consulting approach to problem solving

How Top-Tier Consultants at McKinsey, Bain, and BCG Really Solve Problems 

“Problem-solving” is one of the most overused and least understood phrases in the business world. When many people talk about solving problems, they are rarely talking about a problem-solving process. Instead, they are talking about making the problem disappear, regardless of their own ability to actually “solve” the underlying problem. 

Consider this example: 

Imagine you want to get a single apple from a tree. You could cut down the entire tree and then pick the apple. Or you could use a ladder to reach the branches and then pick the apple. Either way, you’ll get an apple. But the first approach will cost you much more time and energy (not to mention, the tree itself). 

It sounds crazy, but many people end up using an approach analogous to cutting down the entire tree when faced with a problem. They focus on actions like analyzing data or building Excel models instead of thinking about how they are going to solve it. 

What I got wrong about problem-solving 

What are the most important skills you need to be successful?

Communication? Working well with others? Organization?

If you had asked me this question when I was in college, or even after I had worked for a few years, I probably would have given one of those answers. Effective communication tops a lot of lists (it is even the answer I got when I asked ChatGPT this very question). But, after nearly a decade of working in consulting and teaching consulting skills, I’ve come to believe that problem-solving is the most important skill.

But what is problem-solving? It is a term that means different things in different contexts. From a psychological perspective, it’s a cognitive process in which a person uses mental strategies to achieve a goal or find an answer to a challenging situation. From an education perspective, it’s a method where students are encouraged to use critical thinking and reasoning to solve problems given in a textbook or a test.). In rock climbing, it means creatively finding your way forward on your route.

More simply, problem-solving is an intentional approach to arriving at some kind of answer. It is asking,  “What should we do next?” 

When I joined McKinsey, I was struck by how often the term “problem-solving” was used. Instead of being called “meetings,” teams met for what were called “Problem-solving sessions.” These were focused sessions designed to work through specific questions or challenges as part of a broader client engagement. 

But these weren’t just another fancy name for meetings. They were a constant reinforcement of the underlying culture of the firm: one obsessed with the process of ongoing problem-solving. Over time, I internalized the key assumption behind it all: how we approach our work is the most important thing in all the work we do. 

Why is problem-solving at the core of consulting?

Before I started at McKinsey, someone several decades my senior asked me “why would my company hire you, with only a few years of work experience, to solve a problem for us?” He was skeptical of how a team of twenty-something analysts and associates could address problems that his senior leadership team was struggling with.

It’s a fair question. In consulting, you are solving problems that don’t have a clear solution, yet. These are often the most challenging issues the company faces, which is precisely why the consulting team is hired. So how could I actually help him?

But this is exactly what a consulting firm is designed to do. At the highest level, consulting firms are designed to solve customer problems. They build teams and cultures that are obsessed with defining, diagnosing, prioritizing, and taking action on new and novel problems.

And this is exactly what most companies are not designed to do. Most companies have a product or service that they deliver to clients and they are in the business of trying to do that consistently, on time, and on budget. 

When these companies face NEW problems, they don’t actually have many people in the company who are trained to address these problems, not to mention the cultural support to work in a different way. While many companies have started to build internal consulting teams (often of former consultants), there is still clear demand for top consulting firms across industry.

Consulting firms fit into the broader idea of “specialization and trade,” something Jane Jacobs pointed out that emerges naturally in ecosystems, like neighborhoods that are allowed to evolve and emerge naturally. 

Companies rely on consulting firms and vice versa.

The Real Edge Is PROCESS

The real edge that consulting firms have is an obsession with process. And on two levels:

• Problem-solving process: how you solve problems
• Meta-process: how you talk about how you do #1

The best consulting firms are obsessed with both of these things.

In many companies, the time spent on developing problem-solving skills and fundamentals is surprisingly small. But the time spent thinking and talking about how you approach problems is even smaller (or in many cases, when I survey clients, zero minutes a year). 

Then companies come to me and ask , “why is our team struggling to do good work?”

When I worked at McKinsey I was shocked at how much time we spent learning various skills and then talking about them. Once or twice a year, we would attend intensive training weeks, where we would strongly be discouraged from doing client work. The message was clear: learning matters, take this seriously. Outside of training, each week was filled with hours of meta-process discussions:

• How should we structure our team? 
• What hours are we going to work?
• How much time should we spend diagnosing the problem?
• What is the right way to think about prioritizing the various goals of the project? 
• How much time should we spend interviewing the client to understand the situation? 
• Who is responsible for collecting client data? 
• Who is responsible for pushing the project forward with senior leaders?
• How are we planning on formatting various documents?

