case study artificial intelligence in marketing

• Social Media
• Account-Based Marketing
• Affiliate Marketing
• Digital Marketing

AI in Marketing: 4 Real-World Examples and Case Studies

Artificial intelligence (AI) is rapidly transforming the marketing field, offering businesses new ways to personalize their messaging, analyze customer data, and create more effective marketing campaigns.

By using machine learning algorithms and predictive analytics, companies can better understand customer behaviors , preferences, and needs, and tailor their marketing efforts accordingly.

In this blog post, we’ll explore some real-world examples of how businesses are using AI to improve their marketing efforts.

By examining these case studies, we’ll gain a better understanding of the potential benefits of AI in marketing, as well as the challenges and opportunities that this technology presents.

Whether you’re a small business owner or a marketing professional, understanding how AI is being used in marketing can help you stay ahead of the curve and make more informed decisions about your marketing strategy.

So let’s dive in and explore some of the most compelling case studies of AI in marketing .

1. How Netflix Uses AI to Deliver a More Personalized Customer Experience

Netflix is perhaps one of the best-known examples of how AI is being used in marketing.

The streaming giant has long relied on data analysis and machine learning to recommend content to its users. This personalized recommendation system has been a major driver of the company’s success.

Netflix’s AI-powered recommendation system analyzes a wide variety of data points, including a user’s viewing history, search history, and ratings, as well as information about the content itself, such as its genre and director.

Using this data, the system can generate personalized recommendations that are tailored to each user.

The benefits of AI in marketing have been significant

According to Netflix, its recommendation system is responsible for 80% of the content that users watch on the platform.

This has helped to improve customer retention and engagement, as well as drive new sign-ups. The company estimates that its recommendation system has saved it over $1 billion in customer retention costs.

But the benefits of AI in marketing go beyond just recommendations

Netflix is also using AI to optimize its content creation process.

By analyzing viewer data and identifying patterns in what users are watching, the company can make more informed decisions about what types of content to produce, and how to market that content to its users.

Overall, Netflix is a powerful example of how AI can transform marketing.

By using machine learning to understand its users better, the company has improved customer engagement, driven sales, and stayed ahead of the competition.

2. How Sephora Uses AI to Transform the Beauty Industry

Sephora, the cosmetics retailer, is another excellent example of how AI can improve customer experience.

The company has implemented a chatbot on its website and mobile app that uses machine learning algorithms to personalize the customer experience and provide support and recommendations to shoppers.

The chatbot called the Sephora Virtual Artist, uses facial recognition technology to help customers try on different makeup products and experiment with different looks.

Users can upload a photo of themselves to the app, and the chatbot will use machine learning algorithms to analyze their features and provide personalized makeup recommendations.

In addition to the Virtual Artist, Sephora has also implemented an AI-powered chatbot that can answer customer questions and help shoppers find the products they’re looking for.

The chatbot is designed to mimic the experience of talking to a real sales associate and uses natural language processing to understand customer inquiries and respond appropriately.

The benefits of these AI-powered tools have been significant

By providing personalized recommendations and support, Sephora has been able to improve customer satisfaction and drive sales.

The company reports that customers who engage with the Virtual Artist are more likely to make a purchase and that the chatbot has helped to reduce the number of support inquiries that its customer service team receives.

Overall, Sephora’s use of AI in marketing demonstrates the potential for these technologies to transform the customer experience.

By providing personalized recommendations and support, companies can improve customer satisfaction, drive sales, and build long-term customer loyalty.

Suggested Reading: 7 Free Online AI Chatbots to Improve Your Customer Service

3. How Coca-Cola Uses AI to Deliver More Personalized Marketing Campaigns

Coca-Cola is another company that has embraced the power of AI in its marketing efforts.

One notable example is the company’s use of machine learning to optimize its product packaging and distribution.

Coca-Cola worked with a data analytics firm to create an AI system that would analyze sales data and identify patterns in customer preferences.

Using this data, the system was able to recommend specific product packaging and distribution strategies for different markets and demographics.

The results of this approach were impressive.

Coca-Cola was able to improve its sales and distribution efficiencies by up to 30%, resulting in significant cost savings and increased profits.

The company was also able to tailor its marketing efforts more effectively, using data-driven insights to develop more targeted campaigns and messaging.

In addition to its work on product packaging and distribution, Coca-Cola has also used AI to develop a virtual assistant that can help customers find the products they’re looking for and answer common questions.

The virtual assistant is available on the company’s website and mobile app and uses natural language processing to understand customer inquiries and respond appropriately.

By embracing AI in its marketing efforts, Coca-Cola has been able to stay ahead of the competition and drive significant business results.

By using machine learning to better understand customer preferences and optimize its operations, the company has been able to improve its sales, reduce costs, and build a stronger brand.

4. How Unilever Uses AI to Build Brands and Drive Sales

Unilever is a global consumer goods company that has also been at the forefront of using AI in its marketing efforts.

The company has implemented several AI-powered initiatives to improve its customer engagement and marketing ROI.

One of the most notable examples is the company’s use of machine learning to optimize its social media advertising.

Unilever worked with a digital marketing firm to create an AI system that could analyze data from social media platforms and identify the most effective advertising strategies for different demographics and markets.

Using this approach, Unilever was able to improve its advertising performance significantly, with some campaigns achieving up to a 50% increase in engagement and click-through rates.

The company was also able to optimize its advertising spend, reducing costs while improving results.

Unilever has also used AI to develop a personalized shopping assistant for its customers

The assistant, called “Peggy,” uses natural language processing to understand customer inquiries and provide personalized product recommendations and support.

By leveraging AI in its marketing efforts, Unilever has been able to improve its customer engagement, increase sales, and build stronger relationships with its customers.

By optimizing its advertising and providing personalized support, the company has been able to drive significant business results while improving the customer experience.

AI in Marketing: Key Takeaways from Real-World Case Studies

These case studies demonstrate the significant impact that AI can have on marketing efforts.

By leveraging data-driven insights and machine learning algorithms, companies like Netflix, Sephora, Coca-Cola, and Unilever have been able to optimize their marketing strategies and drive significant business results.

However, it’s important to note that AI is not a magic bullet for marketing success.

These companies were able to achieve significant results because they invested time and resources into building and implementing effective AI-powered systems.

It takes careful planning, dedicated resources, and ongoing monitoring and analysis to ensure that an AI system is delivering the desired results.

That being said, the potential benefits of AI in marketing are clear.

By providing more personalized, targeted experiences for customers and optimizing advertising and distribution strategies, companies can improve their ROI and build stronger relationships with their customers.

As AI technology continues to develop and evolve, we can expect to see even more innovative applications in the world of marketing.

By staying up to date on the latest trends and best practices, companies can stay ahead of the competition and drive continued success.

Ali is a digital marketing blogger and author who uses the power of words to inspire and impact others. He has written for leading publications like Business2Community, Inc. Magazine, and Marketing Profs. When not writing, he enjoys spending time with his family.

• Digital Marketing
• Facebook Marketing
• Instagram Marketing
• Ecommerce Marketing
• Content Marketing
• Data Science Certification
• Machine Learning
• Artificial Intelligence
• Data Analytics
• Graphic Design
• Adobe Illustrator
• Web Designing
• UX UI Design
• Interior Design
• Front End Development
• Back End Development Courses
• Business Analytics
• Entrepreneurship
• Supply Chain
• Financial Modeling
• Corporate Finance
• Project Finance
• Harvard University
• Stanford University
• Yale University
• Princeton University
• Duke University
• UC Berkeley
• Harvard University Executive Programs
• MIT Executive Programs
• Stanford University Executive Programs
• Oxford University Executive Programs
• Cambridge University Executive Programs
• Yale University Executive Programs
• Kellog Executive Programs
• CMU Executive Programs
• 45000+ Free Courses
• Free Certification Courses
• Free DigitalDefynd Certificate
• Free Harvard University Courses
• Free MIT Courses
• Free Excel Courses
• Free Google Courses
• Free Finance Courses
• Free Coding Courses
• Free Digital Marketing Courses

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.

• 6 Ways to Pay for an Online Course [2024]
• How to Choose an Online Course? [An Ultimate Checklist] [2024]

Team DigitalDefynd

We help you find the best courses, certifications, and tutorials online. Hundreds of experts come together to handpick these recommendations based on decades of collective experience. So far we have served 4 Million+ satisfied learners and counting.

What are AI Algorithms? [Deep Analysis] [2024]

Top 50 Surprising Artificial Intelligence Facts & Statistics [2024]

How can Leaders make use of Artificial Intelligence [2024]

10 Predictions about Future of AI in the UK & Europe [2024]

7 Hobby Ideas for Artificial Intelligence Engineers [2024]

Is ChatGPT Overhyped? [2024]

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

How to Design an AI Marketing Strategy

• Thomas H. Davenport,
• Abhijit Guha,
• Dhruv Grewal

In order to realize AI’s giant potential, CMOs need to have a good grasp of the various kinds of applications available and how they may evolve. This article guides marketing executives through the current state of AI and presents a framework that will help them classify their existing projects and plan the effective rollout of future ones. It categorizes AI along two dimensions: intelligence level and whether it stands alone or is part of a broader platform. Simple stand-alone task-automation apps are a good place to start. But advanced, integrated apps that incorporate machine learning have the greatest potential to create value, so as firms build their capabilities, they should move toward those technologies.

What the technology can do today—and what’s next

Idea in Brief

The challenge.

At many firms, the marketing function is rapidly embracing artificial intelligence. But in order to fully realize the technology’s enormous potential, chief marketing officers must understand the various types of applications—and how they might evolve.

The Framework

Classifying AI by its intelligence level (whether it is simple task automation or uses advanced machine learning) and structure (whether it is a stand-alone application or is integrated into larger platforms) can help firms plan which technologies to pursue and when.

Implementation

Companies should take a stepped approach, starting with rule-based, stand-alone applications that help employees make better decisions, and over time deploying more-sophisticated and integrated AI systems in customer-facing situations.

Of all a company’s functions, marketing has perhaps the most to gain from artificial intelligence. Marketing’s core activities are understanding customer needs, matching them to products and services, and persuading people to buy—capabilities that AI can dramatically enhance. No wonder a 2018 McKinsey analysis of more than 400 advanced use cases showed that marketing was the domain where AI would contribute the greatest value.

Chances are, you haven’t asked the right questions.

• Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting scholar at the MIT Initiative on the Digital Economy, and a senior adviser to Deloitte’s AI practice. He is a coauthor of All-in on AI: How Smart Companies Win Big with Artificial Intelligence (Harvard Business Review Press, 2023).
• AG Abhijit Guha is an associate professor in the department of marketing at the Darla Moore School of Business at the University of South Carolina. His research and teaching interests include retailing, pricing, and artificial intelligence.
• DG Dhruv Grewal holds the Toyota Chair in Commerce and Electronic Business and is a professor of marketing at Babson College. He is a coauthor of Marketing and Retailing Management and the author of Retail Marketing Management: The 5 Es of Retailing .

Partner Center

AI Marketing Case Study: Discover Success Stories and Cutting-Edge Strategies

Explore a range of successful AI Marketing campaigns in our latest case study. Gain powerful insights to optimize your marketing efforts using AI advancements.

Insights to Innovations — An In-Depth Look at AI Marketing Case Studies

Artificial intelligence (AI) is reshaping the marketing landscape, offering unparalleled opportunities for innovation and precision. AI-based tools are transforming how marketing teams operate, enabling the automation of various marketing tasks such as audience segmentation, search engine optimization, content personalization, and targeted advertising. This technological leap forward allows for more streamlined and effective marketing campaigns, enhancing precision and efficiency.

Chief Marketing Officers are now equipped with AI-driven insights, revealing customer behavior patterns and opportunities that were once elusive. Applying technologies like Natural Language Processing and deep learning extends beyond data analysis. These powerful tools aid in various marketing endeavors, from crafting compelling content to making data-informed decisions about pricing strategies or tailoring marketing messages to specific audience segments. Embrace the AI revolution in marketing, where technology meets creativity to redefine how we connect with audiences.

An Overview of AI in modern marketing efforts

Artificial Intelligence has rapidly become essential in our daily lives, particularly in marketing. What was once considered a far-off concept is now a reality we encounter frequently. From Siri on our smartphones to the customized playlists on Spotify, to Amazon Alexa in our homes, and personalized ads on our social media feeds - AI-powered tools have become ubiquitous. AI marketing refers to incorporating AI technology, such as machine learning, big data, and predictive analytics, into marketing activities to deliver hyper-personalized content and interactive customer experiences in real-time, increasing efficiency and effectiveness.

The importance and potential benefits of AI-driven marketing programs

A comprehensive understanding of AI's capabilities is crucial for businesses looking to remain competitive and relevant in today's rapidly changing market. As companies shift towards more digital and customer-centric marketing strategies, AI-driven marketing solutions offer numerous potential benefits that can completely revolutionize an organization's relationship with its audience. These benefits include, among other marketing objectives, personalized customer interactions, predictive consumer behavior analysis, and enhanced decision-making through comprehensive data analysis. In addition, AI's ability to interpret user behavior patterns enables businesses to anticipate customer needs accurately, thereby dramatically redefining the customer journey while maximizing return on investment (ROI).

Exploring successful case studies of AI marketing campaigns

This in-depth exploration delves into AI's inspiring potential for brands to deploy the technology in their marketing efforts. By exploring case studies of successful AI marketing campaigns, this essay aims to illuminate the various facets of AI marketing - from its underlying technology application to potential challenges - and how businesses can harness this technological marvel to drive success. Anticipate a journey that starts from the granular aspects of AI in Marketing Strategy and scales up to the broader panorama of AI-assisted marketing success stories. The assessment will also reveal the path of innovation paved by AI, culminating in a conclusion highlighting future directions for this fascinating intersection of technology and marketing.

Case Study 1: Dominos’ Dom Assistant and Voice Ordering System

Artificial intelligence type.

Voice AI, Conversational AI

Description of the campaign

Background of the company and its marketing needs.

Domino's, being a global leader in pizza delivery, continually strives to innovate its ordering and delivery processes to enhance customer satisfaction and streamline operations. The inception of voice technology in its marketing and sales channels was aimed to meet the evolving demands of its tech-savvy customer base and to stay competitive in the fast-paced food service industry.

Details on how AI was utilized in the marketing campaign

Domino's introduced voice ordering through its virtual assistant, Dom, enabling customers to place orders using voice commands on their mobile devices and smart home systems. In 2015, the company further expanded this service through the launch of AnyWare, allowing customers to order pizza via Siri, Amazon Echo, and other voice-enabled devices​ 1 ​.

Specific results that were achieved

Voice ordering not only elevated the customer experience by simplifying the ordering process but also optimized operational efficiencies. The initiative reflected a modern, customer-centric approach, aligning with the contemporary trends of voice search and commerce.

Analysis of the success factors

Understanding of the target audience and their needs.

By recognizing the user behavior shift towards voice search and voice-activated devices, Domino's effectively catered to the convenience and preferences of its target audience.

Innovative application of AI technology

The innovative implementation of voice AI technology facilitated an intuitive, hands-free ordering experience, distinguishing Domino's in a competitive market.

Effective integration with other marketing strategies

Domino's integrated voice ordering with its existing digital marketing tools, creating a seamless multi-channel ordering experience for its customers.

Lessons learned for other businesses

Importance of staying updated with technological advancements.

The case illustrates the importance of embracing new technologies to meet evolving customer expectations and stay ahead in the market.

Ways to leverage AI for enhanced customer experiences

Leveraging voice AI can simplify customer interactions, enhance engagement, and provide a modernized customer experience.

Case Study 2: Nike's Personalized Design AI Campaign

Predictive AI, Machine Learning

As a global leader in sports apparel, Nike is known for its innovative and customer-centric marketing strategies. In an increasingly competitive market, Nike recognized the need to strengthen customer engagement and loyalty through highly personalized customer experiences, leading to their decision to incorporate artificial intelligence advertising in their marketing efforts.

Nike's personalized design campaign showcased AI use in its true potency. Leveraging AI-driven marketing campaigns and machine learning in marketing, the company launched a series of personalized shoe designs for its customers. Using an AI-assisted marketing solution, Nike analyzed individual customer data gathered from their app usage patterns, behavior on social media platforms, and previous purchase history. Combining AI data analysis marketing and customer segmentation AI, Nike effectively created unique design recommendations for each customer.

The results were remarkable, with the company registering a surge in customer engagement and sales. Client retention rates increased significantly, and the campaign played a crucial role in reinforcing brand loyalty among customers. This AI application in personalized design recorded an increment in revenue, strengthening Nike's position in the sports apparel market and enhancing its marketing efforts.

The campaign's success can be attributed to Nike's phenomenal understanding of its target audience and their needs. By analyzing customer data effectively, the company was able to identify key customer behaviors and preferences, thereby generating designs that resonated with individual customers.

The innovative use of AI technology, particularly within AI marketing optimization and AI marketing analytics, was another critical success factor. Nike effectively harnessed AI's potential to turn massive amounts of customer data into actionable insights, leading to highly tailored and visually appealing design recommendations.

Moreover, the integration of this AI strategy with other digital marketing automation processes and social media platforms boosted the campaign's reach. This multi-channel marketing approach helped Nike deliver on their marketing objectives to connect with a broader audience, thereby increasing its overall campaign effectiveness.

Importance of personalization

This case study highlights the significant benefits that embracing personalization in AI marketing efforts can bring to businesses. It underscores the need for companies to understand their customers at a granular level and tailor their product or service offerings accordingly.

Ways to leverage AI for unique customer experiences

By exploring how Nike leveraged AI in content marketing and AI in social media posts, other businesses can discern useful insights on using AI to create unique customer experiences. Whether it's through predictive analytics in marketing or AI data analysis, there is much to gain through AI integration into business processes.

Case Study 3: Coca-Cola's "Share a Coke" Voice Campaign

Voice AI & Conversational AI

Coca-Cola, a household name in the beverage industry, has always been at the forefront of innovative marketing strategies. With the advent of voice technology, the company saw an opportunity to engage with customers in a more interactive and personalized manner.

In a campaign titled "Using Your Voice to Share a Coke," Coca-Cola embraced Voice AI to allow consumers to personalize their Coke bottles by simply using their voice. The campaign utilized voice recognition and processing technologies to capture and process the user's input, which was then used to customize labels on the Coke bottles​ 1 ​.

The campaign garnered widespread engagement and showcased how Voice AI can foster unique customer interactions, setting a precedent for other brands to follow suit in leveraging voice technology for personalized marketing endeavors.

By tapping into the growing trend of voice-activated devices and services, Coca-Cola effectively catered to the contemporary consumer's desire for personalized and interactive experiences.

The innovative use of Voice AI technology not only distinguished the campaign but also demonstrated how AI can be harnessed to create unique, memorable customer experiences.

The integration of this Voice AI campaign with other digital marketing strategies and social media platforms amplified its reach and impact, contributing to its overall success.

Importance of innovation in AI application

This case study underscores the importance of being innovative in applying AI technology to marketing strategies to capture the interest and engagement of the target audience.

Exploring new technological frontiers

Brands should not shy away from exploring new technological frontiers like Voice AI to stay competitive and relevant in the evolving digital marketing landscape.

Case Study 4: BuzzFeed's Journey Towards Personalized Quiz Content Through AI

Generative AI

In a quest to further elevate user engagement and satisfaction, BuzzFeed embarked on a unique campaign to personalize quiz content using Artificial Intelligence (AI). The cornerstone of this campaign was to tailor the quiz experience, providing individualized content based on user responses.

BuzzFeed, a titan in the digital media realm, thrives on creating engaging and interactive content. With a vast audience boasting diverse preferences, the need to deliver personalized experiences was paramount. The goal was clear: to enhance user engagement and foster a deeper connection with the audience through personalized content.

Leveraging tools from OpenAI, BuzzFeed crafted a system that tailors quiz content based on user responses. A quintessential example is a quiz that envisages a “new life” for the reader based on their answers, encapsulating the essence of AI's potential in delivering personalized content at scale.

The venture into AI-driven quizzes ushered in a new era of user engagement for BuzzFeed. The personalized quizzes not only enhanced user satisfaction but also showcased the boundless potential of AI in content personalization, setting a new benchmark in user engagement metrics.

The success of the campaign can largely be attributed to a profound understanding of diverse user preferences and the effective utilization of OpenAI tools. The seamless automation of the personalization process was a game-changer, allowing BuzzFeed to cater to individual preferences at an unprecedented scale.

The innovative heart of this campaign was the creative application of AI in crafting personalized responses based on user inputs. This innovative approach not only enriched user engagement but also set a remarkable precedent in AI-driven content personalization.

