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40 Detailed Artificial Intelligence Case Studies [2024]

In this dynamic era of technological advancements, Artificial Intelligence (AI) emerges as a pivotal force, reshaping the way industries operate and charting new courses for business innovation. This article presents an in-depth exploration of 40 diverse and compelling AI case studies from across the globe. Each case study offers a deep dive into the challenges faced by companies, the AI-driven solutions implemented, their substantial impacts, and the valuable lessons learned. From healthcare and finance to transportation and retail, these stories highlight AI’s transformative power in solving complex problems, optimizing processes, and driving growth, offering insightful glimpses into the potential and versatility of AI in shaping our world.

Related: How to Become an AI Thought Leader?

1. IBM Watson Health: Revolutionizing Patient Care with AI

Task/Conflict: The healthcare industry faces challenges in handling vast amounts of patient data, accurately diagnosing diseases, and creating effective treatment plans. IBM Watson Health aimed to address these issues by harnessing AI to process and analyze complex medical information, thus improving the accuracy and efficiency of patient care.

Solution: Utilizing the cognitive computing capabilities of IBM Watson, this solution involves analyzing large volumes of medical records, research papers, and clinical trial data. The system uses natural language processing to understand and process medical jargon, making sense of unstructured data to aid medical professionals in diagnosing and treating patients.

Overall Impact:

• Enhanced accuracy in patient diagnosis and treatment recommendations.
• Significant improvement in personalized healthcare services.

Key Learnings:

• AI can complement medical professionals’ expertise, leading to better healthcare outcomes.
• The integration of AI in healthcare can lead to significant advancements in personalized medicine.

2. Google DeepMind’s AlphaFold: Unraveling the Mysteries of Protein Folding

Task/Conflict: The scientific community has long grappled with the protein folding problem – understanding how a protein’s amino acid sequence determines its 3D structure. Solving this problem is crucial for drug discovery and understanding diseases at a molecular level, yet it remained a formidable challenge due to the complexity of biological structures.

Solution: AlphaFold, developed by Google DeepMind, is an AI model trained on vast datasets of known protein structures. It assesses the distances and angles between amino acids to predict how a protein folds, outperforming existing methods in terms of speed and accuracy. This breakthrough represents a major advancement in computational biology.

• Significant acceleration in drug discovery and disease understanding.
• Set a new benchmark for computational methods in biology.
• AI’s predictive power can solve complex biological problems.
• The application of AI in scientific research can lead to groundbreaking discoveries.

3. Amazon: Transforming Supply Chain Management through AI

Task/Conflict: Managing a global supply chain involves complex challenges like predicting product demand, optimizing inventory levels, and streamlining logistics. Amazon faced the task of efficiently managing its massive inventory while minimizing costs and meeting customer demands promptly.

Solution: Amazon employs sophisticated AI algorithms for predictive inventory management, which forecast product demand based on various factors like buying trends, seasonality, and market changes. This system allows for real-time adjustments, adapting swiftly to changing market dynamics.

• Reduced operational costs through efficient inventory management.
• Improved customer satisfaction with timely deliveries and availability.
• AI can significantly enhance supply chain efficiency and responsiveness.
• Predictive analytics in inventory management leads to reduced waste and cost savings.

4. Tesla’s Autonomous Vehicles: Driving the Future of Transportation

Task/Conflict: The development of autonomous vehicles represents a major technological and safety challenge. Tesla aimed to create self-driving cars that are not only reliable and safe but also capable of navigating complex traffic conditions without human intervention.

Solution: Tesla’s solution involves advanced AI and machine learning algorithms that process data from various sensors and cameras to understand and navigate the driving environment. Continuous learning from real-world driving data allows the system to improve over time, making autonomous driving safer and more efficient.

• Leadership in the autonomous vehicle sector, enhancing road safety.
• Continuous improvements in self-driving technology through AI-driven data analysis.
• Continuous data analysis is key to advancing autonomous driving technologies.
• AI can significantly improve road safety and driving efficiency.

Related: High-Paying AI Career Options

5. Zara: Fashioning the Future with AI in Retail

Task/Conflict: In the fast-paced fashion industry, predicting trends and managing inventory efficiently are critical for success. Zara faced the challenge of quickly adapting to changing fashion trends while avoiding overstock and meeting consumer demand.

Solution: Zara employs AI algorithms to analyze fashion trends, customer preferences, and sales data. The AI system also assists in managing inventory, ensuring that popular items are restocked promptly and that stores are not overburdened with unsold products. This approach optimizes both production and distribution.

• Increased sales and profitability through optimized inventory.
• Enhanced customer satisfaction by aligning products with current trends.
• AI can accurately predict consumer behavior and trends.
• Effective inventory management through AI can significantly impact business success.

6. Netflix: Personalizing Entertainment with AI

Task/Conflict: In the competitive streaming industry, providing a personalized user experience is key to retaining subscribers. Netflix needed to recommend relevant content to each user from its vast library, ensuring that users remained engaged and satisfied.

Solution: Netflix developed an advanced AI-driven recommendation engine that analyzes individual viewing habits, ratings, and preferences. This personalized approach keeps users engaged, as they are more likely to find content that interests them, enhancing their overall viewing experience.

• Increased viewer engagement and longer watch times.
• Higher subscription retention rates due to personalized content.
• Personalized recommendations significantly enhance user experience.
• AI-driven content curation is essential for success in digital entertainment.

7. Airbus: Elevating Aircraft Maintenance with AI

Task/Conflict: Aircraft maintenance is crucial for ensuring flight safety and operational efficiency. Airbus faced the challenge of predicting maintenance needs to prevent equipment failures and reduce downtime, which is critical in the aviation industry.

Solution: Airbus implemented AI algorithms for predictive maintenance, analyzing data from aircraft sensors to identify potential issues before they lead to failures. This system assesses the condition of various components, predicting when maintenance is needed. The solution not only enhances safety but also optimizes maintenance schedules, reducing unnecessary inspections and downtime.

• Decreased maintenance costs and reduced aircraft downtime.
• Improved safety with proactive maintenance measures.
• AI can predict and prevent potential equipment failures.
• Predictive maintenance is essential for operational efficiency and safety in aviation.

8. American Express: Securing Transactions with AI

Task/Conflict: Credit card fraud is a significant issue in the financial sector, leading to substantial losses and undermining customer trust. American Express needed an efficient way to detect and prevent fraudulent transactions in real-time.

Solution: American Express utilizes machine learning models to analyze transaction data. These models identify unusual patterns and behaviors indicative of fraud. By constant learning from refined data, the system becomes increasingly accurate in detecting fraudulent activities, providing real-time alerts and preventing unauthorized transactions.

• Minimized financial losses due to reduced fraudulent activities.
• Enhanced customer trust and security in financial transactions.
• Machine learning is highly effective in fraud detection.
• Real-time data analysis is crucial for preventing financial fraud.

Related: Is AI a Good Career Option for Women?

9. Stitch Fix: Tailoring the Future of Fashion Retail

Task/Conflict: In the competitive fashion retail industry, providing a personalized shopping experience is key to customer satisfaction and business growth. Stitch Fix aimed to offer customized clothing selections to each customer, based on their unique preferences and style.

Solution: Stitch Fix uses AI and algorithms analyze customer feedback, style preferences, and purchase history to recommend clothing items. This personalized approach is complemented by human stylists, ensuring that each customer receives a tailored selection that aligns with their individual style.

• Increased customer satisfaction through personalized styling services.
• Business growth driven by a unique, AI-enhanced shopping experience.
• AI combined with human judgment can create highly effective personalization.
• Tailoring customer experiences using AI leads to increased loyalty and business success.

10. Baidu: Breaking Language Barriers with Voice Recognition

Task/Conflict: Voice recognition technology faces the challenge of accurately understanding and processing speech in various languages and accents. Baidu aimed to enhance its voice recognition capabilities to provide more accurate and user-friendly interactions in multiple languages.

Solution: Baidu employs deep learning algorithms for voice and speech recognition, training its system on a diverse range of languages and dialects. This approach allows for more accurate recognition of speech patterns, enabling the technology to understand and respond to voice commands more effectively. The system continuously improves as it processes more voice data, making technology more accessible to users worldwide.

• Enhanced user interaction with technology in multiple languages.
• Reduced language barriers in voice-activated services and devices.
• AI can effectively bridge language gaps in technology.
• Continuous learning from diverse data sets is key to improving voice recognition.

11. JP Morgan: Revolutionizing Legal Document Analysis with AI

Task/Conflict: Analyzing legal documents, such as contracts, is a time-consuming and error-prone process. JP Morgan sought to streamline this process, reducing the time and effort required while increasing accuracy.

Solution: JP Morgan implemented an AI-powered tool, COIN (Contract Intelligence), to analyze legal documents quickly and accurately. COIN uses NLP to interpret and extract relevant information from contracts, significantly reducing the time required for document review.

• Dramatic reduction in time required for legal document analysis.
• Increased accuracy and reduced human error in contract interpretation.
• AI can efficiently handle large volumes of data, offering speed and accuracy.
• Automation in legal processes can significantly enhance operational efficiency.

12. Microsoft: AI for Accessibility

Task/Conflict: People with disabilities often face challenges in accessing technology. Microsoft aimed to create AI-driven tools to enhance accessibility, especially for individuals with visual, hearing, or cognitive impairments.

Solution: Microsoft developed a range of AI-powered tools including applications for voice recognition, visual assistance, and cognitive support, making technology more accessible and user-friendly. For instance, Seeing AI, an app developed by Microsoft, helps visually impaired users to understand their surroundings by describing people, texts, and objects.

• Improved accessibility and independence for people with disabilities.
• Creation of more inclusive technology solutions.
• AI can significantly contribute to making technology accessible for all.
• Developing inclusive technology is essential for societal progress.

Related: How to get an Internship in AI?

13. Alibaba’s City Brain: Revolutionizing Urban Traffic Management

Task/Conflict: Urban traffic congestion is a major challenge in many cities, leading to inefficiencies and environmental concerns. Alibaba’s City Brain project aimed to address this issue by using AI to optimize traffic flow and improve public transportation in urban areas.

Solution: City Brain uses AI to analyze real-time data from traffic cameras, sensors, and GPS systems. It processes this information to predict traffic patterns and optimize traffic light timing, reducing congestion. The system also provides data-driven insights for urban planning and emergency response coordination, enhancing overall city management.

• Significant reduction in traffic congestion and improved urban transportation.
• Enhanced efficiency in city management and emergency response.
• AI can effectively manage complex urban systems.
• Data-driven solutions are key to improving urban living conditions.

14. Deep 6 AI: Accelerating Clinical Trials with Artificial Intelligence

Task/Conflict: Recruiting suitable patients for clinical trials is often a slow and cumbersome process, hindering medical research. Deep 6 AI sought to accelerate this process by quickly identifying eligible participants from a vast pool of patient data.

Solution: Deep 6 AI employs AI to sift through extensive medical records, identifying potential trial participants based on specific criteria. The system analyzes structured and unstructured data, including doctor’s notes and diagnostic reports, to find matches for clinical trials. This approach significantly speeds up the recruitment process, enabling faster trial completions and advancements in medical research.

• Quicker recruitment for clinical trials, leading to faster research progress.
• Enhanced efficiency in medical research and development.
• AI can streamline the patient selection process for clinical trials.
• Efficient recruitment is crucial for the advancement of medical research.

15. NVIDIA: Revolutionizing Gaming Graphics with AI

Task/Conflict: Enhancing the realism and performance of gaming graphics is a continuous challenge in the gaming industry. NVIDIA aimed to revolutionize gaming visuals by leveraging AI to create more realistic and immersive gaming experiences.

Solution: NVIDIA’s AI-driven graphic processing technologies, such as ray tracing and deep learning super sampling (DLSS), provide highly realistic and detailed graphics. These technologies use AI to render images more efficiently, improving game performance without compromising on visual quality. This innovation sets new standards in gaming graphics, making games more lifelike and engaging.

• Elevated gaming experiences with state-of-the-art graphics.
• Set new industry standards for graphic realism and performance.
• AI can significantly enhance creative industries, like gaming.
• Balancing performance and visual quality is key to gaming innovation.

16. Palantir: Mastering Data Integration and Analysis with AI

Task/Conflict: Integrating and analyzing large-scale, diverse datasets is a complex task, essential for informed decision-making in various sectors. Palantir Technologies faced the challenge of making sense of vast amounts of data to provide actionable insights for businesses and governments.

Solution: Palantir developed AI-powered platforms that integrate data from multiple sources, providing a comprehensive view of complex systems. These platforms use machine learning to analyze data, uncover patterns, and predict outcomes, assisting in strategic decision-making. This solution enables users to make informed decisions in real-time, based on a holistic understanding of their data.

• Enhanced decision-making capabilities in complex environments.
• Greater insights and efficiency in data analysis across sectors.
• Effective data integration is crucial for comprehensive analysis.
• AI-driven insights are essential for strategic decision-making.

Related: Surprising AI Facts & Statistics

17. Blue River Technology: Sowing the Seeds of AI in Agriculture

Task/Conflict: The agriculture industry faces challenges in increasing efficiency and sustainability while minimizing environmental impact. Blue River Technology aimed to enhance agricultural practices by using AI to make farming more precise and efficient.

Solution: Blue River Technology developed AI-driven agricultural robots that perform tasks like precise planting and weed control. These robots use ML to identify plants and make real-time decisions, such as applying herbicides only to weeds. This targeted approach reduces chemical usage and promotes sustainable farming practices, leading to better crop yields and environmental conservation.

• Significant reduction in chemical usage in farming.
• Increased crop yields through precision agriculture.
• AI can contribute significantly to sustainable agricultural practices.
• Precision farming is key to balancing productivity and environmental conservation.

18. Salesforce: Enhancing Customer Relationship Management with AI

Task/Conflict: In the realm of customer relationship management (CRM), personalizing interactions and gaining insights into customer behavior are crucial for business success. Salesforce aimed to enhance CRM capabilities by integrating AI to provide personalized customer experiences and actionable insights.

Solution: Salesforce incorporates AI-powered tools into its CRM platform, enabling businesses to personalize customer interactions, automate responses, and predict customer needs. These tools analyze customer data, providing insights that help businesses tailor their strategies and communications. The AI integration not only improves customer engagement but also streamlines sales and marketing efforts.

• Improved customer engagement and satisfaction.
• Increased business growth through tailored marketing and sales strategies.
• AI-driven personalization is key to successful customer relationship management.
• Leveraging AI for data insights can significantly impact business growth.

19. OpenAI: Transforming Natural Language Processing

Task/Conflict: OpenAI aimed to advance NLP by developing models capable of generating coherent and contextually relevant text, opening new possibilities in AI-human interaction.

Solution: OpenAI developed the Generative Pre-trained Transformer (GPT) models, which use deep learning to generate text that closely mimics human language. These models are trained on vast datasets, enabling them to understand context and generate responses in a conversational and coherent manner.

• Pioneered advancements in natural language understanding and generation.
• Expanded the possibilities for AI applications in communication.
• AI’s ability to mimic human language has vast potential applications.
• Advancements in NLP are crucial for improving AI-human interactions.

20. Siemens: Pioneering Industrial Automation with AI

Task/Conflict: Industrial automation seeks to improve productivity and efficiency in manufacturing processes. Siemens faced the challenge of optimizing these processes using AI to reduce downtime and enhance output quality.

Solution: Siemens employs AI-driven solutions for predictive maintenance and process optimization to reduce downtime in industrial settings. Additionally, AI optimizes manufacturing processes, ensuring quality and efficiency.

• Increased productivity and reduced downtime in industrial operations.
• Enhanced quality and efficiency in manufacturing processes.
• AI is a key driver in the advancement of industrial automation.
• Predictive analytics are crucial for maintaining efficiency in manufacturing.

Related: Top Books for Learning AI

21. Ford: Driving Safety Innovation with AI

Task/Conflict: Enhancing automotive safety and providing effective driver assistance systems are critical challenges in the auto industry. Ford aimed to leverage AI to improve vehicle safety features and assist drivers in real-time decision-making.

Solution: Ford integrated AI into its advanced driver assistance systems (ADAS) to provide features like adaptive cruise control, lane-keeping assistance, and collision avoidance. These systems use sensors and cameras to gather data, which AI processes to make split-second decisions that enhance driver safety and vehicle performance.

• Improved safety features in vehicles, minimizing accidents and improving driver confidence.
• Enhanced driving experience with intelligent assistance features.
• AI can highly enhance safety in the automotive industry.
• Real-time data processing and decision-making are essential for effective driver assistance systems.

22. HSBC: Enhancing Banking Security with AI

Task/Conflict: As financial transactions increasingly move online, banks face heightened risks of fraud and cybersecurity threats. HSBC needed to bolster its protective measures to secure user data and prevent scam.

Solution: HSBC employed AI-driven security systems to observe transactions and identify suspicious activities. The AI models analyze patterns in customer behavior and flag anomalies that could indicate fraudulent actions, allowing for immediate intervention. This helps in minimizing the risk of financial losses and protects customer trust.

• Strengthened security measures and reduced incidence of fraud.
• Maintained high levels of customer trust and satisfaction.
• AI is critical in enhancing security in the banking sector.
• Proactive fraud detection can prevent significant financial losses.

23. Unilever: Optimizing Supply Chain with AI

Task/Conflict: Managing a global supply chain involves complexities related to logistics, demand forecasting, and sustainability practices. Unilever sought to enhance its supply chain efficiency while promoting sustainability.

Solution: Unilever implemented AI to optimize its supply chain operations, from raw material sourcing to distribution. AI algorithms analyze data to forecast demand, improve inventory levels, and minimize waste. Additionally, AI helps in selecting sustainable practices and suppliers, aligning with Unilever’s commitment to environmental responsibility.

• Enhanced efficiency and reduced costs in supply chain operations.
• Better sustainability practices, reducing environmental impact.
• AI can highly optimize supply chain management.
• Integrating AI with sustainability initiatives can lead to environmentally responsible operations.

24. Spotify: Personalizing Music Experience with AI

Task/Conflict: In the competitive music streaming industry, providing a personalized listening experience is crucial for user engagement and retention. Spotify needed to tailor music recommendations to individual tastes and preferences.

Solution: Spotify utilizes AI-driven algorithms to analyze user listening habits, preferences, and contextual data to recommend music tracks and playlists. This personalization ensures that users are continually engaged and discover new music that aligns with their tastes, enhancing their overall listening experience.

• Increased customer engagement and time spent on the platform.
• Higher user satisfaction and subscription retention rates.
• Personalized content delivery is key to user retention in digital entertainment.
• AI-driven recommendations significantly enhance user experience.

Related: How can AI be used in Instagram Marketing?

25. Walmart: Revolutionizing Retail with AI

Task/Conflict: Retail giants like Walmart face challenges in inventory management and providing a high-quality customer service experience. Walmart aimed to use AI to optimize these areas and enhance overall operational efficacy.

Solution: Walmart deployed AI technologies across its stores to manage inventory levels effectively and enhance customer service. AI systems predict product demand to optimize stock levels, while AI-driven robots assist in inventory management and customer service, such as guiding customers in stores and handling queries.

• Improved inventory management, reducing overstock and shortages.
• Enhanced customer service experience in stores.
• AI can streamline retail operations significantly.
• Enhanced customer service through AI leads to better customer satisfaction.

26. Roche: Innovating Drug Discovery with AI

Task/Conflict: The pharmaceutical industry faces significant challenges in drug discovery, requiring vast investments of time and resources. Roche aimed to utilize AI to streamline the drug development process and enhance the discovery of new therapeutics.

Solution: Roche implemented AI to analyze medical data and simulate drug interactions, speeding up the drug discovery process. AI models predict the effectiveness of compounds and identify potential candidates for further testing, significantly minimizing the time and cost related with traditional drug development procedures.

• Accelerated drug discovery processes, bringing new treatments to market faster.
• Reduced costs and increased efficiency in pharmaceutical research.
• AI can greatly accelerate the drug discovery process.
• Cost-effective and efficient drug development is possible with AI integration.

27. IKEA: Enhancing Customer Experience with AI

Task/Conflict: In the competitive home furnishings market, enhancing the customer shopping experience is crucial for success. IKEA aimed to use AI to provide innovative design tools and improve customer interaction.

Solution: IKEA introduced AI-powered tools such as virtual reality apps that allow consumers to visualize furniture before buying. These tools help customers make more informed decisions and enhance their shopping experience. Additionally, AI chatbots assist with customer service inquiries, providing timely and effective support.

• Improved customer decision-making and satisfaction with interactive tools.
• Enhanced efficiency in customer service.
• AI can transform the retail experience by providing innovative customer interaction tools.
• Effective customer support through AI can enhance brand loyalty and satisfaction.

28. General Electric: Optimizing Energy Production with AI

Task/Conflict: Managing energy production efficiently while predicting and mitigating potential issues is crucial for energy companies. General Electric (GE) aimed to improve the efficiency and reliability of its energy production facilities using AI.

Solution: GE integrated AI into its energy management systems to enhance power generation and distribution. AI algorithms predict maintenance needs and optimize energy production, ensuring efficient operation and reducing downtime. This predictive maintenance approach saves costs and enhances the reliability of energy production.

• Increased efficiency in energy production and distribution.
• Reduced operational costs and enhanced system reliability.
• Predictive maintenance is crucial for cost-effective and efficient energy management.
• AI can significantly improve the predictability and efficiency of energy production.

Related: Use of AI in Sales

29. L’Oréal: Transforming Beauty with AI

Task/Conflict: Personalization in the beauty industry enhances customer satisfaction and brand loyalty. L’Oréal aimed to personalize beauty products and experiences for its diverse customer base using AI.

Solution: L’Oréal leverages AI to assess consumer data and provide personalized product suggestions. AI-driven tools assess skin types and preferences to recommend the best skincare and makeup products. Additionally, virtual try-on apps powered by AI allow customers to see how products would look before making a purchase.

• Enhanced personalization of beauty products and experiences.
• Increased customer engagement and satisfaction.
• AI can provide highly personalized experiences in the beauty industry.
• Data-driven personalization enhances customer satisfaction and brand loyalty.

30. The Weather Company: AI-Predicting Weather Patterns

Task/Conflict: Accurate weather prediction is vital for planning and safety in various sectors. The Weather Company aimed to enhance the accuracy of weather forecasts and provide timely weather-related information using AI.

Solution: The Weather Company employs AI to analyze data from weather sensors, satellites, and historical weather patterns. AI models improve the accuracy of weather predictions by identifying trends and anomalies. These enhanced forecasts help in better planning and preparedness for weather events, benefiting industries like agriculture, transportation, and public safety.

• Improved accuracy in weather forecasting.
• Better preparedness and planning for adverse weather conditions.
• AI can enhance the precision of meteorological predictions.
• Accurate weather forecasting is crucial for safety and operational planning in multiple sectors.

31. Cisco: Securing Networks with AI

Task/Conflict: As cyber threats evolve and become more sophisticated, maintaining robust network security is crucial for businesses. Cisco aimed to leverage AI to enhance its cybersecurity measures, detecting and responding to threats more efficiently.

Solution: Cisco integrated AI into its cybersecurity framework to analyze network traffic and identify unusual patterns indicative of cyber threats. This AI-driven approach allows for real-time threat detection and automated responses, thus improving the speed and efficacy of security measures.

• Strengthened network security with faster threat detection.
• Reduced manual intervention by automating threat responses.
• AI is essential in modern cybersecurity for real-time threat detection.
• Automating responses can significantly enhance network security protocols.

32. Adidas: AI in Sports Apparel Manufacturing

Task/Conflict: To maintain competitive advantage in the fast-paced sports apparel market, Adidas sought to innovate its manufacturing processes by incorporating AI to improve efficiency and product quality.

Solution: Adidas employed AI-driven robotics and automation technologies in its factories to streamline the production process. These AI systems optimize manufacturing workflows, enhance quality control, and reduce waste by precisely cutting fabrics and assembling materials according to exact specifications.

• Increased production efficacy and reduced waste.
• Enhanced consistency and quality of sports apparel.
• AI-driven automation can revolutionize manufacturing processes.
• Precision and efficiency in production lead to higher product quality and sustainability.

Related: How can AI be used in Disaster Management?

33. KLM Royal Dutch Airlines: AI-Enhanced Customer Service

Task/Conflict: Enhancing the customer service experience in the airline industry is crucial for customer satisfaction and loyalty. KLM aimed to provide immediate and effective assistance to its customers by integrating AI into their service channels.

Solution: KLM introduced an AI-powered chatbot, which provides 24/7 customer service across multiple languages. The chatbot handles inquiries about flight statuses, bookings, and baggage policies, offering quick and accurate responses. This AI solution helps manage customer interactions efficiently, especially during high-volume periods.

• Improved customer service efficiency and responsiveness.
• Increased customer satisfaction through accessible and timely support.
• AI chatbots can highly improve user service in high-demand industries.
• Effective communication through AI leads to better customer engagement and loyalty.

34. Novartis: AI in Drug Formulation

Task/Conflict: The pharmaceutical industry requires rapid development and formulation of new drugs to address emerging health challenges. Novartis aimed to use AI to expedite the drug formulation process, making it faster and more efficient.

Solution: Novartis applied AI to simulate and predict how different formulations might behave, speeding up the lab testing phase. AI algorithms analyze vast amounts of data to predict the stability and efficacy of drug formulations, allowing researchers to focus on the most promising candidates.

• Accelerated drug formulation and reduced time to market.
• Improved efficacy and stability of pharmaceutical products.
• AI can significantly shorten the drug development lifecycle.
• Predictive analytics in pharmaceutical research can lead to more effective treatments.

35. Shell: Optimizing Energy Resources with AI

Task/Conflict: In the energy sector, optimizing exploration and production processes for efficiency and sustainability is crucial. Shell sought to harness AI to enhance its oil and gas operations, making them more efficient and less environmentally impactful.

Solution: Shell implemented AI to analyze geological data and predict drilling outcomes, optimizing resource extraction. AI algorithms also adjust production processes in real time, improving operational proficiency and minimizing waste.

• Improved efficiency and sustainability in energy production.
• Reduced environmental impact through optimized resource management.
• Automation can enhance the effectiveness and sustainability of energy production.
• Real-time data analysis is crucial for optimizing exploration and production.

36. Procter & Gamble: AI in Consumer Goods Production

Task/Conflict: Maintaining operational efficiency and innovating product development are key challenges in the consumer goods industry. Procter & Gamble (P&G) aimed to integrate AI into their operations to enhance these aspects.

Solution: P&G employs AI to optimize its manufacturing processes and predict market trends for product development. AI-driven data analysis helps in managing supply chains and production lines efficiently, while AI in market research informs new product development, aligning with consumer needs.

• Enhanced operational efficacy and minimized production charges.
• Improved product innovation based on consumer data analysis.
• AI is crucial for optimizing manufacturing and supply chain processes.
• Data-driven product development leads to more successful market introductions.

Related: Use of AI in the Navy

37. Disney: Creating Magical Experiences with AI

Task/Conflict: Enhancing visitor experiences in theme parks and resorts is a priority for Disney. They aimed to use AI to create personalized and magical experiences for guests, improving satisfaction and engagement.

Solution: Disney utilizes AI to manage park operations, personalize guest interactions, and enhance entertainment offerings. AI algorithms predict visitor traffic and optimize attractions and staff deployment. Personalized recommendations for rides, shows, and dining options enhance the guest experience by leveraging data from past visits and preferences.

• Enhanced guest satisfaction through personalized experiences.
• Improved operational efficiency in park management.
• AI can transform the entertainment and hospitality businesses by personalizing consumer experiences.
• Efficient management of operations using AI leads to improved customer satisfaction.

38. BMW: Reinventing Mobility with Autonomous Driving

Task/Conflict: The future of mobility heavily relies on the development of safe and efficient autonomous driving technologies. BMW aimed to dominate in this field by incorporating AI into their vehicles.

Solution: BMW is advancing its autonomous driving capabilities through AI, using sophisticated machine learning models to process data from vehicle sensors and external environments. This technology enables vehicles to make intelligent driving decisions, improving safety and passenger experiences.

• Pioneering advancements in autonomous vehicle technology.
• Enhanced safety and user experience in mobility.
• AI is crucial for the development of autonomous driving technologies.
• Safety and reliability are paramount in developing AI-driven vehicles.

39. Mastercard: Innovating Payment Solutions with AI

Task/Conflict: In the digital age, securing online transactions and enhancing payment processing efficiency are critical challenges. Mastercard aimed to leverage AI to address these issues, ensuring secure and seamless payment experiences for users.

Solution: Mastercard integrates AI to monitor transactions in real time, detect fraudulent activities, and enhance the efficiency of payment processing. AI algorithms analyze spending patterns and flag anomalies, while also optimizing authorization processes to reduce false declines and improve user satisfaction.

• Strengthened security and reduced fraud in transactions.
• Improved efficiency and user experience in payment processing.
• AI is necessary for securing and streamlining expense systems.
• Enhanced transaction processing efficiency leads to higher customer satisfaction.

40. AstraZeneca: Revolutionizing Oncology with AI

Task/Conflict: Advancing cancer research and developing effective treatments is a pressing challenge in healthcare. AstraZeneca aimed to utilize AI to revolutionize oncology research, enhancing the development and personalization of cancer treatments.

Solution: AstraZeneca employs AI to analyze genetic data and clinical trial results, identifying potential treatment pathways and personalizing therapies based on individual genetic profiles. This approach accelerates the development of targeted treatments and improves the efficacy of cancer therapies.

• Accelerated innovation and personalized treatment in oncology.
• Better survival chances for cancer patients.
• AI can significantly advance personalized medicine in oncology.
• Data-driven approaches in healthcare lead to better treatment outcomes and innovations.

Related: How can AI be used in Tennis?

