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Future Problem Solving

Future Problem Solving (FPS) is a competitive educational program based on a six-step problem solving process developed by creativity pioneer Dr. E. Paul Torrance in 1974 to challenge young people to think more creatively about the future. The FPS Program goals are aligned with National Curriculum Standards, National Association for Gifted Children Curriculum and Instruction Standards, and 21st Century Learning. Activities are yearlong and open to students in grades 4-12 in a variety of components: Team Global Issues Problem Solving, Scenario Writing, Community Problem Solving, and Individual Global Issues Problem Solving.

Future Problem Solvers register through affiliate programs, either through a state program in the United States or through a country’s affiliate program in Australia, Canada, Great Britain, Hong Kong, Japan, Korea, Malaysia, New Zealand, and Singapore. 250,000 students from across the globe participate each year. All affiliates operate under the [Future Problem Solving Program International [1] ] (FPSPI), a nonprofit educational corporation that hosts the International Future Problem Solving Conference each June at a rotating site, usually a university.

The course Solving Problems is also available to complement this course.

• 1 21st Century Learning Skills
• 2 The Six Step FPS Process
• 3 Components of Future Problem Solving
• 4 How Schools Can Participate

21st Century Learning Skills [ edit | edit source ]

The Future Problem Solving Program International prepares students for the future by teaching essential problem solving skills. The challenging materials of the Future Problem Solving Program are designed to help students learn how to think, not what to think. Future Problem Solving provides students effective leadership skills, the ability to generate a wide variety of ideas, and decision-making strategies. Team work is an important element for developing the techniques needed to communicate ideas with others, and participants take away critical thinking and creative analysis skills. The program encourages students to develop a vision for the future and improves research proficiency.

The Six Step FPS Process [ edit | edit source ]

The Future Problem Solving process involves a six-step model that identifies possible challenges with a given scenario and produces solution ideas. Students read a future scene provided by competition officials. The scenario relates to the research topic and takes place at least twenty years in the future. Participants determine challenges in the future scene and select an underlying problem, a section of the future scene that they will solve. Students write solutions to the underlying problem, incorporating research from their preparation, and measure the success of their top solution ideas to arrive at their best solution. The team’s action plan is a two-page detailed explanation of the team’s best solution idea.

The FPS process is scored based on clarity of the writing, feasibility of the idea presented, and statement form. The following is an overview of the written structure of the six-step process:

• Step One: Challenges - statements should explain what the challenge is, why it is a challenge, and how it relates to the future scene.
• Step Two: Underlying Problem - paragraph consists of a condition phrase or lead-in from the future scene, the stem, “how might we,” a key verb phrase which mandates what the team must solve, a purpose that describes the outcome of the key verb phrase, and the FPS parameters stated in the future scene, namely the topic, time, and place mentioned in the future scene.
• Step Three: Solutions - ideas identify who will implement a solution idea, what the solution idea is, how the idea will work, and why the solution idea solves the underlying problem.
• Step Four: Criteria - yardsticks to determine the importance of solution ideas should address only one concern, use a superlative to indicate desired direction, and be formed as a question.
• Step Five: The Grid - students apply a grid matrix to their most interesting solution ideas to determine the team’s most successful solution.
• Step Six: Action Plan - the team’s best solution is further expanded to explain in detail who will implement the solution idea and how and why the solution will be carried out.

Components of Future Problem Solving [ edit | edit source ]

Team Global Issues Problem Solving - involves teams of four students who work through the six-step process in a competitive situation. Students are guided through the Future Problem Solving process by their coach. Generally, two practice problems are submitted to state evaluators for feedback, and a state qualifying round determines the teams invited to the affiliate final. Winners at the state or affiliate level are invited to the International FPS Conference.

Scenario Writing - individuals compose an original short story of up to 1,500 words. The story is related to one of the year’s FPS topics and must take place in the future.