Effective integration with other marketing strategies:

The AI-driven personalization was not an isolated strategy; it harmoniously dovetailed with other digital marketing initiatives. The essence of personalization permeated through social media marketing and email campaigns, creating a more personalized user experience across different platforms.

The journey underscored the pivotal role of personalization in enhancing user engagement in the digital landscape. Moreover, it illuminated the potential of AI as a robust tool for automating personalization, providing a scalable solution to cater to diverse user preferences.

The campaign showcased the capability of AI in real-time personalization based on user behavior and preferences. It also highlighted the potential for integrating AI tools with existing digital marketing strategies to enhance user engagement across various digital platforms, opening new horizons for creating enriched user experiences.

Case Study 5: Starbucks’s Predictive Ordering AI Application

Explanation of starbucks' marketing goals and challenges.

As one of the most familiar names in the global coffee industry, Starbucks has often led the pack in terms of innovative marketing solutions. The company identified the need to elevate their customer experience and set a goal to offer customized experiences for its customers worldwide. However, given the multinational status of the company, the challenge lay in predicting and meeting the varying preferences of their diverse consumer base.

Elaboration on how AI was implemented in the marketing strategy

This unprecedented challenge inspired Starbucks to implement AI in their marketing efforts. The company designed an AI-driven marketing campaign using machine learning to predict customer orders. This powerful AI-assisted marketing tool named Deep Brew would analyize customer data to anticipate the customer's order based on factors such as their past orders, the time of the day, the location, and even prevailing weather conditions.

Concrete outcomes that resulted from the campaign

Starbucks's AI marketing optimization with Deep Brew successfully allowed for an exceptional degree of personalization in AI Marketing. The predictive analytics marketing tool managed to boost customer engagement significantly as customers found their needs met even before they verbally articulated them. This surge in engagement translated into higher sales, increased customer loyalty, and elevated Starbucks's reputation as a customer-focused brand.

Deep understanding of customer behavior patterns

A deep understanding of customer behavior patterns was key to the success of Starbucks' AI application. The customer segmentation AI and AI data analysis marketing insights derived from the customers' purchasing patterns provided a nuanced understanding of varying customer preferences that made this personalization possible.

High precision and effectiveness of AI predictive technology

Another significant factor was the high precision and effectiveness of AI predictive technology. In their AI-powered marketing tools, machine learning helped unravel hidden patterns and enabled the company's AI Marketing Technology to provide accurate and efficient predictive ordering.

Seamless integration into customers' everyday lives

The third success factor was the seamless integration of the predictive ordering system into customers' everyday lives. The convenience of having their preferred orders anticipated and served promptly made Starbucks an indispensable part of their daily routines, bolstering brand loyalty and customer satisfaction.

Need for high-quality, comprehensive data for AI prediction

Through Starbucks' experience, it becomes evident that the need for high-quality, comprehensive data is fundamental to the success of AI prediction. Effective Marketing AI Analytics is driven by the depth and breadth of the data analyzed.

Role of AI in enhancing customer convenience and loyalty

This case study also highlights the significant role that AI can play in enhancing customer convenience and loyalty. It showcases what AI in content marketing and AI in social media marketing can truly achieve when effectively incorporated into businesses' marketing strategies.

Case Study 6: Sephora's Virtual Artist Augmented Reality Campaign

Explanation of sephora’s marketing objectives and obstacles.

As a multinational retail giant in personal care and beauty, Sephora constantly seeks to provide its customers with a unique and immersive experience. Understanding the challenge consumers often face in selecting the correct shade or product, Sephora set out to offer a solution through AI and Augmented Reality, thereby addressing its marketing goal of enhancing the customer shopping experience.

Details on how AI and AR were combined for the campaign

Sephora harnessed both Artificial Intelligence (AI) and Augmented Reality (AR) for its groundbreaking campaign – The Virtual Artist. The Virtual Artist, an AI-driven marketing campaign, integrated machine learning in marketing to provide customers with an unparalleled and interactive product-testing experience. The AI marketing technology allowed customers to virtually 'try-on' different makeup products using the camera on their devices and offered recommendations based on the customer's facial analyses and preferences.

Quantifiable results that were recorded from the campaign

The Virtual Artist achieved remarkable results. The interactive and personalized shopping experience increased customer engagement and sales, transforming Sephora's artificial intelligence advertising efforts. The AI-driven campaign also drew plaudits across the industry for its innovative use of technology in enhancing customer experience.

In-depth knowledge of the consumer makeup experience

A deep understanding of the consumer makeup experience and the need for personalization played a key role in the campaign's success. By recognizing and catering to this gap through AI and AR, Sephora's AI marketing strategy hit the mark.

Creative use of AI and AR technologies for product testing

The innovative motion of combining artificial intelligence advertising with AR for virtual makeup trials was another significant factor in the campaign's success. This AI in content marketing transformed how consumers made their product selections, making it a stress-free and fun process.

Effective digital transformation of the shopping experience

Yet another factor contributing to Sephora's success was its ability to effectively blend the physical and digital worlds - a potent combination in the modern retail sector. This blend empowered customers to make more informed purchasing decisions and forge a stronger connection with the brand.

The Potential of AI and AR in enhancing product discovery and trial

Sephora's campaign showcases the vast potential of AI and AR in enhancing product discovery and trial, a crucial takeaway for businesses across sectors. Moreover, it highlights the role of AI and AR not only as tech add-ons but as central components of creating personalized experiences and broader customer-centric AI marketing analytics strategies.

Emphasizing the importance of digital transformation in marketing

Lastly, this case study puts a marked emphasis on the growing importance of digital transformation in marketing. Sephora's campaign shows that successful digital marketing automation is not about displacing the human element in shopping but enhancing it with the help of AI and AR.

Recap of Case Studies and Key Takeaways from Their AI Marketing Campaigns

Throughout this exploration, we immersed ourselves in five distinctive case studies - Dominos Dom assistant, Nike's personalized design AI campaign, Coca-Cola's "Share a Coke" campaign, Starbucks' predictive ordering AI application, and Sephora's virtual artist augmented reality campaign. Each of these campaigns represented different approaches to integrating marketing AI into their strategy, each with its unique set of challenges and achievements.

Dominos demonstrates how you can use an AI assistant to improve the customer experience while Nike showcases how AI can be leveraged in sports apparel marketing to offer personalized designs by integrating machine learning and AI data analysis. The Coca-cola example illustrates how you can use Voice AI for promotions and product sampling. On the other hand, Starbucks showcased how predictive analytics in marketing can refine customer's ordering experience while amplifying brand loyalty. Lastly, Sephora created an engaging and interactive customer experience with its AI and AR-integrated campaigns, revolutionizing the process of product discovery and trial.

The key takeaway from these case studies is that AI, when skillfully integrated into a company's marketing strategy, can revolutionize the customer experience, bolstering engagement and consequently enhancing business profitability and brand loyalty. It can also provide valuable insights into consumer behavior, allowing for unprecedented personalization in marketing content.

The Impact and Future Potential of AI in Marketing Innovation

The transformative power of AI in marketing is evident. As demonstrated through the case studies, AI is already paving the way for innovative and highly personalized marketing strategies. From AI marketing optimization to predictive analytics, the technology offers numerous applications bound to redefine the marketing landscape even further.

The great strides made by AI in the scope of marketing are just the beginning. As AI technology advances and companies become more adept at harnessing its potential, the digital marketing space is set to become even more dynamic and personalized. Businesses that adapt and grow with this technology will remain at the forefront of their respective markets, offering their customers top-notch experiences that extend beyond simple transactions.

The future of marketing lies with AI, and it will continue to rewrite the rules of customer engagement and brand loyalty. As we look towards this future, one thing is certain - companies that embrace AI's capabilities to immerse their customers in highly personalized and interactive experiences will lead in the ever-evolving world of marketing.

Frequently Asked Questions (FAQs) about AI in marketing

How can AI be integrated into existing marketing strategies? AI can be integrated through various tools and platforms designed to automate and optimize marketing processes. This includes customer segmentation, personalization, predictive analytics, and real-time data analysis which can help in better targeting and engagement. Additionally, Conversational AI and Voice technologies can be used to significantly enhance a brand's customer experience by facilitating intuitive interactions, providing instant support, and enabling hands-free engagement which can be particularly beneficial in today's fast-paced, on-the-go culture.

What are some notable success stories of AI in marketing? The page cites Domino's and Nike as companies that have successfully utilized AI in their marketing campaigns, enhancing customer experience and achieving notable campaign success.

How does AI personalize the customer experience? AI allows for the analysis of large datasets to understand individual customer behaviors and preferences. This data can then be used to create personalized experiences, messaging, and offers, improving engagement and conversion rates.

What types of AI technologies are most impactful in marketing? Technologies like machine learning, predictive analytics, natural language processing, and deep learning are especially impactful as they enable real-time decision-making, customer segmentation, and personalized engagement.

How can AI in marketing improve ROI? By automating routine tasks, providing insights through data analysis, and improving customer engagement through personalization, AI can significantly improve the ROI of marketing campaigns.

What are the challenges faced when implementing AI in marketing campaigns? Challenges may include data privacy concerns, the requirement for clean, well-structured data, and the need for skilled personnel to manage AI tools and interpret results.

How does AI in marketing align with data privacy regulations? Ensuring compliance with data privacy regulations is crucial when implementing AI in marketing. Companies need to manage data ethically and within the framework of local and international laws, like GDPR or CCPA, to build trust and avoid legal issues.

Susan Westwater is the CEO and co-founder of Pragmatic and an expert in advising innovative brands on Voice and conversational AI. With 20+ years of experience in top tier agencies and corporate, she helps clients expand their brands into conversational experiences. She is an expert on Voice, conversational AI, and emerging technologies in marketing and business strategy and has published works on the topic. She is an Ambassador of the Open Voice Network, an instructor at the AI Academy for Marketers, and co-author of "Voice Strategy" and "Voice Marketing" (2023).

Explore other topics we've written about

3 ways brands can optimize their mobile experience to better serve their customers, exploring the top use cases for ai in healthcare, the role of personalization in ai marketing strategies.

Stay at the forefront of digital marketing's cutting edge with our monthly newsletter, Pragmatic Advisor, dedicated to the dynamic field of AI in digital marketing.

• Persona Generator

Use Delve AI's persona generator to create AI-powered personas automatically.

• Marketing Advisor

One tool for all your SEO, PPC, social, email, and content marketing needs.

Customer Persona

Generate personas from your CRM data for detailed consumer insights.

Competitor Persona

Create personas to analyze competitors and uncover marketing strategies.

Website Persona

Leverage personas to get a deep understanding of your website visitors.

Quick Persona

Describe your product, industry, or business model to build AI-driven personas.

Social Persona

Build social media personas and gain rich insights from social networks.

Employee Persona

Employ personas to spot your ideal employee and optimize the hiring process.

SEO Advisor

Get guest blog topics, ideas, guidelines, and link bait opportunities.

Social Advisor

Find trending topics, influencers, popular brands, and organizations.

Content Advisor

Identify content ideas and article updates that are critical for SEO.

Media Advisor

Monitors media coverage and pinpoint opportunities to enhance visibility.

PPC Advisor

Boost conversions with keyword research and campaign optimization.

Email Advisor

Receive content and A/B testing tips, with optimal email send times.

E-commerce Personas

Create e-commerce buyer personas.

B2B Personas

Know your buyers with B2B personas.

Personas for Agencies

Generate personas and get marketing ideas.

Business Pricing

Persona by Delve AI for businesses

Agency Pricing

Persona by Delve AI for agencies

Delve AI Blog

Learn about personas and market research.

Templates & Tools

Use free persona tools and templates.

Data Sources

Check data sources and integrations.

Case Studies

Read client case studies and success stories.

Solve common questions and queries.

Knowledge Base

Find all Delve AI related information.

16 Use-cases of AI in Marketing

Table Of Contents

If you see an article which starts by saying that the importance of artificial intelligence (AI) technology to mankind is akin to that of the wheel, would you balk at that thought? Chances are less. Unless you are completely unaware of its influence because you live under a cave, you would be agreeing with it exclaiming at the truth in the statement.

AI has seeped into our lives in a way that it is almost impossible for us to imagine a world without it. In the last four years, AI adoption by businesses has grown by more than 270% , says Gartner. By 2027, the global AI market is expected to reach $267 billion, according to Fortune Business Insights, 2020 . It is a ten-fold increase in eight years as the value of the AI market in 2019 was $27.23 billion.

Most of the popular products that you use today are powered by AI systems. The business in question might not even be a technology company, but many of its functions, such as customer service and operations, could be enabled by AI systems. Naturally, marketers are quick to leverage technology, which is why you will find them using AI to map customer journeys and understand their audience.

Artificial intelligence in its most simple terms means “the science of making machines smart”. Hence, AI marketing would be a step closer to making marketing smart.

Here is how AI is helping marketers and marketing:

#1 audience targeting:.

The audience that you show your ad to matters. You cannot expect great results if your targeting is off the charts. Showing your ads to cops in the Manhattan region when you are targeting dentists in Ohio isn’t the smartest decision. Some of the biggest platforms that you use, Facebook, Google, Quora, Reddit, Instagram, Snapchat, etc., have oodles of data that can be used to segment customers with relative ease. It is impossible to do all of this manually.

AI looks at your past audiences, how your ads performed for that particular set of audiences, and so on. It considers the KPIs you use and your performance data to arrive at audiences who are more likely to buy from you.

AI tools for advertising: AI also helps with performance and spend optimization of the ads as well as its creation. Some of the most popular AI tools for advertising are: Adobe, IBM, Albert, GumGum, Pathmatics, Phrasee, WordStream, etc.

#2 Lead generation:

Based on your existing data, including information about ideal customers, clients, etc., the AI system will be able to show you a list of leads that are closest to becoming your customers. It will even score the lead based on preset conditions. The leads that have the highest score are looking for a solution similar to yours. Your salespeople will be better equipped to provide them with a solution and that too at the right time.

It reduces the time taken for manually scourging through leads from multiple sources. Most of the methods followed to find leads are tiring and are not that effective either. Building an AI system for lead generation allows you to spend time on more important tasks that will add value directly to the company’s bottomline.

AI tools for Lead Generation: LinkedIn’ Sales Navigator Tool helps find leads, it is also powered by AI. Node is a similar program that uses metadata to recommend new customers. Growthbot by HubSpot integrates its AI to CRM systems to generate leads. Conversica is an AI tool that engages in conversations with possible leads to collect real-time information and passes the same to the salespeople.

#3 Personalization:

Sending the right content to your prospects based on his/her needs can make you win deals. Customers these days expect the moon and it is perfectly all right because you can use AI to offer a piece. They expect you to understand what they want and showcase that in front of you. With the help of AI and ML, you will be able to deliver a relevant and personal experience to the visitor. The offers that you send prospective customers should be available across a variety of channels. If not, they will be confused and leave for your competitor.

To provide such an experience, online retailers should gather data within seconds. Is it manually possible? Of course not. This is where AI and ML come together to send contextual content and offers to convert the leads into sales. According to Everstring , 71% of marketers are interested in AI for their marketing mainly because of personalization.

AI tools for Personalization: Skyword, Scripted, Curata, etc., helps you scale your content. Acrolinx uses natural language processing to read all your content and suggests improvement. Atomic Reach tells the business to edit content to increase its engagement. Boomtrain finds your content that is most likely to be ‘engageable’ for the user. OneSpot uses ML to match content to the preference of the individual using website interaction data.

#4 Deep understanding of customers:

“We know enough about our customers now, let’s not spend more time on collecting their data, instead, let’s make some sales” said no business ever. Every business knows that customers keep changing their preferences and are always on the lookout for the next shiny object. So if you need to keep the attention of your prospective customers, you need to keep refining your understanding of them.

Finding buyer personas is one of the prime tasks that you should undertake before planning any marketing campaign. Once you have the personas for your product, you will be able to create content that is tuned towards what each of the personas want to consume.

Persona by Delve AI is an AI-based persona generation tool that uses first-party data (e.g. Google Analytics) and public data sources (e.g. voice of the customer data from social media, review sites, communities and forums) to create personas for your business automatically. It offers incredible insights into what your prospective customers are looking for by using data from more than 20 sources. It even goes a step ahead and segments your audience. You don’t have to worry about customer’s expectations and behaviors changing over time because the personas are automatically updated on a regular basis.

#5 Behavioral analysis:

Retailers love AI and ML because it allows for segmentation of customers and offers products for them based on analyzing and understanding their purchasing habits.

Mere personalization doesn’t hit the perfect pitch, customers are looking for individualized interactions. It implies an offer that is exclusive for the particular customer and not just because they are a part of a customer segment. For a business that has thousands of customers every day, is it even humanly possible? Thankfully, there is an AI system which can make that happen.

AI/ML analytics tools will tell us estimated transaction value, probability of someone making a purchase, their affinity towards our brand, and so on. Based on a report by Salesforce, more than 62% of customers are willing to allow AI to improve their user experience.

Tools: Xineoh, Black Swan Data, Dynamic Yield, Cortex and Logicai.io are some of the AI tools that help businesses predict customer behavior.

#6 Competitor insights:

The importance of knowing what your competitors are doing cannot be discounted. Ever. Everything that your competitor does can either be a lesson or something that you need to adopt for your business. Some of the most successful companies in the world have always made it a point to learn from their competitors. Why else would corporate espionage be a thing? No, getting competitor insights will not put you on the wrong side of the law. We are talking about using tools that are specifically used to understand the visitors on your competitor’s website, the content they create, the tools they use, and so on.

Competitor Persona by Delve AI tool helps you understand details about your competitors and their marketing strategies. Input your competitor’s website domain and the tool will create personas of your competitors’ users automatically. It will help you with market analysis and competitor keyword research and gives you a multitude of ideas on what your competitors are working on. Manually going through each page of your competitor, their social media handles, content on different platforms, etc., might not be a smart decision.

#7 PPC advertising:

When you set up a PPC campaign, you can either choose manual or automatic bidding, placement, etc. The latter is done by AI. While PPC requires AI, it cannot function without the help of a human. AI systems have only managed to take over repetitive tasks of PPC management, but they are great in improving the campaigns. It helps with optimizing your ads, dynamic ads whereby it shows different ads to people based on their browsing history, adjusts your bidding value to help you achieve your objective, help you uncover the most relevant keywords for you, etc.

Tools: Acquisio, Adzooma, and PPCHero are a few of the tools that make PPC management easy with the help of AI.

Intelligent advertising design

With the help of AI, highly personalized design is now possible, allowing for the automatic customization of many aspects of marketing campaigns and advertising materials for particular audiences or even specific individuals. Algorithms can decide which aspects of marketing campaigns are most likely to catch your audience's attention and encourage additional involvement, right down to the design style and color schemes used. The performance of various combinations of design elements and audiences can then be evaluated by algorithms to identify areas where improvements could be made.

For instance, it can be found that younger consumers are more receptive to highly visual advertisements and marketing materials, but elderly consumers prefer more text or in-depth descriptions of goods and services. Advertising creatives would therefore just need to create one version of their assets, which AI algorithms could subsequently tweak and distribute automatically to the right target segments. Advertisers currently employ software like Persado, which uses natural language processing algorithms to generate personalized sales copy.

By identifying and classifying client segments based on their behavioral patterns at scale, AI aids us. By keeping track of how well various pieces of material perform against specific portions, performance can then be optimized.

#8 Search engine optimization:

Using AI for SEO allows you to use much better solutions that will help with your page rankings and create a better strategy. Search engines are getting smarter at identifying irrelevant backlinks, keyword stuffing, poor content, and so on. Marketers are instead using AI to meet the high standards that search engines have.

To give you an example of the kind of power that AI wields in improving SEO performance, let’s say you want to see when your company is mentioned somewhere, you do not have to manually do the checking. There are tools like BuzzSumo and HubSpot that will automatically notify you.

According to research, Google is the starting point for up to 85% of purchasing choices. The most efficient methods for getting your website to the top of the results may have altered as e-commerce has expanded to take up more of our life. In fact, several techniques that in the past would have produced excellent results — such as link-building and keyword stuffing — may today lead to your site being penalized because algorithms are becoming more adept at identifying attempts by marketers to "hack the system."

"Search volume" is a crucial notion in SEO since it lets us know how many people are utilizing specific terms and phrases to look for the goods and services they require. Machine learning algorithms are used in some of today's most sophisticated SEO practices to better comprehend the purpose behind term usage as well as the content of searches. In order to identify any holes in your own SEO strategy and create SEO-friendly marketing content, it may also be utilized to research competitors' SEO efforts.

Tools: WordLift, Bright Edge, MarketBrew, Pave AI, Dialog Flow, and Albert.