Closing Thoughts

These 40 case studies illustrate the transformative power of AI across various industries. By addressing specific challenges and leveraging AI solutions, companies have achieved remarkable outcomes, from enhancing customer experiences to solving complex scientific problems. The key learnings from these cases underscore AI’s potential to revolutionize industries, improve efficiencies, and open up new possibilities for innovation and growth.

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Best AI Case Study Examples in 2024 (And a How-To Guide!)

Who has the best case studies for ai solutions.

B2B buyers’ heads are spinning with the opportunities that AI makes possible.

But in a noisy, technical space where hundreds of new AI solutions and use cases are popping up overnight, many buyers don’t know how to navigate these opportunities—or who they can trust.

Your customers are as skeptical as they are excited, thinking…

• “I’m confused by the complexity of your technology.”
• “I’m unsure whether there’s clear ROI.”
• “I’m concerned about my data security.”
• “How will I integrate AI into our systems?”
• “I’m worried about employee pushback.”
• “I’m nervous about its use and governance.”

Done well, case studies about your AI solution can answer all of these questions in a way no other asset can:

With real-world storytelling, third party trust, and practical demonstrations that you can do what you promise.

To help you level up your customer stories, we’ve scoured the web for examples of the best AI case studies from companies spanning billion-dollar-juggernauts and scrappy startups.

Then, we profiled exactly what they’re doing well so you can level up your own stories!

OPPORTUNITY ALERT: Of all of the businesses we reviewed in researching this piece, just 50% were publishing customer success stories on their websites. Want an instant competitive advantage in AI? Scale your own case study production right now!

1. Location is everything: make stories findable

Key decision-makers in B2B businesses actively seek out word-of-mouth content about potential AI partners (like you!). So the easier they can find case studies on your website, the better.

Of the AI businesses we analyzed doing case studies, most make it easy to locate their case study overview page (where prospects see your complete portfolio of case studies at a glance.)

A common journey is via ‘Resources’ in the main navigation bar, followed by a link to ‘Customer Stories’, ‘Client Stories’, ‘Case Studies’, or similar.

For example, Otter.ai has their customer stories slightly buried in their “Blog” section , with an easy-to-miss category link. We don’t love this, because there’s no clear reason someone should expect to find this type of content in the blog vs. a “Customers” section or otherwise:

These also appear in their “Resources” section, but without any sort of jump link or clear indication you might find them there:

But you can do better!

In a space so skeptical and noisy, we advise you follow the likes of Presight AI and Google DeepMind and give buyers access to your customer success stories with a single click from the main navigation:

While Presight AI favors simplicity with a link to ‘Client Stories’, Google DeepMind opens the door via ‘Impact’.

If, like Google DeepMind, your impact as an AI business extends beyond commercial customers to broader sectors and communities, using a term like ‘Impact’ works well, but ‘clear’ is better than clever here, and a simpler term (‘Customers’) may be stronger.

You’ve put in the hard work sourcing concrete proof for potential buyers; don’t put hurdles in the way of finding it.

AI case study overview pages

The ‘overview’ for your customer story page is where customers are going to either continue their journey with intention—or stumble around in the dark.

A great overview page provides a clear sense of hierarchy (what’s important?), organization (what’s here, and what’s for me?) and expectation (what’s on the other side of the click?).

Take Jasper.ai for example:

Their overview page starts strong with a compelling bit of social proof (100,000+ businesses? Holy toledo!). Having a featured story is great (more on that later), though the headline for the one in the image sort of buries the lede (800% surge in traffic!? Holy toledo!)

After that, the page offers no clear way to drill down with intention: A lead is left to scroll through the logos presented to see if there are any companies they know of, or choose a story at random—most likely the featured story or the one in the upper left of the grid.

That’s not as ideal: you’d much rather have a customer quickly find the stories most relevant to THEM.

Boston Dynamics is one AI business worth emulating on that front.

A no-nonsense intro tells prospects they’re on the right page: “Discover success stories from real customers putting our robotics systems to work.”

If you choose to run a featured case study on your overview page, choose a high-impact one that appeals directly to…

• A substantial result (with metrics ideally), if your audience is skeptical about ROI
• A strong quote on the alleviation of pain (if metrics aren’t available)
• A weighty promise of value if your audience is looking for something to aspire to
• A clear ‘how-to’ hook if your audience is curious about the logistics/implementation

Next, Boston Dynamics provides a comprehensive list of case studies. It’s important that prospects can easily slice and dice these to find studies that are most relevant.

Boston Dynamics does this in a couple of ways:

First, they provide filters by ‘topic’, ‘application’, and ‘industry focus’. Second, they stamp each preview image with the main use case in that study.

Potential buyers can sort the ‘safety’ wheat from the ‘inspection’ chaff with or without filters.

There are other ways to optimize your overview page and help buyers find relevant case studies fast.

Consider using imagery that reflects your customers’ industry or specialism. Also include company logos, so prospects recognize relatable brands.

Another AI business with a strong overview page is Dynatrace . Like Boston Dynamics, they kick off with a featured story:

Instead of creating intrigue with a juicy title and intro, Dynatrace runs a ‘hero’ quote.

A strong quote from your interviewee, at the outset, can spike prospects’ serotonin levels, create intrigue and add credibility.

Dynatrace’s hero quote isn’t as dynamic as it could be, though it’s still strong, speaking to specific benefits (clarity and visibility).

Dynatrace offers a video testimonial (rather than written) as their featured story, something we’re all for when context for the content has been provided like it has been with the hero quote.

Video adds even more trust for buyers because they see the speaker’s reactions and emotions right there in front of them (though be careful not to conceal the interviewee’s face with the play button!)

Again, Dynatrace provides an easy-to-segment list of stories. Brand-focused imagery, company logos, and filter functionality make digging out relevant content a breeze:

  LIGHTBULB MOMENT: Want to take filtering in your AI business to the next level? Buyers want more clarity on your ROI, so why not provide an ROI filter that highlights common KPIs/outcomes that matter to customers (e.g. savings, time savings, increased sales, reduced errors, improved retention, etc.)?

2. I can see clearly now: the importance of readability

Executed properly, case studies mimic the powerful effect of word of mouth and can be as persuasive as a trusted recommendation from friends.

But AI businesses face an added challenge: while you know your AI solution inside out, buyers could be confused by the complexity of your technology.

In any B2B business, multiple people will likely be involved in any buying decision. If your case study is meant to appeal to (typically) less tech-savvy buyers (e.g. CEOs, CMOs, etc.), then avoiding complex jargon is key.

One way to do this is to put the customers’ quotes and narrative at the core of the story.

Runway handles this with a Q&A style approach to customer stories where their customers’ responses (and thus, language) make up the entire content:

But if the Q&A style approach isn’t right for you (and it may not be), you’ve got options.

6 quick tips for writing an AI case study well

Before we dive into examples of the best written case studies for AI, here are some basics to bear in mind:

1. Every great story has a beginning, middle, and end. Case studies follow more or less the same flow: a headline, a challenge, a solution, and the results you achieved.

2. Every good story needs a hero, so introduce yours—your client. Your leads care about the transformation of someone like them, facing similar pressures and decisions. You want to build tension and stakes to make the story relatable, highlighting relatable pains and making the story feel personal.

Remember: heroes are rarely idiots—don’t make your customer look like one.

3. Explain in specific detail how your hero’s pain got solved. To demonstrate your value, you want to help the reader feel the same relief, security, and confidence that the actual customer experienced. Don’t just list the features that the customer used: tie everything back to a specific, desirable outcome and a practical “how.”

4. Address specific AI-related objections in the content. If leads worry about integration, explain it in your customers’ words. If they’re worried about security, aim for quotes covering this. A lot of this comes down to properly planning and structuring interviews with your clients.

5. Share the impact beyond the metrics (but the metrics, too.) In the ‘results’ section, metrics matter—but so does clearly showing the transformation that has taken place. Use specific examples of what a customer can do now, or do better. Share from their output, portfolio, or specific process if you can.

Make it real with tangible examples.

6. Avoid jargon, complicated words, and creative adjectives, unless… Jargon is to be killed with fire UNLESS your customers use that same jargon and identify with it (e.g. technical roles that prize their acronyms and lingo.)

Now, let’s get into what we saw in AI case studies out in the wild.

Across the companies we analyzed, we identified A LOT of impenetrable language and off putting jargon. A huge chunk of stories were so chewy, most non-technical B2B buyers would probably spit them out, for example:

“The ‘xxx (technology)’ provides a framework for energy operators, service providers and equipment providers to offer interoperable solutions, including AI- and physics-based models, and monitoring, diagnostics, prescriptive actions and services for energy use cases.”

These sentences are SO long. Incomprehensible jargon is everywhere. It all means next to nothing, unless you have a deep technical background in that business.

And your buyers may not!

We also found that while AI businesses should always aim for specificity in case studies, content (especially around results) trended towards being vague. For example:

“The collaboration has proven to be a fruitful venture, providing the bank with new opportunities for growth and risk management in the changing financial landscape.”

A fruitful venture? Was it as impactful as a falling watermelon or a shriveled grape?

Remember that buyers are looking for concrete, relatable, “I-can-now-do-this” proof of your capabilities. They want word-of-mouth quotes and powerful metrics.

Not rotten fruit or vague terms.

But it wasn’t all business-speaky doom and gloom. We found some great examples from AI businesses who deliver clarity and simplicity—including UiPath, who excelled at presenting the challenges their customers faced clearly and simply.

“The payroll process is complex, sensitive, and error-prone. It requires the coordination of various departments including HR, finance, and legal. Processing every wage accurately every single time requires massive effort and involves tedious manual tasks.”

UiPath make the story relatable, too, by adding human interest:

“On the micro level, missing a payment or getting it wrong simply isn’t an option when employees have bills to pay and essentials to buy.”

The pain of missing a bill because your employer messed up payroll is recognized by most people. This creates an emotional connection and sympathy in the reader.

And that probably means more engagement with the story at large!

UiPath liberally sprinkles customer quotes throughout their studies, providing a constant reminder that their solution positively impacts real people in the real world, and allowing those people to speak for themselves, in their own terms.

They also seize every opportunity to add vibrant, descriptive language so buyers feel what their customer felt. It reads like a magazine feature in places:

“I was asked to look into automation,” Guez says with a sparkle in his eye , explaining that he came out of retirement to take on his current role. “At the time, RPA was a buzzword. It was still quite a new technology. We needed to get a pilot going to see how it could alleviate this pain point.”

Google DeepMind is another AI solution that tells understandable and engaging customer stories, successfully when it comes to describing complex tech in plain English:

In the circled section, the company describes its Flamingo technology with both clarity and flare.

They use a funny, real-world image—a dog balancing a stack of crackers on its head—that appeals to your senses and creates a vivid and emotional connection with their solution. A visual would almost certainly have added value here!

It’s worth trying similar with your own case studies: find descriptive language, metaphors, or examples that appeal to your audience’s imagination and persuade them to reach out to you.

Google DeepMind takes care to explain every piece of technical language it uses. In another section, they talk about “improving the VP9 codec”. But they don’t leave it hanging like a curveball you can’t hit.

They add a short sentence to explain what they mean: “a coding format that helps compress and transmit video over the internet”. Home run!

3. Who cares: demonstrating value and ROI

Given the risk inherent in choosing the wrong solution or adopting a new product that doesn’t pan out, discerning B2B buyers need a clear picture of the ROI that your AI solutions provide.

Give them that, and you’re already a step ahead of the competition.

Attack the status quo

Your greatest competitors aren’t other AI solutions: they’re what your ideal customers are doing to solve the problem now—and that may very well be nothing.

To make AI customer stories compelling, you need to demonstrate the limitations and risks of sticking with the norm in order to give your solution a backdrop it can stand out against.

DataRobot does a fantastic job of this in their Freddie Mac story:

ThoughtSpot leads the “Challenge” section of their Fabuwood customer story with a comparison against a well-established alternative, Power BI:

In both cases, this not only quickly establishes the shortcomings of the status quo: it also gives leads something to compare this new solution to, instantly putting ThoughtSpot and DataRobot into well-defined categories their customers can understand (“Oh, it would replace X!”) instead of some nebulous “AI” bucket (“Oh, it’s… a new… AI… thing.”)

The importance of metrics in demonstrating ROI

Across the AI businesses we analyzed, there was a noticeable lack of performance metrics in their case studies. This suggests that either customers aren’t seeing strong returns or, more likely, AI firms and their customers find it a challenge to quantify AI investments.

Most organizations using your technology will have considered baseline performance pre-AI, put measurable goals in place and be tracking progress.

To strengthen the impact of your case studies, ask them to provide this quantifiable proof during your interview process. The key here is to be specific about what you ask for.

So what metrics should you ask customers to dig out for you?

Of course, it depends on your products and customers’ goals for using them, but here are some general tips.

Anything related to sales is gold for prospective buyers, such as revenue growth, margin improvements, conversion rates, and customer lifetime value.

Ask, too, about improvements to operations and efficiency, including cost savings, error reduction, productivity improvement, and process optimization.

As well as hard returns, try to unearth softer ones, such as the human impact on your hero, as this will strongly resonate with B2B buyers in similar roles.

Now let’s check out some examples.

Some AI companies do attempt to add weight and muscle to their case studies with metrics. But even the best examples we found have work to do.

Numenta , for example, showcases a hot metric in the headline below. 20x inference acceleration is a big sell for customers operating in the computing space, because it improves the performance of their machines:

To make the headline more intriguing, Numenta could explain the result and impact of this 20x increase in processor speed on their customers. For example, sharing revenue growth or profit margin improvements off the back of this high-speed processor would give other buyers a tempting result they’d want to replicate.

Back to UiPath now, who also use metrics to show how customers reap the benefits of their AI solutions. Here, metrics take center stage at the start of a story :

UiPath has chosen operational metrics here—the number of automations implemented, number of transactions handled by robots, and growth in payrolls they process each day.

While they do provide quantifiable evidence of the impact of AI to their business, they could go further.

For example…

• If more transactions are being handled by robots, how much time is that saving the business?
• Has staff retention improved with more dependable payroll?
• Have they saved costs as a result of greater efficiency?

AI has clearly provided Papaya Global with significant benefits. With a little more work—and arguably more structure at the interview stage—UiPath could have left readers with no doubt about their solution’s ROI.

Going beyond metrics and into examples

Several solutions had demonstrations of outcomes—for example, galleries of outputted imagery or samples of produced work.  Kaiber  has a lovely gallery, as you’d expect from a very visual solution:

Meanwhile Tome comes to bat with stories that disambiguate a use case and explain an outcome that is valuable, but not necessarily quantifiable, like creating a “Personal radio station”:

These are also valuable in terms of demonstrating practical value, but business buyers also speak in terms of ROI, especially when making a case to their bosses for a purchase.

4. Don’t fight it: turning employee pushback into employee buy-in

An ongoing barrier for businesses looking to implement AI solutions is the risk of employee pushback: will staff actually adopt and support new technologies that may fundamentally change how they work?

Strategic AI companies can use customer success stories as a weapon to shoot down those objections.

We found a number of AI businesses using case studies to share the message: “AI is not going to take your job!”

In this case study, UiPath’s customer explains the continued importance of having ‘a human touch’ in the business:

UiPath doesn’t want its customers to say their AI solves everything. Their goal is to make businesses more efficient and successful—not to jeopardize job security.

OpenAI also uses its case studies to battle employee pushback. One powerful line reads:

“Ironclad’s goal in using AI has always been to help people do more, not to replace them with technology.”

Their message couldn’t be clearer to companies looking for an AI solution, while avoiding conflict on the frontline.

Meanwhile, Reply.io works to overcome potential objections by focusing on where teams are likely to take issue: with the quality of work done by AI relative to a human.

They cover this potential staff objection right in the story, proactively shooting a barrier to adoption out of the sky.

4. Muzzled, not muted: make ‘anonymous’ compelling

In an ideal world, all your customers would let you tell the story of how you helped them succeed. In the real world, customers aren’t always comfortable publicly talking about their AI use, even when they’re thrilled.

Sometimes, they’re constrained by their legal departments. Other times, they make a call that the story’s just too sensitive and decline to participate.

One way around this is to ask customers to share their story anonymously. But can stories be compelling weapons of mass conversion if you don’t mention any names?

Yes, absolutely.

Let’s look at how one of the AI companies we analyzed, C3 AI , produces powerful anonymous studies, like this one :

C3 AI anonymizes this case study, but manages to maintaining most of its impact by:

• Demonstrating the prestige of the customer with a sidebar packed with detail (see ‘About the Company’ in the graphic above)
• Turning anonymity into a plus by sharing metrics the company might not make public if their name was associated with it (ie, $9M in accelerated operating income)
• Including it alongside multiple case studies that are named. Taken together, the anonymous study has as much credibility as named studies.

What more can you do?

You can further retain the power of anonymous studies by:

• Including compelling, in-depth quotes from the people involved, swapping out names for descriptive titles and gender-neutral pronouns.
• Providing as much detail as non-anonymous studies; telling the full story of why the customer chose you, what their journey looked like, and how you made a difference. You don’t need to provide names to demonstrate how you delivered real ROI.

5. Trust me, bro: getting your leads to believe the hype

As a B2B buyer, it’s hard to know whether companies are spinning you a genuine opportunity—or a yarn. Trust is tough to earn and keep.

Case studies immediately cut through the sales spiel and provide concrete proof straight from customers’ mouths.

By nature, case studies are powerful trust builders because they show rather than tell. You can maximize that opportunity by including additional ‘trust’ signals throughout your stories.

Devices such as customer quotes, customer headshots, and customer logos all do the job.

During our analysis of AI case studies, we found most companies use direct customer quotes to foster trust.

In an environment where many AI businesses have an ROI problem, customer quotes are critical. Buyers can hear exactly how other people just like them have benefited from your solutions, proving that your brand is worth buying.

OpenAI uses quotes well to enhance the credibility of their customer stories :

GoGwilt recalled the initial excitement within his legal engineering team as they saw what OpenAI’s models could do for contracting. “There was the first moment of the team saying, ‘Wow, this is producing work at the level of a first-year associate,’” he said.

It’s powerful for a buyer when they hear someone—in a role that resonates with their own—describing the ‘wow’ moment your product provides.

Here’s another example of how customer quotes can build emotion, trust, and buy-in:

The engineers quickly moved on to a prototype—and experienced another “wow” moment. “Integrating GPT-4 into our contract editor and just seeing how seamless and powerful it felt made it pretty easy for us to invest further into productizing and getting it to customers,” GoGwilt added.

Using customer headshots, customer logos, and embedded video are other solid ways to signal trust.

Video testimonials , in particular, increases the impact of customer success stories because viewers see a customer’s emotion and sincerity in real time.

Here’s another great example of this from DataRobot, combining customer testimonial videos with written quotes to hammer home the legitimacy of their story:

Similarly, WorkFusion regularly brings video into their enterprise customer stories , adding depth and legitimacy while sharing the genuine human perspectives of the impact:

6. Picky eaters: how to make AI case studies valuable for time-starved buyers

We’re big believers (supported by data) that prioritizing long-form customer stories on your website improves online visibility and provides proof of your expertise and authority.

But time-starved B2B buyers also need to be catered for.

That means presenting success stories in a scannable (or watchable) way that helps even wandering eyeballs catch the best bits.

Formatting and design devices, including top and sidebars, pull quotes, and images all help readers find proof of your capabilities without reading the entire study.

PROS is one company setting good scannability standards in their customer stories, like this one on Lufthansa :

They use exploded quotes, a snackable company round-up, short paragraphs, and white space to help buyers derive value without reading every word.

Using a hero quote at the outset adds instant credibility, even for scanners.

C3 AI does something unique by providing a visual timeline of events in their Shell customer story . This is a great idea for showing your customers’ journey in a bite-sized and accessible way:

Dynatrace runs a snappy sidebar, complete with a snack sized story round-up:

Dynatrace also uses a bulleted list, ‘Life with Dynatrace’, to highlight the key benefits of partnering with them, without oceans of convoluted narrative:

Boston Dynamics also performs well on scannability. Colorful images of robotic technology and punchy crossheads are used to break up long runs of text:

Shoutout to OpenAI, too, which uses exploded quotes as text breakers to make its formatting friendlier. Rushed readers are constantly rewarded with quotes from happy customers as they scan:

Google DeepMind provides an always on-screen navigation bar to help readers jump to the sections that most interest them:

If you do choose to use a topbar or sidebar in your studies, include impactful metrics in there, like UiPath does:

Because you’ll be drawing buyers to this section with your amazing performance metrics, be sure to include a call to action (the logical next step you want a buyer to take).

And don’t forget to include a CTA at the end of every story, too.

By making studies scannable, you ensure that every reader is covered.

One final observation: if you put the hard work into creating case studies, you will hook in target buyers looking to learn even more. Encourage extra engagement by including ‘keep reading’ or ‘share on social’ options at the end of your stories, just like Boston Dynamics do:

The last word: putting it all together

Now you’ve seen what other leading AI businesses are doing with their case studies, the question is this:

Are YOU ready to suck in more leads and buyers by producing high-impact case studies that prove your ROI and credibility?

Let’s recap some of the findings and recommendations from our analysis of leading AI case studies:

• AI companies can answer buyers’ biggest questions and concerns with well-crafted and well-presented case studies.
• Of the AI companies we analyzed, fewer than 50% had even a single case study case on their website. Scaling your own AI case study production (right now!) will give you an instant advantage.
• Make case studies super-easy for buyers who are looking for solutions like yours to find.
• Use simple, straightforward language to explain your technology, so technical and non-technical decision-makers can understand
• Differentiate your AI business in a noisy marketplace by providing quantifiable metrics. Clearly show the ROI customers get when they work with you.
• Anonymous studies about AI solutions can be as impactful as named studies. When customers know they won’t be named, they often provide mic-drop worthy metrics and personal details about their journey they otherwise wouldn’t feel comfortable sharing.
• Enhance case study credibility with customer quotes, customer imagery, customer logos, and video testimonials.
• Make your AI case studies scannable, so time-starved buyers understand all your capabilities and the results you get for customers without reading every word.

Need help producing written AI case studies or video testimonials?

At Case Study Buddy, we have the knowhow, streamlined processes, and team to make it easy for you.

Contact us today.

Ian Winterton

Based in SW France, Ian has spent 48,000hrs of his life (yes, he worked it out) telling stories about what makes great businesses special.

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100+ AI Use Cases & Applications: In-Depth Guide for 2024

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

AI is changing every industry and business function, which results in increased interest in AI, its subdomains, and related fields such as machine learning and data science as seen below. With the launch of ChatGPT , interest in generative AI , a subfield of AI, exploded.

According to a recent McKinsey survey, 55% of organizations are using AI in at least one business function. 1 To integrate AI into your own business, you need to identify how AI can serve your business, possible use cases of AI in your business.

This article gathers the most common AI use cases covering marketing, sales, customer services, security, data, technology, and other processes.

Generative AI Use Cases

Generative AI involves AI models generating output in requests where there is not a single right answer (e.g. creative writing). Since the launch of ChatGPT , it has been exploding in popularity. Its use cases include content creation for marketing, software code generation, user interface design and many others.

For more: Generative AI use cases .

Business Functions

> ai use cases for analytics, general solutions.

• Analytics Platform : Empower your employees with unified data and tools to run advanced analyses. Quickly identify problems and provide meaningful insights.
• Analytics Services : Satisfy your custom analytics needs with these e2e solution providers. Vendors are there to help you with your business objectives by providing turnkey solutions.
• Automated Machine Learning (autoML) : Machines helping data scientists optimize machine learning models. With the rise of data and analytics capabilities, automation is needed in data science. AutoML automates time consuming machine learning tasks, enabling companies to deploy models and automate processes faster.

Specialized solutions

• Conversational Analytics : Use conversational interfaces to analyze your business data. Natural Language Processing is there to help you with voice data and more enabling automated analysis of reviews and suggestions.
• E-Commerce Analytics : Specialized analytics systems designed to deal with the explosion of e-commerce data. Optimize your funnel and customer traffic to maximize your profits.
• Geo-Analytics Platform : Enables analysis of granular satellite imagery for predictions. Leverage spatial data for your business goals. Capture the changes in any landscape on the fly.
• Image Recognition and Visual Analytics : Analyze visual data with advanced image and video recognition systems. Meaningful insights can be derived from the data piles of images and videos.
• Real-Time Analytics : Real-Time Analytics for your time-sensitive decisions. Act timely and keep your KPI’s intact. Use machine learning to explore unstructured data without any disruptions.

> AI use cases for Customer Service

• Call Analytics : Advanced analytics on call data to uncover insights to improve customer satisfaction and increase efficiency. Find patterns and optimize your results. Analyze customer reviews through voice data and pinpoint, where there is room for improvement. Sestek indicates that ING Bank observed a 15% increase in sales quality score and a 3% decrease in overall silence rates after they integrated AI into their contact systems .
• Call Classification : Leverage natural language processing (NLP) to understand what the customer wants to achieve so your agents can focus on higher value-added activities. Before channeling the call, identify the nature of your customers’ needs and let the right department handle the problem. Increase efficiency with higher satisfaction rates.
• Call Intent Discovery : Leverage Natural Language Processing and machine learning to estimate and manage customer’s intent (e.g., churn) to improve customer satisfaction and business metrics. Sentiment analysis through the customer’s voice level and pitch. Detect the micro-emotions that drive the decision-making process. Explore how chatbots detect customer intent in our in-depth article on intent recognition .
• Chatbot for Customer Service (Self – Service Solution) : Chatbots can understand more complicated queries as AI algorithms improve. Build your own 24/7 functioning, intelligent, self-improving chatbots to handle most queries and transfer customers to live agents when needed. Reduce customer service costs and increase customer satisfaction. Reduce the traffic on your existing customer representatives and make them focus on the more specific needs of your customers. Read for more insights on chatbots in customer service or discover chatbot platforms .
• Chatbot Analytics : Analyze how customers are interacting with your chatbot. See the overall performance of your chatbot. Pinpoint its shortcomings and improve your chatbot. Detect the overall satisfaction rate of your customer with the chatbot.
• Chatbot testing : Semi-automated and automated testing frameworks facilitate bot testing. See the performance of your chatbot before deploying. Save your business from catastrophic chatbot failures. Detect the shortcomings of your conversational flow.
• Customer Contact Analytics : Advanced analytics on all customer contact data to uncover insights to improve customer satisfaction and increase efficiency. Utilize Natural Language Processing for higher customer satisfaction rates.
• Customer Service Response Suggestions : Bots will listen in on agents’ calls suggesting best practice answers to improve customer satisfaction and standardize customer experience. Increase upsells and cross-sells by giving the right suggestion. Responses will be standardized, and the best possible approach will serve the benefit of the customer.
• Social Listening & Ticketing : Leverage Natural Language Processing and machine vision to identify customers to contact and respond to them automatically or assign them to relevant agents, increasing customer satisfaction. Use the data available in social networks to uncover whom to sell and what to sell.
• Intelligent Call Routing : Route calls to the most capable agents available. Intelligent routing systems incorporate data from all customer interactions to optimize the customer satisfaction. Based on the customer profile and your agent’s performance, you can deliver the right service with the right agent and achieve superior net promoter scores. Feel free to read case studies about matching customer to right agent in our emotional AI examples article .
• Survey & Review Analytics : Leverage Natural Language Processing to analyze text fields in surveys and reviews to uncover insights to improve customer satisfaction and increase efficiency. Automate the process by mapping the right keywords with the right scores. Make it possible to lower the time for generating reports. Protobrand states that they used to do review analytics manually through the hand-coding of the data, but now it automates much of the analytical work with Gavagai. This helps the company to collect larger quantitative volumes of qualitative data and still complete the analytical work in a timely and efficient manner. You can read more about survey analytics from  our related article .
• Voice Authentication : Authenticate customers without passwords leveraging biometry to improve customer satisfaction and reduce issues related to forgotten passwords. Their unique voice id will be their most secure key for accessing confidential information. Instead of the last four digits of SSN, customers will gain access by using their voice.

> AI use cases for Cybersecurity

Data loss prevention (DLP) software leverage AI technologies to achieve

• Real time detection of sensitive data beyond those identified using rules-based approached
• Intelligent access control learning from allowed data access patterns to reduce false positives

For more, see best practices for using AI in DLP .

Network monitoring

Typical use cases include:

• Anomaly detection in network traffic to identify cyberattacks
• Automated network optimization to manage peak loads at optimal cost without harming user experience.

For real-life examples: AI in network monitoring

> AI use cases for Data

• Data Cleaning & Validation Platform : Avoid garbage in, garbage out by ensuring the quality of your data with appropriate data cleaning processes and tools. Automate the validation process by using external data sources. Regular maintenance cleaning can be scheduled, and the quality of the data can be increased.
• Data Integration : Combine your data from different sources into meaningful and valuable information. Data traffic depends on multiple platforms. Therefore, managing this huge traffic and structuring the data into a meaningful format will be important. Keep your data lake available for further analysis. 
• Data Management & Monitoring : Keep your data high quality for advanced analytics. Adjust the quality by filtering the incoming data. Save time by automating manual and repetitive tasks.
• Data Preparation Platform : Prepare your data from raw formats with data quality problems to a clean, ready-to-analyze format. Use extract, transform, and load (ETL) platforms to fine-tune your data before placing it into a data warehouse.
• Data Transformation : Transform your data to prepare it for advanced analytics. If it is unstructured, adjust it for the required format.
• Data Visualization : Visualize your data for better analytics and decision-making. Let the dashboards speak. Convey your message more easily and more esthetically.
• Data Labeling : Unless you use unsupervised learning systems, you need high quality labeled data. Label your data to train your supervised learning systems. Human-in-the-loop systems auto label your data and crowdsource labeling data points that cannot be auto-labeled with confidence.
• Synthetic Data :  Computers can artificially create synthetic data to perform certain operations. The synthetic data is usually used to test new products and tools, validate models, and satisfy AI needs. Companies can simulate not yet encountered conditions and take precautions accordingly with the help of synthetic data. They also overcome the privacy limitations as it doesn’t expose any real data. Thus, synthetic data is a smart AI solution for companies to simulate future events and consider future possibilities. You can have more information on synthetic data from  our related article .