Community Problem Solving (CmPS) - students choose a real-life problem or threat in their community and use the FPS process to actively solve it. Students may incorporate existing service projects or create a new community initiative. Teams prepare a report based on their service project and document their efforts with a scrapbook, display, and digital presentation.

Individual Global Issues Problem Solving - students acting as individuals complete a shortened version of the FPS process over a competitive future scene.

How Schools Can Participate [ edit | edit source ]

During the school year, students learn the six-step FPS process and work together as a team to solve scenarios based on the future. Parents, teachers, or other volunteers interested in coaching an FPS team should contact their state or country’s affiliate director who will provide training and registration materials. Coaches training sessions may be available, and official publications may be purchased online at the [FPSPI Mart [2] ].

Registration differs from affiliate to affiliate, but registration forms are generally due during the first quarter of the school year. The competitive components are divided into three divisions: junior division (grades 4-6), middle division (grades 7-9), and senior division (grades 10-12). Teams consist of four students, and all written work must be submitted on the FPS booklet pages. Students are responsible for writing 16 Challenges, an Underlying Problem, 16 Solutions, five Criteria, and the Action Plan, all within the two-hour time limit.

Most affiliates receive completed booklets via mail, but others host on-site competitions where competitors meet for two hours and complete a booklet in a competitive environment. Teams invited to the International FPS Conference travel to the appointed host site and spend four days meeting students from other affiliates, showcasing their talents, and competing for top honors.

The program depends on certified evaluators to score booklets and guide teams through the process. Evaluators complete grading rubrics or scoresheets for each booklet and rank teams according to points earned. Individual interested in becoming evaluators should speak with the affiliate director of their program in order to register for this important position.

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Trust in AI is more than a moral problem

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Time's almost up! There's only one week left to request an invite to The AI Impact Tour on June 5th. Don't miss out on this incredible opportunity to explore various methods for auditing AI models. Find out how you can attend here .

The economic potential of AI is uncontested, but it is largely unrealized by organizations, with an astounding 87% of AI projects failing to succeed.

Some consider this a technology problem, others a business problem, a culture problem or an industry problem — but the latest evidence reveals that it is a trust problem.

According to recent research, nearly two-thirds of C-suite executives say that trust in AI drives revenue, competitiveness and customer success.

Trust has been a complicated word to unpack when it comes to AI. Can you trust an AI system ? If so, how? We don’t trust humans immediately, and we’re even less likely to trust AI systems immediately.

June 5th: The AI Audit in NYC

Join us next week in NYC to engage with top executive leaders, delving into strategies for auditing AI models to ensure optimal performance and accuracy across your organization. Secure your attendance for this exclusive invite-only event.

But a lack of trust in AI is holding back economic potential, and many of the recommendations for building trust in AI systems have been criticized as too abstract or far-reaching to be practical.

It’s time for a new “AI Trust Equation” focused on practical application.

The AI trust equation

The Trust Equation, a concept for building trust between people, was first proposed in The Trusted Advisor by David Maister, Charles Green and Robert Galford. The equation is Trust = Credibility + Reliability + Intimacy, divided by Self-Orientation.

It is clear at first glance why this is an ideal equation for building trust between humans, but it does not translate to building trust between humans and machines.

For building trust between humans and machines , the new AI Trust Equation is Trust = Security + Ethics + Accuracy, divided by Control.

Security forms the first step in the path to trust, and it is made up of several key tenets that are well outlined elsewhere. For the exercise of building trust between humans and machines, it comes down to the question: “Will my information be secure if I share it with this AI system?”