#9 Social media listening:

What customers speak about your business and its various aspects like customer service, operations, time taken to order, etc, they tell a lot about you. Not only is their opinion something that you should reflect on, but it should also make you sit up and take notice. Thanks to AI, you can collect this data in real-time. It will help you unearth patterns based on their conversations.

One conversation about your product might be on a subreddit while the same keyword might have been used by a different customer on Twitter while someone on Facebook said a similar thing. By now, the AI system recognizes that it is something that you might want to know. You will be able to come up with brilliant solutions and offer immediate resolution to your customer’s problems if you listen to what your customers and prospects say about you.

Tools: Lately, Genylabs, Synthesio, Socialbakers, Sprout Social, etc., are some of the tools that help businesses with understanding what your customers talk about you on the internet.

#10 Email marketing:

The ability to write attractive subject lines which makes the recipient open the emails is a skill that businesses will pay thousands of dollars. Why? Because email marketing’s ROI is $42 for each dollar spent and the more the emails that you send are opened, the higher are the chances of your prospects converting into customers.

Your customers judge every email that comes to them based on the subject line. Within the few seconds that they read the subject line, they have decided whether to open it or not. More often than not, subject lines are the ones that are created at the end of the campaign which means that businesses do not tend to give them the attention they deserve.

Through natural language technology, AI will find the best words that recipients are likely to respond to, keeping in mind the brand language. Phrasee and Mizy are two of the most popular tools that help businesses in this area.

#11 Predictive marketing:

The choices that we humans make are not usually rational, but they have patterns to it. It cannot be arrived at after going through a few data sets, you need tonnes of data for it. AI has the ability to go through so much data to predict the what, why, where and how of a customer purchase.

It uses various sets of attributes to predict our behavior. Businesses can have better understanding of its customers as it uses text analysis, sentiment analysis, lead scoring based on a set of attributes, etc., to get it right. If a business can use the right channel to offer the right product for a customer, that too at a price they are looking for, you can safely expect the sale to happen with ease.

Tools: Cortex, Crayon, IBM Watson, Google Cloud AI, are some of the tools that help with predictive marketing.

#12 Chatbots:

One in four customer service organizations are currently using AI-powered chatbots. There is no other tool that has changed customer service for the better like Chatbots did. Initially, it was a place for customers or visitors to get information based on a variety of attributes. Thanks to AI-powered chatbots, the systems learn on their own. So with each passing interaction, the system is getting smarter and its responses are becoming more accurate. Chatbots are already a huge part of our lives in the form of Siri, Alexa, Cortana, Google Assistant.

According to The 2022 State of Marketing AI Report , AI-powered chatbots perform the following functions for you:

• Automatically solve common queries in real time
• Answer a broad range of questions using the content on your website
• Eliminate lead generation forms
• Schedule sales calls

Chatbots are also increasingly being used in complex fields such as law. As of now, it helps you file appeals against traffic violations. There are chatbots in the education sector that grade essays. Mental health chatbot? There is Woebot for that. They are revolutionizing business and industries by improving customer service, adding charm to your brand, answering queries, helping them choose the right products based on their recommendations, and so on.

#13 Customer support:

With the help of Machine Learning and Natural Language Processing, customer support becomes a cakewalk for businesses. With its ability to multitask and handle multiple number of queries, AI generates responses with a speed and accuracy that humans cannot. By using tonnes of data, AI can do the following to enable customer support:

• Understand the customer from the information collected
• Identify the issues that they are facing
• Analyze their behavior patterns
• Find out their decisions and preferences
• Create suitable solutions and products
• Respond to messages
• Offer personalized products and discounts
• Minimize the cart abandonment volume

Solvvy, MonkeyLearn, Freshdesk, etc., are some of the AI-based tools for customer support. Gartner predicts that more than 15 percent of customer service interactions will be fully powered by AI by 2021.

#14 Content generation:

Content creation using AI is still in its infancy. There are programs like GPT-3, GROVER, MarketMuse, and XLNET that are known to create content from the AI alone. But these programs haven’t been able to produce exceptional pieces of content. OkWrite, a tool that creates AI-content tells in its blog that while it can produce content, it cannot generate an emotive feel.

They also add that it will be easily discernible to the average reader whether a piece of content is written by an AI or human. As of now, AI cannot replace original content, but it is a great help as it assists with the direction required for a blog post, initial content research or even the strategy for content marketing.

The Washington Post used AI to develop a writer named Heliograf , which used multi-sentence updates based on different data sources during the 2016 Rio Olympics. It was able to write around 850 stories depending on user requests. These were stories to attract pageviews, not longform content with an in-depth analysis though.

#15 Intelligent website audits

It is more crucial than ever to monitor the "flow" of visits to your website and social media accounts in order to identify any "leaks" that are prompting potential buyers to back out before clicking "purchase." Artificial intelligence (AI)-driven services can now automatically examine your website and notify you of any problems that might be affecting your conversion rate. Large, inefficient pictures and videos or slow-loading pages can easily turn off visitors and possibly result in SEO penalties. Regular audits typically take a lot of time and money to complete, and a qualified outside consultant is frequently needed. However, automated AI tools are making it increasingly easier to achieve this, which will increase efficiency and sales.

#16 Market analysis

With the use of computer vision technologies, computer programs can now "see," or comprehend, visual data. In order to better understand how and where products or services are being utilized, marketers can scan millions of photographs that are uploaded to social media networks every day. This offers marketers fresh methods for evaluating elements like brand awareness and market penetration. Additionally, it can be utilized to find influencers that are already associated with your company, resulting in more sincere interactions.

Trend analysis is another effective use case. Here, AI can assist you in identifying evolving habits and behaviors that may have an impact on how your clients and potential customers interact with vendors in your market. Along with your own visual advertising, you may more readily evaluate the success of your rivals' efforts and determine how clients respond to various moods, color schemes, and settings.

In addition to the above-mentioned NLP, image recognition can be utilized to automatically generate product descriptions for sales copy from product images. Additionally, you can utilize it to safeguard your brand by having it instantly notify you of any potential misappropriation of your branding, messaging, or creative IP.

#17 Augmented assistance

By providing augmented reality (AR) technologies that superimpose computer-generated pictures over real-world images, Ikea enables customers to view things in their own homes to see how a new sofa or table would mix with their existing décor. Here, AI is utilized to produce realistic-looking composite images, typically in real-time, as the user views through their phone's camera. The same technology is also used by cosmetic companies like L'Oreal to allow customers to test out makeup and other goods and see how they will look on them. While major businesses have long provided their customers with this kind of functionality, it is being made accessible "as-a-service" through websites like wearfits.com that enable retailers of all sizes to use it.

Ad platforms:

AI powers the purchase and sale of advertising in real-time which digital advertising platforms use. From advertising on platforms like Facebook, Instagram, Snapchat, Reddit, to third-party networks, including programmatic exchanges, AI is a blessing for advertisers .

The performance of your ad and the effectiveness of your budget is decided by the data points that the AI systems use. For example, terms like ad frequency and relevance score are data sets that Facebook uses to dictate how much you should pay and how these ads are displayed.

Non-marketing use cases:

#18 product design:.

AI systems can test the product or features, just like how an actual user would, based on the thousands of user sessions to find bugs in the system, something which even the product manager or a quality tester would have missed. If an AI system is armed with data about how users use a particular product, it will even be able to suggest the team on how it can build better product designs. AI systems can even go through the proposed user flow and determine if a user will complete a desired action or not. Let’s say we are talking about a manufacturing company, it can save millions of dollars in terms of research and development.

#19 Sales forecasting:

Teams that use AI extensively are ten times more likely to experience better forecasting and possess accurate sales pipeline, according to Salesforce . Manufacturing and retail companies will run into huge losses if they either produce more than what is required or lesser than what is necessary.

Human forecasting involves a bunch of formulas that have been refined over a long period of time, but it doesn’t input millions of data sets and there is no way humans can involve a lot of variables into the play. AI and ML are a gift to businesses that want to forecast sales. It will analyze the past opportunities, sales, successes, misses, percentage of win, and so on to forecast the sales. Based on the forecasting, the business only needs to produce as many products.

Challenges for AI in marketing:

More than 9 in 10 top businesses have either invested in AI or are on the verge of doing so, according to the Big Data and AI Executive Survey 2020 . Embracing AI technology is a step that all businesses should do without fail and that too in a war footing. Even though AI-powered platforms are becoming more common than one would think, there are still issues with using AI for your marketing.

Lack of insufficient infrastructure:

If you want AI to power your marketing engine, make sure that there is an IT infrastructure with high-performing hardware which is already in place. Smaller companies that have a low budget might find it difficult to set up such an infrastructure as the computer systems required to set up can be expensive. Thankfully, there are cloud-based solutions which use less resources and don't burn pockets either.

Lack of sufficient data:

The data that an AI system requires should not only be of high-quality but there should be a large amount of data to make sense of it. The existing data sets should be clean. If the data is not of high quality, then the success of the AI campaign will be low because the data isn’t reliable.

Insufficient budget:

There are no questions about the efficacy of AI systems adding a fillip to your marketing efforts. Having a tech stack and other martech tools can itself add a lot to your budget. The leadership team should be persuaded with details including highly relevant business data and forecasting to prove how good AI can be for your marketing.

Lack of skilled professionals:

There is a huge AI skill gap, which is why only large businesses are even thinking about creating in-house AI-based marketing solutions. AI talent is not growing as fast as other technology positions since it requires specialized skill sets. Even businesses that use readymade AI marketing tools should sufficiently train their employees to manage and interpret the results accurately.

While these are some of the challenges that businesses need to consider before jumping into the ‘AI in marketing’ bandwagon, you can always come up with alternative solutions. Businesses should ensure that they use the AI software responsibly. The results from AI systems in your marketing is going to be exponential, this is what you need to keep in mind when assessing the need for it.

Trends and technologies:

Such a recent trend is Artificial Intelligence, although it hasn’t been fully explored yet, there are so many use-cases for it in the marketing function alone. Here are some of the AI marketing trends:

Voice assistance:

With smart homes getting adopted at an incredible pace, the importance of voice search is going to increase. The number of AI-powered voice assistants is forecast to reach 8 billion by 2023 . AI-based voice assistants will save time for the customer from typing long form queries, offer fast solutions, improve sales and expand brand value, and improve customer retention. 43% of owners of voice-assisted devices (ages between 45 and 60) in the US currently use them to purchase items online.

AI Content:

No, artificial intelligence hasn’t been that smart yet to mimic the average human brain yet. They can create content based on data that is already available, but might not be able to add the human element in it, at least not yet. AI helps immensely with strategizing content, all of which helps with increasing traffic and placement on top of the search engine results.

Personalized recommendations:

One of the most popular use cases of artificial intelligence is its ability to recommend products based on previous data. It doesn’t even have to be for a segment of the audience, even if there are millions of users, it can create personal recommendations for each of them. With customers becoming increasingly demanding, personalizing your offerings will put you in their good books.

Hyper-segmentation:

Before AI was a thing, marketing campaigns had more guesswork than data in it. So it was a hit or miss scenario with most of the campaigns. AI equips marketers to process huge amounts of data. With ML algorithms reading through tonnes of data, it will learn more about the audience, create connections between each data point and create hyper-focused and highly segmented groups.

Persona research:

Hyper-segmentation of your audience offers a deep understanding of customer characteristics. Using this, AI-powered software can build personas that go beyond the typical characteristics that are listed in a buyer. Thoroughly analysing each piece of data, you can create extremely targeted personas. They can be then used as super targeted audience segments to send them personalized offerings.

Lead qualification, lead scoring, micro moments, targeted timing, cutting-edge predictive analytics, etc., are some of the other use cases.

How to use AI in marketing?

With the help of AI, marketers can create spectacular offers which will reduce the length of the sales cycle, increase retention rate and bring in more customers. To put it in simple words, AI in marketing uses customer data, historical data, machine learning and other computational data to predict what a prospective customer would do.

Would they buy from you immediately? If not, when would they buy it? What content do you need to show them so that they will be ready to open their purse strings? What exactly are they looking for? What is the price they have in mind? What are the features that they like most about your product? And so on.

The answers to each of the above questions provide you with an opportunity to make business decisions. That’s how powerful AI is for marketers.

Before you jump in the bandwagon, you need to know what exactly you want. You need to have a clear idea of your strategic objectives and understand how AI can help you with it. If there are AI solutions that are already available based on your requirements, then go ahead with it. Does your requirement make it necessary for you to build a custom AI-solution? Then you need to discuss with different stakeholders to understand if it is something that you can afford and what is the opportunity cost of not doing so.

Frequently Asked Questions (FAQs)

How is ai used in marketing.

AI in marketing is used for audience targeting, lead generation, personalization, behavioral analysis, competitor insights, PPC advertising, SEO, social media listening, email marketing, predictive marketing, content creation, market analysis, website audits, and augmented assistance, thus enhancing efficiency across various marketing functions.

Which companies are using AI for marketing?

Famous companies like Netflix, Spotify, Amazon, Coca-Cola, Uber, Salesforce, Zendesk, SEMrush, and HubSpot use AI for marketing automation, predictive analytics, content creation, social media marketing, SEO, advertising, and customer service.

Like what you're reading? Sign up for our email newsletter!

Related articles.

9+ Use-cases of Generative AI in Marketing

AI Generated Persona

How to Create Personas Automatically

Subscribe for blog updates

• Email address

Our products

Live Persona

• E-commerce stores
• B2B/SaaS companies
• Marketing agencies
• Integrations
• Case studies
• Knowledge base
• Persona templates
• Partner program

Drag & Drop Editor

Interactive AMP Emails

Mailmodo AI for Email

Smart Segmentation

Managed Deliverability

Automated Email Journeys

Email API Services

Get ideas to drive high engagement in your next campaign

Discover how brands like you are growing with email marketing

Connect Mailmodo with all your favourite platforms and tools

See 40+ Integrations ->

Explore how Mailmodo can help grow your business

Learn Marketing

Email Templates

Ebooks & Reports

Marketing Stream

Email Masterclass FREE

Generate your email strategy in a single click

• Explore Interactivity

• Get started - it's free
• Case Studies
• For Developers
• Try Mailmodo for free

Table of contents

10 Real-Life AI in Marketing Examples and Use Cases

Last updated: 04 September, 2024

13 mins read

Many people find using AI in marketing intimidating because they believe it could replace humans and even harm us. Although this is a misconception, AI can enhance marketing efforts, cut costs, and save time if used ethically and creatively. Here are some great AI marketing examples and brand use cases to help you get started.

Table of Contents

A deep dive into ai marketing examples.

We have some inspiring generative AI marketing examples, with case studies of Nutella, Netflix, Volkswagen, JP Morgan Chase, etc., to help you better understand AI in marketing and how it generates results for various brands in different industries.

1. Nutella uses AI to create packaging

Industry: Food & Beverage

Use case: Design

The idea: Nutella wanted to stand out in terms of packaging and make its products more desirable and brand more talkable. This transformative challenge was given to Ogilvy Italia, a branding agency. They used AI and a dozen patterns to create 7 million one-of-a-kind labels as unique and expressive as Italian people are. And that's how Nutella Unica was born.

Mira los brillantes diseños de botes que “Nutella Unica” creó en Italia. #Packaging #DiseñoEmpaque pic.twitter.com/CqDQuVjTiT — Soy Marketing (@soy_marketing) June 16, 2017

The result: The 7 million jars manufactured for this campaign were sold out as soon as they hit the market.

How you can replicate this:

AI can be very effective when it comes to design for branding, as you have seen in this example of AI, and if you want to try the power of AI to design your marketing campaigns. There are several tools available for you to try.

Here are some AI platforms for you to explore:

Dream Studio

Here's what AI tool users say about their performance on social media posts.

I used @midjourney_ai to recreate @Delta 's marketing campaign! 📸 Here's how creative teams can unlock cost savings, rapidly ideate and create diverse visuals. #AIArtworks #midjourney #Delta #Marketing https://t.co/HeyZgITw7h pic.twitter.com/W1Mt2oNrIG — Chris McKay (@cmcky) April 24, 2023

2. Cyber Inc. uses AI to create video courses at scale

Industry: IT Services

Use Case: Content Creation

The idea: Using AI for content creation? Could this get even more interesting? A great AI marketing example is Cyber Inc. Cyber Inc. made it happen with Synthesia AI, an AI video creation platform that generates videos for its online courses. Cyber Inc. wanted to quickly scale with content creation and expand its global reach by creating videos in multiple languages.

Not only did they use this AI to create an avatar to replace an actor, but they also used it to cut the costs of video production.

The results: As expected, Cyber Inc. was able to create video 2x faster and scale into new markets much earlier than planned in their roadmap by generating content in multiple languages, which would be a time-consuming and costly process without AI. Talk about scalability at a good price!

You can create content at a faster rate and lower costs with AI. It also gives you an advantage in trying content variations without spending too much time. How? You may ask. We have some great tools for you to discover!

Synthesia AI

Related Guide:

💡 **Related guide:** How to Create a Better Content Marketing Strategy in 7 Steps

3. Cosabella uses AI for ad creation

Industry type: Retail Apparel & Fashion

Use Case: Social Media & Ads

The idea: Social Media and advertising are essential marketing elements, and Cosabella made a big win by letting AI into their marketing operations. As the marketing communication between Cosabella and its agency became time-consuming and difficult, they decided to give an Algorithm named Albert its paid search and digital marketing efforts using creatives and KPIs provided by the brand.

The results: AI taking over the world might be scary, but for Cosabella, Albert taking over their marketing increased its search and social media return-on-ad-spend by 50% and decreased its ad spend by 12%.

"After seeing Albert (AI) handle our paid search and social media marketing, I would never have a human do this again.’’ Courtney Connell, Marketing Director of Cosabella

The advancement of AI has been a miracle for marketing teams, especially regarding social media and advertising. With AI, you could automate tasks and see the numbers increase, just like Cosabella. Check out these AI tools to implement better ads and social media management:

Related guide: 8 Ways to Leverage AI in Email Marketing For Better Campaigns

4. Volkswagen uses AI to forecast buying decisions

Industry type: Automobile

Use Case: Predictive Analysis

The idea: Another great AI in marketing example is Volkswagen. You might have heard Volkswagen automating its vehicles, but have you heard it is automating its ad-buying decisions? Since the ad agency that was working with Volkswagen was giving personal interpretations about the ad buying decisions, the marketers at Volkswagen decided to rely wholly on data for which they decided to trust an AI so that they could invest less in campaigns and upsurge their sales.

The results: Volkswagen cut the hidden costs that the media agency previously charged by better forecasting buying decisions. In addition, Volkswagen dealership sales increased by 20%.

Like Volkswagen, you can improvise your spending decisions and save a lot of bucks. There are tons of AI tools for data analysis that you can try out! We have some good ones ready for you:

Save time with AI-powered email content creation

5. Tomorrow Sleep uses AI to generate content at scale

Industry type: Mattress

Use Case: Content Marketing

The idea: Tomorrow Sleep, a startup, needed help generating organic traffic on its website. What skyrocketed their organic traffic? Well, again, AI happened. Tomorrow Sleep used MarketMuse , an AI-powered tool, to research and generate powerful content that increased traffic quickly and easily.

The results: From organic traffic on their web pages being just 4K per month surged to 400K in just a year. Also, their website gained authority in the eyes of the search engine to gain the featured snippet. Including AI for content marketing was probably the AHA moment they were looking for.

You can use several AI tools in marketing operations to generate organic traffic quickly and efficiently. To get started, here are some tools you can check out:

Content marketing is a great way to get your message out there and reach an audience. But you need to keep your content fresh and relevant. Frase is a tool that will help you easily create high-quality content. https://t.co/FYjZEEgDyG — Marwan Alameddine (@outblogged) April 23, 2023

6. Netflix uses AI to give personalized recommendations

Industry type: Entertainment

Use Case: Personalization

The idea: We all know how excellent Netflix's marketing is, but one thing that stands out is its use of AI for personalization. How do they do it? According to Netflix, ‘’We do this by using the data about what content our members watch and enjoy along with how they interact with our service to get better at figuring out what the next great movie or TV show for them will be.’’

Netflix personalization replaced Cillian Murphy (main character) with Anya Taylor Joy in the box art for Peaky Blinders. Because they know I binged the hell out of Queen’s Gambit. It’s sneaky - but it’s also genius. 😄♟ pic.twitter.com/YtZx88hPHB — Paul Lee (@BeeBimBop) March 12, 2021

The AI collects large amounts of data and analyzes it to target the audience by recommending shows, movies, and artwork. The product feed that you see is personalized according to the user.

The Results: Now that you know how Netflix always keeps you hooked, let's see how profitable the AI is for Netflix. Drum rolls, please! As expected, Netflix’s recommended engine is worth over $1 billion annually, resulting from the personalized grid of suggestions catered only to the viewers’ tastes.