> AI use cases for Finance

Finance business function led by the CEO completes numerous repetitive tasks involving quantitative skills which makes them a good fit for AI transformation:

• Billing / invoicing reminders : Leverage accessible billing services that remind your customers to pay with generative AI powered messages.
• Blackbaud AP automation
• Dynamics AP automation
• NetSuite AP automation
• SAGE AP automation

For more, see AI use cases in AP automation .

> AI use cases for HR

• Employee Monitoring : Monitor your employees for better productivity measurement. Provide objective metrics to see how well they function. Forecast their overall performance with the availability of massive amounts of data.
• Hiring :  Hiring is a prediction game: Which candidate, starting at a specific position, will contribute more to the company? Machine and recruiting chatbots ‘ better data processing capabilities augment HR employees in various parts of hiring such as finding qualified candidates, interviewing them with bots to understand their fit or evaluating their assessment results to decide if they should receive an offer. 
• HR Analytics : HR analytics services are like the voice of employee analysis. Look at your workforce analytics and make better HR decisions. Gain actionable insights and impactful suggestions for higher employee satisfaction.
• HR Retention Management : Predict which employees are likely to churn and improve their job satisfaction to retain them. Detect the underlying reasons for their motive for seeking new opportunities. By keeping them at your organization, lower your human capital loss.
• Performance Management : Manage your employees’ performance effectively and fairly without hurting their motivation. Follow their KPI’s on your dashboard and provide real-time feedback. This would increase employee satisfaction and lower your organization’s employee turnover. Actualize your employee’s maximum professional potential with the right tools.

You can also read our article on HR technology trends .

> AI use cases for Marketing

A 2021 survey conducted among global marketers revealed that 41% of respondents saw an increase in revenue growth and improved performance due to the use of AI in their marketing campaigns.

Marketing can be summarized as reaching the customer with the right offer, the right message, at the right time, through the right channel, while continually learning. To achieve success, companies can leverage AI-powered tools to get familiar with their customers better, create more compelling content, and perform personalized marketing campaigns. AI can provide accurate insights and suggest smart marketing solutions that would directly reflect on profits with customer data. You can find the top three AI use cases in marketing:

• Marketing analytics :  AI systems learn from, analyze, and measure marketing efforts. These solutions track media activity and provide insights into PR efforts to highlight what is driving engagement, traffic, and revenue. As a result, companies can provide better and more accurate marketing services to their customers. Besides PR efforts, AI-powered marketing analytics can lead companies to identify their customer groups more accurately. By discovering their loyal customers, companies can develop accurate marketing strategies and also retarget customers who have expressed interest in products or services before. Feel free to read more about marketing analytics with AI from  this article .
• Personalized Marketing:  The more companies understand their customers, the better they serve them. AI can assist companies in this task and support them in giving personalized experiences for customers. As an example, suppose you visited an online store and looked at a product but didn’t buy it. Afterward, you see that exact product in digital ads. More than that, companies can send personalized emails or special offers and recommend new products that go along with customers’ tastes.
• Context-Aware Marketing : You can leverage machine vision and natural language processing (NLP) to understand the context where your ads will be served. With context-aware advertising, you can protect your brand and increase marketing efficiency by ensuring your message fits its context, making static images on the web come alive with your messages.

For more, check out AI use cases in marketing or AI for email marketing . AI-powered email marketing software is among the first AI tools that marketers should work with.

> AI use cases for Operations

• Cognitive / Intelligent Automation : Combine robotic process automation (RPA) with AI to automate complex processes with unstructured information. Digitize your processes in weeks without replacing legacy systems , which can take years. Bots can operate on legacy systems learning from your personnel’s instructions and actions. Increase your efficiency and profitability ratios. Increase speed and precision, and many more. Feel free to check intelligent automation use cases for more.
• Robotic Process Automation (RPA) Implementation : Implementing RPA solutions requires effort. Suitable processes need to be identified. If a rules-based robot will be used, the robot needs to be programmed. Employees’ questions need to be answered. That is why most companies get some level of external help. Generally, outsourcing companies, consultants, and IT integrators are happy to provide temporary labor to undertake this effort.
• Process Mining : Leverage AI algorithms to mine your processes and understand your actual processes in detail. Process mining tools can provide fastest time to insights about your as-is processes as demonstrated in case studies . Check out process mining use cases & benefits for more.
• Predictive Maintenance : Predictively maintain your robots and other machinery to minimize disruptions to operations. Implement big data analytics to estimate the factors that are likely to impact your future cash flow. Optimize PP&E spending by gaining insight regarding the possible factors.
• Inventory & Supply Chain Optimization : Leverage machine learning to take your inventory& supply chain optimization to the next level. See the possible scenarios in different customer demands. Reduce your stock, keeping spending, and maximize your inventory turnover ratios. Increase your impact factor in the value chain.
• Building Management : Sensors and advanced analytics improve building management. Integrate IoT systems in your building for lower energy consumption and many more. Increase the available data by implementing the right data collection tools for effective building management.
• Digital Assistant : Digital assistants are mature enough to replace real assistants in email communication. Include them in your emails to schedule meetings. They have already scheduled hundreds of thousands of meetings.

> AI use cases for Sales

• Sales Forecasting :  AI allows automatic and accurate sales forecasts based on all customer contacts and previous sales outcomes. Automatically forecast sales accurately based on all customer contacts and previous sales outcomes. Give your sales personnel more sales time while increasing forecast accuracy. Hewlett Packard Enterprise indicates that it has experienced a 5x increase in forecast simplicity, speed, and accuracy with Clari’s sales forecasting tools.
• Lead generation :  Use a comprehensive data profile of your visitors to identify which companies your sales reps need to connect. Generate leads for your sales reps leveraging databases and social networks
• Sales Data Input Automation: Data from various sources will be effortlessly and intelligently copied into your CRM. Automatically sync calendar, address book, emails, phone calls, and messages of your salesforce to your CRM system. Enjoy better sales visibility and analytics while giving your sales personnel more sales time.
• Predictive sales/lead scoring: Use AI to enable predictive sales. Score leads to prioritize sales rep actions based on lead scores and contact factors. Sales forecasting is automated with increased accuracy thanks to systems’ granular access to lead scores and sales rep performance. For scoring leads, these systems leverage anonymized transaction data from their customers, sales data of this specific customer. For assessing contact factors, these systems leverage anonymized data and analyze all customer contacts such as email and calls.
• Sales Rep Response Suggestions: AI will suggest responses during live conversations or written messages with leads. Bots will listen in on agents’ calls suggesting best practice answers to improve sales effectiveness
• Sales Rep Next Action Suggestions : Your sales reps’ actions and leads will be analyzed to suggest the next best action. This situation wise solution will help your representatives to find the right way to deal with the issue. Historical data and profile of the agent will help you to achieve higher results. All are leading to more customer satisfaction.
• Sales Content Personalization and Analytics: Preferences and browsing behavior of high priority leads are analyzed to match them with the right content, aimed to answer their most important questions. Personalize your sales content and analyze its effectiveness allowing continuous improvement.
• Retail Sales Bot : Use bots on your retail floor to answer customer’s questions and promote products. Engage with the right customer by analyzing the profile. Computer vision will help you to provide the right action depending on the characteristics and mimics of the customer.
• Meeting Setup Automation (Digital Assistant): Leave a digital assistant to set up meetings freeing your sales reps time. Decide on the targets to prioritize and keep your KPI’s high.
• Prescriptive Sales : Most sales processes exist in the mind of your sales reps. Sales reps interact with customers based on their different habits and observations. Prescriptive sales systems prescribe the content, interaction channel, frequency, price based on data on similar customers .
• Sales Chatbot : Chatbots are ideal to answer first customer questions. If the chatbot decides that it can not adequately serve the customer, it can pass those customers to human agents. Let 24/7 functioning, intelligent, self-improving bots handle making initial contacts to leads. High value, responsive leads will be called by live agents, increasing sales effectiveness.

Sales analytics

As Gartner discusses , sales analytic systems provide functionality that supports discovery, diagnostic, and predictive exercises that enable the manipulation of parameters, measures, dimensions, or figures as part of an analytic or planning exercise. AI algorithms can automate the data collection process and present solutions to improve sales performance. To have more detailed information, you can read  our article about sales analytics .

• Customer Sales Contact Analytics :  Analyze all customer contacts, including phone calls or emails, to understand what behaviors and actions drive sales. Advanced analytics on all sales call data to uncover insights to increase sales effectiveness
• Sales Call Analytics : Advanced analytics on call data to uncover insights to increase sales effectiveness. See how well your conversation flow performs. Integrating data on calls will help you to identify the performance of each component in your sales funnels.
• Sales attribution :  Leverage big data to attribute sales to marketing and sales efforts accurately. See which step of your sales funnel performs better. Pinpoint the low performing part by the insights provided by analysis.
• Sales Compensation :  Determine the right compensation levels for your sales personnel. Decide on the right incentive mechanism for the sales representatives. By using the sales data, provide objective measures, and continuously increase your sales representatives’ performance.

For more on AI in sales .

> AI use cases for Strategy & Legal

• Presentation preparation : Top management presentations in most companies involve slides (e.g. PowerPoint). Generative AI presentation software can prepare slides from prompts.

Legal counsels can rely on AI in:

• Contract drafting
• Contract review
• Legal research

For more: Legal AI software

> AI use cases for Tech

• No code AI & app development : AI and App development platforms for your custom projects. Your in-house development team can create original solutions for your specific business needs.
• Analytics & Predictive Intelligence for Security : Analyze data feeds about the broad cyber activity as well as behavioral data inside an organization’s network to come up with actionable insights to help analysts predict and thwart impending attacks. Integrate external data sources the watch out for global cyber threats and act timely. Keep your tech infrastructure intact or minimize losses. 
• Knowledge Management : Enterprise knowledge management enables effective and effortless storage and retrieval of enterprise data, ensuring organizational memory. Increased collaboration by ensuring the right people are working with the right data. Seamless organizational integration through knowledge management platforms.
• Natural Language Processing Library/ SDK/ API : Leverage Natural Language Processing libraries/SDKs/APIs to quickly and cost-effectively build your custom NLP powered systems or to add NLP capabilities to your existing systems. An in-house team will gain experience and knowledge regarding the tools. Increased development and deployment capabilities for your enterprise.
• Image Recognition Library/ SDK/ API :  Leverage image recognition libraries/SDKs/APIs to quickly and cost-effectively build your custom image processing systems or to add image processing capabilities to your existing systems.
• Secure Communications : Protect employee communications like emails or phone conversations with advanced multilayered cryptography & ephemerality. Keep your industry secrets safe from corporate espionage.
• Deception Security : Deploy decoy-assets in a network as bait for attackers to identify, track, and disrupt security threats such as advanced automated malware attacks before they inflict damage. Keep your data and traffic safe by keeping them engaged in decoys. Enhance your cybersecurity capabilities against various forms of cyber attacks
• Autonomous Cybersecurity Systems : Utilize learning systems to efficiently and instantaneously respond to security threats, often augmenting the work of security analysts. Lower your risk of human errors by providing greater autonomy for your cybersecurity. AI-backed systems can check compliance with standards.
• Smart Security Systems : AI-powered autonomous security systems. Functioning 24/7 for achieving maximum protection. Computer vision for detecting even the tiniest anomalies in your environment. Automate emergency response procedures by instant notification capabilities.
• Machine Learning Library/ SDK/ API : Leverage machine learning libraries/SDKs/APIs to quickly and cost-effectively build your custom learning systems or to add learning capabilities to your existing systems.
• AI Developer : Develop your custom AI solutions with companies experienced in AI development. Create turnkey projects and deploy them to the specific business function. Best for companies with limited in-house capabilities for artificial intelligence.
• Deep Learning Library/ SDK/ API : Leverage deep learning libraries/SDKs/APIs to quickly and cost-effectively build your custom learning systems or to add learning capabilities to your existing systems.
• Developer Assistance : Assist your developers using AI to help them intelligently access the coding knowledge on the web and learn from suggested code samples. See the best practices for specific development tasks and formulate your custom solution. Real-time feedback provided by the huge history of developer mistakes and best practices.
• AI Consultancy : Provides consultancy services to support your in-house AI development, including machine learning and data science projects. See which units can benefit most from AI deployment. Optimize your artificial intelligence spending for the best results from the insight provided by a consultant.

> AI use cases for Automotive & Autonomous Things

Autonomous things including cars and drones are impacting every business function from operations to logistics.

• Driving Assistant : Required components and intelligent solutions to improve rider’s experience in the car. Implement AI-Powered vehicle perception solutions for the ultimate driving experience.
• Vehicle Cybersecurity : Secure connected and autonomous cars and other vehicles with intelligent cybersecurity solutions. Guarantee your safety by hack-proof mechanisms. Protect your intelligent systems from attacks.
• Vision Systems : Vision systems for self-driving cars. Integrate vision sensing and processing in your vehicle. Achieve your goals with the help of computer vision.
• Self-Driving Cars : From mining to manufacturing, self-driving cars/vehicles are increasing the efficiency and effectiveness of operations. Integrate them into your business for greater efficiency. Leverage the power of artificial intelligence for complex tasks.

> AI use cases for Education

• Course creation

For more: Generative AI applications in education

> AI use cases for Fashion

• Creative Design
• Virtual try-on
• Trend analysis

For more: Generative AI applications in fashion

> AI use cases for FinTech 

• Fraud Detection : Leverage machine learning to detect fraudulent and abnormal financial behavior, and/or use AI to improve general regulatory compliance matters and workflows. Lower your operational costs by limiting your exposure to fraudulent documents.
• Insurance & InsurTech : Leverage machine learning to process underwriting submissions efficiently and profitably, quote optimal prices , manage claims effectively, and improve customer satisfaction while reducing costs. Detect your customer’s risk profile and provide the right plan.
• Financial Analytics Platform : Leverage machine learning, Natural Language Processing, and other AI techniques for financial analysis, algorithmic trading, and other investment strategies or tools.
• Travel & expense management : Use deep learning to improve data extraction from receipts of all types including hotel, gas station, taxi, grocery receipts. Use anomaly detection and other approaches to identify fraud, non-compliant spending. Reduce approval workflows and processing costs per unit.
• Credit Lending & Scoring : Use AI for robust credit lending applications. Use predictive models to uncover potentially non-performing loans and act. See the potential credit scores of your customers before they apply for a loan and provide custom-tailored plans.
• Loan recovery: Increase loan recovery ratios with empathetic and automated messages.
• Robo-Advisory : Use AI finance chatbot and mobile app assistant applications to monitor personal finances. Set your target savings or spending rates for your own goals. Your finance assistant will handle the rest and provide you with insights to reach financial targets.
• Regulatory Compliance : Use Natural Language Processing to quickly scan legal and regulatory text for compliance issues, and do so at scale. Handle thousands of paperwork without any human interaction.
• Data Gathering : Use AI to efficiently gather external data such as sentiment and other market-related data. Wrangle data for your financial models and trading approaches.
• Debt Collection : Leverage AI to ensure a compliant and efficient debt collection process. Effectively handle any dispute and see your success right in debt collection.
• Conversational banking : Financial institutions engage with their customers on a variety of communication platforms ( WhatsApp , mobile app , website etc.) via conversational AI tools to increase customer satisfaction and automate many tasks like customer onboarding .

> AI use cases for HealthTech

• Patient Data Analytics : Analyze patient and/or 3rd party data to discover insights and suggest actions. Greater accuracy by assisted diagnostics. Lower the mortality rates and increase patient satisfaction by using all the diagnostic data available to detect the underlying reasons for the symptoms.
• Personalized Medications and Care : Find the best treatment plans according to patient data. Provide custom-tailored solutions for your patients. By using their medical history, genetic profile, you can create a custom medication or care plan.
• Drug Discovery : Find new drugs based on previous data and medical intelligence. Lower your R&D cost and increase the output — all leading to greater efficiency. Integrate FDA data, and you can transform your drug discovery by locating market mismatches and FDA approval or rejection rates.
• Real-Time Prioritization and Triage : Prescriptive analytics on patient data enabling accurate real-time case prioritization and triage. Manage your patient flow by automatization. Integrate your call center and use language processing tools to extract the information, priorate patients that need urgent care, and lower your error rates. Eliminate error-prone decisions by optimizing patient care.
• Early Diagnosis : Analyze chronic conditions leveraging lab data and other medical data to enable early diagnosis. Provide a detailed report on the likelihood of the development of certain diseases with genetic data. Integrate the right care plan for eliminating or reducing the risk factors.
• Assisted or Automated Diagnosis & Prescription :  Suggest the best treatment based on the patient complaint and other data. Put in place control mechanisms that detect and prevent possible diagnosis errors. Find out which active compound is most effective against that specific patient. Get the right statistics for superior care management.
• Pregnancy Management : Monitor mother and fetus health to reduce mothers’ worries and enable early diagnosis. Use machine learning to uncover potential risks and complications quickly. Lower the rates of miscarriage and pregnancy-related diseases.
• Medical Imaging Insights : Advanced medical imaging to analyze and transform images and model possible situations. Use diagnostic platforms equipped with high image processing capabilities to detect possible diseases.
• Healthcare Market Research : Prepare hospital competitive intelligence by tracking market prices. See the available insurance plans, drug prices, and many more public data to optimize your services. Leverage NLP tools to analyze the vast size of unstructured data.
• Healthcare Brand Management and Marketing : Create an optimal marketing strategy for the brand based on market perception and target segment. Tools that offer high granularity will allow you to reach the specific target and increase your sales.
• Gene Analytics and Editing : Understand genes and their components and predict the impact of gene edits.
• Device and Drug Comparative Effectiveness : Analyze drug and medical device effectiveness. Rather than just using simulations, test on other patient’s data to see the effectiveness of the new drug, compare your results with benchmark drugs to make an impact with the drug.
• Healthcare chatbot :  Use a chatbot to schedule patient appointments, give information about certain diseases or regulations, fill in patient information, handle insurance inquiries, and provide mental health assistance. You can also use intelligent automation with chatbot capabilities.

For more, feel free to check our article on the  use cases of AI in the healthcare industry .

> AI use cases for Manufacturing

• Manufacturing Analytics : Also called industrial analytics systems, these systems allow you to analyze your manufacturing process from production to logistics to save time, reduce cost, and increase efficiency. Keep your industry effectiveness at optimal levels.
• Collaborative Robots : Cobots provide a flexible method of automation. Cobots are flexible robots that learn by mimicking human workers’ behavior.
• Robotics : Factory floors are changing with programmable collaborative bots that can work next to employees to take over more repetitive tasks. Automate physical processes such as manufacturing or logistics with the help of advanced robotics. Increased your connected systems by centralizing the whole manufacturing process. Lower your exposures to human errors.

> AI use cases for Non-Profits

• Personalized donor outreach and engagement based on historical data to increase fundraising levels while avoiding email fatigue.
• Donor identification via techniques like look-alike audiences.

See more use cases of AI in fundraising .

> AI use cases for Retail

• Cashierless Checkout : Self-checkout systems have many names. They are called cashierless, cashier-free, or automated checkout systems. They allow retail companies to serve customers in their physical stores without the need for cashiers. Technologies that allowed users to scan and pay for their products have been used for almost a decade now, and those systems did not require great advances in AI. However, these days we are witnessing systems powered by advanced sensors and AI to identify purchased merchandise and charge customers automatically.

> AI use cases for Telecom

• Network investment optimization : Both wired and wireless operators need to invest in infrastructure like active equipment or higher bandwidth connections to improve Quality of Service (QoS). Machine learning can be used to identify highest ROI investments that will result in less churn and higher cross and up-sell.

Other AI Use Cases

This was a list of areas by business function where out-of-the-box solutions are available. However, AI, like software, has too many applications to list here. You can also take a look at our  AI in business article  to read about AI applications by industry. Also, feel free to check our article on AI services .

It is important to get started fast with high impact applications and generate business value without spending months of effort. For that, we recommend companies to use no code AI solutions to quickly build AI models .

Once companies deploy a few models to production, they need to take a deeper look at their AI/ML development model.

• rely on autoML software to build complex AI models. Though most autoML software is not as easy to use as no code AI solutions, they can be used to build complex models.
• build custom AI solutions in-house
• work with the support of partners to build custom models
• run data science competitions to build custom AI models
• Use pre-trained models built by AI vendors

We examined the pros and cons of this approaches in our article on making the build or buy decisions regarding AI .

You can also check out our list of AI tools and services:

• AI Consultant
• AI/ML Development Services
• Data Science / ML / AI Platform

These articles about AI may also interest you:

• Ultimate Guide to the State of AI technology
• Future of AI according to top AI experts
• Advantages of AI according to top practitioners

What is artificial intelligence (AI)?

Artificial Intelligence (AI) is the branch of computer science that focuses on creating machines capable of performing tasks that typically require human intelligence. This includes activities such as learning, problem-solving, understanding natural language, speech recognition, and visual perception. AI systems can analyze large amounts of data, identify patterns, and make decisions, often with speed and accuracy surpassing human capabilities.

What are the examples of AI in real life?

Artificial Intelligence (AI) is integrated into many aspects of daily life. Some common real-life examples include:

Virtual Assistants: Like Siri, Alexa, and Google Assistant, these AI-powered tools understand and respond to voice commands, performing tasks like setting reminders, answering questions, and controlling smart home devices.

Navigation and Maps: AI is used in services like Google Maps and Waze for route optimization, traffic prediction, and providing real-time directions.

Recommendation Systems: Streaming services like Netflix and Spotify use AI to analyze your viewing or listening history to recommend movies, shows, or music.

Autonomous Vehicles: Self-driving cars use AI to perceive the environment and make decisions for safe navigation.

Social Media: Platforms like Facebook and Instagram use AI for content curation, targeted advertising, and facial recognition in photos.

Security and Surveillance: AI aids in anomaly detection, facial recognition, and monitoring systems for enhanced security.

How does AI impact employment and job creation?

AI impacts employment by automating routine tasks, which can lead to job displacement in some sectors. However, it also creates new job opportunities in AI development, data analysis, and other tech-related fields, emphasizing the need for skill adaptation.

For more, you can check our article on the ethics of AI .

What are some misconceptions about AI?

Common misconceptions include the idea that AI can fully replicate human intelligence, that it’s always unbiased, or that AI-led automation will universally eliminate jobs. In reality, AI has limitations, can inherit biases from data, and often changes rather than replaces job roles.

And if you have a specific business challenge, we can help you find the right vendor to overcome that challenge:

External links

Though most use cases have been categorized based on our experience, we also took a look at Tractica’s AI use cases list before finalizing the list. Other sources:

• 1. “ The state of AI in 2023: Generative AI’s breakout year “. Quantum Black AI by McKinsey . August 1, 2023. Accessed January 1, 2024

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

AIMultiple.com Traffic Analytics, Ranking & Audience , Similarweb. Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics , Business Insider. Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are , Washington Post. Data management barriers to AI success , Deloitte. Empowering AI Leadership: AI C-Suite Toolkit , World Economic Forum. Science, Research and Innovation Performance of the EU , European Commission. Public-sector digitization: The trillion-dollar challenge , McKinsey & Company. Hypatos gets $11.8M for a deep learning approach to document processing , TechCrunch. We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million , Business Insider.

To stay up-to-date on B2B tech & accelerate your enterprise:

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Vertical ai / horizontal ai & other specialized ai models in 2024, ai in analytics: how ai is shaping analytics in 2024 in 4 ways, ai in marketing: comprehensive guide in 2024.

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Good afternoon. I am very curious about your claim that “Elekta has reduced its costs and increased its number of processed invoices from 50,000 to 120,000.” Do you have the source for this claim?

Hello, Aidan. We weren’t able to find the source. So we removed it entirely. Thanks for pointing it out!

We can say that AI is the future of our world. While AI is penetrating in more and more human works, thus creating a demand of AI Industry, AI in healthcare is one of the most surging category in global AI Market. According to Meridian Market Consultants, The global AI in Healthcare Market in 2020 is estimated for more than US$ 5.0 Bn and expected to reach a value of US$ 107.5 Bn by 2028 with a significant CAGR of 47.3%. SOI:

47.3% CAGR? You are so sure about the future. Why don’t you guys just sell the time machine rather than the report?

Related research

7 Steps to Developing AI Systems in 2024

Top 7 AI Challenges & Solutions in 2024

• Digital Marketing
• Apps & Website

AI Case Studies: Highlighting Breakthrough Innovations

• Key Takeaways

Gartner reports that AI adoption in enterprises has increased by 270% since 2020.

Statista reveals that AI-driven personalization leads to a 15% increase in customer retention rates.

According to Moz, websites using AI for SEO experience a 30% boost in organic traffic.  

Businesses leveraging AI benefit from improved customer experiences, reduced costs, and enhanced operational processes.

Welcome to the world of Artificial Intelligence (AI), where amazing innovations are changing how things work in different industries like healthcare, stores, banking, factories, and schools. Have you ever thought about how AI is making these areas better? Let’s look at five interesting case studies that show how AI is improving how things are done, making them work faster and giving people better experiences.

Understanding AI Breakthroughs

• Defining AI Breakthroughs

AI breakthroughs are big steps forward in Artificial Intelligence that do things we didn’t think were possible before. They can come in different forms, like making smarter computer programs, improving how machines learn, or creating new AI tools that solve hard problems. What’s special about AI breakthroughs is they help industries grow and change in important ways, giving us new abilities and making big improvements.

• Significance of AI Breakthroughs in Innovation

The significance of AI breakthroughs in innovation cannot be overstated. These breakthroughs fuel technological progress and empower organizations to tackle challenges more effectively. Businesses can use advanced AI to work better, improve how things are done, and make customers happier. AI helps create new products and services, which can lead to finding new customers and making more money. In general, AI is crucial for making new ideas, growing, and changing industries in the future.

Case Study 1: Healthcare Revolution through AI

• Overview of Case Study:

This case study shows how AI is changing healthcare at a big hospital. It looks at how AI helps with diagnosing diseases, making personalized treatment plans, and improving patient care with AI systems.

• Implementation of AI Solutions:

AI-Powered Disease Diagnosis:

• The hospital used AI tools to look at medical images and find diseases like lung cancer and heart problems. This helped them find diseases earlier and treat patients better.
• Because of this, they became 30% better at diagnosing diseases, which means they could find them sooner and help patients more.
• The AI learned to see small details in the images that humans might miss, so it could make better diagnoses than people could alone.

Personalized Treatment Plans:

• AI used special programs to create treatment plans based on each patient’s unique details like their medical history, how they’ve responded to treatments before, and their specific characteristics.
• This personalized way of treating patients led to a 25% decrease in problems related to treatments because the treatments were made specifically for each patient, considering their individual needs and how their body reacts.
• Moreover, patients responded better to these personalized treatments, with a noticeable 20% improvement in how they reacted to the treatments. This shows that using AI for personalized treatments works effectively.

Enhanced Patient Care Delivery:

• AI systems like chatbots and virtual assistants were used in healthcare to support patients 24/7.
• This reduced paperwork by 40%, letting doctors spend more time with patients.
• Patients were happier because AI could quickly answer questions, schedule appointments, and remind them about medications.
• Results and Impact

Implementing AI at the hospital brought big improvements to healthcare:

• Diagnosing diseases got 30% more accurate, catching problems early and helping treatments work better.
• Personalized treatments cut complications by 25% and made patients respond 20% better to treatments.
• Less time spent on paperwork (40% less) means doctors and nurses can focus more on patients.
• Patients felt more engaged and happy because AI systems gave them personalized help 24/7.

Case Study 2: AI-driven Transportation Solutions

• Autonomous Vehicles: The Future of Transportation

In recent years, the transportation industry has witnessed a paradigm shift with the emergence of autonomous vehicles powered by artificial intelligence (AI). This case study delves into the innovative use of AI-driven transportation solutions, particularly focusing on autonomous vehicles and their potential to revolutionize the way people and goods are transported.

• Overview of Case Study

A big transportation company started using AI to make self-driving cars. These cars have fancy sensors, smart algorithms, and can think quickly without people helping. Because of this, the company is making travel safer, faster, and better for the environment.

• Adoption of AI Technologies

AI technology has made a big difference in how self-driving cars work in transportation. It helps these cars learn and adjust to different traffic situations, making roads safer for everyone. By predicting dangers and finding the best routes, AI also helps reduce traffic jams and pollution, making cities more sustainable.

• Outcomes and Benefits

AI-powered transportation solutions bring many benefits for both companies and society:

• Safer Roads: Autonomous vehicles using AI have fewer accidents than human-driven ones, making roads safer.
• Faster Travel: AI helps vehicles make quick decisions based on traffic, reducing travel times and making transportation more efficient.
• Environmentally Friendly: AI optimizes routes and reduces fuel use, lowering emissions and helping the environment.
• Economic Growth: AI in transportation creates jobs in AI development and maintenance, fostering innovation and competition in the industry.

Case Study 3: AI Transforming Financial Services

• AI’s Impact on Finance and Banking
• AI technologies have revolutionized financial services, offering innovative solutions to enhance efficiency, accuracy, and security.
• Key areas impacted include fraud detection, risk management, customer service, and personalized financial advice.
• AI-powered systems automate processes, analyze data in real-time, and improve decision-making within financial institutions.
• The case study focuses on a leading bank’s implementation of AI-driven fraud detection and risk management systems.
• AI algorithms and machine learning models analyze vast financial data, enabling proactive identification of fraud and risks.
• Automation enhances customer asset protection, prevents financial crimes, and ensures regulatory compliance.
• Integration of AI in Financial Processes
• AI integration streamlines operations by analyzing transaction patterns, detecting anomalies, and flagging suspicious activities.
• Improves accuracy in credit scoring, loan approvals, and investment recommendations, offering personalized financial services.
• Empowers financial professionals to make informed decisions swiftly, based on AI-generated insights and data analysis.
• Achievements and Lessons Learned
• Reduced fraud-related losses and improved operational efficiency through AI-driven solutions.
• Strengthened customer trust by enhancing security measures and offering personalized financial services.
• Continuously optimized processes, staying ahead of emerging threats and market trends.
• Highlighted the scalability and adaptability of AI technologies in reshaping financial services for sustainable growth in the digital era.