Ethics is more complicated than security because it is a moral question rather than a technical question. Before investing in an AI system, leaders need to consider:

• How were people treated in the making of this model, such as the Kenyan workers in the making of ChatGPT? Is that something I/we feel comfortable with supporting by building our solutions with it?
• Is the model explainable? If it produces a harmful output, can I understand why? And is there anything I can do about it (see Control)?
• Are there implicit or explicit biases in the model? This is a thoroughly documented problem, such as the Gender Shades research from Joy Buolamwini and Timnit Gebru and Google’s recent attempt to eliminate bias in their models, which resulted in creating ahistorical biases .
• What is the business model for this AI system? Are those whose information and life’s work have trained the model being compensated when the model built on their work generates revenue?
• What are the stated values of the company that created this AI system, and how well do the actions of the company and its leadership track to those values? OpenAI’s recent choice to imitate Scarlett Johansson’s voice without her consent, for example, shows a significant divide between the stated values of OpenAI and Altman’s decision to ignore Scarlett Johansson’s choice to decline the use of her voice for ChatGPT.

Accuracy can be defined as how reliably the AI system provides an accurate answer to a range of questions across the flow of work. This can be simplified to: “When I ask this AI a question based on my context, how useful is its answer?” The answer is directly intertwined with 1) the sophistication of the model and 2) the data on which it’s been trained.

Control is at the heart of the conversation about trusting AI, and it ranges from the most tactical question: “Will this AI system do what I want it to do, or will it make a mistake?” to the one of the most pressing questions of our time: “Will we ever lose control over intelligent systems?” In both cases, the ability to control the actions, decisions and output of AI systems underpins the notion of trusting and implementing them.

5 steps to using the AI trust equation

•  Determine whether the system is useful: Before investing time and resources in investigating whether an AI platform is trustworthy, organizations would benefit from determining whether a platform is useful in helping them create more value.
• Investigate if the platform is secure: What happens to your data if you load it into the platform? Does any information leave your firewall? Working closely with your security team or hiring security advisors is critical to ensuring you can rely on the security of an AI system.
• Set your ethical threshold and evaluate all systems and organizations against it: If any models you invest in must be explainable, define, to absolute precision, a common, empirical definition of explainability across your organization, with upper and lower tolerable limits, and measure proposed systems against those limits. Do the same for every ethical principle your organization determines is non-negotiable when it comes to leveraging AI.
• Define your accuracy targets and don’t deviate: It can be tempting to adopt a system that doesn’t perform well because it’s a precursor to human work. But if it’s performing below an accuracy target you’ve defined as acceptable for your organization, you run the risk of low quality work output and a greater load on your people. More often than not, low accuracy is a model problem or a data problem, both of which can be addressed with the right level of investment and focus.
• Decide what degree of control your organization needs and how it’s defined: How much control you want decision-makers and operators to have over AI systems will determine whether you want a fully autonomous system, semi-autonomous, AI-powered, or if your organizational tolerance level for sharing control with AI systems is a higher bar than any current AI systems may be able to reach.

In the era of AI, it can be easy to search for best practices or quick wins, but the truth is: no one has quite figured all of this out yet, and by the time they do, it won’t be differentiating for you and your organization anymore.

So, rather than wait for the perfect solution or follow the trends set by others, take the lead. Assemble a team of champions and sponsors within your organization, tailor the AI Trust Equation to your specific needs, and start evaluating AI systems against it. The rewards of such an endeavor are not just economic but also foundational to the future of technology and its role in society.

Some technology companies see the market forces moving in this direction and are working to develop the right commitments, control and visibility into how their AI systems work — such as with Salesforce’s Einstein Trust Layer — and others are claiming that that any level of visibility would cede competitive advantage. You and your organization will need to determine what degree of trust you want to have both in the output of AI systems as well as with the organizations that build and maintain them.

AI’s potential is immense, but it will only be realized when AI systems and the people who make them can reach and maintain trust within our organizations and society. The future of AI depends on it.

Brian Evergreen is author of “Autonomous Transformation: Creating a More Human Future in the Era of Artificial Intelligence .”

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Inclusion is Innovation

Stories behind the innovation.

Freedom in self-expression

Emerging from a journey of self-discovery and embracing their nonbinary identity, Caroline Scalley, senior business administrator at Microsoft, embodies resilience. Drawing from their Puerto Rican heritage and love for self-expression, Caroline blends humor with originality and compassion in their role, redefining norms through unique understanding.