Related guide: How to Master Website Personalization to Improve Customer Experience

Personalizing and experimenting with data is much easier with AI now. All you need is a reliable tool; we have some great ones.

Dynamic Yield

7. JP Morgan Chase’s trust in AI for their copywriting efforts

Industry Type: Banking

Use Case: Copywriting

The Idea: When it realized that AI could create better copy than humans, Chase signed a deal with Persado, a software startup, when the copy generated by AI had higher click rates- even double in most cases. In a statement, Kristin Lemkau, chief marketing officer at JPMorgan Chase, said, “Persado’s technology is incredibly promising. It rewrote copy and headlines that a marketer, using subjective judgment and their experience, likely wouldn’t have. And they worked.”

The Results: As Kristin mentioned, Persado did magic on their copywriting. With their help, Chase saw a 450% lift in CTRs on ads, which led Chase to keep Persado by their side for longer.

Copywriting with AI has been too popular lately, and it can automate many of your writing tasks. Wanna get started? Check these out:

ClosersCopy

8. FARFETCH uses AI to enhance its brand voice

Industry: Luxury Fashion Retail

Use Case: Brand Language Optimisation

The Idea: FARFETCH strives to tailor its communication with millions of users. They shook hands with Phrasee , a tool that generates on-brand content with enterprise-grade controls and content optimization at scale. They made it possible by testing fresh styles, tones, words, and phrases and discovering the language that resonates with its audience in the best way possible. They tried to incorporate this strategy into their email marketing campaigns to target the audience better.

The Results: By giving Phrasee the authority to manage its email marketing copy, FARFETCH saw impressive results, with an increase of 38% in the average click rate and a 31% increase in the average open rate in its trigger campaigns; it captured a unique brand voice and adhered to it consistently.

Your brand language matters a lot when communicating with your audience. Letting AI content in is one way to ensure it's perfect or close to perfect. We have picked some good ones for you:

9. Mastercard applies AI intelligence to tackle competitive threats

Industry Type: Financial Services / Technology

Use Case: Competitive Analysis

The Idea: During the global pandemic, Mastercard Payment Gateway Services (MPGS) faced constant challenges and required intelligence to tackle its competitive threats. Here comes Crayon , an AI-powered platform that MGPS adopted to get the viewpoint of its competitors and predict threats coming their way.

The Results: Crayon made a huge difference in its competitive intelligence strategy. The Director of Global Product Marketing and Sales Enablement, Mike Wienke, says, “Crayon gives participants a clear sense of how each competitor is approaching the market, making it easier to look at Mastercard from an external POV. Not only does this make the exercise much more effective, but it also allows us to bring together people from across the organization and open up a dialogue—one where everyone can contribute valuable ideas.”

Just like MGPS, there's a lot of potential in AI tools for analyzing competitors and revamping your strategies. To closely keep a watch on your competitors, here are a few examples of AI tools that you could invest in:

10. The News Movement uses AI-powered analytics for social listening

Industry Type: Media and Publishing

Use Case: Social Media Management

The Idea: The News Movement, a media platform, wanted its audience to engage and expand as much as possible. For this to happen, they were required to dive deep into data and understand what their audience was thinking and talking about. They use Sprinklers Unified-CXM powered by AI, pull in petabytes of customer engagement data across different platforms, and interpret, recognize, and understand every signal of customer intent.

The Results: With this AI-powered tool, TNM was able to save a lot of hours every week, and they can use their social media data to engage better with their audience on social media, all of this in less than 6 months. It gave them historical data from TikTok that they did not previously have, which changed the social media game for TNM.

Social listening can help you tap into your audiences’ tastes, preferences, and intent. To better understand this, AI-powered social monitoring tools like these can help:

Other applications of AI in marketing

• Amazon uses AI to give better product recommendations based on your previous orders and products searched for.

• Starbucks integrated 'My Starbucks Barista' with Amazon's Alexa to improve customer service. Customers can now place orders, modify orders, and confirm pickup locations over voice commands.

• Lowe introduced a bot to improve the customer shopping experience with personalized suggestions. Also, they used Lowebot to track real-time stock and inventory to understand customer behavior.

In these AI in Marketing examples, you learned how brands, even the biggest ones like Nutella, Volkswagen, Netflix, etc., are winning in their marketing strategies with artificial intelligence. Other popular brands incorporate AI to understand customer needs to get actionable insights in better ways.

With the help of deep learning (machine learning) and AI, companies could utilize product and customer data to target audiences better by understanding customer behavior. Giving AI the responsibility for your marketing activities and operations can make a huge difference and yield astonishing results. If your marketing strategy needs revamping, it's time to incorporate AI. These AI applications in marketing can help you get started with that very well.

To clarify, AI may soon be able to take over business operations. Even popular marketing tools utilize AI to do the heavy lifting for them. So, if you still think it won't be worth your money, think again because you wouldn't want to be far behind the competition.

What you should do next

Hey there, thanks for reading till the end. Here are 3 ways we can help you grow your business:

Talk to an email expert. Need someone to take your email marketing to the next level? Mailmodo’s experts are here for you. Schedule a 30-minute email consultation. Don’t worry, it’s on the house. Book a meet here .

Send emails that bring higher conversions. Mailmodo is an ESP that helps you to create and send app-like interactive emails with forms, carts, calendars, games, and other widgets for higher conversions. Get started for free .

Check out our AI prompts library. If you need AI prompts for ChatGPT or Bing, here's a ready-made database we’ve built to help marketers succeed at prompt engineering. Get your AI prompts here .

Get smarter with our email resources. Explore all our knowledge base here and learn about email marketing, marketing strategies, best practices, growth hacks, case studies, templates, and more. Access guides here .

Frequently asked questions

1. How is AI used for marketing?

AI can be used in marketing for personalization, predictive analytics, content writing, video generation, competitive intelligence, social media marketing, advertising, etc.

2. What is the advantage of AI in marketing?

AI in marketing can help you personalize user experiences, forecast buying ad decisions, create high-quality content in minutes, and predict user behavior.

Hack growth with weekly round up of guides

100k+ marketers like you read these guides every month

What should you do next?

You made it till the end! Here's what you can do next to grow your business:

Get smarter with email resources

Free guides, ebooks, and other resources to master email marketing.

Explore more resources →

Do interactive email marketing with Mailmodo

Send forms, carts, calendars, games and more within your emails to boost ROI.

Try Mailmodo for free →

Consult an email expert

30-min free email consultation with an expert to fix your email marketing.

Book a time →

Get 3X conversions with interactive emails

Popular Categories

Email Automation

Email Deliverability

Email Design

Email Marketing

Email Marketing Use Case

Go to Marketing

Shopify Email Marketing

AI Marketing

You might also like

ai in email marketing

ai marketing tools

ai marketing strategy

ai business tools

Create & send interactive emails without coding

Put revenue on auto-pilot with pre-built journeys.

Try FREE for 21 Days

Talk to a Human

1000+ businesses grew with Mailmodo, including

AI-powered marketing and sales reach new heights with generative AI

Artificial intelligence (AI) and machine learning (ML) continue to push the boundaries of what is possible in marketing and sales. And now, with the ongoing step-change evolution of generative AI (gen AI), we’re seeing the use of open-source platforms penetrating to the sales frontlines, along with rising investment by sales-tech players in gen AI innovations. Given the accelerating complexity and speed of doing business in a digital-first world, these technologies are becoming essential tools.

Inevitably, this will impact how you operate—and how you connect with and serve your customers. In fact, it’s probably already doing so. Forward-thinking C-suite leaders are considering how to adjust to this new landscape. Here, we outline the marketing and sales opportunities (and risks) in this dynamic field and suggest productive paths forward.

Our research suggests that a fifth of current sales-team functions could be automated.

How AI is reshaping marketing and sales

AI is poised to disrupt marketing and sales in every sector. This is the result of shifts in consumer sentiment alongside rapid technological change.

Omnichannel is table stakes

Across industries, engagement models are changing: today’s customers want everything, everywhere, and all the time. While they still desire an even mix of traditional, remote, and self-service channels (including face-to-face, inside sales, and e-commerce), we see continued growth in customer preference for online ordering and reordering.

Winning companies—those increasing their market share by at least 10 percent annually—tend to utilize advanced sales technology; build hybrid sales teams and capabilities; tailor strategies for third-party and company-owned marketplaces; achieve e-commerce excellence across the entire funnel; and deliver hyper-personalization (unique messages for individual decision makers based on their needs, profile, behaviors, and interactions—both past and predictive).

Step changes are occurring in digitization and automation

What is generative ai.

Many of us are already familiar with online AI chatbots and image generators, using them to create convincing pictures and text at astonishing speed. This is the great power of generative AI, or gen AI: it utilizes algorithms to generate new content—writing, images, or audio—from training data.

To do this, gen AI uses deep-learning models called foundation models (FMs). FMs are pre-trained on massive datasets and the algorithms they support are adaptable to a wide variety of downstream tasks, including content generation. Gen AI can be trained, for example, to predict the next word in a string of words and can generalize that ability to multiple text-generation tasks, such as writing articles, jokes, or code.

In contrast, “traditional” AI is trained on a single task with human supervision, using data specific to that task; it can be fine-tuned to reach high precision, but must be retrained for each new use case. Thus gen AI represents an enormous step change in power, sophistication, and utility—and a fundamental shift in our relationship to artificial intelligence.

AI technology is evolving at pace. It is becoming increasingly easy and less costly to implement, while offering ever-accelerating complexity and speed that far exceeds human capacity. Our research suggests that a fifth of current sales-team functions could be automated. In addition, new frontiers are opening with the rise of gen AI (see sidebar “What is generative AI?”). Furthermore, venture capital investment in AI has grown 13-fold over the last ten years. 1 Nestor Maslej et al., “The AI Index 2023 annual report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, April 2023. This has led to an explosion of “usable” data (data that can be used to formulate insights and suggest tangible actions) and accessible technology (such as increased computation power and open-source algorithms). Vast, and growing, amounts of data are now available for foundation-model training, and since 2012 there’s been a millionfold increase in computation capacity—doubling every three to four months. 2 Cliff Saran, “Stanford University finds that AI is outpacing Moore’s Law,” Computer Weekly, December 12, 2019; Risto Miikkulainen, “Creative AI through evolutionary computation: Principles and examples,” SN Computer Science, 2(3): 163, March 23, 2001.

Would you like to learn more about our Growth, Marketing & Sales Practice ?

What does gen ai mean for marketing and sales.

The rise of AI, and particularly gen AI, has potential for impact in three areas of marketing and sales: customer experience (CX), growth, and productivity.

For example, in CX, hyper-personalized content and offerings can be based on individual customer behavior, persona, and purchase history. Growth can be accelerated by leveraging AI to jumpstart top-line performance, giving sales teams the right analytics and customer insights to capture demand. Additionally, AI can boost sales effectiveness and performance by offloading and automating many mundane sales activities, freeing up capacity to spend more time with customers and prospective customers (while reducing cost to serve). In all these actions, personalization is key. AI coupled with company-specific data and context has enabled consumer insights at the most granular level, allowing B2C lever personalization through targeted marketing and sales offerings. Winning B2B companies go beyond account-based marketing and disproportionately use hyper-personalization in their outreach.

Bringing gen AI to life in the customer journey

There are many gen AI-specific use cases across the customer journey that can drive impact:

A gen AI sales use case: Dynamic audience targeting and segmentation

Gen AI can combine and analyze large amounts of data—such as demographic information, existing customer data, and market trends—to identify additional audience segments. Its algorithms then enable businesses to create personalized outreach content, easily and at scale.

Instead of spending time researching and creating audience segments, a marketer can leverage gen AI’s algorithms to identify segments with unique traits that may have been overlooked in existing customer data. Without knowing every detail about these segments, they can then ask a gen AI tool to draft automatically tailored content such as social media posts and landing pages. Once these have been refined and reviewed, the marketer and a sales leader can use gen AI to generate further content such as outreach templates for a matching sales campaign to reach prospects.

Embracing these techniques will require some openness to change. Organizations will require a comprehensive and aggregated dataset (such as an operational data lake that pulls in disparate sources) to train a gen AI model that can generate relevant audience segments and content. Once trained, the model can be operationalized within commercial systems to streamline workflows while being continuously refined by agile processes.

Lastly, the commercial organizational structure and operating model may need to be adjusted to ensure appropriate levels of risk oversight are in place and performance assessments align to the new ways of working.

• At the top of the funnel, gen AI surpasses traditional AI-driven lead identification and targeting that uses web scraping and simple prioritization. Gen AI’s advanced algorithms can leverage patterns in customer and market data to segment and target relevant audiences . With these capabilities, businesses can efficiently analyze and identify high-quality leads, leading to more effective, tailored lead-activation campaigns (see sidebar “A gen AI sales use case: Dynamic audience targeting and segmentation”). Additionally, gen AI can optimize marketing strategies through A/B testing of various elements such as page layouts, ad copy, and SEO strategies, leveraging predictive analytics and data-driven recommendations to ensure maximum return on investment. These actions can continue through the customer journey, with gen AI automating lead-nurturing campaigns based on evolving customer patterns.
• Within the sales motion, gen AI goes beyond initial sales-team engagement, providing continuous critical support throughout the entire sales process, from proposal to deal closure. With its ability to analyze customer behavior, preferences, and demographics, gen AI can generate personalized content and messaging. From the beginning, it can assist with hyper-personalized follow-up emails at scale and contextual chatbot support . It can also act as a 24/7 virtual assistant for each team member, offering tailored recommendations, reminders, and feedback, resulting in higher engagement and conversion rates. As the deal progresses, gen AI can provide real-time negotiation guidance and predictive insights based on comprehensive analysis of historical transaction data, customer behavior, and competitive pricing.
• There are many gen AI use cases after the customer signs on the dotted line, including onboarding and retention. When a new customer joins, gen AI can provide a warm welcome with personalized training content , highlighting relevant best practices. A chatbot functionality can provide immediate answers to customer questions and enhance training materials for future customers. Gen AI can also offer sales leadership with real-time next-step recommendations and continuous churn modeling based on usage trends and customer behavior. Additionally, dynamic customer-journey mapping can be utilized to identify critical touchpoints and drive customer engagement.

This revolutionary approach is transforming the landscape of marketing and sales, driving greater effectiveness and customer engagement from the very start of the customer journey.

Winning tomorrow’s car buyers using artificial intelligence in marketing and sales

Commercial leaders are optimistic—and reaping benefits.

We asked a group of commercial leaders to provide their perspective on use cases and the role of gen AI in marketing and sales more broadly. Notably, we found cautious optimism across the board: respondents anticipated at least moderate impact from each use case we suggested. In particular, these players are most enthusiastic about use cases in the early stages of the customer journey lead identification, marketing optimization, and personalized outreach (Exhibit 1).

These top three use cases are all focused on prospecting and lead generation, where we’re witnessing significant early momentum. This comes as no surprise, considering the vast amount of data on prospective customers available for analysis and the historical challenge of personalizing initial marketing outreach at scale.

Various players are already deploying gen AI use cases, but this is undoubtedly only scratching the surface. Our research found that 90 percent of commercial leaders expect to utilize gen AI solutions “often” over the next two years (Exhibit 2).

Our research found that 90 percent of commercial leaders expect to utilize gen AI solutions “often” over the next two years.

Overall, the most effective companies are prioritizing and deploying advanced sales tech, building hybrid teams, and enabling hyper-personalization. And they’re maximizing their use of e-commerce and third-party marketplaces through analytics and AI. At successful companies, we’ve found:

• There is a clearly defined AI vision and strategy.
• More than 20 percent of digital budgets are invested in AI-related technologies.
• Teams of data scientists are employed to run algorithms to inform rapid pricing strategy and optimize marketing and sales.
• Strategists are looking to the future and outlining simple gen AI use cases.

Such trailblazers are already realizing the potential of gen AI to elevate their operations.

Our research indicates that players that invest in AI are seeing a revenue uplift of 3 to 15 percent and a sales ROI uplift of 10 to 20 percent.

Anticipating and mitigating risks in gen AI

While the business case for artificial intelligence is compelling, the rate of change in AI technology is astonishingly fast—and not without risk. When commercial leaders were asked about the greatest barriers limiting their organization’s adoption of AI technologies, internal and external risk were at the top of the list.

From IP infringement to data privacy and security, there are a number of issues that require thoughtful mitigation strategies and governance. The need for human oversight and accountability is clear, and may require the creation of new roles and capabilities to fully capitalize on opportunities ahead.

In addition to immediate actions, leaders can start thinking strategically about how to invest in AI commercial excellence for the long term. It will be important to identify which use cases are table stakes, and which can help you differentiate your position in the market. Then prioritize based on impact and feasibility.

The AI landscape is evolving very quickly, and winners today may not be viable tomorrow. Small start-ups are great innovators but may not be able to scale as needed or produce sales-focused use cases that meet your needs. Test and iterate with different players, but pursue partnerships strategically based on sales-related innovation, rate of innovation versus time to market, and ability to scale.

AI is changing at breakneck speed, and while it’s hard to predict the course of this revolutionary tech, it’s sure to play a key role in future marketing and sales. Leaders in the field are succeeding by turning to gen AI to maximize their operations, taking advantage of advances in personalization and internal sales excellence. How will your industry react?

Richelle Deveau is a partner in McKinsey’s Southern California office, Sonia Joseph Griffin is an associate partner in the Atlanta office, where Steve Reis is a senior partner.

The authors wish to thank Michelle Court-Reuss, Will Godfrey, Russell Groves, Maxim Lampe, Siamak Sarvari, and Zach Stone for their contributions to this article.

Explore a career with us

Related articles.

A CEO’s guide to the metaverse

The multiplier effect: How B2B winners grow

AI for Businesses: Eight Case Studies and How You Can Use It

Updated: July 18, 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

What did you think of this article?

Give Feedback

Don't forget to share this post!

Related articles.

Is localization easier with LLMs or do legacy systems still have the edge? Experts Weigh In

How to Identify and Mitigate AI Bias in Marketing

Is AI companionship the answer to loneliness?

Here's Your Rundown of TikTok's New Suite of AI Tools

How The HubSpot Video Team is Freeing Up Its Time with AI Video Generators

Reviewing Some of the Top Gen AI Video Tools [+ Thoughts On Where They're Headed]

How This Content Marketer Uses AI to Be a Better Manager + Kickstart Projects

How Creators are Using AI to Drive Social Commerce

OpenAI Launches SearchGPT: What We Know + What SEO Experts are Saying

AI's Role in Protecting Consumer Data + 5 Steps Companies Can Take Today

Discover the key to unlocking unparalleled productivity with this ultimate guide to revolutionizing your workflow.

Marketing software that helps you drive revenue, save time and resources, and measure and optimize your investments — all on one easy-to-use platform

• Free Resources

Artificial Intelligence Case Studies: Two companies that boosted brand awareness with AI and another marketer that used humans instead

This article was published in the MarketingSherpa email newsletter .

“Apply to join our new research cohort where we will help you test and build an AI-calibrated MECLABS SuperFunnel,” Flint McGlaughlin offered in Landing Page Conversion: 4 powerful ways to develop a cohesive, effective strategy .

It’s part of our effort (MECLABS Institute is the parent organization of MarketingSherpa) to work with marketers and entrepreneurs to determine how to best use current and emerging AI-driven technologies to build an effective funnel.

But rest assured, to serve you, this reporter has to remain a skeptic of artificial intelligence, machine learning, and really, every other “next best thing.” In much the same way I’m guessing a magician can’t muster up the suspension of disbelief necessary to watch someone else’s magic show, after a lifetime in the marketing and advertising industry I just can’t hop on a bandwagon without a healthy dose of skepticism.

So, to help you determine when to use (and avoid) AI, today we bring you two stories of when brands leveraged AI…but also a story about a brand that avoided using the technology.

First up, a television network that used artificial intelligence and machine learning to get more traffic from organic distribution. Then, how SAP used AI to avoid cookies and increase brand awareness with a paid campaign. And finally, an adventure travel marketplace that invested in humans instead of AI to increase sales from organic traffic.

Quick Case Study #1: How artificial intelligence and machine learning helped television network increase pageviews 63% from Twitter

Sistema Brasileiro de Televisão (SBT) is a free-to-air Brazilian broadcaster with 114 television stations and over 6,000 employees. The network’s website attracts 11 million unique viewers and more than 99 million pageviews per month. The TV network as 12 million followers on its main Facebook page.

Social media challenge

On an average day, SBT posts between 100 to 150 pieces of content to Facebook alone.