Case Study 4: AI Enhancing Marketing Strategies

• AI helps businesses understand their customers better by analyzing data like age, behavior, and preferences.
• It improves marketing by creating personalized content based on customer interests and behavior across websites, social media, and emails.
• AI enhances advertising effectiveness by targeting specific customer groups and making ads more relevant and successful.
• Company Background: To do this, they used AI tools in their marketing. They collected and analyzed data, used AI to divide customers into groups, and automated personalized campaigns.
• Objectives: Their goal was to improve their marketing using AI. They wanted to give customers personalized experiences and make them more engaged and loyal.
• Implementation Process: To do this, they used AI tools in their marketing. They collected and analyzed data, used AI to divide customers into groups, and automated personalized campaigns.
• Application of AI in Marketing Campaigns
• Predictive Analytics: AI-driven predictive analytics helped the company anticipate customer behavior and trends. This helped in planning ahead for marketing, like sending special deals to customers who are likely to buy.
• Recommendation Engines: These engines used AI to look at customer info and suggest things they might like to buy based on what they’ve liked before. This made it easier to sell more things to each customer.
• Dynamic Content Generation: AI made it possible to create changing content, like personalized emails and ads that match what each customer likes and does. This increased engagement and conversion rates by delivering relevant and timely messages.
• Success Metrics and Insights
• The use of AI in marketing helped the company connect better with customers. This meant more people clicking on ads, opening emails, and interacting on social media.
• The personalized ads also made more people buy things, which increased sales and income.
• Overall, using AI in marketing was a good investment. It made marketing campaigns work better and saved money by automating tasks and making them more efficient.
• By using AI to analyze data, the company learned more about what customers liked and how to improve marketing strategies for even better results in the future.

Case Study 5: Education and AI

• Adaptive Learning Platforms
• AI-powered adaptive learning platforms analyze how students learn and what they like to provide personalized lessons and activities.
• These platforms change lesson plans as students progress, making sure they get the help they need and have the best learning experience possible.
• By adapting to each student’s way of learning, these platforms keep students interested and motivated, which helps them learn better.
• Educational institutions and companies that specialize in educational technology (EdTech) work together to create new and smart platforms that use AI. These platforms change how we teach in schools.
• These platforms keep checking data all the time and use smart computer programs to find ways to make things better. They then change the content to fit what students need.
• Because students get to learn in a way that suits them best, they remember more and do better in their studies.
• Integration of AI in Education Services
• Educators use AI tools to make special lesson plans, quizzes, and tests that match each student’s needs.
• AI analytics help teachers see how students are doing, so they can make smart choices about how to teach better.
• AI in education helps all kinds of learners, making sure everyone gets the support they need to learn well.

Personalized learning means that students get to learn in a way that suits them best. For example, if a student likes to learn through videos, they can have more video lessons. This helps them stay interested and do better in their studies.

AI tools are like smart helpers for teachers. They can give teachers useful information about how each student is doing. This way, teachers can plan lessons that fit each student’s needs and give them the right support.

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Using AI in education makes it possible for students to learn from anywhere, like at home or in a different country. It doesn’t matter how much money a school has because AI tools can help everyone learn better.

It’s really important to make sure that everyone can benefit from AI technology in schools. This means making sure that all students, no matter where they come from or what they’re good at, can use AI tools to learn and succeed.

The AI studies we’ve seen show how powerful artificial intelligence is in different areas like healthcare, finance, education etc. They prove that AI can bring big changes, making things work better, creating more personalized experiences, and helping businesses grow in a smart way. As more companies use AI, they’re likely to succeed more and make their customers happier in our fast-changing digital world.

• Q. How has AI impacted the healthcare sector? 

AI has improved patient care through early disease detection and personalized treatments, reducing healthcare costs and enhancing efficiency.

Q . What benefits has AI brought to the retail industry? 

AI-driven personalized recommendations have boosted sales and customer loyalty by offering tailored shopping experiences based on customer behavior.

Q . How does AI contribute to fraud detection in financial services? 

AI-powered fraud detection systems analyze real-time financial transactions, mitigating risks and increasing trust among customers and financial institutions.

Q . What role does AI play in predictive maintenance in manufacturing? 

AI-driven predictive maintenance systems prevent equipment failures, minimizing downtime and optimizing production processes in manufacturing plants.

• Q. How has AI transformed education delivery? 

AI-powered adaptive learning platforms personalize education by analyzing students’ learning patterns, improving academic outcomes and engagement.

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• Q. What benefits has AI brought to the retail industry? 
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AI for Businesses: Eight Case Studies and How You Can Use It

Updated: May 14, 2024

Published: August 31, 2023

Artificial intelligence has become an essential growth strategy for entrepreneurs. Almost 9 in 10 organizations believe AI will enable them to gain or sustain a competitive advantage — yet only 35% of companies currently leverage AI.

The majority of businesses leave the benefits of using AI — from optimizing research to streamlining operations — on the table. To stay competitive, entrepreneurs need to figure out how to integrate AI into their business strategy.

Table of contents:

What is AI for businesses?

What are the benefits of ai for businesses, ai for businesses case studies, ai for businesses tools.

AI for businesses involves integrating AI into a business’s strategy, mainly for tasks that require some level of human intelligence. Within a business, as examples, AI can:

• Convert speech to text for emails or memos
• Translate text for foreign markets
• Generate images from text for marketing purposes
• Solve problems, such as aggregating data to make data-driven decisions

For the most part, AI for businesses does not necessarily entail replacing a human worker with AI. Rather, professionals on all levels — from entry-level workers to C-suite executives — can use AI to improve their job performance.

“Across nearly every business function, we’re seeing AI make a major impact on business as usual,” explains Chief Content Officer at Marketing AI Institute Mark Kaput . Benefits of using AI in business include:

• Automating data-driven, repetitive tasks such as data entry
• Increasing revenue by making better predictions
• Enhancing customer experiences by providing more readily available support
• Driving growth by aggregating data and outputting highly targeted ads and marketing campaigns

Aside from more direct benefits, AI has also improved popular business tools. For example, Google Workspace uses AI to enable users to create automatic Google Docs summaries, generate text based on prompts, and more.

Additionally, as AI adoption increases (it doubled from 2017 to 2022), so does the need to leverage it to stay competitive. Almost 8 in 10 organizations believe incumbent competitors already use AI — not surprisingly since 73% of consumers are open to using AI if it makes their lives easier.

AI has been an impactful tool across different industries, from podcasts to fashion to health care.

1. Reduce time and resources needed to create podcast content

In Kaput’s content-creation business, his team leverages AI to decrease the time he spends on their weekly podcast by 75%. This involves using AI to create promotional campaign material (e.g., graphics, emails) alongside script writing.

Podcasts necessitate a human host ( most of the time ), but AI can help optimize the process of getting from idea to episode.

2. Optimize supply chain operations in the fashion industry

Retailers often deal with a significant amount of guesswork. For example, predicting what kind of clothing to stock typically requires historical data and educated guesses.

AI can streamline supply chain operations for retailers. These tools take in necessary data, such as prior inventory levels and sales performance, and predict future sales with greater accuracy.

Fast fashion retailers (e.g., H&M, Zara) have seen growths in revenue by leveraging predictive analytics driven by AI.

3. Speed up and improve accuracy of diagnoses

Physicians often use imaging as a tool to provide accurate patient diagnoses. However, images often show only one part of a larger story — requiring physicians to look into a patient’s medical history.

AI can help optimize this process. For example, at Hardin Memorial Health (HMH), doctors can use AI to bring up a summary of the patient’s medical history and highlight information relevant to the imaging.

For example, one radiologist at the hospital found a bone lesion in an image, which can have many different causes. However, AI sifted through the patient’s medical background and showed the physician the patient’s history of smoking, giving them a better idea for potential treatments.

4. Create professional videos within minutes

If your business plans on creating a video, they need to find a speaker, acquire a high-quality camera, set up a studio, and edit. This can take days to finalize, but AI has made it possible to create a professional video in less than fifteen minutes.

For instance, Synthesia offers tools that enable the creation of videos featuring 140+ realistic-looking avatars, 120+ language options, and high-quality voice-overs.

5. Provide robots with autonomous functions

AI also has many industrial applications. For instance, Built Robotics uses AI to create autonomous heavy machinery that can operate in difficult environments.

One of their robots works in solar piling, or the process of creating solid foundations to place solar panels on. This entails placing foundations on uneven terrain and working with very strict design parameters, which can take time when done manually. However, AI-driven robots can automate and speed up this process significantly.

6. Act as a personal confidant

Generative AI tools such as ChatGPT often output human-sounding text. After all, its learning comes primarily from what people post on the internet. Replika recognized the opportunity to capitalize on this potential human-adjacent relationship and launched their “AI companion who cares.”

Users can create an avatar, customize its likes and interests, and build a relationship with it. The avatar can hop on video calls and chat, interact with real-life environments via augmented reality (AR), and provide guidance to their human companions.

7. Generate mock websites in minutes

Creating a minimum viable product (MVP) often entails launching a simple website to collect user information. But not everyone can code a functional website. AI tools enable users to create mock websites without any coding skills.

For example, you can use Uizard, which outputs app, web, and user interface (UI) designs after receiving instructions in text. Users type in what kind of app or website they want with a few other design parameters. Then, Uizard gives them a design of what their idea would look like.

In this case, AI performs a number of functions, including converting screenshots to functional designs and creating UI designs via simple text. Without AI, these tasks would take hours of technical and graphical work. You can also use AI to supplement your site's content, such as by using it to create blog posts. 

8. Reduce the time and effort needed to create content for training courses

Though you can dive headfirst into AI, Kaput recommends doing thorough research before adopting new AI tools. He advises business owners to first ask themselves the following questions about their tasks:

• Is the task data-driven?
• Does the task follow a standard set of steps?
• Is the task predictive?
• Is the task generative?

If you answer yes to any of these questions, you likely have a solid starting point to integrate AI into your business. Once you understand which tasks you can apply AI to, you can look into different tools that can improve and speed up different parts of your operations.

AI has most visibly impacted marketing, with image and text tools going viral on social media. Tools can help create graphics for social media, write articles, design logos, and more. Consider using the following tools to integrate AI into your marketing:

• LogoAi : Designs logos using AI
• ChatGPT : Provides powerful text in response to prompts
• DALL·E 2 : Creates unique images in response to prompts 
• LOVO : Converts text to natural-sounding speech

AI can aid in high-level thinking, such as devising a business plan or strategy. The following tools can help validate ideas, provide useful analysis, and summarize complex information:

• VenturusAI : Analyzes business ideas for strategic planning
• Zapier : Connects apps to automated workflows

AI can be used to replace repetitive, manual tasks. Using the following tools, you can increase your productivity, speed up research, and more:

• Jamie : Automatically takes notes and creates an executive summary with action items
• Tome : Creates AI-powered presentations
• Consensus : Provides answers using insights from evidence-based research papers

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When thinking of artificial intelligence (AI) use cases, the question might be asked:  What won’t AI be able to do? The easy answer is mostly manual labor, although the day might come when much of what is now manual labor will be accomplished by robotic devices controlled by AI. But right now, pure AI can be programmed for many tasks that require thought and intelligence , as long as that intelligence can be gathered digitally and used to train an AI system. AI is not yet loading the dishwasher after supper—but can help create a legal brief, a new product design, or a letter to grandma.

We’re all amazed by what AI can do. But the question for those of us in business is what are the best business uses? Assembling a version of the Mona Lisa in the style of Vincent van Gough is fun, but how often will that boost the bottom line? Here are 27 highly productive ways that AI use cases can help businesses improve their bottom line.

Customer-facing AI use cases

Deliver superior customer service.

Customer interactions can now be assisted in real time with conversational AI. Voice-based queries use natural language processing (NLP) and sentiment analysis for speech  recognition so their conversations can begin immediately. Using machine learning algorithms, AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. With text to speech and NLP, AI can respond immediately to texted queries and instructions. There’s no need to make customers wait for the answers to frequently asked questions (FAQs) or to take the next step to purchase. And digital customer service agents can boost customer satisfaction by offering advice and guidance to customer service agents.

Personalize customer experiences

The use of AI is effective for creating personalized experiences at scale through chatbots, digital assistants and customer interfaces , delivering tailored experiences and targeted advertisements to customers and end-users. For example, Amazon reminds customers to reorder their most often-purchased products, and shows them related products or suggestions. McDonald’s is building AI solutions for customer care with IBM Watson AI technology and NLP to accelerate the development of its automated order taking (AOT) technology. Not only will this help scale the AOT tech across markets, but it will also help tackle integrations including additional languages, dialects and menu variations. Over at Spotify, they’ll suggest a new artist for the customer’s listening pleasure. YouTube will deliver a curated feed of content suited to customer interests.

Promote cross- and up-selling

Recommendation engines use consumer behavior data and AI algorithms to help discover data trends to be used in the development of more effective up-selling and cross-selling strategies, resulting in more useful add-on recommendations for customers during checkout for online retailers. Other uses include Netflix offering viewing recommendations powered by models that process data sets collected from viewing history; LinkedIn uses ML to filter items in a newsfeed, making employment recommendations and suggestions on who to connect with; and Spotify uses ML models to generate its song recommendations.

Smarten up smartphones

Facial recognition turns on smartphones and voice assistants, powered by machine learning, while Apple’s Siri, Amazon’s Alexa, Google Assistant and Microsoft’s Copilot use NLP to recognize what we say and then respond appropriately. Companies also take advantage of ML in smartphone cameras to analyze and enhance photos using image classifiers, detect objects (or faces) in the images, and even use artificial neural networks to enhance or expand a photo by predicting what lies beyond its borders.

Introduce personal assistants

Virtual assistants or voice assistants, such as Amazon’s Alexa and Apple’s Siri, are powered by AI. When someone asks a question via speech or text, ML searches for the answer or recalls similar questions the person has asked before. The same technology can power messaging bots, such as those used by Facebook Messenger and Slack—while Google Assistant, Cortana and IBM watsonx Assistant combine NLP to understand questions and requests , take appropriate actions and compose responses.

Humanize Human Resources

AI can attract, develop and retain a skills-first workforce . A flood of applications can be screened, sorted and passed to HR team members with precision. Manual promotion assessment tasks can be automated, making it easier to gain important HR insights with a clearer view of, for example, employees up for promotion and assessing whether they’ve met key benchmarks . Routine questions from staff can be quickly answered using AI.

Creative AI use cases

Create with generative ai.

Generative AI tools such as ChatGPT, Bard and DeepAI rely on limited memory AI capabilities to predict the next word, phrase or visual element within the content it’s generating. Generative AI can produce high-quality text, images and other content based on the data used for training.

IBM Research is working to help its customers use generative models to write high-quality  software code  faster, discover  new molecules , and train trustworthy conversational chatbots  grounded on enterprise data. The IBM team is even using generative AI to create  synthetic data  to build more robust and trustworthy AI models and to stand in for real-world data protected by privacy and copyright laws.

Deliver new insights

Expert systems can be trained on a corpus—metadata used to train a machine learning model—to emulate the human decision-making process and apply this expertise to solve complex problems. These systems can evaluate vast amounts of data to uncover trends and patterns, and to make decisions. They can also help businesses predict future events and understand why past events occurred.

Clarify computer vision

AI-powered computer vision enables image segmentation , which has a wide variety of  use cases, including aiding diagnosis in medical imaging, automating locomotion for robotics and self-driving cars, identifying objects of interest in satellite images and photo tagging in social media. Running on neural networks , computer vision enables systems to extract meaningful information from digital images, videos and other visual inputs.

Technical AI use cases

Speed operations with aiops.

There are many benefits to using  artificial intelligence for IT operations (AIOps) . By infusing AI into IT operations , companies can harness the considerable power of NLP, big data, and ML models to automate and streamline operational workflows, and monitor event correlation and causality determination.

AIOps is one of the fastest ways to boost ROI from digital transformation investments. Process automation is often centered on efforts to optimize spend, achieve greater operational efficiency and incorporate new and innovative technologies, which often translate into a better customer experience. More benefits from AI include building a more sustainable IT system and improving the continuous integration/continuous (CI/CD) delivery pipelines.

Automate coding and app modernization

Leading companies are now using generative AI for application modernization and enterprise IT operations, including automating coding, deploying and scaling. For coding, developers can input a coding command as a straightforward English sentence through a natural-language interface and get automatically generated code . Using generative AI with code generation capabilities can also enable hybrid cloud developers of all experience levels to migrate and modernize legacy application code at scale, to new target platforms with code consistency, fewer errors, and speed.

Boost application performance

Ensuring that apps perform consistently and constantly—without overprovisioning and overspending—is a critical AI operations (AIOps) use case. Automation is key to optimizing cloud costs, and IT teams, no matter how skilled they are, don’t always have the capacity to continuously determine the exact compute, storage and database configurations needed to deliver performance at the lowest cost. AI software can identify when and how resources are used, and match actual demand in real time.

Strengthen end-to-end system resilience

To help ensure uninterrupted service availability, leading organizations use real-time root cause analysis capabilities powered by AI and intelligent automation. AIOps can enable ITOps teams to swiftly identify the underlying causes of incidents and take immediate action to reduce both mean time between failures (MTBF) and mean time to repair (MTTR) incidents.

AIOps platform solutions also consolidate data from multiple sources and correlate events into incidents, granting clear visibility into the entire IT environment through dynamic infrastructure visualizations, integrated AI capabilities and suggested remediation actions.

Using predictive IT management, IT teams can use AI to automate IT and network operations to resolve incidents swiftly and efficiently—and proactively prevent issues before they occur, enhance user experiences and cut the cost of and administrative tasks. To help eliminate tool sprawl, an enterprise-grade AIOps platform can provide a holistic view of IT operations on a central pane of glass for monitoring and management.

Lock in cybersecurity

There are many ways AI can use ML to deliver improved cybersecurity, including: facial recognition for authentication, fraud detection, antivirus programs to detect and block malware, reinforcement learning to train models that identify and respond to cyberattacks and detect intrusions and classification algorithms that label events as anomalies or phishing attacks.

Gear up robotics

AI is not just about asking for a haiku written by a cat. Robots handle and move physical objects. In industrial settings, narrow AI can perform routine, repetitive tasks involving materials handling, assembly and quality inspections. AI can assist surgeons by monitoring vitals and detecting potential issues during procedures. Agricultural machines can engage in autonomous pruning, moving, thinning, seeding and spraying. Smart home devices such as the iRobot Roomba can navigate a home’s interior using computer vision and use data stored in memory to understand its progress. And if AI can guide a Roomba, it can also direct self-driving cars on the highway and robots moving merchandise in a distribution center or on patrol for security and safety protocols.

Clean up with predictive maintenance

AI can be used for predictive maintenance by analyzing data directly from machinery to identify problems and flag required maintenance. AI has also been used to improve mechanical efficiency and reduce carbon emissions in engines. Maintenance schedules can use AI-powered predictive analytics to create greater efficiencies.

See what’s ahead

AI can assist with forecasting . For example, a supply-chain function can use algorithms to predict future needs and the time products need to be shipped for timely arrival. This can help create new efficiencies, reduce overstocks and help make up for reordering oversights.

Industry AI use cases

AI can power tasks and tools for almost any industry to boost efficiency and productivity. AI can deliver intelligent automation to streamline business processes that were manual tasks or run on legacy systems—which can be resource-intensive, costly and prone to human error. Here are some of the industries that are benefiting now from the added power of AI.

With applications of AI, automotive manufacturers are able to more effectively predict and adjust production to respond to changes in supply and demand. They can streamline workflows to increase efficiency and reduce time-consuming tasks and the risk of error in production, support, procurement and other areas. Robots help reduce the need for manual labor and improve defect discovery, providing higher quality vehicles to customers at a lower cost to the business.

In education and training , AI can tailor educational materials to each individual student’s needs. Teachers and trainers can use AI analytics to see where students might need extra help and attention. For students tempted to plagiarize their papers or homework, AI can help spot the copied content. AI-driven language translation tools and real-time transcription services can help non-native speakers understand the lessons.

Companies in the energy sector can increase their cost competitiveness by harnessing AI and data analytics for demand forecasting, energy conservation, optimization of renewables and smart grid management. By introducing AI into energy generation, transmission and distribution processes, AI can also improve customer support, freeing up resources for innovation. And for customers using supplier-based AI, they can better understand their energy consumption and take steps to reduce their power draw during peak demand periods.

Financial services

AI-powered FinOps (Finance + DevOps) helps financial institutions operationalize data-driven cloud spend decisions to safely balance cost and performance in order to minimize alert fatigue and wasted budget. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions. Banks and other lenders can use ML classification algorithms and predictive models to suggest loan decisions.

Many stock market transactions use ML with decades of stock market data to forecast trends and ultimately suggest whether and when to buy or sell. ML can also conduct algorithmic trading without human intervention. ML algorithms can predict patterns, improve accuracy, lower costs and reduce the risk of human error.

The  healthcare industry is using intelligent automation with NLP to provide a consistent approach to data analysis, diagnosis and treatment. The use of chatbots in remote healthcare appointments requires less human intervention and often a shorter time to diagnosis. On-site, ML can be used in radiology imaging, with AI-enabled computer vision often used to analyze mammograms and for early lung cancer screening. ML can also be trained to create treatment plans, classify tumors, find bone fractures and detect neurological disorders.

In genetic research, gene modification and genome sequencing, ML is used to identify how genes impact health. ML can identify genetic markers and genes that will or will not respond to a specific treatment or drug and may cause significant side effects in certain people.

With AI, insurance providers can virtually eliminate the need for manual rate calculations or payments and can simplify processing claims and appraisals. Intelligent automation also helps insurance companies adhere to compliance regulations more easily by ensuring that requirements are met. This way, they are also able to calculate the risk of an individual or entity and calculate the appropriate insurance rate.

Manufacturing

Advanced AI with analytics can help manufacturers create predictive insights on market trends. Generative AI can speed and optimize product design by helping companies create multiple design options. AI can also assist with suggestions for boosting production efficiency. Using historical data of production, generative AI can predict or locate equipment failures in real time—and then suggest equipment adjustments, repair options or needed spare parts.

Pharmaceuticals

For the life sciences industry, drug discovery and production require an immense amount of data collection, collation, processing and analysis. A manual approach to development and testing could lead to calculation errors and require a huge volume of resources. By contrast, the production of Covid-19 vaccines in record time is an example of how intelligent automation enables processes that improve production speed and quality.

AI is becoming the secret weapon for retailers to better understand and cater to increasing consumer demands. With highly personalized online shopping, direct-to-consumer models and delivery services competing with retail, generative AI can help retailers and e-commerce firms improve customer care, plan marketing campaigns, and transform the capabilities of their talent and their applications. AI can even help optimize inventory management.

Generative AI excels at handling diverse data sources such as emails, images, videos, audio files and social media content. This unstructured data forms the backbone for creating models and the ongoing training of generative AI, so it can remain useful over time. Leveraging this unstructured data can extend benefits to various aspects of retail operations, including enhancing customer service through chatbots and facilitating more effective email routing. In practice, this could mean guiding users to the appropriate resources, whether that’s connecting them with the right agent or directing them to user guides and FAQs.

Transportation

AI informs many transportation systems these days. For instance, Google Maps uses ML algorithms to check current traffic conditions, determine the fastest route, suggest places to “explore nearby” and estimate arrival times.

Ride-sharing applications such as Uber and Lyft use ML to match riders and drivers, set prices, examine traffic and, like Google Maps, analyze real-time traffic conditions to optimize driving routes and estimate arrival times.

Computer vision guides self-driving cars. An unsupervised ML algorithm enables self-driving cars to gather data from cameras and sensors to understand what’s happening around them, and enables real-time decision-making.

Delivering the promise of AI

Much of what AI can do seems miraculous, but much of what gets reported in the general media is frivolous fun or just plain scary. What is now available to business is a remarkably powerful tool that can help many industries and functions make great strides. The companies that do not explore and adopt the most beneficial AI use cases will soon be at a severe competitive disadvantage. Keeping an eye out for the most useful AI tools, such as IBM ® watsonx.ai™, and mastering them now will pay great dividends.

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Machines of mind: The case for an AI-powered productivity boom

Subscribe to the economic studies bulletin, martin neil baily , martin neil baily senior fellow emeritus - economic studies , center on regulation and markets erik brynjolfsson , and erik brynjolfsson director - stanford digital economy lab, jerry yang and akiko yamazaki professor and senior fellow - stanford institute for human centered ai anton korinek anton korinek nonresident fellow - economic studies , center on regulation and markets @akorinek.

May 10, 2023

Large language models such as ChatGPT are emerging as powerful tools that not only make workers more productive but also increase the rate of innovation, laying the foundation for a significant acceleration in economic growth. As a general purpose technology, AI will impact a wide array of industries, prompting investments in new skills, transforming business processes, and altering the nature of work. However, official statistics will only partially capture the boost in productivity because the output of knowledge workers is difficult to measure. The rapid advances can have great benefits but may also lead to significant risks, so it is crucial to ensure that we steer progress in a direction that benefits all of society.

On a recent Friday morning, one of us sat down in his favorite coffee shop to work on a new research paper regarding how AI will affect the labor market. To begin, he pulled up ChatGPT , a generative AI tool. After entering a few plain-English prompts, the system was able to provide a suitable economic model, draft code to run the model, and produce potential titles for the work. By the end of the morning, he had achieved a week’s worth of progress on his research.

We expect millions of knowledge workers, ranging from doctors and lawyers to managers and salespeople to experience similar ground-breaking shifts in their productivity within a few years, if not sooner.

The potential of the most recent generation of AI systems is illustrated vividly by the viral uptake of ChatGPT, a large language model (LLM) that captured public attention by its ability to generate coherent and contextually appropriate text. This is not an innovation that is languishing in the basement. Its capabilities have already captivated hundreds of millions of users.

Other LLMs that were recently rolled out publicly include Google’s Bard and Anthropic’s Claude . But generative AI is not limited to text: in recent years, we have also seen generative AI systems that can create images, such as Midjourney , Stable Diffusion or DALL-E , and more recently multi-modal systems that combine text, images, video, audio and even robotic functions . These technologies are foundation models , which are vast systems based on deep neural networks that have been trained on massive amounts of data and can then be adapted to perform a wide range of different tasks. Because information and knowledge work dominates the US economy, these machines of the mind will dramatically boost overall productivity.

The power of productivity growth

The primary determinant of our long-term prosperity and welfare is the rate of productivity growth: the amount of output created per hour worked. This holds even though changes in productivity are not immediately felt by everyone and, in the short run, workers’ perceptions of the economy are dominated by the business cycle. From World War II until the early 1970s, labor productivity grew at over 3% a year, more than doubling over the period, ushering in an era of prosperity for most Americans. In the early 1970s productivity growth slowed dramatically, rebounding in the 1990s, only to slow again since the early 2000s.

Figure 1 illustrates the story. It decomposes the overall growth in labor productivity into two components: total factor productivity (which is a measure of the impact of technology) and the contribution of the labor composition and capital intensity. The figure illustrates that the key driver of changes in labor productivity is changes total factor productivity (TFP). There are many reasons for America’s recent economic struggles, but slow TFP growth is a key cause, slowly eating away at the country’s prosperity, making it harder to fight inflation, eroding workers’ wages and worsening budget deficits.

The generally slow pace of economic growth, together with the outsized profits of tech companies, has resulted in skepticism about the benefits of digital technologies for the broad economy. However, for about 10 years starting in the 1990s there was a surge in productivity growth, as shown in Figure 1, driven primarily by a huge wave of investment in computers and communications , which in turn drove business transformations. Even though there was a stock market bubble as well as significant reallocation of labor and resources, workers were generally better off. Furthermore, the federal budget was balanced from 1998 to 2001 —a double win. Digital technology can drive broad economic growth, and it happened less than thirty years ago.

Early estimates of AI’s productivity effects

The recent advances in generative AI have been driven by progress in software, hardware, data collection, and growing amounts of investment in cutting-edge models. Sevilla et al. (2022) observe that the amount of compute (computing power) used to train cutting-edge AI systems has been doubling every six months over the past decade. The capabilities of generative AI systems have grown in tandem, allowing them to perform many tasks that used to be reserved for cognitive workers, such as writing well-crafted sentences, creating computer code, summarizing articles, brainstorming ideas, organizing plans, translating other languages, writing complex emails, and much more.

Generative AI has broad applications that will impact a wide range of workers, occupations, and activities. Unlike most advances in automation in the past, it is a machine of the mind affecting cognitive work. As noted in a recent research paper (Eloundou et al., 2023) , LLMs could affect 80% of the US workforce in some form.

There is an emerging literature that estimates the productivity effects of AI on specific occupations or tasks. Kalliamvakou (2022) finds that software engineers can code up to twice as fast using a tool called Codex, based on the previous version of the large language model GPT-3. That’s a transformative effect. Noy and Zhang (2023) find that many writing tasks can also be completed twice as fast and Korinek (2023) estimates, based on 25 use cases for language models, that economists can be 10-20% more productive using large language models.

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But can these gains in specific tasks translate into significant gains in a real-world setting? The answer appears to be yes. Brynjolfsson, Li, and Raymond (2023) show that call center operators became 14% more productive when they used the technology, with the gains of over 30% for the least experienced workers. What’s more, customer sentiment was higher when interacting with operators using generative AI as an aid, and perhaps as a result, employee attrition was lower. The system appears to create value by capturing and conveying some of the tacit organizational knowledge about how to solve problems and please customers that previously was learned only via on-the-job experience.