“You are enough. You wouldn’t be here if someone here didn’t think you were incredible.”

Raised with values rooted in her Japanese heritage, Megumi Voight found solace in community during deployments while in the military. Now as a managing editor of customer storytelling at Microsoft, she empowers global voices, advocating for authenticity and reminding others of their inherent worth.

Utilizing your superpower

Armed with what he calls “super empathy,” Joao Madureira, principal customer reliability engineer at Microsoft, harnesses inclusivity as his strength. As a gay man and a Latino, he activates allyship in the workplace, ensuring all voices are heard. His actions have not only brought in new talent but also propelled inclusivity forward.

Near and dear

Keepsakes that get passed through generations of a connected family can unlock hidden characteristics of our colleagues. Cynthia Bryant presents us with some of hers that showcase her mom’s creativity and how she encourages herself to fly.

Dismantling the stereotype

Empowered to explore their gender expression fully while working from home, Iain Raleigh, a software engineer at Microsoft, felt supported when it was time to work in an office again. To dismantle the idea of what a software engineer should look like, they advocate for each one of us showing up as our full selves.

“When you know what you’re worth, when you know what you stand for, it changes how you show up.”

Nurtured by the wealth of knowledge found in public libraries, Aleenah Ansari, a product marketing manager at Microsoft, noticed the lack of stories that reflected her experience as a queer Pakistani woman. Determined to bridge this gap, she strives to make technology more accessible through inclusive and authentic storytelling.

“Your uniqueness makes you who you are.” 

Inspired by her early love for fantasy and gaming, Christina Parker champions diversity and representation in the gaming industry, explaining the accuracy of portrayals and the importance of players seeing themselves in the virtual worlds they love. 

Tosh’s journey through time

There are artifacts in our lives that represent how we connect to the world around us. Tosh Hudson shares how journaling, music, and plants, for him, represent a willingness to release, learn, and grow.

Art of cherishing memories

Sometimes our possessions remind us of our favorite places or home. Athena Chang shares the items that take her back to Taiwan, Prague, and New York.

When innovation and passion collide

Jerome Collins discusses the influence of his father’s guidance, his passion for art and music, and his innovative approach to driving positive change and representation in his professional sphere.

“At the end of the day, I think that’s what people want: to be heard.” 

Guided by a gift for listening and a commitment to motherhood, Erin Jagelski shares how she navigated post-maternity challenges and pioneered support networks for parents in the workplace by blending her passion and leadership to foster inclusive environments. 

Melissa Curry’s treasures of heritage and achievement

Our possessions showcase the things that really matter to us. Melissa Curry unveils the artifacts that encapsulate her heritage, achievements, and bonds that shape her. Tell us about the artifacts that weave the fabric of your story. 

I don’t have the option to be shy

Gonçalo Mendes has always yearned for independence. His journey with cerebral palsy led him from relying on others for care to taking care of others.

Embrace your identity, embrace life

Kimberly Marreros Chuco discusses embracing one’s unique identity and learning from challenges, emphasizing the importance of adaptability and accepting mistakes as part of growth, inspired by her experiences moving from an Andean mining town. Featured Artist: Tai Silva

Ashley Witherspoon Innovator’s Inventory and the big plans she’s made

Our personal treasures hold the stories of who we are. Ashley Witherspoon shares the tangible symbols of her values and journey. What mementos narrate your life’s chapters?

This is my sazón

Ivelisse Capellan Heyer is a user experience designer who uses patience and her family to combat her own self-doubt. Featured Artist: Sol Cotti

Nurturing inner peace

When Ethan Alexander started at Microsoft, he prioritized money over his wellbeing. Twelve years later, the senior customer success account manager and D&I storytelling host knows that the only way to truly take care of others is to first take care of yourself. Discover his story of gratitude and growth. Featured Artist: Camila Abdanur

Master of messiness

As a mom and a tech leader, Elaine Chang has learned to embrace the chaos and put her “octopus mind” to work in service of innovation, at work and at home. Featured Artist: Niege Borges

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Real progress requires real work

Innovation demands intention., innovation thrives on insight., innovation requires introspection., innovation calls for investment..