“We do not have a centralized social media team, so every content production team ends up posting independently,” says Rodrigo Hornhardt, Journalism Integration and Planning Manager, Sistema Brasileiro de Televisão . In total, he estimates that around 20 people throughout the company post regularly to SBT’s social media accounts, rendering the post scheduling process time-consuming and convoluted.

SBT posts content to a wide variety of social platforms, but Facebook is the most important. According to Hornhardt, “around 40% to 50% of our web traffic comes from Facebook.”

Social media content approach – leverage artificial intelligence and machine learning

To solve these challenges, SBT turned to marketing automation technology powered by artificial intelligence (AI) and machine learning.

Before, SBT’s staff had undertaken the laborious task of posting new content manually — a workflow that was as time consuming as it was inefficient, especially given the decentralized nature of SBT’s social media management. “With so many people posting so much content across the company on different platforms, AI is so important in keeping everything aligned,” notes Hornhardt.

SBT’s reasons for selecting an AI solution were manifold: “The possibility of having AI recommend the best content to post was important, but so, too, was the ease with which multiple people can manage posting for a unified output. The ability to automate workflows was also a key consideration,” he said.

To increase the reach, visibility, and engagement on posts, the team makes regular use of real-time platform trend data alongside SBT’s own audience data to determine the best time to share content. This not only reflects audience habit — when do SBT’s readers most often engage with posts? — but also the ever-changing factors of Facebook’s Feed algorithm. Hornhardt notes that SBT News in particular uses AI to understand the optimal time to publish and maximize the impact of the multiple news items it posts each hour.

AI technology determines optimal share timing by constantly analyzing and computing the predicted performance of shares to maximize their inclusion in the Feed. Using advanced machine learning, it becomes possible to continuously reverse engineer the workings of Facebook’s algorithm, producing a sophisticated and accurate picture of the best time to post to drive engagement.

When posts are shared, AI automatically continues to update each post’s optimal time based on the latest data, pushing certain posts back if new, higher-potential posts are added, or bringing posts forward if their short-term potential goes up. This is just one example of how using AI technology to augment SBT’s social sharing has meant, in Hornhardt’s word’s, “increased reach and organic and substantial growth.”

Social media results

By incorporating artificial intelligence into its social media strategy, SBT saw strong improvements in performance on social media. Within four months of adopting this AI technology, SBT’s social media pages saw increases in daily clicks of 25%, whilst daily organic impressions rose by 61%. On Facebook, SBT saw a 52% increase in daily impressions, while the company also saw improvement gains on Twitter — pageviews from the platform increased by 63%.

In addition to these performance gains, SBT has increased workflow efficiency using automation, bringing time savings in the process. Within four months of adopting AI, the team shared almost 40,000 posts; making these shares via AI and using automation has saved the company 14 hours per day.

“Brands and content producers can greatly benefit from intelligent automation, but it must be tailored to their content and audience,” says Simran Cashyap, Head of Product & Design, Echobox (SBT’s content automation provider). “While each brand must experiment to determine the ideal level of automation for them, we tend to find that the more a brand automates, the bigger the performance gains, as the AI algorithms have more opportunities to continuously learn from the content and audiences.”

By relying on AI’s capability of calculating the optimal share time, the team has increased traffic and impressions whilst saving significant amounts of time. Daniela Nobre, Customer Success Representative, Echobox, explains AI and automation “completely overhaul a company's organic marketing strategy by digging into granular data, something little to no marketing teams could try to replicate.”

For Hornhardt, this is only the start of what could become a more specialized social strategy. “We need to dedicate more working time to planning and defining strategies for networks,” he told us. “One way would be to have specialists dedicated to this strategy with the possibility of creating an audience/networking team. We see AI supporting us in this endeavor.”

Quick Case Study #2: How SAP used AI-powered contextual intelligence to increase brand awareness 4% without cookies

The demise of the third-party cookie has left data-driven companies with a new focus: to build cookie-less alternatives into their marketing strategy. And that’s exactly what global software company SAP did this year.

The team has always had a robust marketing strategy in place, but with the end of the third-party cookies looming, it recognized the increasingly pressing need to focus on privacy-first, cookie-less campaigns.  

The contextual experiment to overcome the cookie-less challenge

The team chose to place its ads alongside contextually relevant content, targeting key business decision makers based on what they had chosen to look at in the moment.

They used contextual targeting technology that uses AI to see and analyze the full content of a page by scanning all of the main content data signals – from text and imagery to audio and video – for accurate and safe ad placement, without the need for third-party cookies or any personally identifiable information (PII).

The team used a wrap-style ad that wraps around the content the visitor is viewing. The ad had an entrance animation to attract the user’s attention. The right panel had a countdown to the advertised webcast. And the left panel had a message and CTA.

Creative Sample #1: Wrap ad for SAP

When the user scrolled, the background changed to another visual. The headline was repositioned onto the left panel, so it had a consistent presence.

Creative Sample #2: Scrolled state of wrap ad for SAP

“Throughout the campaign, the aim was to boost brand and product awareness,” commented Moritz Fisecker, Integrated Media Manager EMEA, SAP .

Advertising campaign measurement methodology

In order to successfully establish the impact of SAP’s campaign, the team analyzed the difference in results for a control sample (users who were not exposed to the high-impact ad creative) and the target audience (users who were exposed to the ad creative).

Finally, they used eye tracking to understand the level of consumer attention the ads garnered.

Advertising campaign results

“Overall, the campaign results proved that brands can reach consumers just as effectively – if not more effectively – using a completely cookie-less approach to digital marketing rather than relying on behavioral data,” Fisecker said.

More than half (61%) of users exposed to the campaign took or intended to take some sort of action; and the ads had a 93.2% viewability rate.

Others results included:

• 0.9% click-through rate
• 4% uplift in overall awareness as a direct result of the campaign
• 5% positive shift among the target audience
• 5% improvement in perceptions of SAP as offering the best business software and solutions
• 7% improvement in perceptions of SAP as a trusted brand 
• 1 in 3 users felt the ad was informative

In addition, the study concluded that the ad creative drove significantly more users to become “very interested” in finding out more after being exposed to the ad.

“In addition to awareness, Lumen Research and On Device Research (ODR) – SAP’s measurement partners for the campaign – confirmed that users who saw the ads had a heightened interest in finding out more. The results also affirm the role of contextual data in the future of mar tech. In this new cookie-less, privacy-first era, tapping into the customer’s mindset ‘in the moment’ will be the key to inspiring them, and ultimately, influencing their behavior,” said Peter Wallace, General Manager EMEA, GumGum (SAP’s contextual intelligence partner).

Quick Case Study #3: How adventure travel marketplace generated $1 million in sales from organic traffic by having humans (instead of machines) write content

10Adventures is an online marketplace that connects adventure travel enthusiasts with local guides around the world. In addition to allowing users to book adventure travel, it recently launched a subscription-based GPS-trail app that enables people to safely explore the outdoors.

“We launched 10Advnetures in 2019, with a WordPress MVP (minimum viable product) and great content,” said Richard Campbell, Founder & CEO, 10Adventures . The site currently has more than 1.5 million annual unique visitors.

Let’s take a look at the most – and the least – effective tactic the team used to grow website traffic.

Most Effective Tactic – Human-written content paired with SEO basics and site speed enhancements

“We have had an incredible journey to build our skills in ranking high in Google,” Campbell said. “There are literally dozens of small bits of work we have done to get our content to the top of Google and Bing.”

The team invested the time necessary to create engaging, factual, useful content that targeted the right keywords.

 “Users want quality content. The web is filled with superficial content, written by content farms with lots of mistakes. This is especially true in the outdoors market,” Campbell said. “In the world of content, the cost of efficiency usually comes at the expense of quality. Many businesses will turn to cheaper or faster solutions like AI writers or outsourcing to meet their goals but won’t see the desired performance if the quality isn’t there.”

The team’s most popular content is their free route guides, especially regional pages of the best hikes (or road bike rides, backpacking routes, cross-country skiing, etc.) in a geographic region.

Creative Sample #3: Route guide content on adventure travel marketplace website

The team then implemented the following elements of on-site SEO:

• URL structure
• Page titles
• Internal links

For external links, the team tried third-party backlink providers but found that doing it themselves was cheaper and ensured that all backlinks were organic.

The team felt a Google algorithm change punished the site for having slow site speed. “Algorithm changes are a constant reality, and Google places plenty of demands on websites to perform,” Campbell said. “Being listed first in Google is tough, and recently site speed has been an important metric that we felt was hindering our ability to be first in Google.”

Campbell continued, “Unfortunately, our site was based on WordPress, and we had done everything we could to improve site speed.” This included identifying performance bottlenecks and optimizing the databases and code behind them.

Ultimately, though, the team had to implement a new solution, and moved to a static site, rebuilding the entire front-end in a new language. This meant each page is rendered and doesn’t need database calls, creating a quicker user experience and greatly improved page speed performance. In addition, they now have edge servers around the world. In the month since this change, organic traffic is up 67% compared to last year. 

For this reason, the team has baked in regular content reviews to look for opportunities to rank higher, as well as evaluate the website’s performance.

Least Effective Tactic – Paid marketing

The team found that paid marketing was not only expensive when compared to what they were doing with organic, but it also resulted in a lower conversion rate – their paid advertising leads convert at 10% of their organic leads.

The team has created many permutations of different ads, but in general can’t replicate what worked pre-pandemic. In 2019 when they launched, customer acquisition cost was about $300. Since the end of last year and the first seven months of this year, it was around $1,000.

Thanks to the traffic generated by their organic tactics, they now have a registered user base of more than 50,000.

The previously mentioned increase in organic traffic has also led to growth in another key area of the business – sales. When looking at the 10Adventures marketplace and tour sales over the last 18 months, the team saw steady growth that has culminated in nearly $1 million of sales over that period, and almost 5x growth year-on-year.

Going forward, the team is working on experiments with their new site to understand their ability to influence rankings from on-page rewrites and other small-scale optimizations. They want to know if it’s easier to move content from third place to second, from eighth to third, or from 40th to eight, etc.  Does this ability vary based on the type of content, or the location the content describes? These discoveries will guide their work in the coming year to continue to grow their organic traffic.

Related Resources

High-Converting Landing Page: If you don’t ask this question you will never maximize conversion

Artificial Intelligence and Machine Learning in Marketing: What marketers (even those who don’t care about tech) should know about AI and ML

Marketing Funnel: 3 quick case studies to help you increase conversion

Improve Your Marketing

Join our thousands of weekly case study readers.

Enter your email below to receive MarketingSherpa news, updates, and promotions:

Note: Already a subscriber? Want to add a subscription? Click Here to Manage Subscriptions

Get Better Business Results With a Skillfully Applied Customer-first Marketing Strategy

The customer-first approach of MarketingSherpa’s agency services can help you build the most effective strategy to serve customers and improve results, and then implement it across every customer touchpoint.

Get headlines, value prop, competitive analysis, and more.

Marketer Vs Machine

Marketer Vs Machine: We need to train the marketer to train the machine.

Free Marketing Course

Become a Marketer-Philosopher: Create and optimize high-converting webpages (with this free online marketing course)

Project and Ideas Pitch Template

A free template to help you win approval for your proposed projects and campaigns

Six Quick CTA checklists

These CTA checklists are specifically designed for your team — something practical to hold up against your CTAs to help the time-pressed marketer quickly consider the customer psychology of your “asks” and how you can improve them.

Infographic: How to Create a Model of Your Customer’s Mind

You need a repeatable methodology focused on building your organization’s customer wisdom throughout your campaigns and websites. This infographic can get you started.

Infographic: 21 Psychological Elements that Power Effective Web Design

To build an effective page from scratch, you need to begin with the psychology of your customer. This infographic can get you started.

Receive the latest case studies and data on email, lead gen, and social media along with MarketingSherpa updates and promotions.

• Your Email Account
• Customer Service Q&A
• Search Library
• Content Directory:

Questions? Contact Customer Service at [email protected]

© 2000-2024 MarketingSherpa LLC, ISSN 1559-5137 Editorial HQ: MarketingSherpa LLC, PO Box 50032, Jacksonville Beach, FL 32240

The views and opinions expressed in the articles of this website are strictly those of the author and do not necessarily reflect in any way the views of MarketingSherpa, its affiliates, or its employees.

4 Incredible AI Case Studies in Content Marketing

By Ashley Sams on March 10, 2022

Wondering how to get started with AI? Take our on-demand Piloting AI for Marketers Series.

Artificial intelligence (AI) is giving businesses the ability to create and promote content at scale.

Which means every business that does content marketing needs to pay attention...

Because if your competitors start adopting AI for content marketing before you, you're toast.

That's because there's more than one AI case study where companies are using AI technology and machine learning to make their content marketing campaigns insanely successful.

Here are four AI case studies to keep an eye on.

1. Vanguard Increases Conversion Rates by 15% with AI

Vanguard is one of the world's biggest investment firms, with $7 trillion under management.

The company needed to promote its Vanguard Institutional business, but it had a problem:

The company does business in an industry that highly regulates what you can say in advertising. As a result, it was hard to stand out in the financial services ad landscape, since everyone used the same type of language.

That's when Vanguard turned to AI language platform Persado. Using AI from Persado, Vanguard was able to personalize its ads based on the specific messaging that resonated most with consumers.

2. Tomorrow Sleep Boosts Web Traffic 10,000%

Sleep system startup Tomorrow Sleep started creating content shortly after its launch with the hope of attracting droves of web visitors.

After several months of pushing out top-quality content and manually tracking and analyzing keyword analytics, they were averaging around 4,000 users to their site every month.

Not bad, but not great. If they wanted to compete with long-standing players in the crowded sleep market, something had to change.

Sleep Tomorrow needed a way to plan and produce content at scale that would reach their target audience.

Enter artificial intelligence.

Tomorrow Sleep began using an AI solution called MarketMuse. MarketMuse's AI-powered content intelligence and strategy platform.

It used the platform's AI research application to understand which high-value topics the company needed to be talking about. Next, it used one of the tool's advanced analytics applications to see where competitors ranked for each of these topics.

This intel illuminated the gaps and opportunities in the current content plan, leading Tomorrow Sleep to create content around key topics where it could quickly establish itself as an expert.

The result?

• 400,000 monthly visits to its website (a 10,000% increase).
• Ranked for multiple positions in a single search result.
• Domain authority to secure Google's featured snippet for specific results.

MarketMuse is an AI-driven assistant for building content strategies. It will show you exactly what terms you need to target to compete in certain topic categories. It'll also surface topics you may need to target if you want to own certain topics.

3. The American Marketing Association Automatically Writes and Hyper-Personalizes Its Newsletter

The American Marketing Association (AMA) strives to be the most relevant voice shaping marketing around the world.

Its website is a marketplace of industry knowledge and resources on branding, careers customer experience, digital marketing, ethics, and more.

One unique aspect of its community is the vast number of industries it represents. Because every business has marketing needs, its members hail from industries across the globe such as education, finance, healthcare, insurance, manufacturing, real estate, and more.

It shares its wealth of knowledge with over 100,000 subscribers in its email newsletter.

However, to serve its subscribers only the most relevant and deserving content, it pulled in rasa.io.

This AI system uses natural language processing and machine learning to generate personalized Smart Newsletters and provide newsletter automation. By doing so, it dramatically increases reader engagement and provides rich insights back to the brand, while saving organizations time.

To personalize each newsletter to a subscriber, the solution uses AI for both curation and filtering content from sources chosen by the AMA. This includes the selection of each individual piece of content, the placement of articles, and the subject line selected for each reader.

The result? A newsletter that provides a perfectly personalized experience to each and every reader.

Plus, the platform is able to infuse the newsletters with AMA's internally produced content and feature it at the top of the newsletter, maximizing visibility.

4. Adobe Generates $10M+ in Revenue with an AI Chatbot + Content

Website content is a key way for consumers to learn about your products and solutions, and find answers to their top questions. And boy does software giant Adobe have a lot of website content.

However, with all the website content the company has, it's sometimes hard to keep consumers engaged and find them exactly what they need at any given moment.

To solve this challenge, Adobe turned to conversational AI from Drift. Drift's chatbot uses AI to have natural language conversations with site visitors at every stage of their journey. The bot was able to direct visitors to what they needed when they needed it. It was also able to hand off conversations to humans when the time was right.

Ashley Sams

Ashley Sams is director of marketing at Ready North. She joined the agency in 2017 with a background in marketing, specifically for higher education and social media. Ashley is a 2015 graduate of The University of Mount Union where she earned a degree in marketing.

Related Posts

How olay uses artificial intelligence to double conversion rate.

Our team reads articles every day to deliver the best artificial intelligence and machine learning news to you. This week we can't stop reading about Olay's use of AI for a doubled conversion rate, Element AI's new podcast for real business applications and Yext's partnership with Amazon Alexa.

5 Powerful Examples of AI in Marketing

How do real brands use AI to improve their marketing? Keep reading to learn about actionable ways top companies we’ve interviewed use marketing AI.

How Brands Target Consumers Better and Sell More with Artificial Intelligence [Case Studies Included]

Real brands are using AI to better target and sell to consumers. Discover actual case studies on how they’re doing it in this post.

• What Is Purpose Branding?
• Social Impact: SOAR
• Social Impact: Primo Center
• Social Impact: Fair Chance Program
• Our Services
• Purpose Branding and Brand Strategy
• Media and Public Relations
• Content Marketing With Digital Capabilities
• Community Engagement
• Web Design and Web Development
• Special Events and Experiential Marketing
• Video Marketing
• About Video
• Types of Video
• Video Production for Corporations
• Video Production for Real Estate
• Video Production for Nonprofits
• Video Production for Fashion
• Training and Workshops
• Case Studies
• Client List
• ADAPT: Scaling Purpose in a Divisive World
• ALL Report: Culture, Diversity, Leadership – Stop Talking and Execute
• The Purpose Report: Corporate Purpose, People, Products and the Will to Adapt
• Keynote Speaker

The AI Revolution in Marketing: Content Creation Case Studies

Home - Blog - The AI Revolution in Marketing: Content Creation Case Studies

Published on November 28, 2023

Some of the world’s most powerful marketers are finding creative uses for artificial intelligence in content creation.

Global marketing organizations have a sharp edge to hone using emerging AI technologies: Their purpose-built AI systems can mine a wealth of internal consumer data. But small and midsize business marketers can adopt their best practices in their own AI content creation experiments.

Here are case studies of AI advancements from global marketing organizations.

Unilever’s Recipe for Fresh AI Insights

Brand managers have been adept at finding AI applications for content creation using Unilever’s custom OpenAI interface. A Thanksgiving promotion serves up menu suggestions with a bit of AI dressing. An AI-augmented search finds recipes using food on hand. Our test found many appealing alternatives to turkey sandwiches with Hellmann’s mayonnaise, but few ways to rescue leftover pumpkin from the back of the fridge.

Unilever was one of the original brand marketers ; the Lever brothers named and packaged their laundry soap in 1884. Now, Unilever innovates in a wide range of AI applications. Several early uses make supply chains more sustainable , analyzing palm oil sourcing through previously neglected data such as plantation traffic, crowdsourced market reports and cloudy aerial views.

Several of Unilever’s GPT-3 solutions write marketing copy. One filters consumer emails to understand messages’ substance and tone, then drafts replies in Salesforce. Dubbed Alex, the sentiment analysis tool has cut agents’ time responding to emails by 90%. Another app, Homer, writes Amazon product descriptions with the proper brand voice and tone.

Few marketers will match Unilever with automated solutions, but they can build better ChatGPT prompts or train their own AI applications using the knowledge bases they have built for customers. Smaller organizations also can learn from the ways Unilever uses AI in sorting sales tasks such as tracking SKUs , identifying both poor-performing items to discontinue and sleepers to activate with marketing support.

PepsiCo Comes Alive in the AI Generation

PepsiCo has barred AI use for employee recruitment or one-to-one consumer targeting, working with Stanford to develop a framework for ethical use. Yet it applies AI widely in marketing and product research, forecasting consumer demand and inventory needs. In employee development, an AI bot suggests job openings and stretch assignments.

An in-house AI tool, named Ada for 19th-century mathematician Ada Lovelace, tests creative ideas to gauge audience reaction, speeding turnaround times and evaluating return on ad spend. Marketing gambits include personalized messages from soccer star Lionel Messi and speeding the development of healthy snacks by analyzing social posts.

ESG and sustainability loom large in investor relations. PepsiCo gives farmers AI tools to farmers to help raise yields and employs machine learning to refine operations to meet greenhouse gas emissions targets. A custom sustainability report assembles sections of its ESG summary report that drill down into agriculture, value chain and product offerings.

PepsiCo’s aggressive, yet standards-based use of AI is a lesson for marketers conducting their own content creation experiments. By setting guardrails and training teams in responsible practices, its brands get an early jump on trends and a better fix on how to make human connections.