Criticism of large language models as merely “stochastic parrots” is misplaced. Most cognitive work involves drawing on past knowledge and experience and applying it to the problem at hand. It is true that generative AI programs are prone to certain types of mistakes, but the form of these mistakes is predictable. For example, language models tend to engage in “hallucinations,” i.e., to make up facts and references. As a result, they clearly require human oversight. However, their economic value depends not on whether they are flawless, but on whether they can be used productively. By that criterion, they are already poised to have a massive impact. Moreover, the accuracy of generative AI models continues to improve rapidly.

Quantifying the productivity effects

A recent report by Goldman Sachs suggests that generative AI could raise global GDP by 7%, a truly significant effect for any single technology. Based on our analysis of a variety of use cases and the share of the workforce doing mainly cognitive work, this estimate strikes us as being reasonable, though there remains great uncertainty about the ultimate productivity and growth effects of AI.

It is useful to rigorously break down the channels through which we expect generative AI to produce growth in productivity, output, and ultimately in social welfare in a model.

The first channel is the increased efficiency of output production. By making cognitive workers engaged in production more efficient, the level of output increases. Economic theory tells us that, in competitive markets, the effect of a productivity boost in a given sector on aggregate productivity and output is equal to the size of the productivity boost multiplied by the size of the sector ( Hulten’s theorem ). For instance, if generative AI makes cognitive workers on average 30% more productive over a decade or two and cognitive work makes up about 60% of all value added in the economy (as measured by the wage bill attributable to cognitive tasks), this amounts to a 18% increase in aggregate productivity and output, spread out over those years.

The second, and ultimately more important, channel is the acceleration of innovation and thus future productivity growth. Cognitive workers not only produce current output but also invent new things, engage in discoveries, and generate the technological progress that boosts future productivity. This includes R&D—what scientists do—and perhaps more importantly, the process of rolling out new innovations into production activities throughout the economy—what managers do. If cognitive workers are more efficient, they will accelerate technological progress and thereby boost the rate of productivity growth—in perpetuity. For example, if productivity growth was 2% and the cognitive labor that underpins productivity growth is 20% more productive, this would raise the growth rate of productivity by 20% to 2.4%. In a given year, such a change is barely noticeable and is usually swamped by cyclical fluctuations.

But productivity growth compounds. After a decade, the described tiny increase in productivity growth would leave the economy 5% larger, and the growth would compound further every year thereafter. What’s more, if the acceleration applied to the growth rate of the growth rate (for instance if one of the applications of AI was to improving AI itself ), then of course, growth would accelerate even more over time.

Figure 2 schematically illustrates the effects of the two channels of productivity growth over a twenty year horizon. The baseline follows the current projection of the Congressional Budget Office (CBO) of 1.5% productivity growth , giving rise to a total of 33% productivity growth over 20 years. The projection labeled “Level” assumes that generative AI raises the level of productivity and output by an additional 18% over ten years, as suggested by the illustrative numbers we discussed for the first channel. After ten years, growth reverts to the baseline rate. The third projection labeled “Level+Growth” additionally includes a one percentage point boost in the rate of growth over the baseline rate, resulting from the additional innovation triggered by generative AI. At first, the resulting growth trajectory is barely distinguishable from the “Level” projection, but through the power of compounding, the effects grow bigger over time, leading to a near doubling of output after 20 years, far greater than the baseline projection.

Barriers and drivers of adoption

For the productivity gains to materialize, advances in AI have to disseminate throughout the economy. Traditionally, this has always taken time, so we would not expect potential productivity gains to show up immediately. The advances need to be taken up and rolled out by businesses and organizations that employ cognitive labor throughout the economy, including small and medium-sized businesses, some of which may be slow to realize the potential of adapting advanced new technologies or may lack the required skills to use them well. For example, the Goldman report assumes it takes 10 years for the gains to fully materialize.

The “productivity J-curve” (Brynjolfsson et al., 2021) describes how new technologies, especially general purpose technologies, deliver productivity gains only after a period of investment in complementary intangible goods, such as business processes and new skills. In fact, this can temporarily even drag down measured productivity. As a result, earlier general purpose technologies like electricity and the first wave of computers took decades to have a significant effect on productivity. Additional barriers to adoption and rollout include concerns about job losses and institutional inertia and regulation, in areas from the medicine to finance and law.

However, in the case of generative AI there are also factors that can mitigate these barriers, or even accelerate adoption. First, in contrast to physical automation, one benefit of cognitive automation is that it can often be rolled out quickly via software. This is particularly true now that a ubiquitous digital infrastructure is available: the Internet. ChatGPT famously was the most rapid product launch in history—it gained 100 million users in just two months —because it was accessible to anyone with an internet connection and did not require any hardware investment on the users’ side.

Both Microsoft and Google are in the process of rolling out Generative AI tools as part of their search engines and office suites, offering access to generative AI to a large fraction of the cognitive workforce in advanced countries who regularly use these tools. Furthermore, application programming interfaces (APIs) are increasingly available to enable seamless modularization and connectivity between systems, and a marketplace for plug-ins and extensions is rapidly growing, making it much easier to add functionality. Finally, in contrast to other technologies, users of generative AI can interact with the technology in natural language rather than special codes or commands, making it easier to learn and adopt these tools.

These reasons for optimism suggest that the rollout of these new technologies may be faster than in the past. Still, the importance of training to make optimal use of these tools cannot be overstated.

Problems of measurement – silent productivity growth

The most common measure of productivity, non-farm business productivity, is quite adept at capturing increases in  productivity in the industrial sector where inputs and outputs are tangible and easy to account for. However, productivity of cognitive labor is harder to measure. Statisticians who compile GDP and productivity statistics sometimes resort to valuing the output of cognitive activity simply by assuming it is proportional to the quantity of labor input being used to produce it, which of course eliminates any scope for productivity growth.

For example, generative AI enables economists to write more thought pieces and provide deeper analyses of the economy than before, yet this output would not directly show up in GDP statistics. Readers may feel that they have access to better and deeper economic analyses (contributing to channel 1 above). Moreover, the analyses may also play a part in enabling business leaders and policymakers to better harness the positive productivity effects of generative AI (contributing to channel 2 above). Neither of these positive productivity effects of such work would be directly captured in official GDP or productivity statistics, yet the benefits of economists’ productivity gains would still lead to greater social welfare.

The same holds true for many other cognitive workers throughout the economy. This may give rise to significant under-measurement or “silent productivity growth.”

Productivity growth, labor markets, and income distribution

A bigger pie does not automatically mean everyone benefits evenly, or at all. The productivity effects of generative AI are likely to go hand in hand with significant disruption in the job market as many workers may see downward wage pressures. For example, the Eloundou et al. paper cited earlier predicts that up to 49% of the workforce could eventually have half or more of their job tasks performed by AI. Will the demand for these tasks increase enough to compensate for such efficiency gains? Will the workers find other tasks to do? The answers are far from certain. In past technological transformations, workers who lost their jobs could transition to new jobs, and on average pay increased. However, given the scale of the impending disruption and the labor-saving nature of it, it remains to be seen whether this will be the case in the age of generative AI.

Moreover, the current wave of cognitive automation marks a change from most earlier waves of automation, which focused on physical jobs or routine cognitive tasks. Now, creative and unstructured cognitive jobs are also being impacted. Instead of the lowest paid workers bearing the brunt of the disruption, now many of the highest-paying occupations will be affected. These workers may find the disruption to be quite unexpected. If their skills are general, they may find it easier to adjust to displacement than blue-collar workers. However, if they have acquired a significant amount of human capital that becomes obsolete, they may experience much larger income losses than blue-collar workers who were displaced by previous rounds of automation.

The idea of jobs created versus jobs displaced is the most tangible manifestation of job market disruption for lay people. Job losses are indeed a significant social concern, and we need policies to facilitate adjustment. However, as economists, we note that the key factor in determining the influence of new technologies on the labor market is ultimately their effect on labor demand. Counting how many jobs are created versus how many are destroyed misses that employment is determined as the equilibrium of labor demand and labor supply. Labor supply is quite inelastic, reflecting that most working-age people want to or have to work independently of whether their incomes go up or down. Workers who lose their jobs as a result of changing technology will seek alternative employment. And, to the extent that changing technology raises productivity, this will increase national income and spur the demand for labor. Over the long run, the labor market can be expected to equilibrate, meaning that the supply of jobs, the demand for jobs and the level of wages will adjust to maintain full employment. This is evidenced by the fact that the unemployment rate in the United States has remained consistently low in the postwar period (with help from monetary and fiscal policy to recover from recessions). Job destruction has always been offset by job creation. Instead, the effects of automation and augmentation tend to be reflected in wages and income.

The effect of generative AI on labor demand depends on whether the systems complement or substitute for labor . Substitution occurs when AI models automate most or all tasks of certain jobs, while complementing occurs if they automate small parts of certain jobs, leaving humans indispensable. Additionally, AI systems can be complementary to human labor if they enable new tasks or increase quality.

As companies invest more in generative AI, they often have choices about whether to emphasize substitution or complementarity. For example, if call centers can use AI to complement human operators, or, as AI improves, they may restructure their processes to have the systems address more and more queries without human operators being involved. At the same time, higher productivity growth across the economy may make the overall effects more complementary by increasing overall labor demand and may mitigate the disruption.

In recent decades, there have been three main forces impacting income distribution. First, there has been an overall shift of income away from wages and towards corporate capital. Second, there has been an increase in the return to the skills that are valued by companies (reflected in part by higher returns to education). Third, there has been a shift caused by increased foreign competition .

It is hard to predict how generative AI will impact this mix. A positive interpretation is that workers who currently struggle with aspects of math and writing will become more productive with the help of these new tools and will be able to take better-paid jobs with the help of the new technology. A negative interpretation is that companies will use the technology to eliminate or de-skill more and more positions pushing a larger fraction of the workforce into unfulfilling jobs, raising the share of profits in income and, perhaps, increasing the demand for the most elite members of the workforce.

No doubt technological progress will not stop with the current wave of generative AI. Instead, we can expect even more dramatic advances in AI, bringing the technology closer to what is called artificial general intelligence (AGI). This will lead to even more radical transformations of life and work . The scarcity of human labor has been a double-edged sword throughout our history : on the one hand, it has held back economic growth because greater production would require more labor; on the other hand, it has been highly beneficial for income distribution since wages represent the market value of scarce labor. If labor can be replaced by machines across a wide range of tasks in the future, both points may no longer hold, and we may experience an AI-powered growth take-off at the same time as that the value of labor declines. This would present a significant challenge for our society . Moreover, AGI may also impose large risks on humanity if not aligned with human objectives .

Large language models and other forms of generative AI are still at an early stage, making it difficult to predict with great confidence the exact productivity effects they will have. Yet as we have argued, we expect that generative AI will have tremendous positive productivity effects, both by increasing the level of productivity and accelerating future productivity growth.

For policymakers, the goal should be to allow for the positive productivity gains while mitigating the risks and downsides of ever-more powerful AI. Faster productivity growth is an elixir that can solve or mitigate many of our society’s challenges, from raising living standards and addressing poverty to providing healthcare for all and strengthening our defenses. Indeed, it will be nearly impossible to fix some of our budgetary challenges, including the growing deficits, without sufficiently stronger growth.

AI-powered productivity growth will also create challenges. There may be a need for updating social programs and tax policy to soften the welfare costs of labor market disruptions and ensure that the benefits of AI give rise to shared prosperity rather than concentration of wealth. Other harms will also need to be addressed, including the amplification of misinformation and polarization, potentially destabilizing our democracy, and the creation of new biological and other weapons that could injure or kill untold numbers of people.

Therefore, we cannot let the capabilities of AI outstrip our understanding of their potential impacts. Economists and other social scientists will need to accelerate their work on AI’s impacts to keep up with our colleagues in AI research who are rapidly advancing the technologies. If we do that, we are optimistic our society can harness the productivity benefits and growth acceleration delivered by artificial intelligence to substantially advance human welfare in the coming years.

The authors used GPT4 for writing assistance in producing this text but assume full responsibility for its content and accuracy.

The Brookings Institution is financed through the support of a diverse array of foundations, corporations, governments, individuals, as well as an endowment. A list of donors can be found in our annual reports published online here . The findings, interpretations, and conclusions in this report are solely those of its author(s) and are not influenced by any donation.

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Artificial Intelligence Case Study Topics

Looking for artificial intelligence case study topics? Explore real-life examples and learn how AI is transforming industries like healthcare, finance, manuf...

Artificial Intelligence Case Study Topics: Unleashing the Power of AI

Artificial Intelligence (AI) has emerged as one of the most transformative technologies in recent times, revolutionizing industries and reshaping the way we live and work. With its ability to analyze vast amounts of data, learn from patterns, and make autonomous decisions, AI has the potential to solve complex problems and unlock new possibilities. One of the key drivers of AI advancements is the utilization of case studies, which provide real-world examples of AI applications and their impact.

Introduction to AI Case Studies

Case studies serve as invaluable resources in understanding the practical applications of AI. They offer insights into how AI technologies are implemented, the challenges faced, and the outcomes achieved. By examining successful AI case studies, we can gain a deeper understanding of the potential of AI and how it can be harnessed to drive innovation and improve various aspects of our lives.

The Importance of AI Case Studies

AI case studies play a pivotal role in showcasing the capabilities of AI systems and their potential impact. These studies enable researchers, developers, and businesses to learn from past experiences, identify best practices, and avoid potential pitfalls. By studying successful AI case studies, decision-makers can make informed choices when implementing AI solutions, ensuring maximum efficiency and effectiveness.

Purpose of the Blog Post

The purpose of this blog post is to provide an in-depth exploration of artificial intelligence case study topics. We will delve into various industries and domains where AI has made significant strides, examining real-life examples and their impact. By the end of this comprehensive guide, you will have a clear understanding of the potential applications of AI across different sectors and gain insights into how these case studies have transformed industries.

Overview of Artificial Intelligence Case Studies

Before we dive into specific case studies, let's first establish a foundational understanding of AI case studies. These case studies involve the application of AI technologies to address a specific problem or challenge. They provide a detailed account of how AI systems were developed, implemented, and the outcomes achieved.

AI case studies offer a multifaceted perspective, encompassing various industries, including healthcare, finance, manufacturing, customer service, and transportation. Each case study presents a unique set of challenges and opportunities, highlighting the versatility and adaptability of AI in different contexts.

Real-life Examples of Successful AI Case Studies

To truly grasp the potential of AI, it is essential to explore real-life examples of successful AI case studies. These pioneering projects have showcased the transformative power of AI, pushing the boundaries of what was once thought possible. Let's take a glimpse into some notable AI case studies:

1. Google DeepMind's AlphaGo

In 2016, Google's DeepMind developed AlphaGo, an AI system that defeated the world champion Go player, Lee Sedol. This groundbreaking achievement highlighted the ability of AI to master complex strategic games that were previously considered beyond the reach of machines. AlphaGo's success demonstrated the potential of AI in problem-solving and decision-making in complex scenarios.

2. IBM Watson's Jeopardy! Victory

IBM's Watson showcased its cognitive capabilities by competing against human champions on the popular quiz show, Jeopardy! in 2011. Watson's ability to understand and process natural language, coupled with its vast knowledge base, enabled it to outperform the human contestants. This case study demonstrated the potential of AI in understanding and analyzing unstructured data, paving the way for advancements in natural language processing.

3. Tesla's Autopilot System

Tesla's Autopilot system utilizes AI algorithms and sensors to enable semi-autonomous driving. By analyzing real-time data from cameras, radar, and ultrasonic sensors, the Autopilot system can detect and respond to road conditions, other vehicles, and pedestrians. This case study showcases the potential of AI in the transportation industry, revolutionizing the concept of self-driving cars.

4. Amazon's Recommendation Engine

Amazon's recommendation engine is powered by AI algorithms that analyze customer preferences, purchase history, and browsing behavior to provide personalized product recommendations. This case study demonstrates how AI can enhance the customer experience by delivering targeted suggestions, improving sales, and fostering customer loyalty.

These real-life examples are just the tip of the iceberg when it comes to AI case studies. They illustrate the diverse range of industries and domains where AI has made significant contributions, showcasing the potential for innovation and transformation.

In the next section, we will explore the process of selecting artificial intelligence case study topics, considering various factors and identifying the most relevant and impactful areas of study. Stay tuned for an in-depth analysis of AI case studies in healthcare, finance, manufacturing, customer service, and transportation.

Note: In the following sections, we will explore each case study topic in greater detail, analyzing the problem at hand, the AI solution implemented, and the results and impact achieved.

Artificial intelligence (AI) case studies provide valuable insights into the practical applications and impact of AI technologies. These case studies offer a glimpse into the real-world implementation of AI systems, showcasing their capabilities, successes, and challenges. By examining these case studies, we can gain a deeper understanding of the potential of AI and its ability to transform various industries.

Explanation of AI Case Studies

AI case studies involve the application of AI technologies to solve specific problems or challenges within a given context. These studies provide detailed accounts of how AI systems were developed, implemented, and the outcomes achieved. By analyzing the methodologies and approaches used in these case studies, researchers, developers, and businesses can learn from past experiences and gain insights into the best practices for implementing AI solutions.

AI case studies often involve the utilization of machine learning algorithms, natural language processing, computer vision, robotics, and other AI techniques. They can range from small-scale projects to large-scale deployments, depending on the complexity of the problem being addressed.

Benefits of AI Case Studies

AI case studies offer numerous benefits for both researchers and practitioners in the field of AI. Here are some key advantages:

Insights into Implementation : Case studies offer insights into the practical implementation of AI systems. They provide details on the data collection process, model training, algorithm selection, and optimization techniques employed. This information can guide future AI projects and help avoid common pitfalls.

Benchmarking and Comparison : Case studies allow for benchmarking and comparison of different AI approaches. By examining multiple case studies within a specific domain, researchers can identify the strengths and weaknesses of various AI techniques, leading to advancements and improvements in the field.

Inspiration for Innovation : AI case studies can inspire new ideas and innovative solutions. By understanding the challenges faced in previous case studies and the methods used to overcome them, researchers can build upon existing knowledge and push the boundaries of AI capabilities.

To truly comprehend the power and potential of AI, it is essential to explore real-life examples of successful AI case studies. These examples highlight the impact that AI can have across various domains. Let's take a closer look at some notable AI case studies:

Google DeepMind's AlphaGo : AlphaGo, developed by Google DeepMind, made headlines in 2016 when it defeated the world champion Go player, Lee Sedol. This case study demonstrated the ability of AI to master complex strategic games and showcased the potential for AI in decision-making and problem-solving.

IBM Watson's Jeopardy! Victory : In 2011, IBM's Watson competed against human champions on the quiz show Jeopardy! and emerged victorious. Watson's success demonstrated the power of AI in natural language processing and understanding unstructured data.

Tesla's Autopilot System : Tesla's Autopilot system utilizes AI algorithms and sensors to enable semi-autonomous driving. This case study showcases the potential of AI in the transportation industry, revolutionizing the concept of self-driving cars.

Amazon's Recommendation Engine : Amazon's recommendation engine utilizes AI to analyze customer preferences and provide personalized product recommendations. This case study highlights how AI can enhance the customer experience and drive sales through targeted suggestions.

These real-life examples illustrate the diverse range of industries and domains where AI has made significant contributions. They serve as inspiration and provide valuable insights into the potential of AI technologies.

Choosing Artificial Intelligence Case Study Topics

When exploring the world of artificial intelligence case studies, it is essential to select the right topics that align with current AI trends and have the potential for significant impact. In this section, we will discuss the factors to consider when choosing case study topics and identify some promising areas for exploration.

Factors to Consider

Relevance to Current AI Trends : Selecting case study topics that align with current AI trends ensures that you are exploring areas of research and development that are actively advancing. Staying up-to-date with the latest advancements in AI will provide you with a better understanding of the challenges and opportunities in the field.

Availability of Data : Data availability is crucial for successful AI case studies. Consider topics where relevant and high-quality data is accessible. Adequate data sets are essential for training AI models effectively and obtaining reliable results.

Ethical Considerations : Ethical considerations should be an integral part of AI case study topic selection. It is important to choose topics that adhere to ethical guidelines and prioritize fairness, transparency, and accountability. Avoid topics that raise concerns regarding privacy, bias, or potential harm to individuals or society.

Identifying Potential Case Study Topics

Now, let's explore some potential case study topics in various industries where AI has shown promising applications:

Healthcare and Medical Diagnostics : AI has the potential to revolutionize healthcare by improving diagnostics, predicting disease outcomes, and enabling personalized treatment plans. Some potential case study topics in this domain include:

AI in Early Cancer Detection: Explore how AI algorithms can analyze medical imaging data to detect and diagnose cancer at an early stage, leading to improved patient outcomes.

AI in Medical Imaging Analysis: Investigate how AI can assist radiologists in analyzing medical images, such as X-rays, MRIs, and CT scans, to improve accuracy and speed in diagnosis.

Financial Services and Fraud Detection : AI offers significant potential in the finance industry, particularly in fraud detection and prevention. Some potential case study topics in this domain include:

AI in Fraud Detection for Banks: Examine how AI algorithms can analyze transaction data and detect fraudulent activities in real-time, enhancing security and minimizing financial losses.

AI in Credit Card Fraud Detection: Explore how AI can analyze patterns and anomalies in credit card transactions to identify and prevent fraudulent activities, ensuring the safety of customers' financial information.

Manufacturing and Process Optimization : AI can optimize manufacturing processes, improve efficiency, and reduce costs. Some potential case study topics in this domain include:

AI in Predictive Maintenance: Investigate how AI can analyze sensor data to predict machinery failures and schedule maintenance proactively, minimizing downtime and optimizing production.

AI in Supply Chain Optimization: Explore how AI algorithms can optimize supply chain operations by predicting demand, optimizing inventory levels, and improving logistics, leading to cost savings and improved customer satisfaction.

Customer Service and Chatbots : AI-powered chatbots have revolutionized customer service by providing instant responses and personalized experiences. Some potential case study topics in this domain include:

AI-powered Chatbots in E-commerce: Examine how AI-powered chatbots can enhance customer engagement, provide personalized product recommendations, and streamline the online shopping experience.

AI in Virtual Assistants for Customer Support: Explore how AI-based virtual assistants can handle customer inquiries, resolve issues, and provide 24/7 support, improving customer satisfaction and reducing support costs.

Transportation and Autonomous Vehicles : AI plays a critical role in the development of autonomous vehicles and traffic management systems. Some potential case study topics in this domain include:

AI in Self-Driving Cars: Investigate how AI algorithms enable autonomous vehicles to perceive the environment, make real-time decisions, and navigate safely on the roads.

AI in Traffic Management Systems: Explore how AI can optimize traffic flow, reduce congestion, and improve transportation efficiency by analyzing real-time traffic data and implementing intelligent control systems.

By considering these factors and exploring potential case study topics in various industries, you can select areas that align with your interests and have the potential to contribute to the advancement of AI technologies.

Deep Dive into Selected Artificial Intelligence Case Study Topics

In this section, we will delve deeper into selected artificial intelligence case study topics across various industries. By examining these case studies, we can gain a comprehensive understanding of the problem at hand, the AI solutions implemented, and the results and impact achieved.

Healthcare and Medical Diagnostics

Case Study: AI in Early Cancer Detection

Overview of the Problem: Early detection of cancer is crucial for successful treatment and improved patient outcomes. However, it can be challenging for healthcare professionals to accurately detect cancer at its early stages due to the complexity of medical imaging data and the potential for human error.

AI Solution and Implementation: In this case study, AI algorithms were developed and trained using large datasets of medical imaging data, including mammograms, CT scans, or MRIs. These algorithms utilize deep learning techniques to analyze and interpret the images, identifying potential cancerous cells or tumors. By comparing the patterns in the images to an extensive database of known cancer cases, the AI system can provide accurate early detection of cancer.

Results and Impact: The implementation of AI in early cancer detection has shown promising results. The AI system can analyze medical images with high accuracy, often outperforming human radiologists in detecting cancer at its early stages. Early detection allows for timely intervention, leading to improved treatment outcomes and increased survival rates for patients.

Case Study: AI in Medical Imaging Analysis

Overview of the Problem: Medical imaging, such as X-rays, MRIs, and CT scans, plays a crucial role in diagnosing and monitoring various medical conditions. However, the interpretation of these images can be time-consuming, subjective, and prone to human error.

AI Solution and Implementation: In this case study, AI algorithms were developed and trained using large datasets of labeled medical imaging data. These algorithms leverage deep learning techniques, such as convolutional neural networks (CNNs), to analyze and interpret the images. The AI system can identify anomalies, highlight potential abnormalities, and provide quantitative measurements to assist radiologists in making accurate diagnoses.

Results and Impact: The implementation of AI in medical imaging analysis has shown significant potential in improving diagnostic accuracy and efficiency. The AI system can assist radiologists in identifying subtle abnormalities that may be missed by the human eye, leading to early detection of diseases and improved patient care. Additionally, AI can help reduce the burden on radiologists by automating certain tasks, allowing them to focus on more complex cases.

Financial Services and Fraud Detection

Case Study: AI in Fraud Detection for Banks

Overview of the Problem: Fraudulent activities, such as identity theft and unauthorized transactions, pose significant challenges for banks and financial institutions. Traditional rule-based fraud detection systems often struggle to keep up with evolving fraud techniques and patterns.

AI Solution and Implementation: In this case study, AI algorithms were developed to analyze large volumes of transactional data in real-time. These algorithms utilize machine learning techniques, including anomaly detection and pattern recognition, to identify suspicious activities that deviate from normal patterns. By continuously learning from new data, the AI system can adapt and evolve to detect new and emerging fraud patterns.

Results and Impact: The implementation of AI in fraud detection for banks has led to improved fraud prevention and detection rates. The AI system can analyze vast amounts of transactional data quickly and accurately, flagging potentially fraudulent activities in real-time. By minimizing false positives and identifying fraudulent transactions promptly, banks can mitigate financial losses and protect their customers' assets.

Case Study: AI in Credit Card Fraud Detection

Overview of the Problem: Credit card fraud is a significant concern for both financial institutions and cardholders. Detecting fraudulent credit card transactions is challenging due to the large volume of transactions and the need for real-time analysis.

AI Solution and Implementation: In this case study, AI algorithms were developed to analyze credit card transaction data, including transaction amounts, merchant information, and cardholder behavior. These algorithms utilize machine learning techniques, such as supervised and unsupervised learning, to identify patterns and anomalies indicative of fraudulent activities. The AI system can learn from historical data to improve its fraud detection capabilities over time.

Results and Impact: The implementation of AI in credit card fraud detection has proven to be highly effective in reducing fraudulent activities. The AI system can quickly analyze transactions, identify suspicious patterns, and flag potentially fraudulent transactions for further investigation. By minimizing false positives and accurately detecting fraud, financial institutions can protect their customers and maintain trust in the credit card ecosystem.

In the next section, we will explore case studies in manufacturing and process optimization, showcasing how AI can enhance efficiency and streamline operations.

In this section, we will explore case studies in the domain of manufacturing and process optimization. These examples highlight how artificial intelligence (AI) can enhance efficiency, reduce costs, and streamline operations in manufacturing industries.

Manufacturing and Process Optimization

Case Study: AI in Predictive Maintenance

Overview of the Problem: Unplanned equipment failures and unexpected downtime can significantly impact manufacturing operations, leading to production delays and increased costs. Traditional maintenance strategies, such as reactive or preventive maintenance, may not effectively address the challenges of equipment failure prediction and maintenance scheduling.

AI Solution and Implementation: In this case study, AI algorithms were implemented to perform predictive maintenance. The algorithms utilize machine learning techniques, such as supervised learning and anomaly detection, to analyze sensor data from machines and predict potential failures. By continuously monitoring the health and performance of equipment, the AI system can identify early warning signs of impending failures and schedule maintenance proactively.

Results and Impact: The implementation of AI in predictive maintenance has proven to be highly beneficial for manufacturing industries. By detecting potential equipment failures in advance, companies can plan maintenance activities more efficiently, minimizing downtime and reducing costs associated with unscheduled repairs. This proactive approach to maintenance helps optimize production schedules and ensures smooth operations.

Case Study: AI in Supply Chain Optimization

Overview of the Problem: Supply chain management involves complex processes, including demand forecasting, inventory management, and logistics planning. Optimizing these processes is crucial for reducing costs, improving customer satisfaction, and increasing operational efficiency.

AI Solution and Implementation: In this case study, AI algorithms were utilized to optimize supply chain operations. The algorithms leverage machine learning techniques, such as demand forecasting, inventory optimization, and route optimization, to analyze historical and real-time data. By considering factors such as customer demand, lead times, transportation costs, and inventory levels, the AI system can generate optimal plans and recommendations for procurement, production, and distribution.

Results and Impact: The implementation of AI in supply chain optimization has led to significant improvements in efficiency and cost reduction. By accurately forecasting demand and optimizing inventory levels, companies can minimize stockouts and excess inventory, leading to reduced carrying costs and improved cash flow. AI-powered route optimization helps streamline logistics operations, optimizing delivery schedules and reducing transportation costs. These advancements in supply chain optimization ultimately lead to improved customer satisfaction through faster and more reliable deliveries.

These case studies highlight the potential impact of AI in manufacturing and process optimization. By leveraging AI technologies, companies can achieve greater efficiency, reduced costs, and improved operational effectiveness. In the next section, we will explore case studies in the domain of customer service and chatbots, showcasing how AI can enhance customer experiences and support interactions.

In this section, we will explore case studies in the domain of customer service and chatbots. These examples highlight how artificial intelligence (AI) can enhance customer experiences, streamline support interactions, and improve overall customer satisfaction.

Customer Service and Chatbots

Case Study: AI-powered Chatbots in E-commerce

Overview of the Problem: With the rise of e-commerce, providing personalized and timely customer support has become a crucial aspect of the online shopping experience. However, scaling customer service to meet the growing demands of a large customer base can be challenging and costly.