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What is decision making?

Decisions, decisions. When was the last time you struggled with a choice? Maybe it was this morning, when you decided to hit the snooze button—again. Perhaps it was at a restaurant, with a miles-long menu and the server standing over you. Or maybe it was when you left your closet in a shambles after trying on seven different outfits before a big presentation. Often, making a decision—even a seemingly simple one—can be difficult. And people will go to great lengths—and pay serious sums of money—to avoid having to make a choice. The expensive tasting menu at the restaurant, for example. Or limiting your closet choices to black turtlenecks, à la Steve Jobs.

Get to know and directly engage with senior McKinsey experts on decision making

Aaron De Smet is a senior partner in McKinsey’s New Jersey office, Eileen Kelly Rinaudo  is McKinsey’s global director of advancing women executives and is based in the New York office, Frithjof Lund is a senior partner in the Oslo office, and Leigh Weiss is a senior adviser in the Boston office.

If you’ve ever wrestled with a decision at work, you’re definitely not alone. According to McKinsey research, executives spend a significant portion of their time— nearly 40 percent , on average—making decisions. Worse, they believe most of that time is poorly used. People struggle with decisions so much so that we actually get exhausted from having to decide too much, a phenomenon called decision fatigue.

But decision fatigue isn’t the only cost of ineffective decision making. According to a McKinsey survey of more than 1,200 global business leaders, inefficient decision making costs a typical Fortune 500 company 530,000 days  of managers’ time each year, equivalent to about $250 million in annual wages. That’s a lot of turtlenecks.

How can business leaders ease the burden of decision making and put this time and money to better use? Read on to learn the ins and outs of smart decision making—and how to put it to work.

Learn more about our People & Organizational Performance Practice .

How can organizations untangle ineffective decision-making processes?

McKinsey research has shown that agile is the ultimate solution for many organizations looking to streamline their decision making . Agile organizations are more likely to put decision making in the right hands, are faster at reacting to (or anticipating) shifts in the business environment, and often attract top talent who prefer working at companies with greater empowerment and fewer layers of management.

For organizations looking to become more agile, it’s possible to quickly boost decision-making efficiency by categorizing the type of decision to be made and adjusting the approach accordingly. In the next section, we review three types of decision making and how to optimize the process for each.

What are three keys to faster, better decisions?

Business leaders today have access to more sophisticated data than ever before. But it hasn’t necessarily made decision making any easier. For one thing, organizational dynamics—such as unclear roles, overreliance on consensus, and death by committee—can get in the way of straightforward decision making. And more data often means more decisions to be taken, which can become too much for one person, team, or department. This can make it more difficult for leaders to cleanly delegate, which in turn can lead to a decline in productivity.

Leaders are growing increasingly frustrated with broken decision-making processes, slow deliberations, and uneven decision-making outcomes. Fewer than half  of the 1,200 respondents of a McKinsey survey report that decisions are timely, and 61 percent say that at least half the time they spend making decisions is ineffective.

What’s the solution? According to McKinsey research, effective solutions center around categorizing decision types and organizing different processes to support each type. Further, each decision category should be assigned its own practice—stimulating debate, for example, or empowering employees—to yield improvements in effectiveness.

Here are the three decision categories  that matter most to senior leaders, and the standout practice that makes the biggest difference for each type of decision.

• Big-bet decisions are infrequent but high risk, such as acquisitions. These decisions carry the potential to shape the future of the company, and as a result are generally made by top leaders and the board. Spurring productive debate by assigning someone to argue the case for and against a potential decision can improve big-bet decision making.
• Cross-cutting decisions, such as pricing, can be frequent and high risk. These are usually made by business unit heads, in cross-functional forums as part of a collaborative process. These types of decisions can be improved by doubling down on process refinement. The ideal process should be one that helps clarify objectives, measures, and targets.
• Delegated decisions are frequent but low risk and are handled by an individual or working team with some input from others. Delegated decision making can be improved by ensuring that the responsibility for the decision is firmly in the hands of those closest to the work. This approach also enhances engagement and accountability.