Salesforce: Prompt Engineering as a Service

AI enhancements to the Salesforce platform are not limited to global clients such as Unilever. Its Einstein-branded automation tools give marketers a version of ChatGPT that does not start from scratch but is pre-loaded with typical outreach scenarios. Generative AI then tailors the narratives for specific customers.

The blank slate of a chatbot prompt frustrates many marketers. To get past generic answers, content strategists must enter extensive details on the business context of a message. Yet ChatGPT will retain user personas and other proprietary data in its chat history and could reuse them on competitors’ products. Entering confidential customer data poses an even greater risk. ChatGPT users can opt out of model training to prevent reuse, yet they still face a cumbersome workflow to use chatbots as helpers.

A paid Salesforce pilot program expands marketing access to AI without hiring a chatbot wrangler for the emerging, in-demand prompt engineer role. Email admins can build workflows to generate audience segments and descriptions from sales data; draft email subject lines and body copy; and review campaign performance. Once it is generally available, Einstein for Marketing will plug and play industry- or vendor-specific prompts to speed PR’s sluggish adoption of AI tools and create email content with more compelling narratives.

B2C PR Case Studies

• The Placement With the Helpful Millennial Focus
• Staging a Welcoming Homecoming Journey
• Social Media Framework Promotes Gifts with Social Impact
• Three Public Transit Agencies. One New Payment Method. Millions of Customers Onboard.

Fortune 500 Marketing

• Best Purpose Statement Examples From the Fortune 500
• Corporate Diversity Data: What Fortune 500 DEI Reports Reveal
• 2023 Sustainability Reports Track Fortune 500 Corporate Climate Action
• 2023 Fortune 500 ESG Reports Build on Solid Marketing Pillars
• In Corporate Responsibility Marketing, It’s a Small World After All

Back to Blog →

Enter Your Email Address

Recent Posts

• AI Is Old News. Generative AI is a Marketing Gamechanger.
• The Race Is On: Marketing Disciplines Show AI’s PR Potential
• Custom Content at Scale: Top 10 Generative AI Marketing Examples
• Best AI Tools for PR Tasks: What to Try, When to Buy, How to Choose

Download Report

As soon as you submit the form, we will send to you a link to download our report.

First Name *

Last Name *

Position / Title

Work Email *

* Required field

Sign up for marketing tips.

Meet our team., connect with us.

Optimal planning with AI

• Call for Change
• When Tech Meets Human Ingenuity
• A Valuable Difference
• Related Capabilities

Call for change

Every year, a top American retailer sees some $14-15 billion in marketing-driven sales, which means decisions on how to allocate marketing dollars—and specifically, media spend—aren’t taken lightly.

But using historical data to decide where to spend among the dozens of channels available—from traditional TV to Tik Tok—isn’t easy. The data is often stale by the time it’s available to analyze, and the number of new channels and platforms grows all the time.

With so much money at stake and the difficulty in getting quick answers, increased speed and agility were at the top of the retailer’s wish list, and the company issued a challenge to Accenture: To get more specific, actionable insights faster.

When tech meets human ingenuity

Accenture partnered with the retailer to design an AI-powered solution that would enable faster and better data collection and more precise modeling to optimize media spend. The first task was speeding up the existing data flow process, then aggregating and processing all the data from media channels, sales and spend that fed the measurement model. By customizing AIP+ , Accenture’s pre-integrated AI services and capabilities, to do the data aggregation, we helped cut the existing process by 80% using automation to accelerate processing and validation.

With data flow addressed, the team looked next to alter the underlying model that produced the measurement. Previously, these models were hypothesis-driven, i.e., people would painstakingly hypothesize every possible interdependency between different channels. New machine learning was introduced to the process, helping to proactively identify those interdependencies between channels that potentially drive sales. With the new monthly cadence, the team could refresh the models every month, iterating from the previous month’s model instead of starting from scratch. By hosting deep-dive training sessions for employees on the modeling methodology, the team offered them transparency that earned buy-in and trust in the solution.

The number of marketing channels included in the modeling was increased nearly 40%, allowing them to thinly slice the data (for example, by breaking out a catch all “social media” channel into each social media platform).

A valuable difference

The results were significant.

The solution shortened the lag between the measurement period and performance insights from five months to five weeks, opening up a 10 and a half month planning runway for the same period the following year. Also, going from one annual measurement (where performance was expressed as an average) to monthly measurements meant that insights were more nuanced, so the team could see how one channel or another might vary in performance throughout the year.

Even more concretely, the team estimates that $300 million in media buying opportunities and value creation was unlocked by implementing the new tool. This meant the team could spend the same amount on media and generate an additional $300 million in sales.

Related capabilities

Artificial intelligence, solutions.ai for marketing, data-driven reinvention.

Generative AI

Artificial intelligence isn’t going anywhere.

Create once. Distribute forever.

The b2b buyer journey: using content to influence modern enterprise decisions, what's generative engine optimization (geo) & how to do it, about us overview.

• Book a Call
• “Boring” Industries
• Software Companies
• Generative AI Companies
• Manufacturing Companies
• Professional Services Industries
• Social Media
• Link Building
• GTM Services
• Reddit Marketing
• Content Creation
• Content Distribution
• Content Repurposing
• Conversion Rate Optimization Services
• Technical Search Engine Optimization
• Search Engine Optimization
• Generative Engine Optimization
• AI Marketing Services
• Training & Development
• See Our Case Studies
• Our Leadership

Subscribe For Exclusive Trends, Research & Data

Gain access to exclusive research, training, trends and support from the best marketers in the world.

Foundation Labs provides you with timely, meaningful, and relevant data that enables you to grow your company in a meaningful way. The world’s top SaaS companies subscribe to Foundation Labs to receive industry news and data driven insights to create a marketing culture that drives results.

We have two different plans:

→ Exclusive B2B SaaS growth, SEO & content case studies​ → Quarterly reports on data-backed B2B SaaS trends, correlations & more​ → Weekly Insiders-only email on trends, data & research​ → Insiders-only webinars on B2B SaaS content marketing​ → Two weekly newsletters with case studies & SaaS stories​

→ Exclusive B2B SaaS growth, SEO & content case studies​ → Quarterly reports on data-backed B2B SaaS trends, correlations & more​ → Weekly Insiders-only email on trends, data & research​ → Insiders-only webinars on B2B SaaS content marketing​ → Two weekly newsletters with case studies & SaaS stories​ → Invite-only fireside chats with marketing leaders at B2B SaaS giants → SaaS reports breaking down what’s working across industries today

Learn How The Best B2B SaaS Companies Do Marketing.

• Name * First Last
• Enter your email address

Last updated on May 31st, 2024

AI in Marketing: Research Study, Stats, Industry Trends & Data

Article's Content

Generative AI represents both the biggest threat and biggest opportunity to this segment of the workforce.  

Since ChatGPT first came on the scene in late 2022, marketers and content creators have been one of the roles most interested in its use. It’s not surprising that this is the case. 

Experts project that generative AI will add nearly $500 billion in value to the global marketing industry through productivity lifts. That’s a massive overall impact. 

But what about at the organizational and individual levels? How are marketers using generative AI in their day-to-day lives? And how do they feel about this impact?

We fired up and sent out a poll to better understand how marketers and management feel about using tools like ChatGPT, DALL-E, and Jasper (among countless others) in the workplace. 

Over 360 marketers weighed in, with strong representation from leaders, managers, specialists, and SEO experts. Of these marketers, 247 work for brands with 50 employees or fewer, but there was good representation from marketers at companies with over 100 employees (84). 

To get the lay of the AI marketing landscape, we asked participants questions from the following categories: 

Current Usage of AI in the Workplace

• Biggest Concerns About AI

The Impact of Artificial Intelligence on the Industry and Beyond

• How Leaders Are Looking to Implement AI in the Organization

Level Up Your Skill Set in 2024 with the AI Marketing Console

The data from these questions provides insights into the state of AI in marketing after the technology’s first full year in the tech limelight. 

In this comprehensive article, we’re going to dive into how artificial intelligence is impacting the marketing industry, from the thoughts, concerns, and usage of individuals to company-wide planning. 

Let’s get started. 

One of the main benefits of working in SaaS marketing at Foundation is that we learn about and experiment with technologies before many others are able to use them. Well, that advantage is crucially important given the long-ranging impacts of artificial intelligence. 

Building up skills like prompt engineering or developing new AI content workflows can translate into massive gains over competitors. 

Naturally, the first question on everyone’s mind is: Are people actually using artificial intelligence as much as they say they are?

Yes, Marketers Use Artificial Intelligence in the Workplace  

Sure, there’s lots of hype going around on LinkedIn, X, and subreddits about using ChatGPT or Jasper to gain an edge, but just how many people are actually building up that knowledge and applying it day in and day out?

As you probably suspect, the vast majority of our respondents do use it—nearly 85% , in fact.  

Over 300 of the marketing pros we surveyed use artificial intelligence daily to improve their performance. If we extend that to the industry in general, it looks like everyone is looking to level up their AI skills. The race is on. 

Content Creation Is Far and Away the Most Common AI Use Case for Marketers

In terms of how marketers are using AI in the workplace, the most common usage is content creation—cited by 87% of our respondents. Given that the most popular AI tool, ChatGPT, is a text-based generative tool, this isn’t surprising either. 

From leading SaaS brands and agencies to major publications like Wired, companies are using AI to help streamline the content creation process. The final product is typically polished up by a number of real people, but the fingerprints of AI are still all over the content we see across the web and social media. 

We’ve established that content creation is the most popular usage of AI. Now, let’s look at some more specific use cases. 

The next most popular applications of artificial intelligence among our marketing respondents are keyword research (42%), social media (39%), email marketing (39%), and note-taking (36%). 

Generative AI is proving to be a good resource for preliminary keyword research, allowing marketers to quickly generate a list of short-, mid-, and long-tail keywords to include in their content. Some of the most popular ChatGPT plugins are SEO-focused and can help with analyzing content and extracting the most important terms.

One of the most underrated applications of AI at the moment is taking meeting recording and note-taking to the next level. For example, tools like Avoma can transcribe call recordings and create a searchable archive of meeting notes. 

Instead of skipping through hours of video recordings to find key information, AI helps turn your archive into a searchable library complete with summaries and revenue intelligence. 

ChatGPT, Canva, and Grammarly Lead the Way in Terms of Common Tools

As expected, ChatGPT is the AI tool of choice among marketers— 99.65% of our respondents are familiar with it. The tool has dominated headlines for the past year to the point that it’s almost synonymous with artificial intelligence itself. 

It’s also one of the best horses to back in the race. ChatGPT is affordable, powerful, and constantly being improved by industry-leading researchers. This year alone, OpenAI has improved its utility with features like APIs, plugins, and now customizable, task-based GPTs. It’s essentially a one-stop shop for marketers at this point. 

But , it’s also something of a generalist. 

If you want to zero in on a specific domain, like image creation or copy editing, then a targeted application of AI is a better choice. Canva, Grammarly, Midjourney, and DALL-E are some of the other AI (or AI-enhanced) tools that marketers are familiar with.

It’s interesting to note that AI detection tools haven’t gained traction in the marketing space—at least not yet. Just under 30% (89) of our respondents said they use AI detection tools at work. 

Most of us are familiar with the AI-isms, so it’s just a matter of refining outputs so they read more humanly. The use of AI for marketing is essentially undeniable at this point; now, it’s about establishing workflows that produce content that reads like a human wrote it. 

While AI is abundantly popular among marketers, the usage of AI detection tools is lagging behind. 

AI-Written Content Still Isn’t Up to the Standard of Human-Written Content

When OpenAI released ChatGPT and GPT4, the company included long, detailed studies explaining how these models were capable of creating content at a university level and beyond. 

Well, it looks like it left marketers out of that report. 

We asked our respondents how they compare AI-written content to human-written content—the majority aren’t too impressed with the former. Only about 11% of our respondents believe AI-written content is better than human-written content. That’s in contrast to the nearly 63% who believe it is worse. 

Interestingly, about a quarter of respondents believe that the content of ChatGPT, Jasper, and other generative tools is similar to the content marketers create. It could be a matter of not using AI the right way , and it will be interesting to see how this opinion evolves alongside the technology itself. 

The Majority of Marketers Believe AI Is Important to Doing Their Job

Despite the many perceived shortcomings of AI (like content creation), nearly 75% of respondents believe that artificial intelligence is at least moderately important to doing their job well. Over 150 believe that it’s very or extremely important. 

That number will certainly increase as knowledge of how marketers can use artificial intelligence to improve results spreads throughout the industry. 

For instance, as more and more SaaS companies integrate AI into their product suites through APIs, prompt engineering and precise language manipulations will become a coveted skill. 

Biggest Concerns About AI 

It’s still very early days in the commercialization of artificial intelligence . That means it’s just as important to understand human concerns about AI’s trajectory as it is to understand how they currently use it. 

With that in mind, we asked respondents some questions about the biggest concerns they have about the impact AI will have in the marketing industry and beyond. One of the immediate concerns that began circulating after the release of ChatGPT and other LLM-based generative AIs is the impact that they will have on certain job segments. 

In the same way automation has replaced jobs across everything from manufacturing to front-line retail, experts have projected that the same will inevitably happen for jobs in the information sectors. 

So far, marketers feel good about their chances. 

Over 55% of our respondents are very or extremely confident that AI won’t replace them . Another 24% are moderately confident that they won’t be replaced. Just over 20% of marketers have low confidence in their ability to hold back the inevitable tide of job replacement.

Interestingly, this high level of confidence doesn’t extend to marketing in general. 

Marketers Believe Writers, Email Marketers, and Graphic Designers Are Most at Risk

We asked our respondents which marketing roles are most at risk due to the rise in artificial intelligence—covering everything from writing and SEO strategies to media buying and web development. Unsurprisingly, marketers have lower confidence in the marketing domains most closely impacted by generative AI . 

Nearly 75% of our respondents —over 250 marketers—believe that the role of content writer is at risk due to AI (*Gulp*). And 44% believe the same is true of email marketing. 

This makes sense, considering the widespread adoption of tools like ChatGPT, Jasper, and Writer, which can churn out decent content at an incredible rate. A McKinsey report of thousands of business leaders reveals that marketing and sales are the top domains for AI at the enterprise level, with content first drafts being the top use case. 

The popularity of text-to-image generation tools is another major factor with an impact on our results. Nearly 40% of our respondents cited graphic designers as a marketing role that will be outsourced to artificial intelligence. Considering how easy it is to create visual assets with DALL-E and Midjourney prompts, this isn’t surprising. 

The best SaaS marketing agencies are responding accordingly and investing in leveling up their AI work.

False Information Is the Biggest Risk Marketers Associate with AI

Since generative AI first hit the mainstream in the form of ChatGPT, everyone from teachers to researchers to business leaders has highlighted the same risk: the tendency to produce misinformation. 

There’s been extensive coverage of the impacts this AI-generated misinformation can have on everything from elections to crisis management. Given all the attention, it’s not surprising that nearly half of our respondents cited false information as the top risk associated with AI. 

A significant number of marketers are also concerned with the impact artificial intelligence will have on intellectual property. 

Because LLMs are, well, large, they thrive on new information. Companies can upload brand guidelines, strategies, and even user behavior data to generate everything from marketing content to GTM strategies. But this comes with a lot of risk . 

Most companies choose to keep their most sensitive information out of large language models like ChatGPT to maintain security. OpenAI even has guidelines on how users can opt out of model training . 

Marketers Are Much More Concerned with Deepfakes than Visual AI

We also asked our respondents how concerned they are with the growing use of visual and video AI creation tools. A slight majority of respondents, 57% , indicated no concern. 

It seems there’s already a level of comfort or familiarity with the technology. Visual AI has been increasingly incorporated into various marketing activities, such as automated graphic design, photography enhancements, and content creation.

There is not the same level of ease surrounding deepfake technology. 

We asked marketers how concerned they are with AI’s ability to create hyper-realistic video content— 78% of them expressed a high level of concern with this technology. 

In marketing and beyond, the use of generative AI for video raises serious ethical concerns, particularly regarding misinformation, identity theft, and the manipulation of media. 

Of course, not everyone in the industry is so pessimistic about the outcomes of video AI 👀.

Despite major concerns about job replacement and the negative impact of deepfakes, our respondents are resoundingly positive about the impact that artificial intelligence will have on the marketing industry. 

More than 93% of our respondents feel that AI will have a positive impact on their work. 

Venture capital and major companies continue to pour resources into the top AI model developers, resulting in products that perform well across an increasing range of tasks. Plus, companies continue to integrate them into tech stacks, giving marketers an increasing opportunity to test out these tools. 

Time Savings Is the Biggest Benefit of Using AI 

Our marketing respondents gave a resounding answer to the biggest benefit of using artificial intelligence in the workplace—time savings. Nearly 64% have experienced the benefits of using AI to create everything from emails to wedding vows (don’t recommend this one). 

This overwhelming majority indicates a strong belief in AI’s efficiency and its ability to streamline processes. Marketers evidently value the time AI saves, which can be reallocated to strategic, creative, or more complex tasks that require human intervention.

The second most recognized benefit is the acquisition of new capabilities. 

Almost 20% of our respondents highlighted AI’s role as a transformative tool that unlocks new possibilities in marketing, such as advanced data analysis, customer behavior prediction, and sophisticated content generation. AI capabilities are reshaping marketing strategies and operations , allowing for innovative approaches to customer engagement and market analysis.

Notably, cost savings, which is often touted as a major advantage of AI, received a relatively low response rate of 6% . For many marketers, the apparent value of AI lies more in its operational enhancements than direct financial savings. That said, I have a sneaking suspicion that CMOs and high-level decision-makers are a lot more interested in this benefit.

Interestingly, only a minimal percentage saw AI contributing to better decisions (4%) and new revenue generation (3%). It seems the perceived value of AI in strategic decision-making and direct financial impact is still evolving. It could just be the early days of AI integration in high-level decision-making or revenue-focused strategies in marketing. 

Most Marketers Believe AI Will Have a Positive Impact on Content 

In terms of the impact AI is having on content, marketers are predominantly optimistic. Of our respondents, 62% believe that tools like ChatGPT and Jasper will boost the quality of content in the long term. Generating ideas, enhancing language, and automating routine tasks are some of the ways this technology will help marketers create better content in less time. AI is emerging as an augmentative force, boosting creativity and efficiency.

This is an interesting finding, considering most of our respondents also believe that AI-written copy is worse than that written by humans. In my opinion, this points to the rising trend of human-AI content teams that work together to produce better outputs than either would on its own. The content centaurs are coming .

Marketers Are Skeptical About AI for Sales, Strategy, HR, and More

Our survey also unearthed some skepticism about AI’s capacity to fully replace certain human-centric tasks within our lifetime. 

The most doubted capability is AI’s ability to engage with sales prospects on calls, with 45% expressing disbelief in AI’s potential to replicate this complex human interaction. This skepticism likely stems from the nuanced nature of sales calls, which often require empathy, adaptability, and a deep understanding of human emotions and reactions—qualities that are inherently human and challenging to replicate in AI.

Creating strategic plans (40%) and hiring and training employees (42%) are also seen as tasks beyond AI’s reach. This is likely because high-level decision-making and understanding of organizational culture are still closely linked to human insight and intuition.

The data collectively reflects a cautious yet realistic view of AI’s limitations, recognizing its supportive role while affirming the indispensable value of human skills and attributes in certain professional domains.

How Leaders Are Looking to Implement AI in the Organization  

Despite all the potential benefits and pitfalls of artificial intelligence, there’s no doubt that leaders in marketing and other departments want to use it. With each new day, it seems like another SaaS company is releasing a feature powered by ChatGPT or another generative AI. 

So, we included a few questions in the survey to understand the relationship between leaders and artificial intelligence. 

A combined 45% of respondents believe their leaders are very or extremely informed about AI. That’s a substantial segment of companies where leadership is highly knowledgeable about AI trends, applications, and implications.

In contrast, 29% of respondents believe that leaders lack sufficient understanding of AI and are only slightly informed or not informed at all . This gap could impact these organizations’ ability to effectively implement and leverage AI technologies, potentially hindering their competitive edge and innovation capacity.

While many companies aren’t “very” knowledgeable about AI, that isn’t stopping leadership from wanting to see it implemented. 

Most Marketing Leaders Want to See More AI Use in the Workplace

Nearly 60% of our marketing respondents state that leadership at their workplace is advocating for increased AI usage. It seems that companies are taking a proactive stance towards embracing AI technologies, reflecting an awareness of AI’s potential benefits, such as enhanced efficiency, data-driven decision-making, and a competitive advantage in the market.