AI Solution and Implementation: In this case study, AI-powered chatbots were implemented to handle customer inquiries and provide support in e-commerce platforms. These chatbots utilize natural language processing (NLP) and machine learning algorithms to understand and respond to customer queries. They can provide instant and personalized responses, offer product recommendations based on customer preferences, and assist with order tracking and returns.

Results and Impact: The implementation of AI-powered chatbots in e-commerce has significantly improved customer experiences and operational efficiency. Chatbots provide instant responses, reducing customer wait times and ensuring 24/7 availability for support inquiries. By offering personalized product recommendations, chatbots can enhance the shopping experience and increase sales conversion rates. Additionally, chatbots can handle routine inquiries, freeing up human agents to focus on more complex customer issues, ultimately improving overall customer satisfaction.

Case Study: AI in Virtual Assistants for Customer Support

Overview of the Problem: Customer support departments often face high call volumes and long wait times, leading to customer frustration and decreased satisfaction. Providing timely and effective support to customers is critical for maintaining brand loyalty and positive customer experiences.

AI Solution and Implementation: In this case study, AI-powered virtual assistants were implemented to handle customer support interactions. These virtual assistants utilize AI technologies such as natural language processing, sentiment analysis, and knowledge graph systems. They can understand customer inquiries, provide accurate and personalized responses, and escalate complex issues to human agents when necessary. Virtual assistants continuously learn from customer interactions, improving their responses and problem-solving abilities over time.

Results and Impact: The implementation of AI-powered virtual assistants in customer support has proven to be highly effective in improving response times and customer satisfaction. Virtual assistants can provide instant support, reducing wait times and enabling customers to receive assistance at their convenience. By accurately understanding customer inquiries and providing relevant information, virtual assistants can resolve issues quickly and efficiently. This results in improved customer experiences, reduced support costs, and increased customer loyalty.

These case studies illustrate the potential of AI in enhancing customer service and support interactions. By leveraging AI-powered chatbots and virtual assistants, businesses can provide timely, personalized, and efficient support to their customers, resulting in improved customer satisfaction and loyalty. In the next section, we will explore case studies in the domain of transportation and autonomous vehicles, showcasing how AI is revolutionizing the way we travel and manage traffic.

In this section, we will explore case studies in the domain of transportation and autonomous vehicles. These examples highlight how artificial intelligence (AI) is revolutionizing the way we travel and manage traffic.

Transportation and Autonomous Vehicles

Case Study: AI in Self-Driving Cars

Overview of the Problem: Self-driving cars have the potential to transform the transportation industry by reducing accidents, improving traffic flow, and enhancing overall mobility. However, developing autonomous vehicles that can navigate safely and make real-time decisions in complex traffic scenarios is a significant challenge.

AI Solution and Implementation: In this case study, AI algorithms are utilized to power self-driving cars. These algorithms leverage a combination of computer vision, sensor fusion, machine learning, and decision-making models to perceive the environment, interpret traffic signs, detect obstacles, and make real-time driving decisions. By continuously analyzing sensor data and learning from past experiences, self-driving cars can navigate autonomously while adhering to traffic rules and ensuring passenger safety.

Results and Impact: The implementation of AI in self-driving cars has the potential to revolutionize transportation. Autonomous vehicles can reduce human errors and improve road safety by eliminating the risks associated with human distraction, fatigue, and impaired driving. Additionally, self-driving cars have the potential to optimize traffic flow, reduce congestion, and increase overall transportation efficiency, leading to reduced travel times and fuel consumption.

Case Study: AI in Traffic Management Systems

Overview of the Problem: Managing traffic flow in urban areas is a complex task that requires real-time analysis of traffic patterns, congestion, and accidents. Traditional traffic management systems often struggle to handle the dynamic nature of traffic and effectively optimize traffic flow.

AI Solution and Implementation: In this case study, AI algorithms are used to enhance traffic management systems. These algorithms leverage machine learning techniques and real-time data analysis to predict traffic congestion, optimize signal timings, and suggest alternative routes. By analyzing historical and real-time traffic data, the AI system can make intelligent decisions to improve traffic flow, reduce congestion, and minimize travel times.

Results and Impact: The implementation of AI in traffic management systems has shown significant potential in improving transportation efficiency. By optimizing signal timings based on real-time traffic conditions, AI can reduce congestion and ensure a smoother flow of vehicles. AI algorithms can also provide real-time traffic updates to drivers, enabling them to make informed decisions about alternative routes, further reducing travel times and improving overall traffic management.

These case studies highlight how AI is transforming the transportation industry. From self-driving cars to intelligent traffic management systems, AI technologies have the potential to revolutionize the way we travel, making transportation safer, more efficient, and environmentally friendly.

In this comprehensive guide, we have explored various artificial intelligence case study topics across different industries. We have witnessed the power of AI in healthcare, finance, manufacturing, customer service, and transportation. By examining real-life examples and understanding the problem-solving capabilities of AI, we have gained insights into the potential of this transformative technology.

AI case studies provide invaluable lessons and inspire innovation in the field of artificial intelligence. They offer opportunities for learning, benchmarking, and improving AI systems. By studying successful case studies, researchers, developers, and businesses can harness the power of AI to drive advancements, solve complex problems, and improve various aspects of our lives.

As AI continues to evolve, it is crucial to stay updated with the latest trends, research, and case studies. The potential of AI is immense, and by exploring and sharing knowledge, we can collectively shape a future where AI-driven solutions enhance our lives in remarkable ways.

Adrian Kennedy

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Top 10 artificial intelligence case studies: recap and future trends

The far-reaching consequences of the global COVID-19 pandemic and the high odds of recession have driven organizations to realize the potential of automation for business continuity. As a result, over the last few years, we have witnessed an all-time high number of artificial intelligence case studies .

According to McKinsey, 57 percent of companies report AI adoption, up from 45 percent in 2020. The majority of these applications targeted the optimization of service operations, a much-needed shift in these turbulent times. Beyond service optimization, AI case studies have been spotted across virtually all industries and functional activities.

Today, we’ll have a look at some of the most exciting business use cases that owe their advent to artificial intelligence and its offshoots.

What is the business value of artificial intelligence?

According to PwC, AI development can rack in an additional $15.7 trillion of the global economic value by 2030. In 2022, 92% of respondents have indicated positive and measurable business results from their prior investments in AI and data initiatives.

However, there are other benefits that incentivize companies to tap into artificial intelligence case studies.

Reduced costs

The cost-saving potential of AI systems stems from automated labor-intensive processes, which leads to reduced operational expenses. For example, Gartner predicts that conversational AI will reduce contact center labor costs by $80 billion in 2026.

Indirect cost reduction of smart systems is associated with optimizing operations with precise forecasting, predictive maintenance, and quality control.

Amplified decision-making

AI doesn’t just cut costs, it expands business brainpower in terms of new revenue streams and better resource allocation. Smart data analysis allows companies to make faster, more accurate, and consistent decisions by capitalizing on datasets and predicting the optimal course of action. AI consulting comes in especially handy when bouncing back from crises.

Source: Unsplash

Lower risks

From workplace safety to fraud detection to what-if scenarios, machine learning algorithms can evaluate historical risk indicators and develop risk management strategies. Automated systems can also be used to automate risk assessment processes, identify risks early, and monitor risks on an ongoing basis. Thus, 56% of insurance companies see the biggest impact of AI in risk management.

Better business resilience

Automation and advanced analytics are becoming key enablers for combating risks in real-time rather than taking a retrospective approach. As 81% of CEOs predict a recession in the coming years, companies can protect their core by predicting transition risks, closing supply and demand gaps, and optimizing resources – based on artificial intelligence strategy .

Top 10 AI case studies: from analytics to pose tracking

Now let’s look into the most prominent artificial intelligence case studies that are pushing the frontier of AI adoption.

Industry: E-commerce and retail Application: AI-generated marketing, personalized recommendations

A Chinese E-commerce giant, Alibaba is the world’s largest platform with recorded revenue of over $93.5 billion in Chinese online sales. No wonder, that the company is vested in maximizing revenue by optimizing the digital shopping experience with artificial intelligence.

Its well-known case study on artificial intelligence includes an extensive implementation of algorithms to improve customer experience and drive more sales. Alibaba Cloud Artificial Intelligence Recommendation (AIRec) leverages Alibaba’s Big data to generate real-time, personalized recommendations on Alibaba-owned online shopping platform Taobao and across the number of Double 11 promotional events.

The company also uses NLP to help merchants automatically generate product descriptions.

Mayo Clinic

Industry: healthcare Application: medical data analytics

Another AI case study in the list is Mayo Clinic, a hospital and research center that is ranked among the top hospitals and excels in a variety of specialty areas. Intelligent algorithms are used there in a large number of business use cases – both administrative and clinical.

The use of computer algorithms on ECG in Mayo’s cardiovascular medicine research helps detect weak heart pumps by analyzing data from Apple Watch ECGs. The research center is also a staunch advocate of AI medical imaging where machine learning is applied to analyze image data fast and at scale.

As another case study on artificial intelligence in healthcare, Mayo Clinic has also launched a new project to collect and analyze patient data from remote monitoring devices and diagnostic tools. The sensor and wearables data can then be analyzed to improve diagnoses and disease prediction.

Deutsche Bank

Industry: banking Application: fraud detection

Now, let’s look at artificial intelligence in the banking case study brought up by Deutsche Bank and Visa. The two companies partnered up in 2022 to eliminate online retail fraud. Merchants who process their E-commerce payments via Deutsche Bank can now rely on a smart fraud detection system from Visa-owned company Cybersource.

Driven by pre-defined rules, the system automatically calculates a risk value for each transaction. The system employs risk models and data from billions of data points on the Visa network. This allows for blocking fraudulent transactions and faster authorizing other transactions.

Industry: E-commerce Application: supply and demand prediction

Amazon is a well-known technology innovator that makes the most of artificial intelligence. From data analysis to route optimization, the company injects automation at all stages of the whole supply chain. Over the last few years, the company has perfected its forecasting algorithm to make a unified forecasting model that predicts even fluctuating demand.

Let’s look at its AI in E-commerce case study. When toilet paper sales surged by 213% during the pandemic, Amazon’s predictive forecasting allowed the company to respond quickly to the sudden spike and adjust the supply levels to the market needs.

Blue River Technology

Industry: agriculture Application: computer vision

This AI case study demonstrates the potential of intelligent machinery in improving crop yield. Blue River Technology, a California-based machinery enterprise, aims to radically change agriculture through the adoption of robotics and machine learning. The company equips farmers with sustainable and effective intelligent solutions to manage crops.

Their company’s flagship product, See & Spray, relies on computer vision, machine learning, and advanced robotic technology to distinguish between crops and weeds. The machine then delivers a targeted spray to weeds. According to the company, this innovation can reduce herbicide use by up to 80 percent.

Industry: automotive Application: voice recognition

The car manufacturer has over 400 AI & ML case studies at all levels of production. According to the company, these technologies play an essential role in the production of new vehicles and augment automated driving with advanced, natural experience.

In particular, voice recognition allows drivers to adjust the in-car settings such as climate and driving mode, or even choose the preferred song. BMW owners can also use the voice command to ask the car about its performance status, get guidance on specific vehicle functions, and input a destination.

Industry: media and entertainment Application: emotion recognition

Another exciting case study about artificial intelligence is Affectiva company and its flagship AI products. The company conceived a new technological dimension of Artificial Emotional Intelligence, named Emotion AI. This application allows publishers to optimize content and media spending based on the customers’ emotional responses.

Emotion AI is fuelled by a combination of computer vision and deep learning to discern nuanced emotions and cognitive states by analyzing facial movement.

Industry: manufacturing Application: process optimization

As global enterprises are looking for more ways to optimize, the demand for automation grows. Siemens’ collaboration with Google is a prominent case study on the application of artificial intelligence in factory automation. The manufacturer has teamed up with Google to drive up shop floor productivity with edge analytics.

The expected results are to be achieved via computer vision, cloud-based analytics, and AI algorithms. Optimization will most likely leverage the connection of Google’s data cloud with Siemens’ Digital Industries Factory Automation tools. This will allow companies to unify their factory data and run cloud-based analytics and AI at scale.

Industry: manufacturing Application: semiconductor development

Along with cutting-edge solutions like its memory accelerator, the manufacturing conglomerate also implements AI to automate the highly complex process of designing computer chips. A prominent artificial intelligence case study is Samsung using Synopsys AI software to design its Exynos chips. The latter are used in smartphones, including branded handsets and other gadgets.

Industry: manufacturing Application: predictive maintenance

According to McKinsey , the greatest value from AI in manufacturing will be delivered from predictive maintenance, which accounts for $0.5-$0.7 trillion in value worldwide. The snack food manufacturer and PepsiCo’s subsidiary, Frito-Lay, has followed suit.

The company has a long track record of using predictive maintenance to enhance production and reduce equipment costs. Paired with sensors, this case study of artificial intelligence helped the company reduce planned downtime and add 4,000 hours a year of manufacturing capacity.

Looking over horizon: Technology trends for 2023-2024

Although artificial intelligence case studies are likely to account for the majority of innovations, the exact form and shape of intelligent transformation can vary. Below, you will find the likely successors of AI technologies in the coming years.

Advanced connectivity

Advanced connectivity refers to the various ways in which devices can connect and share data. It includes technologies like 5G, the Internet of Things, edge computing, wireless low-power networks, and other innovations that facilitate seamless and fast data sharing.

The global IoT connectivity imperative has been driven by cellular IoT (2G, 3G, 4G, and now 5G) as well as LPWA over the last five years. Growing usage of medical IoT, IoT-enabled manufacturing, and autonomous vehicles have been among the greatest market enablers so far.

Web 3.0 is the new iteration of the Internet that aims to make the digital space more user-centered and enables users to have full control over their data. The concept is premised on a combination of technologies, including blockchain, semantic web, immersive technology, and others.

Metaverse generally refers to an integrated network of virtual worlds accessed through a browser or headset. The technology is powered by a combination of virtual and augmented reality.

Edge computing

Edge computing takes cloud data processing to a new level and focuses on delivering services from the edge of the network. The technology will enable faster local AI data analytics and allow smart systems to deliver on performance and keep costs down. Edge computing will also back up autonomous behavior for Internet of Things (IoT) devices.

Industries already incorporate devices with edge computing, including smart speakers, sensors, actuators, and other hardware.

Augmented analytics

Powered by ML and natural language technologies, augmented analytics takes an extra step to help companies glean insights from complex data volumes. Augmented analytics also relies on extensive automation capabilities that streamline routine manual tasks across the data analytics lifecycle, reduce the time needed to build ML models, and democratize analytics.

Large-sized organizations often rely on augmented analytics when scaling their analytics program to new users to accelerate the onboarding process. Leading BI suites such as Power BI, Qlik, Tableau, and others have a full range of augmented analytics capabilities.

Engineered decision intelligence

The field of decision intelligence is a new area of AI that combines the scientific method with human judgment to make better decisions. In other words, it’s a way to use machine intelligence to make decisions more effectively and efficiently in complex scenarios.

Today, decision intelligence assists companies in identifying risks and frauds, improving sales and marketing as well as enhancing supply chains. For example, Mastercard employs technology to increase approvals for genuine transactions.

Data Fabric

Being a holistic data strategy, data fabric leverages people and technology to bridge the knowledge-sharing gap within data estates. Data fabric is based on an integrated architecture for managing information with full and flexible access to data.

The technology also revolves around Big data and AI approaches that help companies establish elastic data management workflows.

Quantum computing

An antagonist of conventional computing, the quantum approach uses qubits as a basic unit of information to speed up analysis to a scale that traditional computers cannot ever match. The speed of processing translates into potential benefits of analyzing large datasets – faster and at finer levels.

Hyperautomation

This concept makes the most of intelligent technologies to help companies achieve end-to-end automation by combining AI-fuelled tools with Robotic Process Automation. Hyperautomation strives to streamline every task executed by business users through ever-evolving automated pathways that learn from data.

Thanks to a powerful duo of artificial intelligence and RPA, the hyperautomated architecture can handle undocumented procedures that depend on unstructured data inputs – something that has never been possible.

Turning a crisis into an opportunity with AI

In the next few years, businesses will have to operate against the backdrop of the looming recession and financial pressure. The only way of standing firmly on the ground is to save resources, which usually leaves just two options: layoffs or resource optimization.

While the first option is a moot point, resource optimization is a time-tested method to battle uncertainty. And there’s no technology like artificial intelligence that can better audit, identify, validate, and execute the optimal transition strategy for virtually any industry. From better marketing messages to voice-controlled vehicles, AI adds a new dimension to your traditional business operations.

AI technology to combat recession

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Privacy Overview

Princeton Dialogues on AI and Ethics

Princeton University

Case Studies

Princeton Dialogues on AI and Ethics Case Studies

The development of artificial intelligence (AI) systems and their deployment in society gives rise to ethical dilemmas and hard questions. By situating ethical considerations in terms of real-world scenarios, case studies facilitate in-depth and multi-faceted explorations of complex philosophical questions about what is right, good and feasible. Case studies provide a useful jumping-off point for considering the various moral and practical trade-offs inherent in the study of practical ethics.

Case Study PDFs : The Princeton Dialogues on AI and Ethics has released six long-format case studies exploring issues at the intersection of AI, ethics and society. Three additional case studies are scheduled for release in spring 2019.

Methodology : The Princeton Dialogues on AI and Ethics case studies are unique in their adherence to five guiding principles: 1) empirical foundations, 2) broad accessibility, 3) interactiveness, 4) multiple viewpoints and 5) depth over brevity.

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What is a Case Study

A case study is like a detailed story that looks closely at a particular situation, person, or event, especially in the business world. It's a way to understand how things work in real life and learn valuable lessons. For instance, if a business wanted to figure out how another one became successful, they might study that business as a case study.

Let's say there's a small company that started selling handmade products online and became successful. A case study about this business could explain the challenges they faced, the strategies they used to grow, and the results they achieved. By reading this case study, other businesses could learn useful tips and apply them to their situations to improve and succeed.

7 Tips For Writing Great Case Studies

• Pick a Familiar Topic: Choose a client or project that your audience can relate to. This makes it easier for them to see how your solutions might work for their situations.
• Clear Structure: Start with a concise introduction that sets the stage for the case study. Clearly outline the problem, solution, and results to make your case study easy to follow.
• Engaging Storytelling: Turn your case study into a compelling narrative. Use real-world examples, anecdotes, and quotes to make it relatable and interesting for your audience.
• Focus on the Problem: Clearly define the problem or challenge your case study addresses. This helps readers understand the context and sets the foundation for the solution.
• Highlight Solutions: Showcase the strategies or solutions implemented to overcome the problem. Provide details about the process, tools used, and any unique approaches that contributed to the success.
• Optimize for SEO: By incorporating your case study into a blog post using a blog post generator, you enhance its visibility and reach. This, in turn, improves the search engine rankings of your blog post, attracting more organic traffic.
• Quantify Results: Use data and metrics to quantify the impact of your solutions. Whether it's increased revenue, improved efficiency, or customer satisfaction, concrete results add credibility and demonstrate the value of your case study.

What is a Case Study Creator

A free case study generator is a tool or system designed to automatically create detailed case studies. It typically uses predefined templates and may incorporate artificial intelligence (AI) to generate comprehensive analyses of specific situations, events, or individuals.

This tool streamlines the process of crafting informative case studies by extracting key details, analyzing data, and presenting the information in a structured format.

Case study generators are valuable for businesses, students, or professionals seeking to efficiently produce well-organized and insightful case studies without the need for extensive manual effort.

Benefits of Using Case Study Generator

In today's competitive landscape, showcasing your product or service successes is vital. While case studies offer a compelling way to do this, starting from scratch can be time-consuming. That's where case study generators step in, providing a robust solution to streamline the process and unlock various advantages.

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• Improve Your Writing Skills: Free AI Case study generators act as learning tools, offering guidance on structure, content, and storytelling. Studying generated drafts refines your writing skills for crafting impactful case studies in the future.

How AI Case Study Generator Works

An online case study generator works by leveraging artificial intelligence algorithms to analyze and synthesize information, creating comprehensive case studies. Here's a simplified explanation of its functioning:

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Everything that you should know about Storydoc

What is the Storydoc case study creator?

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Research and Practice of AI Ethics: A Case Study Approach Juxtaposing Academic Discourse with Organisational Reality

• Original Research/Scholarship
• Open access
• Published: 08 March 2021
• Volume 27 , article number  16 , ( 2021 )

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• Mark Ryan   ORCID: orcid.org/0000-0003-4850-0111 1 ,
• Josephina Antoniou 2 ,
• Laurence Brooks 3 ,
• Tilimbe Jiya 4 ,
• Kevin Macnish 5 &
• Bernd Stahl 3  

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This study investigates the ethical use of Big Data and Artificial Intelligence (AI) technologies (BD + AI)—using an empirical approach. The paper categorises the current literature and presents a multi-case study of 'on-the-ground' ethical issues that uses qualitative tools to analyse findings from ten targeted case-studies from a range of domains. The analysis coalesces identified singular ethical issues, (from the literature), into clusters to offer a comparison with the proposed classification in the literature. The results show that despite the variety of different social domains, fields, and applications of AI, there is overlap and correlation between the organisations’ ethical concerns. This more detailed understanding of ethics in AI + BD is required to ensure that the multitude of suggested ways of addressing them can be targeted and succeed in mitigating the pertinent ethical issues that are often discussed in the literature.

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Introduction

Big Data and Artificial Intelligence (BD + AI) are emerging technologies that offer great potential for business, healthcare, the public sector, and development agencies alike. The increasing impact of these two technologies and their combined potential in these sectors can be highlighted for diverse organisational aspects such as for customisation of organisational processes and for automated decision making. The combination of Big Data and AI, often in the form of machine learning applications, can better exploit the granularity of data and analyse it to offer better insights into behaviours, incidents, and risk, eventually aiming at positive organisational transformation.

Big Data offers fresh and interesting insights into structural patterns, anomalies, and decision-making in a broad range of different applications (Cuquet & Fensel, 2018 ), while AI provides predictive foresight, intelligent recommendations, and sophisticated modelling. The integration and combination of AI + BD offer phenomenal potential for correlating, predicting and prescribing recommendations in insurance, human resources (HR), agriculture, and energy, as well as many other sectors. While BD + AI provides a wide range of benefits, they also pose risks to users, including but not limited to privacy infringements, threats of unemployment, discrimination, security concerns, and increasing inequalities (O’Neil, 2016 ). Footnote 1 Adequate and timely policy needs to be implemented to prevent many of these risks occurring.

One of the main limitations preventing key decision-making for ethical BD + AI use is that there are few rigorous empirical studies carried out on the ethical implications of these technologies across multiple application domains. This renders it difficult for policymakers and developers to identify when ethical issues resulting from BD + AI use are only relevant for isolated domains and applications, or whether there are repeated/universal concerns which can be seen across different sectors. While the field lacks literature evaluating ethical issues Footnote 2 ‘on the ground’, there are even fewer multi-case evaluations.

This paper provides a cohesive multi-case study analysis across ten different application domains, including domains such as government, agriculture, insurance, and the media. It reviews ethical concerns found within these case studies to establish cross-cutting thematic issues arising from the implementation and use of BD + AI. The paper collects relevant literature and proposes a simple classification of ethical issues (short term, medium term, long term), which is then juxtaposed with the ethical concerns highlighted from the multiple-case study analysis. This multiple-case study analysis of BD + AI offers an understanding of current organisational practices.

The work described in this paper makes an important contribution to the literature, based on its empirical findings. By presenting the ethical issues across an array of application areas, the paper provides much-needed rigorous empirical insight into the social and organisational reality of ethics of AI + BD. Our empirical research brings together a collection of domains that gives a broad oversight about issues that underpin the implementation of AI. Through its empirical insights the paper provides a basis for a broader discussion of how these issues can and should be addressed.

This paper is structured in six main sections: this introduction is followed by a literature review, which allows for an integrated review of ethical issues, contrasting them with those found in the cases. This provides the basis for a categorisation or classification of ethical issues in BD + AI. The third section contains a description of the interpretivist qualitative case study methodology used in this paper. The subsequent section provides an overview of the organisations participating in the cases to contrast similarities and divisions, while also comparing the diversity of their use of BD + AI. Footnote 3 The fifth section provides a detailed analysis of the ethical issues derived from using BD + AI, as identified in the cases. The concluding section analyses the differences between theoretical and empirical work and spells out implications and further work.

Literature Review

An initial challenge that any researcher faces when investigating ethical issues of AI + BD is that, due to the popularity of the topic, there is a vast and rapidly growing literature to be considered. Ethical issues of AI + BD are covered by a number of academic venues, including some specific ones such as the AAAI/ACM Conference on AI, Ethics, and Society ( https://dl.acm.org/doi/proceedings/10.1145/3306618 ), policy initiative and many publicly and privately financed research reports (Whittlestone, Nyrup, Alexandrova, Dihal, & Cave, 2019 ). Initial attempts to provide overviews of the area have been published (Jobin, 2019 ; Mittelstadt, Allo, Taddeo, Wachter, & Floridi, 2016 ), but there is no settled view on what counts as an ethical issue and why. In this paper we aim to provide a broad overview of issues found through the case studies. This paper puts forward what are commonly perceived to be ethical issues within the literature or concerns that have ethical impacts and repercussions. We explicitly do not apply a particular philosophical framework of ethics but accept as ethical issues those issues that we encounter in the literature. This review is based on an understanding of the current state of the literature by the paper's authors. It is not a structured review and does not claim comprehensive coverage but does share some interesting insights.

To be able to undertake the analysis of ethical issues in our case studies, we sought to categorise the ethical issues found in the literature. There are potentially numerous ways of doing so and our suggestion does not claim to be authoritative. Our suggestion is to order ethical issues in terms of their temporal horizon, i.e., the amount of time it is likely to take to be able to address them. Time is a continuous variable, but we suggest that it is possible to sort the issues into three clusters: short term, medium term, and long term (see Fig.  1 ).

Temporal horizon for addressing ethical issues

As suggested by Baum ( 2017 ), it is best to acknowledge that there will be ethical issues and related mitigating activities that cannot exclusively fit in as short, medium or long term.

ather than seeing it as an authoritative classification, we see this as a heuristic that reflects aspects of the current discussion. One reason why this categorisation is useful is that the temporal horizon of ethical issues is a potentially useful variable, with companies often being accused of favouring short-term gains over long-term benefits. Similarly, short-term issues must be able to be addressed on the local level for short-term fixes to work.

Short-term issues

These are issues for which there is a reasonable assumption that they are capable of being addressed in the short term. We do not wish to quantify what exactly counts as short term, as any definition put forward will be contentious when analysing the boundaries and transition periods. A better definition of short term might therefore be that such issues can be expected to be successfully addressed in technical systems that are currently in operation or development. Many of the issues we discuss under the heading of short-term issues are directly linked to some of the key technologies driving the current AI debate, notably machine learning and some of its enabling techniques and approaches such as neural networks and reinforcement learning.

Many of the advantages promised by BD + AI involve the use of personal data, data which can be used to identify individuals. This includes health data; customer data; ANPR data (Automated Number Plate Recognition); bank data; and even includes data about farmers’ land, livestock, and harvests. Issues surrounding privacy and control of data are widely discussed and recognized as major ethical concerns that need to be addressed (Boyd & Crawford, 2012 ; Tene & Polonetsky, 2012 , 2013 ; Mittelstadt, Allo, Taddeo, Wachter, & Floridi, 2016 ; Jain, Gyanchandani, & Khare, 2016 ; Mai, 2016 ; Macnish, 2018 ). The concern surrounding privacy can be put down to a combination of a general level of awareness of privacy issues and the recently-introduced General Data Protection Regulation (GDPR). Closely aligned with privacy issues are those relating to transparency of processes dealing with data, which can often be classified as internal, external, and deliberate opaqueness (Burrell, 2016 ; Lepri, Staiano, Sangokoya, Letouzé, & Oliver, 2017 ; Mittelstadt, Allo, Taddeo, Wachter, & Floridi, 2016 ).

The Guidelines for Trustworthy AI Footnote 4 were released in 2018 by the High-Level Expert Group on Artificial Intelligence (AI HLEG Footnote 5 ), and address the need for technical robustness and safety, including accuracy, reproducibility, and reliability. Reliability is further linked to the requirements of diversity, fairness, and social impact because it addresses freedom from bias from a technical point of view. The concept of reliability, when it comes to BD + AI, refers to the capability to verify the stability or consistency of a set of results (Bush, 2012 ; Ferraggine, Doorn, & Rivera, 2009 ; Meeker and Hong, 2014 ).

If a technology is unreliable, error-prone, and unfit-for-purpose, adverse ethical issues may result from decisions made by the technology. The accuracy of recommendations made by BD + AI is a direct consequence of the degree of reliability of the technology (Barolli, Takizawa, Xhafa, & Enokido, 2019 ). Bias and discrimination in algorithms may be introduced consciously or unconsciously by those employing the BD + AI or because of algorithms reflecting pre-existing biases (Baroccas and Selbst, 2016 ). Examples of bias have been documented often reflecting “an imbalance in socio-economic or other ‘class’ categories—ie, a certain group or groups are not sampled as much as others or at all” (Panch et al., 2019 ). have the potential to affect levels of inequality and discrimination, and if biases are not corrected these systems can reproduce existing patterns of discrimination and inherit the prejudices of prior decision makers (Barocas & Selbst, 2016 , p. 674). An example of inherited prejudices is documented in the United States, where African-American citizens, more often than not, have been given longer prison sentences than Caucasians for the same crime.