In addition, business leaders can take the following four actions to help sustain rapid decision making :

• Focus on the game-changing decisions, ones that will help an organization create value and serve its purpose.
• Convene only necessary meetings, and eliminate lengthy reports. Turn unnecessary meetings into emails, and watch productivity bloom. For necessary meetings, provide short, well-prepared prereads to aid in decision making.
• Clarify the roles of decision makers and other voices. Who has a vote, and who has a voice?
• Push decision-making authority to the front line—and tolerate mistakes.

Introducing McKinsey Explainers : Direct answers to complex questions

How can business leaders effectively delegate decision making.

Business is more complex and dynamic than ever, meaning business leaders are faced with needing to make more decisions in less time. Decision making takes up an inordinate amount of management’s time—up to 70 percent for some executives—which leads to inefficiencies and opportunity costs.

As discussed above, organizations should treat different types of decisions differently . Decisions should be classified  according to their frequency, risk, and importance. Delegated decisions are the most mysterious for many organizations: they are the most frequent, and yet the least understood. Only about a quarter of survey respondents  report that their organizations make high-quality and speedy delegated decisions. And yet delegated decisions, because they happen so often, can have a big impact on organizational culture.

The key to better delegated decisions is to empower employees by giving them the authority and confidence to act. That means not simply telling employees which decisions they can or can’t make; it means giving employees the tools they need to make high-quality decisions and the right level of guidance as they do so.

Here’s how to support delegation and employee empowerment:

• Ensure that your organization has a well-defined, universally understood strategy. When the strategic intent of an organization is clear, empowerment is much easier because it allows teams to pull in the same direction.
• Clearly define roles and responsibilities. At the foundation of all empowerment efforts is a clear understanding of who is responsible for what, including who has input and who doesn’t.
• Invest in capability building (and coaching) up front. To help managers spend meaningful coaching time, organizations should also invest in managers’ leadership skills.
• Build an empowerment-oriented culture. Leaders should role model mindsets that promote empowerment, and managers should build the coaching skills they want to see. Managers and employees, in particular, will need to get comfortable with failure as a necessary step to success.
• Decide when to get involved. Managers should spend effort up front to decide what is worth their focused attention. They should know when it’s appropriate to provide close guidance and when not to.

How can you guard against bias in decision making?

Cognitive bias is real. We all fall prey, no matter how we try to guard ourselves against it. And cognitive and organizational bias undermines good decision making, whether you’re choosing what to have for lunch or whether to put in a bid to acquire another company.

Here are some of the most common cognitive biases and strategies for how to avoid them:

• Confirmation bias. Often, when we already believe something, our minds seek out information to support that belief—whether or not it is actually true. Confirmation bias  involves overweighting evidence that supports our belief, underweighting evidence against our belief, or even failing to search impartially for evidence in the first place. Confirmation bias is one of the most common traps organizational decision makers fall into. One famous—and painful—example of confirmation bias is when Blockbuster passed up the opportunity  to buy a fledgling Netflix for $50 million in 2000. (Actually, that’s putting it politely. Netflix executives remember being “laughed out” of Blockbuster’s offices.) Fresh off the dot-com bubble burst of 2000, Blockbuster executives likely concluded that Netflix had approached them out of desperation—not that Netflix actually had a baby unicorn on its hands.
• Herd mentality. First observed by Charles Mackay in his 1841 study of crowd psychology, herd mentality happens when information that’s available to the group is determined to be more useful than privately held knowledge. Individuals buy into this bias because there’s safety in the herd. But ignoring competing viewpoints might ultimately be costly. To counter this, try a teardown exercise , wherein two teams use scenarios, advanced analytics, and role-playing to identify how a herd might react to a decision, and to ensure they can refute public perceptions.
• Sunk-cost fallacy. Executives frequently hold onto underperforming business units or projects because of emotional or legacy attachment . Equally, business leaders hate shutting projects down . This, researchers say, is due to the ingrained belief that if everyone works hard enough, anything can be turned into gold. McKinsey research indicates two techniques for understanding when to hold on and when to let go. First, change the burden of proof from why an asset should be cut to why it should be retained. Next, categorize business investments according to whether they should be grown, maintained, or disposed of—and follow clearly differentiated investment rules  for each group.
• Ignoring unpleasant information. Researchers call this the “ostrich effect”—when people figuratively bury their heads in the sand , ignoring information that will make their lives more difficult. One study, for example, found that investors were more likely to check the value of their portfolios when the markets overall were rising, and less likely to do so when the markets were flat or falling. One way to help get around this is to engage in a readout process, where individuals or teams summarize discussions as they happen. This increases the likelihood that everyone leaves a meeting with the same understanding of what was said.
• Halo effect. Important personal and professional choices are frequently affected by people’s tendency to make specific judgments based on general impressions . Humans are tempted to use simple mental frames to understand complicated ideas, which means we frequently draw conclusions faster than we should. The halo effect is particularly common in hiring decisions. To avoid this bias, structured interviews can help mitigate the essentializing tendency. When candidates are measured against indicators, intuition is less likely to play a role.

For more common biases and how to beat them, check out McKinsey’s Bias Busters Collection .

Learn more about Strategy & Corporate Finance consulting  at McKinsey—and check out job opportunities related to decision making if you’re interested in working at McKinsey.

Articles referenced include:

• “ Bias busters: When the crowd isn’t necessarily wise ,” McKinsey Quarterly , May 23, 2022, Eileen Kelly Rinaudo , Tim Koller , and Derek Schatz
• “ Boards and decision making ,” April 8, 2021, Aaron De Smet , Frithjof Lund , Suzanne Nimocks, and Leigh Weiss
• “ To unlock better decision making, plan better meetings ,” November 9, 2020, Aaron De Smet , Simon London, and Leigh Weiss
• “ Reimagine decision making to improve speed and quality ,” September 14, 2020, Julie Hughes , J. R. Maxwell , and Leigh Weiss
• “ For smarter decisions, empower your employees ,” September 9, 2020, Aaron De Smet , Caitlin Hewes, and Leigh Weiss
• “ Bias busters: Lifting your head from the sand ,” McKinsey Quarterly , August 18, 2020, Eileen Kelly Rinaudo
• “ Decision making in uncertain times ,” March 24, 2020, Andrea Alexander, Aaron De Smet , and Leigh Weiss
• “ Bias busters: Avoiding snap judgments ,” McKinsey Quarterly , November 6, 2019, Tim Koller , Dan Lovallo, and Phil Rosenzweig
• “ Three keys to faster, better decisions ,” McKinsey Quarterly , May 1, 2019, Aaron De Smet , Gregor Jost , and Leigh Weiss
• “ Decision making in the age of urgency ,” April 30, 2019, Iskandar Aminov, Aaron De Smet , Gregor Jost , and David Mendelsohn
• “ Bias busters: Pruning projects proactively ,” McKinsey Quarterly , February 6, 2019, Tim Koller , Dan Lovallo, and Zane Williams
• “ Decision making in your organization: Cutting through the clutter ,” McKinsey Quarterly , January 16, 2018, Aaron De Smet , Simon London, and Leigh Weiss
• “ Untangling your organization’s decision making ,” McKinsey Quarterly , June 21, 2017, Aaron De Smet , Gerald Lackey, and Leigh Weiss
• “ Are you ready to decide? ,” McKinsey Quarterly , April 1, 2015, Philip Meissner, Olivier Sibony, and Torsten Wulf.

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