Our survey also found that the overwhelming majority ( 88% ) of leaders included AI in their planning for 2024. 

But exactly how much AI usage do leaders mean when they say they want to see an increase? 

Most Companies Only Implement AI Across Limited Use Cases

Only about 22% of respondents report that there has been “widespread adoption” of AI into processes and tech stacks. However, the largest group, 47% , said that AI is currently only used in “limited use cases.” This implies that while AI is being utilized, its application is restricted to specific areas or projects. This selective approach could be due to various reasons, such as budget constraints, limited AI expertise, or a cautious attitude towards new technologies.

Meanwhile, “experimenting but not using’” AI is reported by 21% , reflecting a stage of exploration and evaluation of AI’s potential benefits and drawbacks before full-scale implementation.

Overall, the data indicates a broad spectrum of AI adoption in the business landscape, ranging from experimental phases to full integration, highlighting the diverse approaches companies are taking in leveraging AI technology.

You have no doubt already noticed, but the numbers confirm that AI is going to be a massive part of marketing (and a lot of other spaces) in 2024 and beyond. 

But adoption and implementation aren’t uniform. Some people are going to be using AI a lot more than others and, in the process, building up an incredibly valuable skill set. Whether it’s engineering prompts for better social and blog posts or using AI for more efficient data analysis, you need to experiment with these tools as much as possible. 

Well, here’s your chance to get ahead (or catch up). 

We recently launched our AI Marketing Console —a comprehensive course that includes over 130 battle-tested prompts you can use for landing page copy, video scripts, blog post creation, email writing, product descriptions, and more.

Find out more about our AI Marketing Console here!

Did you enjoy this post?

Other reads on this topic, search engine optimization (seo) vs generative engine optimization (geo): key differences and strategies, how guild conducted a rebrand & site migration with excellence, how pipedrive uses comparison pages to close high-intent prospects.

Strategy session

Let's talk about your project

Thank you for reaching out, !

Make sure to check for details.

• Artificial Intelligence
• Marketing technologies

Using AI in Marketing: Top 5 Cases & Examples

• Link Copied

Table of Contents

When was the last time you booked a flight for a business trip or vacation? Regardless of whether you booked it yourself or asked your assistant for help, you most likely noticed that prices vary between flights to the same destination with the same set of added services. The reason is that airlines use d ynamic pricing — one of the most common examples of using AI in marketing.

Companies in a range of industries, including aviation, hotels, and ride-hailing apps, use AI to adjust the prices of their services to account for factors like supply and demand, whether it be demand for seats on a plane, or hotel rooms at a given property, or cars. The main goal is to generate more revenue from the limited number of resources available.

Recent advances in artificial intelligence (AI) and growing quantities of data give marketing and sales specialists numerous opportunities to leverage technology to optimize workflows and eventually reduce business operating expenses. In this article, I’ll discuss how AI helps in marketing and how your business can benefit from using it.

AI in modern digital marketing

So what’s the role of AI in marketing and sales and how does it affect business KPIs?

According to a HubSpot study , marketing specialists spend 16 hours per week on average performing routine tasks. This includes tasks like entering information about leads into a CRM system, checking competitors’ prices on similar products and services, answering customer inquiries in chats, segmenting clients, and so on. Sixteen hours is nearly half of a full workweek.

This is where CMOs can take action. Integrating AI into marketing and sales departments and letting algorithms take over routine tasks releases resources for more creative work.

IT research and consulting firm Gartner Inc. expects global spending on AI to hit $62.5 bn in 2022, up 21.3% from 2021. Marketing and sales will get their fair share of this spending. 

According to McKinsey’s recent global survey on AI , marketing is one of the top three domains, where the use of AI has been common over the past few years. The biggest increase in the use of AI in 2021 was in companies’ marketing-budget allocation and spending effectiveness. Over 33% of survey respondents reported AI use cases in marketing and sales — 17% in customer service analytics and 16% in customer segmentation. What’s even more interesting is that marketing and sales showed one of the biggest year-over-year changes in the shares reporting cost takeout from using AI.

That’s no surprise, since using AI in marketing allows specialists to identify which ads generate more revenue than others, spot declining CTRs on time, and even anticipate changes in customer behavior. Then they can properly look into each case based on changes identified in the data. 

These uses, together with other, similar data-driven campaigns, promise to increase product or service sales by more than 10%. Let’s take a closer look at how companies use AI in marketing to drive sales.

Sales forecasting

Sales forecasting may be one of the most common examples of using AI in marketing. AI-enabled software can use predictive analytics to forecast future sales. It relies on machine learning (ML) and big data containing information about previous sales. Those data train ML models to detect patterns in new data and predict future outcomes. 

The longer you use the technology, the higher accuracy it delivers since it learns from additional data received over time. This means that in the long run, the software gives more and more benefits, enabling sales executives to predict KPIs more accurately (CPAs, conversion rates, customer retention rate, etc.) and adjust marketing strategies accordingly. 

Levi Strauss & Co. revealed it leveraged AI and a massive data repository it built on Alphabet Inc.’s Google Cloud to boost revenue growth and improve its margins. The clothing company fed its ML pipeline with information about its shoppers from the repository, supplemented by external data received from private and public sources that track consumer buying behaviors, weather forecasts, and more. Levi Strauss & Co. used the ML model to predict demand, enhance the personalization of consumer marketing, make informed pricing decisions, and predict sales.

In one case, an ML model spotted that a particular T-shirt had gained popularity with female consumers in China. Despite the sales department’s incentive to discount the product, ML analysis suggested that the company kept offering it at the same price based on predicted volume of sales and anticipated revenue. 

Dynamic pricing

I mentioned this technique at the beginning of this post and it’s the one that most customers encounter when purchasing goods or services online. 

Brands use this method to efficiently manage the prices of their products and services. AI-enabled tools with integrated dynamic pricing algorithms monitor competition and automatically reprice the company’s offerings when they spot an opportunity. Other AI tools enable better customer segmentation in terms of pricing decisions, helping retailers set different prices for different groups of customers.

A core estimation at the heart of dynamic pricing algorithms is a correlation between price and demand. The algorithm changes the price, adding a demand function to the product’s basic pricing equation. The demand function usually includes competitor prices, inventory costs, procurement expenses, promotional campaigns, and other factors. The more diverse the factors that the pricing algorithm considers, the better results it generates.

The majority of dynamic pricing algorithms use historical sales data to estimate the demand function. 

Take Amazon for example. It’s one of the biggest e-commerce platforms, with over 300 million active users , and it changes product prices 2.5 million times a day ! This means that an average product changes cost every 10 minutes. The company relies on tons of data that it collects about shoppers who visit the platform, enabling it to efficiently analyze customer behavior and set prices accordingly. Amazon managed to boost revenue by 25% using dynamic pricing and the company continues to use the technique extensively.

Customer targeting and personalization

Brands use AI at scale to reach out to various groups of customers with content that triggers them to perform certain actions (e.g., buy a product, subscribe to promo emails, contact a manager). Algorithms collect and analyze data about customers’ previous interactions with the brand’s website, to see how they engaged with the content and what they liked. 

Based on this information, AI segments customers into groups and can offer content or digital ads that would be more relevant to particular groups. Combined with predictive analytics, AI can identify additional target audiences for a brand. AI can also help predict the actions that customers from each segment of the target audience will take after receiving a message from the company. Will a customer open it or not? Will they read it all the way through? Will they make a purchase? The more information about customers’ previous actions the algorithm has, the more accurate its predictions about outcomes are.

Starbucks is a vivid example of a company that uses loyalty cards they issue to customers and a mobile app to collect and analyze data about them. The coffee shop chain’s app has information about purchases including location, time of day, and demographics. Predictive analytics process this information to offer customers even more personalized ads. The ads can include promotions or recommendations whenever a person approaches a Starbucks.

“We’re leveraging AI to deepen our digital relationships, architecting experiences that provide that experiential retail touchpoint. We want to both surprise and delight our customers in new and different ways, ” said Rajesh Naidu, Starbucks VP of Architecture, Data, & Analytics Technology.

Sales outreach automation

This technique is new to the market but is a promising one.

ML algorithms can be trained to automate the time-consuming process of sales outreach. In particular, it can analyze a database of leads in the company’s CRM platform, locate leads, and book meetings.

The AI startup Kalendar claims to have developed a tool that relies on AI sales bots to send personalized pitches to potential clients. According to the company’s representative, the software shortens the lead time to start conversations to set appointments from seven days to 30 seconds. The best markets for the product so far are marketing and IT. 

AI-enabled chatbots

AI-enabled chatbots are marketers’ true saviors since they can conserve lots of time and resources for a company.

Built on ML algorithms, chatbots can be triggered by customers’ specific behavior, such as reading certain types of content or interacting with web page elements. Or when directly contacted in any type of messaging app, chatbots can perform the tasks of human customer support managers. Algorithms can be programmed according to a business’s specific buyer personas, and lead conversations in a specific tone of voice to maximize engagement with those personas.

Benefits of integrating AI chatbots into a marketing strategy for business include:

• increasing the volume of conversations with customers
• nurturing leads
• personalizing user experiences
• qualifying of leads automatically
• automating part of the marketing process

Beauty brand Sephora is an early adopter of AI in marketing automation. In 2017, it launched a Kik bot that gave beauty advice to teenagers. It asks customers questions about their preferences to help narrow down their purchasing choices. The company has since launched more chatbots on Facebook’s Messenger app. 

Leverage the AI in marketing 

Postindustria’s team of ML engineers harnesses years of experience in training algorithms for a variety of purposes across industries — from efficiently filling publishers’ ad space with programmatic advertising banners to developing high-precision hand tracking models for virtual try-ons.

We rely on the only viable approach to managing ML projects — building ML pipelines and making sure the system changes automatically as datasets evolve. Once a pipeline is delivered, you won’t need to come back to us for constant upgrades. 

Postindustria will help you develop AI-enabled solutions that make your marketing strategy more efficient. Book a strategy session with us to learn more.

Book a strategy session _

Get actionable insights for your product

Thank you for reaching out, User !

Using AI Technology for Fashion Trends Prediction and Demand Forecasting

AutoML Current Uses and Approaches

Why Everyone is Seeking AI Engineers: An Emerging Trend That Is Here to Stay

How Artificial Intelligence Has Revolutionized Marketing [Case Study]

Pratiksha Bajikar

We have seen automation happening in manual habitats but AI has brought us a whole new world to figure out. Computers are slowly taking over manual tasks and this has triggered fear in a tech giant you might be familiar with. Using artificial intelligence in marketing has now become a norm as it has transformed the Marketing Industry.

Yes, I'm talking about Elon Musk fearing that the advancements in AI might create human hunting robots and it would be us, mortals, against AI-powered geniuses. However, there has been an increase in companies using AI for marketing and ad campaigns.

What is AI Marketing? Why Is AI A Good Approach For Marketing? How Companies Are Using AI For Marketing? Most Successful Marketing Campaigns Designed By AI How Small Businesses Are Using Ai Tools Conclusion FAQs

What is AI Marketing?

Artificial intelligence plays a big role in marketing, as it helps companies to make decisions based on the data collected by a company. The use of artificial intelligence in marketing will help you get the data could be anything from the customer’s favourite hangout places to their birthdays.

Any AI marketing strategy helps data to be collected from general behaviours of consumers from polls on social media, surveys, and their interests when surfing the internet. Marketing teams along with AI study consumer patterns and accordingly place and plan their AI marketing campaigns.

AI has suspended a large area of the consumer field where everything can be predicted. The AI marketing research mainly consists of collecting and analyzing data, media buy-in, personalization, content creation, and much more. This case study on artificial intelligence shows you how most of the marketing tasks are highly dependent on AI these days.

Why Is AI A Good Approach For Marketing?

AI-based marketing has entirely changed the way people use social media. Before it sneaked into every device, social media was leisure, rather, luxury.

Cut to today, every individual and even kids are owners of smartphones enabling them access to social media and shopping sites because of the artificial intelligence advertising campaigns. Your everyday social media behaviour is what fuels the study of AI-based marketing tools . With this being done, marketers can reach up to a larger demographic without spending a fortune on marketing campaigns .

How Companies Are Using AI For Marketing?

Many AI marketing companies use core elements like Big Data, Machine Learning, and other powerful solutions that allow AI to get adopted quickly by a marketer. Big data is nothing but a prodigious amount of data aggregated by the marketer and segmented into various categories with a minimal amount of manual work.

With the help of big data, marketers can personalize every message sent to their customers. Like mentioned earlier, several marketers keep an eye on repetitive actions which offer deeper insight into responses. It is a demanding task when we try to make sense out of the massive data collected. This is where machine learning and AI in marketing are used.

You may have been familiar with pop-up messages or push notifications from the apps you install from time to time. These messages are powered by AI-programmed systems and automated to deliver context in a specified manner. There are several other AI marketing strategies that marketers have adopted to keep you hooked and engaged. Let us have a look at what they are.

Pay-per-click (PPC) is an online advertising model used to drive viewer traffic to the website. Nowadays AI-based marketing is used in every blog, website, and video. When you visit a certain page and click on the link containing an advertisement, it directs you to the said website.

This is an AI marketing analytics used by the advertiser where they pay the publisher every time a user clicks on the link. Some of the tops AI marketing companies like Facebook ads , Instagram , Twitter Ads , Google Ads make PPC Ads.

Personalization

If you have your mailbox overflowing with promotional emails , worry not, you’re not alone. Personalization has become a crucial contributor to every company where they want you to feel like the most sought-after customers.

Personalized messaging has a direct influence over your mind giving you the impression that this auto-generated message was especially typed for you because it has your name on it. Artificial intelligence marketing helps organizations to place bids on relevant ad spaces in real-time. With critical analytics and results from big data, artificial intelligence, and marketing you can send personalized messages to individuals.

The AI tools for marketing are not limited to just emails. Have you noticed how Netflix uses your previously watched movies and gives recommendations based on past experience? Or how it shows you different imageries for the same movie every time you open it? Their blog, Netflix tech blog , talks about how creating different imageries for the same movie or show has got viewers hooked to Netflix all day long.

A movie title may not be that intriguing. But a picture is worth a thousand words . It may contain a familiar face, your favourite actor, something that draws you in and keeps you engaged.

Predictive Analytics

Predictive analytics is based on processing the historical data of a demographic to understand or carve a pattern or predict future behaviour. Predictive analytics is an AI marketing strategy that compares the trends and patterns of different data sets and pulls out a new analysis using a mathematical ‘model which helps companies prepare for what’s coming.

It could be identifying a customer likely to ditch a service or product, or a customer who is likely to stick around and send them marketing campaigns. AI marketing companies use predictive analysis to improve current services and make organizational changes accordingly.

Deep learning

Deep learning is a class of machine learning algorithms used to extract higher-level data from raw input and is considered to be the future of AI in digital marketing. Machine learning and AI in marketing are important because they help in creating voice-controlled chatbots, image recognition (in the case of Facebook), and predicting customer interests.

Many brick-and-mortar stores are turning to AI systems enabled by deep learning such as cashier-less counters, contactless payment options, and virtual baskets. One of the popular AI in marketing examples is Amazon GO stores that have adopted auto checkouts where AI-enabled cameras detect the movements of customers and add items to a virtual basket. Customers can check themselves out after making payments online.

Most Successful Marketing Campaigns Designed By AI

Chase+persado.

Chase, a New York-based consumer bank upgraded its copywriting by collaborating with Persado, is one of the tops AI marketing companies that use AI in its creative processes. Chase bank wanted a humanitarian perspective to their AI marketing campaigns and after a successful pilot, the bank saw a 450% surge in the click-through ads created by Persado and both the companies have ties hands for a five-year contract.

Starbucks is one of the other companies using AI for marketing. The company uses predictive analytics by making use of its loyalty cards and mobile app to collect and analyze customer data. The coffee giant has also used AI marketing research to optimize the user experience to an extent where it records details of purchases, including what time and what buy. It is concentrating on a model “ AI for Humanity ” where they believe will create better connections with humans surrounding them.

In a busy world, as is ours, Starbucks makes unique use of artificial intelligence in marketing , as it aims to recreate human interaction which is seemingly blurred since everyone is literally into their devices. This artificial intelligence marketing initiative may be invisible to the customers as it focuses on inventory management, processing orders, staffing requirements, and much more so that there are more interactions between customers and partners at Starbucks. You would leave the café with much more than coffee for sure.

Alibaba is known to use many AI tools for marketing, it has now opened its first AI fashion store in Hong Kong. This store has deployed AI in streamlining fashion retail. The store has introduced Intelligent garment tags with radio-frequency identification, gyro-sensors, and low-energy Bluetooth chips. This can enable the garment to carry information such as colour and size.

The gyro-sensors will recognize patterns when an item is touched, moved, or picked up. Another intriguing feature is its smart mirrors located on sales floors and changing rooms that help customers find related items along with the ones picked up and also add them to a virtual basket. Alibaba uses different types of AI in marketing in order to provide a one of the kind experience to its customers.

Nike launched the “ Nike Makers’ experience ” campaign in 2017 that allowed customers to design their own Nike pair. Using AI marketing analytics, the shoe brand can encourage their customers to choose their designs and graphics using projections and augmented reality on blank Nike X Presto sneakers.

This is the future of AI in digital marketing, as customers then chose their designs and they were delivered the shoes in 90 minutes. Not only their sales soared but Nike used this opportunity to collect customer preferences using machine learning and design future products.

Sephora is one of the few companies using ai for marketing and is also an early adaptor of AI-based marketing. The AI marketing strategy that the company uses is responsible for three bots that interact with customers on a personal level to understand their needs and wants. It is really daunting to order a foundation or lipstick that may or may not be your perfect match.

Sephora’s Kik bot helps you with an interactive quiz, make-up tips, how-to videos, and reviews. Using AI in marketing will help your customers can even scan images of celebrities to find matching make-up products.

How Small Businesses Are Using Ai Tools

The use of artificial intelligence in marketing has proven to be quite a revolution in the industry. Recent studies and case studies on artificial intelligence, predict that almost 70% of marketing jobs will be replaced by automation . The vital role of artificial intelligence in digital marketing has made many AI marketing companies successful, while the small businesses are no longer shying away from

Here’s a list of AI tools you could check out while you chalk out your next marketing plan.

Concured is an AI content strategy platform that gauges what your audience is looking for. This AI tool for marketing offers three exclusive tools for content- research, creation, and personalization. It has other beneficial tools which include live audits, automation of content and removing of content waste by 90%, planning tool for detailed content brief, performance tracking, and analysis for better ROI and user engagement .

Import.io is a data extraction tool that helps you with everything from extracting data from the sea that is the internet , even hidden pages, with anomaly detection, and validation rules. These AI-based marketing tools allow you to optimize your campaigns with access to competitor price search and analyze all your customer reviews.

Grammarly is a popular AI tool for marketing and is considered a savior for all the content creators out there. Writers, please take note. Grammarly not only eases the spell checks but suggests you rephrase your sentences in case they appear to be blurry or unclear. Ultimately, it makes you a better writer over the period.

Cortex helps you build AI-based marketing strategies based on visual analytics, that is what images or video ideas are trending on social platforms. These AI marketing strategy helps you gain insights on what will work better and what’s suited better for your company. After you have analyzed your competitors’ social media efforts, it pushes you and your team to do better by refining your marketing and content strategies.

If you break a sweat every time copywriting challenges come up, then Phrasee is here to ease that out. With AI-powered language optimization , you can create professional and compelling copies for your audience. These AI-based marketing tools help you create email subject lines, push notifications, social media posts, and much more with, what they call a “Magic” button.

Sentinent Ascend

Sentient Ascend is a testing tool for marketers that helps enhance conversion rate optimization (CRO). AI for marketing has proven to be 100 times better than the current A/B testing multivariate solutions and gives marketers a free hand in trying several copies, images, designs, and interaction changes that speed up revenues and conversions.

Drift helps in AI marketing research as it connects you to potential clients in real-time. It is a marketing and sales chatbot that helps you connect with business leads . It personalizes every experience of your website visitors by interacting with them while turning conversations into conversions.

FindTheRipple

FindTheRipple is an AI-driven platform that enables you to create content with impact, find untapped trends, and alert you with emerging engagement peaks to target your audience. It also recommends you match digital assets to optimize your content creation.

Lucy is an AI-powered knowledge management assistant that helps you leverage business insights across your company. You ask her a question, and she answers you with the best possible solutions for your challenges. Lucy’s integrations allow it to quickly enter your systems and access information hence helping you the right answers to your business needs.

Nudge.ai is a relationship management tool that helps you connect better within and outside your organization. Its powerful AI leverages big data and data mining brings better connectivity to your business and helps you make meaningful relationships.