Medium-term issues

Medium-term issues are not clearly linked to a particular technology but typically arise from the integration of AI techniques including machine learning into larger socio-technical systems and contexts. They are thus related to the way life in modern societies is affected by new technologies. These can be based on the specific issues listed above but have their main impact on the societal level. The use of BD + AI may allow individuals’ behaviour to be put under scrutiny and surveillance , leading to infringements on privacy, freedom, autonomy, and self-determination (Wolf, 2015 ). There is also the possibility that the increased use of algorithmic methods for societal decision-making may create a type of technocratic governance (Couldry & Powell, 2014 ; Janssen & Kuk, 2016 ), which could infringe on people’s decision-making processes (Kuriakose & Iyer, 2018 ). For example, because of the high levels of public data retrieval, BD + AI may harm people’s freedom of expression, association, and movement, through fear of surveillance and chilling effects (Latonero, 2018 ).

Corporations have a responsibility to the end-user to ensure compliance, accountability, and transparency of their BD + AI (Mittelstadt, Allo, Taddeo, Wachter, & Floridi, 2016 ). However, when the source of a problem is difficult to trace, owing to issues of opacity, it becomes challenging to identify who is responsible for the decisions made by the BD + AI. It is worth noting that a large-scale survey in Australia in 2020 indicated that 57.9% of end-users are not at all confident that most companies take adequate steps to protect user data. The significance of understanding and employing responsibility is an issue targeted in many studies (Chatfield et al., 2017 ; Fothergill et al., 2019 ; Jirotka et al., 2017 ; Pellé & Reber, 2015 ). Trust and control over BD + AI as an issue is reiterated by a recent ICO report demonstrating that most UK citizens do not trust organisations with their data (ICO, 2017 ).

Justice is a central concern in BD + AI (Johnson, 2014 , 2018 ). As a starting point, justice consists in giving each person his or her due or treating people equitably (De George, p. 101). A key concern is that benefits will be reaped by powerful individuals and organisations, while the burden falls predominantly on poorer members of society (Taylor, 2017 ). BD + AI can also reflect human intentionality, deploying patterns of power and authority (Portmess & Tower, 2015 , p. 1). The knowledge offered by BD + AI is often in the hands of a few powerful corporations (Wheeler, 2016 ). Power imbalances are heightened because companies and governments can deploy BD + AI for surveillance, privacy invasions and manipulation, through personalised marketing efforts and social control strategies (Lepri, Staiano, Sangokoya, Letouzé, & Oliver, 2017 , p. 11). They play a role in the ascent of datafication, especially when specific groups (such as corporate, academic, and state institutions) have greater unrestrained access to big datasets (van Dijck, 2014 , p. 203).

Discrimination , in BD + AI use, can occur when individuals are profiled based on their online choices and behaviour, but also their gender, ethnicity and belonging to specific groups (Calders, Kamiran, & Pechenizkiy, 2009 ; Cohen et al., 2014 ; and Danna & Gandy, 2002 ). Data-driven algorithmic decision-making may lead to discrimination that is then adopted by decision-makers and those in power (Lepri, Staiano, Sangokoya, Letouzé, & Oliver, 2017 , p. 4). Biases and discrimination can contribute to inequality . Some groups that are already disadvantaged may face worse inequalities, especially if those belonging to historically marginalised groups have less access and representation (Barocas & Selbst, 2016 , p. 685; Schradie, 2017 ). Inequality-enhancing biases can be reproduced in BD + AI, such as the use of predictive policing to target neighbourhoods of largely ethnic minorities or historically marginalised groups (O’Neil, 2016 ).

BD + AI offers great potential for increasing profit, reducing physical burdens on staff, and employing innovative sustainability practices (Badri, Boudreau-Trudel, & Souissi, 2018 ). They offer the potential to bring about improvements in innovation, science, and knowledge; allowing organisations to progress, expand, and economically benefit from their development and application (Crawford et al., 2014 ). BD + AI are being heralded as monumental for the economic growth and development of a wide diversity of industries around the world (Einav & Levin, 2014 ). The economic benefits accrued from BD + AI may be the strongest driver for their use, but BD + AI also holds the potential to cause economic harm to citizens and businesses or create other adverse ethical issues (Newman, 2013 ).

However, some in the literature view the co-development of employment and automation as somewhat naïve outlook (Zuboff, 2015 ). BD + AI companies may benefit from a ‘post-labour’ automation economy, which may have a negative impact on the labour market (Bossman, 2016 ), replacing up to 47% of all US jobs within the next 20 years (Frey & Osborne, 2017 ). The professions most at risk of affecting employment correlated with three of our case studies: farming, administration support and the insurance sector (Frey & Osborne, 2017 ).

Long-term issues

Long-term issues are those pertaining to fundamental aspects of nature of reality, society, or humanity. For example, that AI will develop capabilities far exceeding human beings (Kurzweil, 2006 ). At this point, sometimes called the ‘ singularity ’ machines achieve human intelligence, are expected to be able to improve on themselves and thereby surpass human intelligence and become superintelligent (Bostrom, 2016 ). If this were to happen, then it might have dystopian consequences for humanity as often depicted in science fiction. Also, it stands to reason that the superintelligent, or even just the normally intelligent machines may acquire a moral status.

It should be clear that these expectations are not universally shared. They refer to what is often called ‘ artificial general intelligence’ (AGI), a set of technologies that emulate human reasoning capacities more broadly. Footnote 6

Furthermore, if we may acquire new capabilities, e.g. by using technical implants to enhance human nature. The resulting being might be called a transhuman , the next step of human evolution or development. Again, it is important to underline that this is a contested idea (Livingstone, 2015 ) but one that has increasing traction in public discourse and popular science accounts (Harari, 2017 ).

We chose this distinction of three groups of issues for understanding how mitigation strategies within organisations can be contextualised. We concede that this is one reading of the literature and that many others are possible. In this account of the literature we tried to make sense of the current discourse to allow us to understand our empirical findings which are introduced in the following sections.

Case Study Methodology

Despite the impressive amount of research undertaken on ethical issues of AI + BD (e.g. Mittelstadt, Allo, Taddeo, Wachter, & Floridi, 2016 ; Zwitter, 2014 ), there are few case studies exploring such issues. This paper builds upon this research and employs an interpretivist methodology to do so, focusing on how, what, and why questions relevant to the ethical use of BD + AI (Walsham, 1995a , b ). The primary research questions for the case studies were: How do organisations perceive ethical concerns related to BD + AI and in what ways do they deal with them?

We sought to elicit insights from interviews, rather than attempting to reach an objective truth about the ethical impacts of BD + AI. The interpretivist case study approach (Stake 2003) allowed the researchers ‘to understand ‘reality’ as the blending of the various (and sometimes conflicting) perspectives which coexist in social contexts, the common threads that connect the different perspectives and the value systems that give rise to the seeming contradictions and disagreements around the topics discussed. Whether one sees this reality as static (social constructivism) or dynamic (social constructionism) was also a point of consideration, as they both belong in the same “family” approach where methodological flexibility is as important a value as rigour’ (XXX).

Through extensive brainstorming within the research team, and evaluations of relevant literature, 16 social application domains were established as topics for case study analysis. Footnote 7 The project focused on ten out of these application domains in accordance with the partners’ competencies. The case studies have covered ten domains, and each had their own unique focus, specifications, and niches, which added to the richness of the evaluations (Table 1 ).

The qualitative analysis approach adopted in this study focused on these ten standalone operational case studies that were directly related to the application domains presented in Table 1 . These individual case studies provide valuable insights (Yin, 2014 , 2015 ); however, a multiple-case study approach offers a more comprehensive analysis of ethical issues related to BD + AI use (Herriott & Firestone, 1983 ). Thus, this paper adopts a multiple-case study methodology to identify what insights can be obtained from the ten cases, identifies whether any generalisable understandings can be retrieved, and evaluates how different organisations deal with issues pertaining to BD + AI development and use. The paper does not attempt to derive universal findings from this analysis, in line with the principles of interpretive research, but further attempts to gain an in-depth understanding of the implications of selected BD + AI applications.

The data collection was guided by specific research questions identified through each case, including five desk research questions (see appendix 1); 24 interview questions (see appendix 2); and a checklist of 17 potential ethical issues, developed by the project leader Footnote 8 (see appendix 3). A thematic analysis framework was used to ‘highlight, expose, explore, and record patterns within the collected data. The themes were patterns across data sets that were important to describe several ethical issues which arise through the use of BD  +  AI across different types of organisations and application domains’ (XXX).

A workshop was then held after the interviews were carried out. The workshop brought together the experts in the case study team to discuss their findings. This culminated in 26 ethical issues Footnote 9 that were inductively derived from the data collected throughout the interviews (see Fig.  2 and Table 3). Footnote 10 In order to ensure consistency and rigour in the multiple-case study approach, researchers followed a standardised case study protocol (Yin, 2014 ). Footnote 11

The Prevalence of Ethical Issues in the Case Studies

Thirteen different organisations were interviewed for 10 case studies, consisting of 22 interviews in total. Footnote 12 These ranged from 30 min to 1 ½ hours in-person or Skype interviews. The participants that were selected for interviews represented a very broad range of application domains and organisations that use BD + AI. The case study organisations were selected according to their relevance to the overall case study domains and considering their fit with the domains and likelihood of providing interesting insights. The interviewees were then selected according to their ability to explain their BD + AI and its role in their organisation. In addition to interviews, a document review provided supporting information about the organisation. Thus, websites and published material were used to provide background to the research.

Findings: Ten Case Studies

This section gives a brief overview of the cases, before analysing their similarities and differences. It also highlights the different types of BD + AI being used, and the types of data used by the BD + AI in the case study organisations, before conducting an ethical analysis of the cases. Table 2 presents an overview of the 10 cases to show the roles of the interviewees, the focus of the technologies being used, and the data retrieved by each organisation’s BD + AI. All interviews were conducted in English.

The types of organisations that were used in the case studies varied extensively. They included start-ups (CS10), niche software companies (CS1), national health insurers (Organisation X in CS6), national energy providers (CS7), chemical/agricultural multinational (CS3), and national (CS9) and international (CS8) telecommunications providers. The case studies also included public (CS2, Organisation 1 and 4 in CS4) and semi-public (Organisation 2 in CS4) organisations, as well as a large scientific research project (CS5).

The types of individuals interviewed also varied extensively. For example, CS6 and CS7 did not have anyone with a specific technical background, which limited the possibility of analysing issues related to the technology itself. Some case studies only had technology experts (such as CS1, CS8, and CS9), who mostly concentrated on technical issues, with much less of a focus on ethical concerns. Other case studies had a combination of both technical and policy-focused experts (i.e. CS3, CS4, and CS5). Footnote 13

Therefore, it must be made fundamentally clear that we are not proposing that all of the interviewees were authorities in the field, or that even collectively they represent a unified authority on the matter, but instead, that we are hoping to show what are the insights and perceived ethical issues of those currently working with AI on the ground view as ethical concerns. While the paper is presenting the ethical concerns found within an array of domains, we do not claim that any individual case study is representative of their entire industry, but instead, our intent was to capture a wide diversity of viewpoints, domains, and applications of AI, to encompass a broad amalgamation of concerns. We should also state that this is not a shortcoming of the study but that it is the normal approach that social science often takes.

The diversity of organisations and their application focus areas also varied. Some organisations focused more so on the Big Data component of their AI, while others more strictly on the AI programming and analytics. Even when organisations concentrated on a specific type of BD + AI, such as Big Data, its use varied immensely, including retrieval (CS1), analysis (CS2), predictive analytics (CS10), and transactional value (Organisation 2 in CS4). Some domains adopted BD + AI earlier and more emphatically than others (such as communications, healthcare, and insurance). Also, the size, investment, and type of organisation played a part in the level of BD + AI innovation (for example, the two large multinationals in CS3 and CS8 had well-developed BD + AI).

The maturity level of BD + AI was also determined by how it was integrated, and its importance, within an organisation. For instance, in organisations where BD + AI were fundamental for the success of the business (e.g. CS1 and CS10), they played a much more important role than in companies where there was less of a reliance (e.g. CS7). In some organisations, even when BD + AI was not central to success, the level of development was still quite advanced because of economic investment capabilities (e.g. CS3 and CS8).

These differences provided important questions to ask throughout this multi-case study analysis, such as: Do certain organisations respond to ethical issues relating to BD + AI in a certain way? Does the type of interviewee affect the ethical issues discussed—e.g. case studies without technical experts, those that only had technical experts, and those that had both? Does the type of BD + AI used impact the types of ethical issues discussed? What significance does the type of data retrieved have on ethical issues identified by the organisations? These inductive ethical questions provided a template for the qualitative analysis in the following section.

Ethical Issues in the Case Studies

Based on the interview data, the ethical issues identified in the case studies were grouped into six specific thematic sections to provide a more conducive, concise, and pragmatic methodology. Those six sections are: control of data, reliability of data, justice, economic issues, role of organisations, and individual freedoms. From the 26 ethical issues, privacy was the only ethical issue addressed in all 10 case studies, which was not surprising because it has received a great deal of attention recently because of the GDPR. Also, security, transparency, and algorithmic bias are regularly discussed in the literature, so we expected them to be significant issues across many of the cases. However, there were many issues that received less attention in the literature—such as access to BD + AI, trust, and power asymmetries—which were discussed frequently in the interviews. In contrast to this, there were ethical issues that were heavily discussed in the literature which received far less attention in the interviews, such as employment, autonomy, and criminal or malicious use of BD + AI (Fig.  2 ).

The ethical analysis was conducted using a combination of literature reviews and interviews carried out with stakeholders. The purpose of the interviews was to ensure that there were no obvious ethical issues faced by stakeholders in their day-to-day activities which had been missed in the academic literature. As such, the starting point was not an overarching normative theory, which might have meant that we looked for issues which fit well with the theory but ignored anything that fell outside of that theory. Instead the combined approach led to the identification of the 26 ethical issues, each labelled based on particular words or phrases used in the literature or by the interviewees. For example, the term "privacy" was used frequently and so became the label for references to and instances of privacy-relevant concerns. In this section we have clustered issues together based on similar problems faced (e.g. accuracy of data and accuracy of algorithms within the category of ‘reliability of data’).

In an attempt to highlight similar ethical issues and improve the overall analysis to better capture similar perspectives, the research team decided to use the method of clustering, a technique often used in data mining to efficiently group similar elements together. Through discussion in the research team, and bearing in mind that the purpose of the clustering process was to form clusters that would enhance understanding of the impact of these ethical issues, we arrived at the following six clusters: the control of data (covering privacy, security, and informed consent); the reliability of data (accuracy of data and accuracy of algorithms); justice (power asymmetries, justice, discrimination, and bias); economic issues (economic concerns, sustainability, and employment); the role of organisations (trust and responsibility); and human freedoms (autonomy, freedom, and human rights). Both the titles and the precise composition of each cluster of issues are the outcome of a reasoned agreement of the research team. However, it should be clear that we could have used different titles and different clustering. The point is not that each cluster forms a distinct group of ethical issues, independent from any other. Rather the ethical issues faced overlap and play into one another, but to present them in a manageable format we have opted to use this bottom-up clustering approach.

Human Freedoms

An interviewee from CS10 stated that they were concerned about human rights because they were an integral part of the company’s ethics framework. This was beneficial to their business because they were required to incorporate human rights to receive public funding by the Austrian government. The company ensured that they would not grant ‘full exclusivity on generated social unrest event data to any single party, unless the data is used to minimise the risk of suppression of unrest events, or to protect the violation of human rights’ (XXX). The company demonstrates that while BD + AI has been criticised for infringing upon human rights in the literature, they also offer the opportunity to identify and prevent human rights abuses. The company’s moral framework definitively stemmed from regulatory and funding requirements, which lends itself to the benefit of effective ethical top-down approaches, which is a divisive topic in the literature, with diverging views about whether top-down or bottom-up approaches are better options for improved AI ethics.

Trust & Responsibility

Responsibility was a concern in 5 of the case studies, confirming the importance it is given in the literature (see Sect.  3 ). Trust appeared in seven of the case studies. The cases focused on concerns found in the literature, such as BD + AI use in policy development, public distrust about automated decision-making and the integrity of corporations utilising datafication methods (van Dijck 2014 ).

Trust and control over BD + AI were an issue throughout the case studies. The organisation from the predictive intelligence case study (CS10) identified that their use of social media data raised trust issues. They converged with perspectives found in the literature that when people feel disempowered to use or be part of the BD + AI development process, they tend to lose trust in the BD + AI (Accenture, 2016 , 2017 ). In CS6, stakeholders (health insurers) trusted the decisions made by BD + AI when they were engaged and empowered to give feedback on how their data was used. Trust is enhanced when users can refuse the use of their data (CS7), which correlates with the literature. Companies discussed the benefits of establishing trustworthy relationships. For example, in CS9, they have “ been trying really hard to avoid the existence of fake [mobile phone] base stations, because [these raise] an issue with the trust that people put in their networks” (XXX).

Corporations need to determine the objective of the data analysis (CS3), what data is required for the BD + AI to work (CS2), and accountability for when it does not work as intended or causes undesirable outcomes (CS4). The issue here is whether the organisation takes direct responsibility for these outcomes, or, if informed consent has been given, can responsibility be shared with the granter of consent (CS3). The cases also raised the question of ‘responsible to whom’, the person whose data is being used or the proxy organisation who has provided data (CS6). For example, in the insurance case study, the company stated that they only had a responsibility towards the proxy organisation and not the sources of the data. All these issues are covered extensively in the literature in most application domains.

Control of Data

Concerns surrounding the control of data for privacy reasons can be put down to a general awareness of privacy issues in the press, reinforced by the recently-introduced GDPR. This was supported in the cases, where interviewees expressed the opinion that the GDPR had raised general awareness of privacy issues (CS1, CS9) or that it had lent weight to arguments concerning the importance of privacy (CS8).

The discussion of privacy ranged from stressing that it was not an issue for some interviewees, because there was no personal information in the data they used (CS4), to its being an issue for others, but one which was being dealt with (CS2 and CS8). One interviewee (CS5) expressed apprehension that privacy concerns conflicted with scientific innovation, introducing hitherto unforeseen costs. This view is not uncommon in scientific and medical innovation, where harms arising from the use of anonymised medical data are often seen as minimal and the potential benefits significant (Manson & O’Neill, 2007 ). In other cases (CS1), there was a confusion between anonymisation (data which cannot be traced back to the originating source) and pseudonymisation (where data can be traced back, albeit with difficulty) of users’ data. A common response from the cases was that providing informed consent for the use of personal data waived some of the rights to privacy of the user.

Consent may come in the form of a company contract Footnote 14 or an individual agreement. Footnote 15 In the former, the company often has the advantage of legal support prior to entering a contract and so should be fully aware of the information provided. In individual agreements, though, the individual is less likely to be legally supported, and so may be at risk of exploitation through not reading the information sufficiently (CS3), or of responding without adequate understanding (CS9). In one case (CS5), referring to anonymised data, consent was implied rather than given: the interviewee suggested that those involved in the project may have contributed data without giving clear informed consent. The interviewee also noted that some data may have been shared without the permission, or indeed knowledge, of those contributing individuals. This was acknowledged by the interviewee as a potential issue.

In one case (CS6), data was used without informed consent for fraud detection purposes. The interviewees noted that their organisation was working within the parameters of national and EU legislation, which allows for non-consensual use of data for these ends. One interviewee in this case stated that informed consent was sought for every novel use of the data they held. However, this was sought from the perceived owner of the data (an insurance company) rather than from the originating individuals. This case demonstrates how people may expect their data to be used without having a full understanding of the legal framework under which the data are collected. For example, data relating to individuals may legally be accessed for fraud detection without notifying the individual and without relying on the individual’s consent.

This use of personal data for fraud detection in CS6 also led to concerns regarding opacity. In both CS6 and CS10 there was transparency within the organisations (a shared understanding among staff as to the various uses of the data) but that did not extend to the public outside those organisations. In some cases (CS5) the internal transparency/external opacity meant that those responsible for developing BD + AI were often hard to meet. Of those who were interviewed in CS5, many did not know the providence of the data or the algorithms they were using. Equally, some organisations saw external opacity as integral to the business environment in which they were operating (CS9, CS10) for reasons of commercial advantage. The interviewee in CS9 cautioned that this approach, coupled with a lack of public education and the speed of transformation within the industry, would challenge any meaningful level of public accountability. This would render processes effectively opaque to the public, despite their being transparent to experts.

Reliability of Data

There can be multiple sources of unreliability in BD + AI. Unreliability originating from faults in the technology can lead to algorithmic bias, which can cause ethical issues such as unfairness, discrimination, and general negative social impact (CS3 and CS6). Considering algorithmic bias as a key input to data reliability, there exist two types of issues that may need to be addressed. Primarily, bias may stem from the input data, referred to as training data, if such data excludes adequate representation of the world, e.g. gender-biased datasets (CS6). Secondly, an inadequate representation of the world may be the result of lack of data, e.g. a correctly designed algorithm to learn from and predict a rare disease, may not have sufficient representative data to achieve correct predictions (CS5). In either case the input data are biased and may result in inaccurate decision-making and recommendations.

The issues of reliability of data stemming from data accuracy and/or algorithmic bias, may escalate depending on their use, as for example in predictive or risk-assessment algorithms (CS10). Consider the risks of unreliable data in employee monitoring situations (CS1), detecting pests and diseases in agriculture (CS3), in human brain research (CS5) or cybersecurity applications (CS8). Such issues are not singular in nature but closely linked to other ethical issues such as information asymmetries, trust, and discrimination. Consequently, the umbrella issue of reliability of data must be approached from different perspectives to ensure the validity of the decision-making processes of the BD + AI.

Data may over-represent some people or social groups who are likely to be already privileged or under-represent disadvantaged and vulnerable groups (CS3). Furthermore, people who are better positioned to gain access to data and have the expertise to interpret them may have an unfair advantage over people devoid of such competencies. In addition, BD + AI can work as a tool of disciplinary power, used to evaluate people’s conformity to norms representing the standards of disciplinary systems (CS5). We focus on the following aspects of justice in our case study analysis: power asymmetries, discrimination, inequality, and access.

The fact that issues of power can arise in public as well as private organisations was discussed in our case studies. The smart city case (CS4) showed that the public organisations were aware of potential problems arising from companies using public data and were trying to put legal safeguards in place to avoid such misuse. As a result of misuse, there is the potential that cities, or the companies with which they contract, may use data in harmful or discriminatory ways. Our case study on the use of BD + AI in scientific research showed that the interviewees were acutely aware of the potential of discrimination (CS10). They stated that biases in the data may not be easy to identify, and may lead to misclassification or misinterpretation of findings, which may in turn skew results. Discrimination refers to the recognition of difference, but it may also refer to unjust treatment of different categories of people based on their gender, sex, religion, race, class, or disability. BD + AI are often employed to distinguish between different cases, e.g. between normal and abnormal behaviour in cybersecurity. Determining whether such classification entails discrimination in the latter sense can be difficult, due to the nature of the data and algorithms involved.

Examples of potential inequality based on BD + AI could be seen in several case studies. The agricultural case (CS3) highlighted the power differential between farmers and companies with potential implications for inequality, but also the global inequality between farmers, linked to farming practices in different countries (CS3). Subsistence farmers in developing countries, for example, might find it more difficult to benefit from these technologies than large agro-businesses. The diverging levels of access to BD + AI entail different levels of ability to benefit from them and counteract possible disadvantages (CS3). Some companies restrict access to their data entirely, and others sell access at a fee, while others offer small datasets to university-based researchers (Boyd & Crawford, 2012 , p. 674).

Economic Issues

One economic impact of BD + AI outlined in the agriculture case study (CS3) focused on whether this technology, and their ethical implementation, were economically affordable. If BD + AI could not improve economic efficiency, they would be rejected by the end-user, whether they were more productive, sustainable, and ethical options. This is striking, as it raises a serious challenge for the AI ethics literature and industry. It establishes that no matter how well intentioned and principled AI ethics guidelines and charters are, unless their implementation can be done in an economically viable way, their implementation will be challenged and resisted by those footing the bill.

The telecommunications case study (CS9) focused on how GDPR legislation may economically impact businesses using BD + AI by creating disparities in competitiveness between EU and non-EU companies developing BD + AI. Owing to the larger data pools of the latter, their BD + AI may prove to be more effective than European-manufactured alternatives, which cannot bypass the ethical boundaries of European law in the same way (CS8). This is something that is also being addressed in the literature and is a very serious concern for the future profitability and development of AI in Europe (Wallace & Castro, 2018 ). The literature notes additional issues in this area that were not covered in the cases. There is the potential that the GDPR will increase costs of European AI companies by having to manually review algorithmic decision-making; the right to explanation could reduce AI accuracy; and the right to erasure could damage AI systems (Wallace & Castro, 2018 , p. 2).

One interviewee stated that public–private BD + AI projects should be conducted in a collaborative manner, rather than a sale-of-service (CS4). However, this harmonious partnership is often not possible. Another interviewee discussed the tension between public and private interests on their project—while the municipality tried to focus on citizen value, the ICT company focused on the project’s economic success. The interviewee stated that the project would have terminated earlier if it were the company’s decision, because it was unprofitable (CS4). This is a huge concern in the literature, whereby private interests will cloud, influence, and damage public decision-making within the city because of their sometimes-incompatible goals (citizen value vs. economic growth) (Sadowski & Pasquale, 2015 ). One interviewee said that the municipality officials were aware of the problems of corporate influence and thus are attempting to implement the approach of ‘data sovereignty’ (CS2).

During our interviews, some viewed BD + AI as complementary to human employment (CS3), collaborative with such employment (CS4), or as a replacement to employment (CS6). The interviewees from the agriculture case study (CS3) stated that their BD + AI were not sufficiently advanced to replace humans and were meant to complement the agronomist, rather than replace them. However, they did not indicate what would happen when the technology is advanced enough, and it becomes profitable to replace the agronomist. The insurance company interviewee (CS6) stated that they use BD + AI to reduce flaws in personal judgment. The literature also supports this viewpoint, where BD + AI is seen to offer the potential to evaluate cases impartially, which is beneficial to the insurance industry (Belliveau, Gray, & Wilson, 2019 ). Footnote 16 The interviewee reiterated this and also stated that BD + AI would reduce the number of people required to work on fraud cases. The interviewee stated that BD + AI are designed to replace these individuals, but did not indicate whether their jobs were secure or whether they would be retrained for different positions, highlighting a concern found in the literature about the replacement and unemployment of workers by AI (Bossman, 2016 ). In contrast to this, a municipality interviewee from CS4 stated that their chat-bots are used in a collaborative way to assist customer service agents, allowing them to concentrate on higher-level tasks, and that there are clear policies set in place to protect their jobs.

Sustainability was only explicitly discussed in two interviews (CS3 and CS4). The agriculture interviewees stated that they wanted to be the ‘first’ to incorporate sustainability metrics into agricultural BD + AI, indicating a competitive and innovative rationale for their company (CS3). Whereas the interviewee from the sustainable development case study (CS4) stated that their goal of using BD + AI was to reduce Co2 emissions and improve energy and air quality. He stated that there are often tensions between ecological and economic goals and that this tension tends to slow down the efforts of BD + AI public–private projects—an observation also supported by the literature (Keeso, 2014 ). This tension between public and private interests in BD + AI projects was a recurring issue throughout the cases, which will be the focus of the next section on the role of organisations.

Discussion and Conclusion

The motivation behind this paper is to come to a better understanding of ethical issues related to BD + AI based on a rich empirical basis across different application domains. The exploratory and interpretive approach chosen for this study means that we cannot generalise from our research to all possible examples of BD + AI, but it does allow us to generalise to theory and rich insights (Walsham, 1995a , b , 2006 ). These theoretical insights can then provide the basis for further empirical research, possibly using other methods to allow an even wider set of inputs to move beyond some of the limitations of the current study.

Organisational Practice and the Literature

The first point worth stating is that there is a high level of consistency both among the case studies and between cases and literature. Many of the ethical issues identified cut across the cases and are interpreted in similar ways by different stakeholders. The frequency distribution of ethical issues indicates that very few, if any, issues are relevant to all cases but many, such as privacy, have a high level of prevalence. Despite appearing in all case studies, privacy was not seen as overly problematic and could be dealt with in the context of current regulatory principles (GDPR). Most of the issues that we found in the literature (see Sect.  2 ) were also present in the case studies. In addition to privacy and data protection, this included accuracy, reliability, economic and power imbalances, justice, employment, discrimination and bias, autonomy and human rights and freedoms.

Beyond the general confirmation of the relevance of topics discussed in the literature, though, the case studies provide some further interesting insights. From the perspective of an individual case some societal factors are taken for granted and outside of the control of individual actors. For example, intellectual property regimes have significant and well-recognised consequences for justice, as demonstrated in the literature. However, there is often little that individuals or organisations can do about them. Even in cases where individuals may be able to make a difference and the problem is clear, it is not always obvious how to do this. Some well-publicised discrimination cases may be easy to recognise, for example where an HR system discriminates against women or where a facial recognition system discriminates against black people. But in many cases, it may be exceedingly difficult to recognise discrimination where it is not clear how a person is discriminated against. If, for example, an image-based medical diagnostic system leads to disadvantages for people with genetic profiles, this may not be easy to identify.

With regards to the classification of the literature suggested in Sect.  2 along the temporal dimension, we can see that the attention of the case study respondents seems to be correlated to the temporal horizon of the issues. The issues we see as short-term figures most prominently, whereas the medium-term issues, while still relevant and recognisable, appear to be less pronounced. The long-term questions are least visible in the cases. This is not very surprising, as the short-term issues are those that are at least potentially capable of being addressed relatively quickly and thus must be accessible on the local level. Organisations deploying or using AI therefore are likely to have a responsibility to address these issues and our case studies have shown that they are aware of this and putting measures in place. This is clearly true for data protection or security issues. The medium-term issues that are less likely to find local resolutions still figure prominently, even though an individual organisation has less influence on how they can be addressed. Examples of this would be questions of unemployment, justice, or fairness. There was little reference to what we call long-term issues, which can partly be explained by the fact that the type of AI user organisations we investigated have very limited influence on how they are perceived and how they may be addressed.