One of the AI tools for marketing helps develop relationships that are organic and natural and shows you how to reach out with a perspective thereby creating a genuine sense of urgency which is vital during closing a deal.

Cogito is a real-time conversational and analysis system that identifies human signals on calls and predicts the emotional intelligence of the caller. It incorporates AI and machine learning to voice calls and assists call centres to be more empathetic while conversing.

It monitors speed, volume, and pauses in the conversation between agents and customers records the transcripts of the call and detects keywords used by the customers, and helps executives with apt responses.

Processing emails manually can be extremely time-consuming and expensive. A manual process is always prone to human error. Hence, Optimail enables marketers to continuously and automatically optimize their email and AI marketing campaigns. It not only promotes campaigns but adjusts the timings, content, and personalization. It builds better engagement and also reduces spam.

Aizimov is an AI social selling platform that writes ultra-personal messages for each of your target audiences. It’s basically like having a personal secretary. It optimizes the time spent on sales leads and improvises with every feedback and response. The smart AI also analyses the psychological profile of a prospect and crafts its message carefully. It takes a step further and chooses the best channel to reach the potential client.

Pathmatics offers solutions to what, when, where, who, and how-to questions in marketing. You can see ads served, impressions, and digital strategies of brands and publishers across Facebook, Twitter, and Instagram. Marketers can take a jiffy from aggregating data and use digital insights to pitch new ideas and create more effective campaigns.

Emarsys is an omnichannel customer engagement platform that helps you accelerate your business outcomes. These AI-based marketing tools include industry-specific analytics and use-cases that cater to every business. By one’s own bootstraps, Emarsys powers all your marketing needs with 1:1 personalization across all channels and devices.

AI has become indispensable in many areas of our life. The importance of artificial intelligence in marketing is that it leads to Driverless cars, Automation Marketing, conversational bots like Siri and Alexa are the new highs of AI technology. We’re yet to comprehend what AI can accomplish.

Could they completely replace human interference and take over our world like Elon Musk fears? We don’t know. But it surely has prodigious potential to make things happen which we never imagined were possible. Hopefully, this artificial intelligence case study would have helped you understand the importance of artificial intelligence in marketing.

How is AI used in marketing?

AI tools are used in marketing to learn how to best communicate with customers, then serve them tailored messages at the right time.

What are the 3 stages of AI?

The three stages of AI are artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial superintelligence (ASI).

How AI is used in mobile marketing?

AI used to craft more targeted and personalized marketing messages such as special offers and ads by spotting consumers' behavioural trends and patterns.

Ho does AI work in digital marketing?

Artificial intelligence is transforming customer-facing services for digital marketers by increasing efficiency and optimizing user experience.

Is AI the future of marketing?

AI is changing the future of digital marketing, that is certain. It's not so much about what new developments are happening, but also what new trends are likely to emerge over the next few years.

How is AI used in Marketing?

One common example of AI across the web is the use of chatbots to provide customer services to users.

What is AI marketing?

Artificial intelligence plays a big role in marketing, as it helps companies to make decisions based on the data collected by a company.

Must have tools for startups - Recommended by StartupTalky

• Convert Visitors into Leads- SeizeLead
• Manage your business smoothly- Google Workspace
• International Money transfer- XE Money Transfer

Niche Edits 2024: Proven Strategies for Niche Link Building

A popular website serves review blogs on electronic wearables. You have a website that sells high-quality wearables like smartwatches, ear pods and so on. So, Niche edit = getting your link added to some specific wearable review blog. How this might look- "For an immense audio experience, you must try

The Evolution of Engagement Rings: From Classic Cuts to Modern Designs

Engagement rings have long been a symbol of love, commitment, and promises for the future. This tradition has evolved over centuries, adapting to cultural shifts, technological advances, and individual preferences. While giving a ring to mark a commitment dates back thousands of years, engagement rings' style, craftsmanship, and symbolism

India's Semicon Mission Set to Receive Crucial 8-Year Financial Boost

Reports indicate that government officials are contemplating a five-year extension of the budgetary assistance period under the second phase of the India Semiconductor Mission (ISM) to eight years. According to a senior government official, applicants whose projects are approved under ISM Phase II may be eligible for additional financial grants

Ather Energy: Building the Connected and Electric Future Of Mobility

Company Profile is an initiative by StartupTalky to publish verified information on different startups and organizations. Fueling with electricity offers a whole lot of advantages that are clearly not available with the conventional internal combustion engine vehicles that we have been using to date. Electric motors react quicker than the

Artificial intelligence in automated decision-making in tax administration: the case for legal, justiciable and enforceable safeguards

Kunal Nathwani

Published on 11 September 2024

This paper focuses on the use of AI in ADM in tax administration to make discretionary or subjective decisions.

• Taxes and benefits
• Tax administration

Download report PDF 

PDF | 368.41 KB

HM Revenue & Customs (HMRC) has a broad range of powers, which can be divided into powers which (i) are administrative or mechanical in nature, and (ii) require the exercise of an element of discretion or subjectivity (including the imposition of penalties). 

Automated decision-making (ADM) is any decision or process where the whole or part of the decision or process is made without human intervention (through technology), irrespective of whether the decision or output is subsequently reviewed by HMRC. Broadly, the technology underpinning ADM can be divided into:

• artificial intelligence (AI), and 
• algorithmic systems developed through conventional programming (i.e. algorithmic systems or rules-based systems).

Although there is no comprehensive list of ADM technology published by HMRC, it is understood that, at present, HMRC employs both types of technology. This paper focuses on the use of AI in ADM in tax administration to make discretionary or subjective decisions.

This discussion paper was written for the Tax Law Review Committee (TLRC) by Kunal Nathwani. The Committee has authorised its publication to inform and promote debate in this area. The views expressed do not necessarily represent the views of the Committee. The Institute for Fiscal Studies has no corporate views.

Related content

Tax law review committee.

Solicitor Associate, Kirkland & Ellis

Report details

Suggested citation.

Nathwani, K. (2024). Artificial intelligence in automated decision-making in tax administration: the case for legal, justiciable and enforceable safeguards . London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/artificial-intelligence-automated-decision-making-tax-administration-case-legal (accessed: 15 September 2024).

More from IFS

Understand this issue.

How could the Chancellor raise more tax?

28 August 2024

Election Special: Your questions answered

27 June 2024

Election Special: The big issues politicians haven't spoken about

25 June 2024

Policy analysis

How would the parties’ tax and spending plans affect Scotland and Wales?

28 June 2024

What are the parties’ plans for benefits and taxes?

24 June 2024

Lower oil and gas prices hit Scotland’s underlying public finances in 2023-24

14 August 2024

Academic research

Identifying network ties from panel data: Theory and an application to tax competition

9 September 2024

Focal pricing and pass-through

21 August 2024

Does the value-added tax add value? Lessons using administrative data from a diverse set of countries

9 February 2024

Advertisement

Pakistani students’ perceptions about knowledge, use and impact of artificial intelligence (AI) on academic writing: a case study

• Published: 11 September 2024

Cite this article

• Shaista Rashid 1 ,
• Sadia Malik   ORCID: orcid.org/0000-0002-4989-2359 2 ,
• Faheem Abbas 2 &
• Javaria Ahmad Khan 3  

20 Accesses

Explore all metrics

Integrating artificial intelligence (AI) in language pedagogy can help learn and develop many skills. In this context, this study explores Pakistani students' perceptions and trends regarding the knowledge, use, and impact of AI on their academic writing. The data was collected using a quantitative method, using a questionnaire through cluster sampling of four faculties and random sampling of 229 students from Bahuddin Zakariya University, Multan, Pakistan. Data is subjected to frequency analysis, Kruskal–Wallis hypothesis test, and chi-square association test using SPSS. The findings reveal that most students agree regarding the knowledge, use, and impact of AI on their academic writing. For the Kruskal–Wallis test, significant variations are seen in semesters and age groups for all three variables; however, only the knowledge variable shows significant variation across faculties. Moreover, chi-square test results indicate an association among components of knowledge, use, and impact of AI. The research suggests that academia should introduce AI as a pedagogical tool to improve students' comprehension, productivity, and writing quality. Furthermore, trends indicate that comprehensive policy formulation should be implemented to equip students of all faculties, semesters, and age groups to use technology equally.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Artificial intelligence in higher education: the state of the field

Algorithmically-driven writing and academic integrity: exploring educators' practices, perceptions, and policies in AI era

Learning What Works in Improving Writing: A Meta-Analysis of Technology—Oriented Studies Across Saudi Universities

Explore related subjects.

• Artificial Intelligence
• Digital Education and Educational Technology

Data availability

The data was collected through questionnaire using Google Forms; SPSS file can be provided if requested.

Ahmed, M., Siddiqui, M., & Usman, T. (2024). Impact of artificial intelligence-based writing assistant on the academic writing skills of university faculty in Pakistan. International Journal of Human and Society, 4 (1), 539–545.

Google Scholar  

Aladini, A. (2023). AI applications impact on improving EFL university academic writing skills and their logical thinking. Al-’ulūm Al-Tarbawiyyaẗ, 31 (2), 25–44. https://doi.org/10.21608/ssj.2023.320166

Article   Google Scholar  

Ali, Z. (2020). Artificial intelligence (AI): A review of its uses in language teaching and learning. IOP Conference Series: Materials Science and Engineering, 769 (1), 012043. https://doi.org/10.1088/1757-899x/769/1/012043

Almusharraf, N., & Bailey, D. (2021). A regression analysis approach to measuring the influence of student characteristics on language learning strategies. International Journal of Instruction, 14 (4), 463–482. https://doi.org/10.29333/iji.2021.14428a

Beiki, M. (2022). Review of writing-related theories. Cultural Arts Research and Development, 2 (1), 27–33. https://doi.org/10.55121/card.v2i1.20

Burkhard. (2022). Student perceptions of AI-powered writing tools: Towards individualized teaching strategies. 19th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2022) . https://doi.org/10.33965/celda2022_202207l010

Chaudhry, I. S., Sarwary, S. A. M., Refae, G. A. E., & Chabchoub, H. (2023). Time to revisit existing student’s performance evaluation approach in higher education sector in a new era of Chatgpt — a case study. Cogent Education, 10 (1), 2210461. https://doi.org/10.1080/2331186x.2023.2210461

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8 , 75264–75278. https://doi.org/10.1109/access.2020.2988510

Cunningham, U., Rashid, S., & Van Le, T. (2019). The effect of learner training on the use of digital tools to support English writing skills. Asian EFL Journal , 21 , 27–49. http://uu.diva-portal.org/smash/record.jsf?pid=diva2:1300583

Dhara, S., Chatterjee, S., Chaudhuri, R., Goswami, A., & Ghosh, S. K. (2022). Artificial intelligence in assessment of students’ performance. In CRC Press eBooks (pp. 153–167). https://doi.org/10.1201/9781003184157-8

Dizon, G., & Gayed, J. M. (2021). Examining the impact of Grammarly on the quality of mobile L2 writing. The JALT CALL Journal, 17 (2), 74–92. https://doi.org/10.29140/jaltcall.v17n2.336

Feise, R. J. (2002). Do multiple outcome measures require p-value adjustment? BMC Medical Research Methodology, 2 (1), 8. https://doi.org/10.1186/1471-2288-2-8

Fitria, T. N. (2021). Grammarly as AI-powered English writing assistant: students’ alternative for writing English. Metathesis, 5 (1), 65–78. https://doi.org/10.31002/metathesis.v5i1.3519

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34 (10), 906–911. https://doi.org/10.1037/0003-066x.34.10.906

Ghafar, Z. N., Salh, H. F., Abdulrahim, M. A., Farxha, S. S., Arf, S. F., & Rahim, R. I. (2023). The role of artificial intelligence technology on English language learning: a literature review. Canadian Journal of Language and Literature Studies, 3 (2), 17–31. https://doi.org/10.53103/cjlls.v3i2.87

Ginting, P., Batubara, H. M., & Hasnah, Y. (2023). Artificial intelligence powered writing tools as adaptable aids for academic writing: Insight from EFL college learners in writing final project. Zenodo, 6 (10), 4640–4650. https://doi.org/10.5281/zenodo.8407887

Grájeda, A., Burgos, J., Olivera, P. C., & Sanjinés, A. (2023). Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent Education, 11 (1), 2287917. https://doi.org/10.1080/2331186x.2023.2287917

Imran, M., & Almusharraf, N. (2023). Analyzing the role of ChatGPT as a writing assistant at higher education level: A systematic review of the literature. Contemporary Educational Technology, 15 (4), ep464. https://doi.org/10.30935/cedtech/13605

Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. Biostatistics, 4 (1), 60–67. https://doi.org/10.4103/ijam.ijam_7_18

Keleş, P. U., & Aydın, S. (2021). University students’ perceptions about artificial intelligence. Shanlax International Journal of Education, 9 , 212–220. https://doi.org/10.34293/education.v9is1-may.4014

Knowles, E. (2005). The Oxford dictionary of phrase and fable. In Oxford University Press eBooks . https://doi.org/10.1093/acref/9780198609810.001.0001

Kothari, C. R. (2004). Research methodology: Methods and techniques . New Age International.

LeCompte, M. D., & Schensul, J. J. (2015). Ethics in ethnography: A mixed methods approach . AltaMira Press.

Lei, H. (2022). High school students’ foreign language vocabulary acquisition in the era of artificial intelligence. Advances in Social Science, Education and Humanities Research, 637 , 669–662. https://doi.org/10.2991/assehr.k.220131.121

Li, A. W. (2023). Using Perceptive to support AI-based online writing assessment across the disciplines. Assessing Writing, 57 , 100746. https://doi.org/10.1016/j.asw.2023.100746

Mahmud, F. A. (2023). Investigating EFL students’ writing skills through artificial intelligence: Wordtune application as a tool. Journal of Language Teaching and Research, 14 (5), 1395–1404. https://doi.org/10.17507/jltr.1405.28

Malik, A. R., Pratiwi, Y., Andajani, K., Numertayasa, I. W., Suharti, S., Darwis, A., & Marzuki, M. (2023). Exploring artificial intelligence in academic essay: Higher education student’s perspective. International Journal of Educational Research Open, 5 , 100296. https://doi.org/10.1016/j.ijedro.2023.100296

Malik, S., Sadiq, U., & Khan, J. A. (2021). Belief, practices, and challenges of Pakistani primary grade government school teachers: Variable analysis affecting pronunciation and phonics teaching. Humanities & Social Sciences Reviews, 9 (1), 206–217. https://doi.org/10.18510/hssr.2021.9122

Marzuki, Widiati, U., Rusdin, D., Darwin, D., & Indrawati, I. (2023). The impact of AI writing tools on the content and organization of students’ writing: EFL teachers’ perspective. Cogent Education, 10 (2), 1–17. https://doi.org/10.1080/2331186x.2023.2236469

Mayo, D. G., & Cox, D. R. (2006). Frequentist statistics as a theory of inductive inference. In Institute of Mathematical Statistics eBooks (pp. 77–97). https://doi.org/10.1214/074921706000000400

McHugh, M. L. (2013). The chi-square test of independence. Biochemia Medica, 23 (2), 143–149. https://doi.org/10.11613/bm.2013.018

Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of artificial intelligence powered digital writing assistant in higher education: Randomized controlled trial. Heliyon, 7 (5), e07014. https://doi.org/10.1016/j.heliyon.2021.e07014

Ostertagová, E., Ostertag, O., & Kováč, J. (2014). Methodology and application of the Kruskal-Wallis test. Applied Mechanics and Materials, 611 , 115–120. https://doi.org/10.4028/www.scientific.net/amm.611.115

Pacheco-Mendoza, S., Guevara, C., Samaniego, J., & Fernandez, J. (2023). Artificial intelligence in higher education: A predictive model for academic performance. Education Sciences, 13 (10), 990. https://doi.org/10.3390/educsci13100990

Park, J. (2019). An AI-based English grammar checker vs. human raters in evaluating EFL learners’ writing. Multimedia-Assisted Language Learning , 22 (1), 112–131. https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002449524

Rashid, S., Cunningham, U., Watson, K., & Howard, J. (2018). Revisiting the digital divide(s): Technology-enhanced English language practices at a university in Pakistan. Australian Journal of Applied Linguistics, 1 (2), 64–87. https://doi.org/10.29140/ajal.v1n2.7

Ribeiro, R. (2022). AI in English Language Learning | Cambridge English . World of Better Learning | Cambridge University Press. https://www.cambridge.org/elt/blog/2020/03/09/artificial-intelligence-english-language-learning/

Rothman, K. J. (1990). No adjustments are needed for multiple comparisons. Epidemiology, 1 (1), 43–46. https://doi.org/10.1097/00001648-199001000-00010

Rusmiyanto, R., Huriati, N., Fitriani, N., Tyas, N. K., Rofi’i, A., & Sari, M. N. (2023). The role of artificial intelligence (AI) in developing English language learner’s communication skills. Journal on Education, 6 (1), 750–757. https://doi.org/10.31004/joe.v6i1.2990

Saini, N. (2023). Research paper on artificial intelligence and its applications. International Journal for Research Trends and Innovation, 8 (4), 356–360.

Sharifi, A., Ahmadi, M., & Ala, A. (2021). The impact of artificial intelligence and digital style on industry and energy post-COVID-19 pandemic. Environmental Science and Pollution Research, 28 (34), 46964–46984. https://doi.org/10.1007/s11356-021-15292-5

Slimi, Z. (2023). The impact of artificial intelligence on higher education: An empirical study. European Journal of Educational Sciences . https://doi.org/10.19044/ejes.v10no1a17

Sumakul, D. T. Y. G., Hamied, F. A., & Sukyadi, D. (2022). Students’ perceptions of the use of AI in a writing class. Advances in Social Science, Education and Humanities Research, 624 , 52–57. https://doi.org/10.2991/assehr.k.220201.009

Toncic, J. (2020). Teachers, AI grammar checkers, and the newest literacies: Emending writing pedagogy and assessment. Digital Culture & Education, 12 (1), 26–51.

Turner, E. (2021). Causes for leaving jobs: A comparative analysis. 2nd International Conference on Research in Management . https://doi.org/10.33422/2nd.icrmanagement.2021.02.43

Utami, S. P. T., Andayani, A., Winarni, R., & Sumarwati, S. (2023). Utilization of artificial intelligence technology in an academic writing class: How do Indonesian students perceive? Contemporary Educational Technology, 15 (4), ep450. https://doi.org/10.30935/cedtech/13419

Verma, M. (2018). Artificial intelligence and its scope in different areas with special reference to the field of education. International Journal of Advanced Educational Research , 3 (1), 5–10. http://files.eric.ed.gov/fulltext/ED604401.pdf

Victorivna, K. L., Oleksandrovych, V. A., Oleksandrivna, K. I., & Oleksandrivna, K. N. (2022). Artificial intelligence in language learning: What are we afraid of? Arab World English Journal, 8 , 262–273. https://doi.org/10.24093/awej/call8.18

Wang, T., Lund, B., Marengo, A., Pagano, A., Mannuru, N. R., Teel, Z. A., & Pange, J. (2023). Exploring the potential impact of artificial intelligence (AI) on international students in higher education: Generative AI, chatbots, analytics, and international student success. Applied Sciences, 13 (11), 6716. https://doi.org/10.3390/app13116716

Zhao, X. (2022). Leveraging artificial intelligence (AI) technology for English writing: Introducing Wordtune as a digital writing assistant for EFL writers. RELC Journal, 54 (3), 890–894. https://doi.org/10.1177/00336882221094089

Download references

Acknowledgements

The authors would like to thank Prince Sultan University for its support.

No funding was sought from any external source for this research.

Author information

Authors and affiliations.

Prince Sultan University, Riyadh, Saudi Arabia

Shaista Rashid

Department of English, Bahauddin Zakariya University, Multan, Pakistan

Sadia Malik & Faheem Abbas

Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan

Javaria Ahmad Khan

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sadia Malik .

Ethics declarations

Conflict of interest.

There is no conflict of interest of any sort.

Ethical statement

All participants gave their informed consent for inclusion before they participated in the study. Their responses were evaluated anonymously as the data was presented in aggregate form.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Rashid, S., Malik, S., Abbas, F. et al. Pakistani students’ perceptions about knowledge, use and impact of artificial intelligence (AI) on academic writing: a case study. J. Comput. Educ. (2024). https://doi.org/10.1007/s40692-024-00338-7

Download citation

Received : 15 May 2024

Revised : 08 August 2024

Accepted : 14 August 2024

Published : 11 September 2024

DOI : https://doi.org/10.1007/s40692-024-00338-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Artificial intelligence
• Academic writing
• Higher education
• Language pedagogy
• Find a journal
• Publish with us
• Track your research