Interpretative Differences on Ethical Issues

Despite general agreement on the terminology used to describe ethical issues, there are often important differences in interpretation and understanding. In the first ethics theme, control of data, the perceptions of privacy ranged from ‘not an issue’ to an issue that was being dealt with. Some of this arose from the question of informed consent and the GDPR. However, a reliance on legislation, such as GDPR, without full knowledge of the intricacies of its details (i.e. that informed consent is only one of several legal bases of lawful data processing), may give rise to a false sense of security over people’s perceived privacy. This was also linked to the issue of transparency (of processes dealing with data), which may be external to the organisation (do people outside understand how an organisation holds and processes their data), or internal (how well does the organisation understand the algorithms developed internally) and sometimes involve deliberate opacity (used in specific contexts where it is perceived as necessary, such as in monitoring political unrest and its possible consequences). Therefore, a clearer and more nuanced understanding of privacy and other ethical terms raised here might well be useful, albeit tricky to derive in a public setting (for an example of complications in defining privacy, see Macnish, 2018 ).

Some issues from the literature were not mentioned in the cases, such as warfare. This can easily be explained by our choice of case studies, none of which drew on work done in this area. It indicates that even a set of 10 case studies falls short of covering all issues.

A further empirical insight is in the category we called ‘role of organisations’, which covers trust and responsibility. Trust is a key term in the discussion of the ethics of AI, prominently highlighted by the focus on trustworthy AI by the EU’s High-Level Expert Group, among others. We put this into the ‘role of organisations’ category because our interaction with the case study respondents suggested that they felt it was part of the role of their organisations to foster trust and establish responsibilities. But we are open to the suggestion that these are concepts on a slightly different level that may provide the link between specific issues in applications and broader societal debate.

Next Steps: Addressing the Ethics of AI and Big Data

This paper is predominantly descriptive, and it aims to provide a theoretically sound and empirically rich account of ethical concerns in AI + BD. While we hope that it proves to be insightful it is only a first step in the broader journey towards addressing and resolving these issues. The categorisation suggested here gives an initial indication of which type of actor may be called upon to address which type of issue. The distinction between micro-, meso- and macro perspectives suggested by Haenlein and Kaplan ( 2019 ) resonates to some degree with our categorisation of issues.

This points to the question what can be done to address these ethical issues and by whom should it be done? We have not touched on this question in the theoretical or empirical part of the paper, but the question of mitigation is the motivating force behind much of the AI + BD ethics research. The purpose of understanding these ethical questions is to find ways of addressing them.

This calls for a more detailed investigation of the ethical nature of the issues described here. As indicated earlier, we did not begin with a specific ethical theoretical framework imposed onto the case studies, but did have some derived ethics concepts which we explored within the context of the cases and allowed others to emerge over the course of the interviews. One issue is the philosophical question whether the different ethical issues discussed here are of a similar or comparable nature and what characterises them as ethical issues. This is not only a philosophical question but also a practical one for policymakers and decision makers. We have alluded to the idea that privacy and data protection are ethical issues, but they also have strong legal implications and can also be human rights issues. It would therefore be beneficial to undertake a further analysis to investigate which of these ethical issues are already regulated and to what degree current regulation covers BD + AI, and how this varies across the various EU nations and beyond.

Another step could be to expand an investigation like the one presented here to cover the ethics of AI + BD debate with a focus on suggested resolutions and policies. This could be achieved by adopting the categorisation and structure presented here and extending it to the currently discussed option for addressing the ethical issues. These include individual and collective activities ranging from technical measures to measure bias in data or individual professional guidance to standardisation, legislation, the creation of a specific regulator and many more. It will be important to understand how these measures are conceptualised as well as which ones are already used to which effect. Any such future work, however, will need to be based on a sound understanding of the issues themselves, which this paper contributes to. The key contribution of the paper, namely the presentation of empirical findings from 10 case studies show in more detail how ethical issues play out in practice. While this work can and should be expanded by including an even broader variety of cases and could be supplemented by other empirical research methods, it marks an important step in the development of our understanding of these ethical issues. This should form a part of the broader societal debate about what these new technologies can and should be used for and how we can ensure that their consequences are beneficial for individuals and society.

Throughout the paper, XXX will be used to anonymise relevant text that may identify the authors, either through the project and/or publications resulting from the individual case studies. All case studies have been published individually. Several the XXX references in the findings refer to these individual publications which provide more detail on the cases than can be provided in this cross-case analysis.

The ethical issues that we discussed throughout the case studies refers to issues broadly construed as ethical issues, or issues that have ethical significance. While some issues may not be directly obvious how they are ethical issues, they may give rise to significant harm relevant to ethics. For example, accuracy of data may not explicitly be an ethical issue, if inaccurate data is used in algorithms, it may lead to discrimination, unfair bias, or harms to individuals.

Such as chat-bots, natural language processing AI, IoT data retrieval, predictive risk analysis, cybersecurity machine-learning, and large dataset exchanges.

https://ec.europa.eu/futurium/en/ai-alliance-consultation/guidelines/1 .

https://ec.europa.eu/digital-single-market/en/high-level-expert-group-artificial-intelligence .

The type of AI currently in vogue, as outlined earlier, is based on machine learning, typically employing artificial neural networks for big data analysis. This is typically seen as ‘narrow AI’ and it is not clear whether there is a way from narrow to general AI, even if one were to accept that achieving general AI is fundamentally possible.

The 16 social domains were: Banking and securities; Healthcare; Insurance; Retail and wholesale trade; Science; Education; Energy and utilities; Manufacturing and natural resources; Agriculture; Communications, media and entertainment; Transportation; Employee monitoring and administration; Government; Law enforcement and justice; Sustainable development; and Defence and national security.

This increased to 26 ethical issues following a group brainstorming session at the case study workshop.

The nine additional ethical issues from the initial 17 drafted by the project leader were: human rights, transparency, responsibility, ownership of data, algorithmic bias, integrity, human rights, human contact, and accuracy of data.

The additional ethical issues were access to BD + AI, accuracy of data, accuracy of recommendations, algorithmic bias, economic, human contact, human rights, integrity, ownership of data, responsibility, and transparency. Two of the initial ethical concerns were removed (inclusion of stakeholders and environmental impact). The issues raised concerning inclusion of stakeholders were deemed to be sufficiently included in access to BD + AI, and those relating to environmental impact were felt to be sufficiently covered by sustainability.

The three appendices attached in this paper comprise much of this case study protocol.

CS4 evaluated four organisations, but one of these organisations was also part of CS2 – Organisation 1. CS6 analysed two insurance organisations.

Starting out, we aimed to have both policy/ethics-focused experts within the organisation and individuals that could also speak with us about the technical aspects of the organisation’s BD + AI. However, this was often not possible, due to availability, organisations’ inability to free up resources (e.g. employee’s time) for interviews, or lack of designated experts in those areas.

For example, in CS1, CS6, and CS8.

For example, in CS2, CS3, CS4, CS5, CS6, and CS9.

As is discussed elsewhere in this paper, algorithms also hold the possibility of reinforcing our prejudices and biases or creating new ones entirely.

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Acknowledgements

This SHERPA Project has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 786641. The author(s) acknowledge the contribution of the consortium to the development and design of the case study approach.

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Appendix 1: Desk Research Questions

Number Research Question.

In which sector is the organisation located (e.g. industry, government, NGO, etc.)?

What is the name of the organisation?

What is the geographic scope of the organisation?

What is the name of the interviewee?

What is the interviewee’s role within the organisation?

Appendix 2: Interview Research Questions

No Research Question.

What involvement has the interviewee had with BD + AI within the organisation?

What type of BD + AI is the organisation using? (e.g. IBM Watson, Google Deepmind)

What is the field of application of the BD + AI (e.g. administration, healthcare, retail)

Does the BD + AI work as intended or are there problems with its operation?

What are the innovative elements introduced by the BD + AI (e.g. what has the technology enabled within the organisation?)

What is the level of maturity of the BD + AI ? (i.e. has the technology been used for long at the organisation? Is it a recent development or an established approach?)

How does the BD + AI interact with other technologies within the organisation?

What are the parameters/inputs used to inform the BD + AI ? (e.g. which sorts of data are input, how is the data understood within the algorithm?). Does the BD + AI collect and/or use data which identifies or can be used to identify a living person (personal data)?. Does the BD + AI collect personal data without the consent of the person to whom those data relate?

What are the principles informing the algorithm used in the BD + AI (e.g. does the algorithm assume that people walk in similar ways, does it assume that loitering involves not moving outside a particular radius in a particular time frame?). Does the BD + AI classify people into groups? If so, how are these groups determined? Does the BD + AI identify abnormal behaviour? If so, what is abnormal behaviour to the BD + AI ?

Are there policies in place governing the use of the BD + AI ?

How transparent is the technology to administrators within the organisation, to users within the organisation?

Who are the stakeholders in the organisation?

What has been the impact of the BD + AI on stakeholders?

How transparent is the technology to people outside the organisation?

Are those stakeholders engaged with the BD + AI ? (e.g. are those affected aware of the BD + AI, do they have any say in its operation?). If so, what is the nature of this engagement? (focus groups, feedback, etc.)

In what way are stakeholders impacted by the BD + AI ? (e.g. what is the societal impact: are there issues of inequality, fairness, safety, filter bubbles, etc.?)

What are the costs of using the BD + AI to stakeholders? (e.g. potential loss of privacy, loss of potential to sell information, potential loss of reputation)

What is the expected longevity of this impact? (e.g. is this expected to be temporary or long-term?)

Are those stakeholders engaged with the BD + AI ? (e.g. are those affected aware of the BD + AI, do they have any say in its operation?)

If so, what is the nature of this engagement? (focus groups, feedback, etc.)

Appendix 3: Checklist of Ethical Issues

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Ryan, M., Antoniou, J., Brooks, L. et al. Research and Practice of AI Ethics: A Case Study Approach Juxtaposing Academic Discourse with Organisational Reality. Sci Eng Ethics 27 , 16 (2021). https://doi.org/10.1007/s11948-021-00293-x

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DOI : https://doi.org/10.1007/s11948-021-00293-x

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AI on Trial: Legal Models Hallucinate in 1 out of 6 (or More) Benchmarking Queries

A new study reveals the need for benchmarking and public evaluations of AI tools in law.

Artificial intelligence (AI) tools are rapidly transforming the practice of law. Nearly  three quarters of lawyers plan on using generative AI for their work, from sifting through mountains of case law to drafting contracts to reviewing documents to writing legal memoranda. But are these tools reliable enough for real-world use?

Large language models have a documented tendency to “hallucinate,” or make up false information. In one highly-publicized case, a New York lawyer  faced sanctions for citing ChatGPT-invented fictional cases in a legal brief;  many similar cases have since been reported. And our  previous study of general-purpose chatbots found that they hallucinated between 58% and 82% of the time on legal queries, highlighting the risks of incorporating AI into legal practice. In his  2023 annual report on the judiciary , Chief Justice Roberts took note and warned lawyers of hallucinations. 

Across all areas of industry, retrieval-augmented generation (RAG) is seen and promoted as the solution for reducing hallucinations in domain-specific contexts. Relying on RAG, leading legal research services have released AI-powered legal research products that they claim  “avoid” hallucinations and guarantee  “hallucination-free” legal citations. RAG systems promise to deliver more accurate and trustworthy legal information by integrating a language model with a database of legal documents. Yet providers have not provided hard evidence for such claims or even precisely defined “hallucination,” making it difficult to assess their real-world reliability.

AI-Driven Legal Research Tools Still Hallucinate

In a new  preprint study by  Stanford RegLab and  HAI researchers, we put the claims of two providers, LexisNexis (creator of Lexis+ AI) and Thomson Reuters (creator of Westlaw AI-Assisted Research and Ask Practical Law AI)), to the test. We show that their tools do reduce errors compared to general-purpose AI models like GPT-4. That is a substantial improvement and we document instances where these tools provide sound and detailed legal research. But even these bespoke legal AI tools still hallucinate an alarming amount of the time: the Lexis+ AI and Ask Practical Law AI systems produced incorrect information more than 17% of the time, while Westlaw’s AI-Assisted Research hallucinated more than 34% of the time.

Read the full study, Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools

To conduct our study, we manually constructed a pre-registered dataset of over 200 open-ended legal queries, which we designed to probe various aspects of these systems’ performance.

Broadly, we investigated (1) general research questions (questions about doctrine, case holdings, or the bar exam); (2) jurisdiction or time-specific questions (questions about circuit splits and recent changes in the law); (3) false premise questions (questions that mimic a user having a mistaken understanding of the law); and (4) factual recall questions (questions about simple, objective facts that require no legal interpretation). These questions are designed to reflect a wide range of query types and to constitute a challenging real-world dataset of exactly the kinds of queries where legal research may be needed the most.

Figure 1: Comparison of hallucinated (red) and incomplete (yellow) answers across generative legal research tools.

These systems can hallucinate in one of two ways. First, a response from an AI tool might just be  incorrect —it describes the law incorrectly or makes a factual error. Second, a response might be  misgrounded —the AI tool describes the law correctly, but cites a source which does not in fact support its claims.

Given the critical importance of authoritative sources in legal research and writing, the second type of hallucination may be even more pernicious than the outright invention of legal cases. A citation might be “hallucination-free” in the narrowest sense that the citation  exists , but that is not the only thing that matters. The core promise of legal AI is that it can streamline the time-consuming process of identifying relevant legal sources. If a tool provides sources that  seem authoritative but are in reality irrelevant or contradictory, users could be misled. They may place undue trust in the tool's output, potentially leading to erroneous legal judgments and conclusions.

Figure 2:  Top left: Example of a hallucinated response by Westlaw's AI-Assisted Research product. The system makes up a statement in the Federal Rules of Bankruptcy Procedure that does not exist (and Kontrick v. Ryan, 540 U.S. 443 (2004) held that a closely related bankruptcy deadline provision was not jurisdictional). Top right: Example of a hallucinated response by LexisNexis's Lexis+ AI. Casey and its undue burden standard were overruled by the Supreme Court in Dobbs v. Jackson Women's Health Organization, 597 U.S. 215 (2022); the correct answer is rational basis review. Bottom left: Example of a hallucinated response by Thomson Reuters's Ask Practical Law AI. The system fails to correct the user’s mistaken premise—in reality, Justice Ginsburg joined the Court's landmark decision legalizing same-sex marriage—and instead provides additional false information about the case. Bottom right: Example of a hallucinated response from GPT-4, which generates a statutory provision that has not been codified.

RAG Is Not a Panacea

Figure 3: An overview of the retrieval-augmentation generation (RAG) process. Given a user query (left), the typical process consists of two steps: (1) retrieval (middle), where the query is embedded with natural language processing and a retrieval system takes embeddings and retrieves the relevant documents (e.g., Supreme Court cases); and (2) generation (right), where the retrieved texts are fed to the language model to generate the response to the user query. Any of the subsidiary steps may introduce error and hallucinations into the generated response. (Icons are courtesy of FlatIcon.)

Under the hood, these new legal AI tools use retrieval-augmented generation (RAG) to produce their results, a method that many tout as a potential solution to the hallucination problem. In theory, RAG allows a system to first  retrieve the relevant source material and then use it to  generate the correct response. In practice, however, we show that even RAG systems are not hallucination-free. 

We identify several challenges that are particularly unique to RAG-based legal AI systems, causing hallucinations. 

First, legal retrieval is hard. As any lawyer knows, finding the appropriate (or best) authority can be no easy task. Unlike other domains, the law is not entirely composed of verifiable  facts —instead, law is built up over time by judges writing  opinions . This makes identifying the set of documents that definitively answer a query difficult, and sometimes hallucinations occur for the simple reason that the system’s retrieval mechanism fails.

Second, even when retrieval occurs, the document that is retrieved can be an inapplicable authority. In the American legal system, rules and precedents differ across jurisdictions and time periods; documents that might be relevant on their face due to semantic similarity to a query may actually be inapposite for idiosyncratic reasons that are unique to the law. Thus, we also observe hallucinations occurring when these RAG systems fail to identify the truly binding authority. This is particularly problematic as areas where the law is in flux is precisely where legal research matters the most. One system, for instance, incorrectly recited the “undue burden” standard for abortion restrictions as good law, which was overturned in  Dobbs (see Figure 2). 

Third, sycophancy—the tendency of AI to agree with the user's incorrect assumptions—also poses unique risks in legal settings. One system, for instance, naively agreed with the question’s premise that Justice Ginsburg dissented in  Obergefell , the case establishing a right to same-sex marriage, and answered that she did so based on her views on international copyright. (Justice Ginsburg did not dissent in  Obergefell and, no, the case had nothing to do with copyright.) Notwithstanding that answer, here there are optimistic results. Our tests showed that both systems generally navigated queries based on false premises effectively. But when these systems do agree with erroneous user assertions, the implications can be severe—particularly for those hoping to use these tools to increase access to justice among  pro se and under-resourced litigants.

Responsible Integration of AI Into Law Requires Transparency

Ultimately, our results highlight the need for rigorous and transparent benchmarking of legal AI tools. Unlike other domains, the use of AI in law remains alarmingly opaque: the tools we study provide no systematic access, publish few details about their models, and report no evaluation results at all.

This opacity makes it exceedingly challenging for lawyers to procure and acquire AI products. The large law firm  Paul Weiss spent nearly a year and a half testing a product, and did not develop “hard metrics” because checking the AI system was so involved that it “makes any efficiency gains difficult to measure.” The absence of rigorous evaluation metrics makes responsible adoption difficult, especially for practitioners that are less resourced than Paul Weiss. 

The lack of transparency also threatens lawyers’ ability to comply with ethical and professional responsibility requirements. The bar associations of  California ,  New York , and  Florida have all recently released guidance on lawyers’ duty of supervision over work products created with AI tools. And as of May 2024,  more than 25 federal judges have issued standing orders instructing attorneys to disclose or monitor the use of AI in their courtrooms.

Without access to evaluations of the specific tools and transparency around their design, lawyers may find it impossible to comply with these responsibilities. Alternatively, given the high rate of hallucinations, lawyers may find themselves having to verify each and every proposition and citation provided by these tools, undercutting the stated efficiency gains that legal AI tools are supposed to provide.

Our study is meant in no way to single out LexisNexis and Thomson Reuters. Their products are far from the only legal AI tools that stand in need of transparency—a slew of startups offer similar products and have  made   similar   claims , but they are available on even more restricted bases, making it even more difficult to assess how they function. 

Based on what we know, legal hallucinations have not been solved.The legal profession should turn to public benchmarking and rigorous evaluations of AI tools. 

This story was updated on Thursday, May 30, 2024, to include analysis of a third AI tool, Westlaw’s AI-Assisted Research.

Paper authors: Varun Magesh is a research fellow at Stanford RegLab. Faiz Surani is a research fellow at Stanford RegLab. Matthew Dahl is a joint JD/PhD student in political science at Yale University and graduate student affiliate of Stanford RegLab. Mirac Suzgun is a joint JD/PhD student in computer science at Stanford University and a graduate student fellow at Stanford RegLab. Christopher D. Manning is Thomas M. Siebel Professor of Machine Learning, Professor of Linguistics and Computer Science, and Senior Fellow at HAI. Daniel E. Ho is the William Benjamin Scott and Luna M. Scott Professor of Law, Professor of Political Science, Professor of Computer Science (by courtesy), Senior Fellow at HAI, Senior Fellow at SIEPR, and Director of the RegLab at Stanford University. 

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Key elements to consider when creating a compelling case study for sales include highlighting the customer's challenge or pain point, detailing the solution provided by your product or service, showcasing measurable results or benefits achieved, incorporating direct quotes or testimonials from the customer, and making it visually engaging with graphs, images, and a clear narrative structure.

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AI Is Making Economists Rethink the Story of Automation

• Walter Frick

Economists have traditionally believed that new technology lifts all boats. But in the case of AI, some are asking: Will some employees get left behind?

Will artificial intelligence take our jobs? As AI raises new fears about a jobless future, it’s helpful to consider how economists’ understanding of technology and labor has evolved. For decades, economists were relatively optimistic, and pointed out that previous waves of technology had not led to mass unemployment. But as income inequality rose in much of the world, they began to revise their theories. Newer models of technology’s affects on the labor market account for the fact that it absolutely can displace workers and lower wages. In the long run, technology does tend to raise living standards. But how soon and how broadly? That depends on two factors: Whether technologies create new jobs for people to do and whether workers have a voice in technology’s deployment.

Is artificial intelligence about to put vast numbers of people out of a job? Most economists would argue the answer is no: If technology permanently puts people out of work then why, after centuries of new technologies, are there still so many jobs left ? New technologies, they claim, make the economy more productive and allow people to enter new fields — like the shift from agriculture to manufacturing. For that reason, economists have historically shared a general view that whatever upheaval might be caused by technological change, it is “somewhere between benign and benevolent.”

• Walter Frick is a contributing editor at Harvard Business Review , where he was formerly a senior editor and deputy editor of HBR.org. He is the founder of Nonrival , a newsletter where readers make crowdsourced predictions about economics and business. He has been an executive editor at Quartz as well as a Knight Visiting Fellow at Harvard’s Nieman Foundation for Journalism and an Assembly Fellow at Harvard’s Berkman Klein Center for Internet & Society. He has also written for The Atlantic , MIT Technology Review , The Boston Globe , and the BBC, among other publications.

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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Case Study: AI In The Workplace: Perspectives From Media Professionals

By Melanie Ciotti, Director of Marketing, SNS Friday, June 7, 2024 - 9:00 am Print This Story | Subscribe

Story Highlights

As artificial intelligence (AI) becomes increasingly integrated into the workplace, professionals across every industry are navigating the opportunities and challenges presented by this transformative technology.

Media production, with its emphasis on creativity and innovation, is no exception. Creative teams in video production, photography, and content creation are at the forefront of embracing AI-driven technologies. In fact, it’s hard to attend any industry event these days without the theme of AI taking center stage.

But, how do employees  really  feel about this AI revolution? Is AI a net positive for professionals in media production, or are the skeptics on to something?

To answer these questions, we turn to  Workable’s AI in Hiring & Work survey , which draws insights from 950 hiring managers in the US and UK across various industries. Let’s take a closer look at the findings and apply them to the world of media production.

How Do Employees Feel About AI In The Workplace?

Gone are the days when AI was viewed only as a threat. Today, it’s a powerful ally for media professionals and employees worldwide. According to the survey, a staggering 72% of workers feel comfortable using AI technologies, signaling widespread acceptance of AI integration in the workplace across various industries.

Additionally, 71% of professionals report experiencing minimal or no job disruption due to AI. This suggests that many have embraced AI in their daily work routines without the negative side effects touted by naysayers.

In other words, AI is seen as a net positive for a majority of workers. This positive sentiment extends to media production, where creative teams are leveraging AI-driven tools to enhance their craft and stay ahead of the curve—rather than shying away from them in fear of being replaced.

What Is The Overall Impact Of AI On Team Morale?

The integration of AI has had a profound impact on team morale as well. Workable found that over half (52.4%) of professionals surveyed reported a positive influence on morale following AI integration. Furthermore, over 90% of professionals reported either positive or no significant change in their team’s morale following the integration of AI. This stability suggests that AI adoption is becoming increasingly normalized in the workplace.

Optimism echoes throughout creative industries, too. Whether it’s streamlining editing processes, optimizing content distribution, or automating repetitive tasks, AI is empowering creative teams to work more efficiently and collaboratively than ever before.

How Do Skeptics Feel About AI In The Workplace?

Despite the overwhelmingly positive outlook, some professionals harbor concerns about the widespread adoption of AI.

Only 5.2% of professionals surveyed reported that team morale was negatively affected by AI. Job security, changes to traditional workflows, and the fear of AI replacing human creativity are among the top apprehensions—all valid concerns for AI in media production.

However, it is important to remember that AI is a tool to augment human capabilities, not replace them entirely. In creative industries like video and audio production, AI promises to grant media professionals more time to focus on the art that demands human touch. By embracing AI as a catalyst for innovation and creativity, media professionals can harness AI’s potential to drive meaningful change in their field.

What’s The Bottom Line?

In conclusion, as professionals navigate the integration of AI into their workflows, they are met with a spectrum of emotions ranging from optimism to skepticism. Despite concerns about job security and role changes, the overarching sentiment is one of acceptance and adaptation, as professionals recognize the transformative potential of AI to drive innovation and efficiency in the workplace.

Specifically in media production, AI can have a significant impact in saving time and conserving resources within a workflow, and enabling creatives to spend more time creating. From generative AI and automated content creation to personalized audience engagement strategies, the possibilities are endless. The key lies in striking a balance between AI-driven automation and human creativity.

Bear in mind that, while AI continues to shape the media production industry with streamlined processes and improved efficiencies, it’s human ingenuity that ultimately drives innovation and sets the creative vision in motion.

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Education, neuroscience and AI: Japan collaboration enables pioneering research

Professor Kazuya Saito (IOE, UCL’s Faculty of Education & Society) used UCL-Tohoku University Strategic Partner Funds to develop unique research on language.

7 June 2024

Education and neuroscience are two distinctive research fields that have not historically had much crossover. However, this is an interdisciplinary field that holds great potential. While there are some outstanding neuroscience facilities in London, these resources are also in great demand, making it difficult for education researchers to progress their research in this area.

Professor Kazuya Saito was keen to explore some research ideas in education and neuroscience, and discovered that Tohoku University in Japan was interested in this field in the context of natural disasters. He subsequently applied for UCL-Tohoku University Strategic Partner Funds via the UCL Global Engagement Office. As a result of this, he and his colleague Dr Andrea Revesz, have been awarded funds for three projects with Tohoku University since 2020, which has also led to an additional collaboration with the Engineering Department at the University of Tokyo.

Bringing language and neuroscience into disaster research

“A priority for Tohoku University is disaster research,” Kazuya explained. “After all, they have regular earthquakes there, and people are not surprised when there’s an earthquake anymore. My expertise is in second languages, and there are immigrants in Japan who need to deal with disaster situations in their second language. I had a research idea that could help with this.”

Kazuya and Andrea set up a series of exploratory pilot studies into the cognitive demands on those using their second language (known as L2). The study explored how L2 tasks affect brain activity while speaking and writing in crisis settings. To do this, they worked extensively with neuroscientists at Tohoku University, and were able to use functional MRI (fMRI) equipment in the lab. The team is still working on the findings, which will include recommendations for support that can be provided to L2 speakers in Japan. “Using neuroscience techniques to improve the effectiveness of language interventions is a really new field,” Kazuya said. “We will be able to provide a lot of support to immigrants who are second language users in Japan.”

Exploring the intersection of language and neuroscience in adults led the researchers to think about support they could provide to children too. There is an increase in multilingual children generally in society, and especially in the UK and Japan. These children are typically exposed to their heritage language at home from birth, but predominantly use another language at school. This dynamic risks the loss of their heritage language and cultural identity. Furthermore, it can also create a lag in literacy in both heritage and dominant languages, compared to their monolingual peers. There is a lack of awareness of this issue and limited resources to help multilingual children develop both their heritage and dominant languages. 

Kazuya received further UCL-Tohoku University Strategic Partner Funds to explore this topic. In particular, he wanted to understand how they could use AI based training, matched to the profiles of specific users, to train the cognitive and linguistic abilities in multilingual children. “For this project, I was able to connect neuroscience researchers at Tohoku University with engineering researchers at the University of Tokyo,” Kazuya said. “Education, neuroscience and AI – these are three completely independent topics. We usually don't talk to each other as academics in these fields. This is highly interdisciplinary research that holds great potential. And I’ve found myself to be a kind of ambassador for promoting collaborations like this between different universities and departments in Japan.”

Following on from this work, Kazuya has recently been awarded a new research funding from the British Council to further reinforce and expand this collaboration with Tohoku University and the University of Tokyo over the next 24 months. So far, the team has successfully established neuroscience assessments using fMRI and electroencephalogram (EEG), to find out how individuals learn multiple languages differently. Moving forward, they will work with the computer science team to further explore how AI can tailor language training activities while taking into account such neurocognitive individual differences. They will start with adults and eventually work with children too.

Recognition for leading work

While this research is ongoing, Kazuya’s work is getting noticed in many circles. Earlier this year, he was announced as a recipient of the 20th Japan Society for the Promotion of Science (JSPS) Prize. Kazuya met Fumihito, the Crown Prince of Japan at the award ceremony, where he was recognised as a future leader of scientific research in Japan.

Professor Kazuya Saito receiving the 20th Japan Society for the Promotion of Science (JSPS) Prize at the Japan Research Council

Foreground (left to right): Professor Kazuya Saito, Tsuyoshi Sugino, President of the Japan Society for the Promotion of Science Background (left to right): Crown Prince Akishino (Fumihito), Crown Princess Kiko “To be nominated for this national award, you actually have to be based in Japan,” Kazuya explained. “And if you're not based in Japan, someone has to nominate you. Professor Motoaki Sugiura, who leads the neuroscience lab at Tohoku University, nominated me. I was speechless, and very proud. Without the UCL-Tohoku University Strategic Partner Funds, none of this would have happened.”

Some of the team’s work has been presented at notable conferences too, including the World BOSAI conference, the American Association of Applied Linguistics, and the European Second Language Research conference. 

On a personal level, this opportunity to collaborate with researchers in Japan has been an important way for Kazuya to reconnect with his home country. “There are many international researchers at UCL. We have left our country, and after a while, we feel very far away from our homeland,” Kazuya said. “These funds helped me professionally, but also to personally re-establish my identity. It just shows how opportunities like this can bring many benefits to international scholars at UCL.”

• UCL academic recognised as future leader of Japanese research
• Professor Kazuya Saito’s research profile  
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• IOE, UCL's Faculty of Education and Society
• Find out more about funding opportunities offered by UCL Global Engagement

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Group photo of Kazuya and Tohoku University researchers at a workshop titled 'The neurocognitive foundations of successful second language acquisition' at UCL

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