At GE, where I worked previously, no one talked about anything beyond how to format spreadsheets to avoid triggering the managers and higher-ups.  But at GE, we were trying to keep the metaphorical trains running on time. While at McKinsey, we were trying to solve something we had never solved before!

Consulting Firms See Problem-Solving As A Linear Process

Given that problem-solving is the core of the work firms like McKinsey, Bain, and BCG do (and I would argue, their true competitive edge), it makes sense that there is a lot of buzz online trying to understand and decode their problem-solving process. 

Several prominent firms have spoken on podcasts or published memos outlining their views on and approach to problem-solving. McKinsey’s 7-step approach to problem-solving is by far the most dominant (and many articles or videos you see on “bulletproof” or “expert” problem-solving are derived from their approach). 

In 2007 McKinsey published a memo detailing their approach. Here is a screenshot from that memo:

I can tell you from my experience working at McKinsey: that this visual is used all the time internally: in new hire onboarding, in manager training, when creating project timelines, in project kick-offs with clients, or in client workshops. The steps are clear:

• Define the problem
• Structure the problem
• Prioritize the issues
• Plan analyses and work
• Conduct analyses and work
• Synthesize findings
• Recommendations

Though this original representation of 7 steps is somewhat sacrosanct, I’ve always had a strong belief that a linear process doesn’t capture what happens at these firms. In addition to this, McKinsey now works on a much wider range of projects, such as year-long implementation projects, large-scale operations improvement projects, and even data analytics and AI projects. These different types of projects mean there is some variation in the approach. 

But at the core of it, the fundamental idea of moving through a process remains the same. Follow the steps and you will end up somewhere good . 

Bain also has their framework for problem-solving: the RAPID decision-making framework. It doesn’t cover the entire problem-solving process, just the latter half of it. It’s a proprietary decision-making tool created to “clarify decision accountabilities with multiple stakeholders.” Among other benefits, it is designed to ensure all voices are heard in the process. Here’s an image from an article Bain published in 2023:

Other leading consulting firms have written about their problem-solving approach, though not as explicitly and prolifically as McKinsey. LEK’s “What We Do” page includes a quote from their founder, Iain Evans: 

“The most important principles are around the nature of problem-solving. You will very rarely hear anyone at L.E.K. say, ‘The L.E.K. view of this is X.’ You will very often hear people say, ‘We will have to work out from first principles what the answer is for you in your current circumstance.’ We think each problem is unique in itself, and it needs to be worked on.”

This is all helpful context, but I think these frameworks and quotes only provide just that – a framework, or scaffolding, to understand problem-solving. To be effective at it, I think you need to go a layer deeper and understand what makes consultants so skilled at problem-solving.

My Hot Take: Problem-Solving Is Not A Linear Process

I’ve studied these varying approaches to problem-solving. And I’ve seen it in action firsthand at BCG, McKinsey, and with my freelance clients. While I respect McKinsey and the other firms, as well as the white papers they publish, I have found that a linear process comes up short in helping people understand what it feels like during a problem-solving process.

In almost any intensive process-based knowledge work environment, I’ve seen a process like this, moving between two modes of thinking and processing information: top-down and bottom-up. 

If you are writing a book, you can think about the top-down mode as the outline you would create and revisit throughout the process.  The bottom-up mode would be the writing and would include the surprising insights that emerge as you move through the document. 

In consulting:

• Top-down: includes defining the process, structuring the key insights and core ideas, outlining the story, developing a ghost deck, and pulling together the overall messages at the end of a project
• Bottom-up: Includes digging into the data, doing interviews, organizing and synthesizing information, designing individual slides, documents or memos 

The problem-solving process happens over time and shifts into these different modes throughout the process. If it’s a longer process these shifts may take days or weeks. If it’s a shorter process, they may only take hours. In a large team, you may have specific members of the team solely focused on the bottom-up mode. For example, someone responsible for an advanced data model. 

You may also have someone else, like a project manager or junior partner, responsible for “owning” the overall story. If three different mini-teams are working on three different workstreams, or hypotheses, the project manager would need to make sure the work doesn’t overlap and that the elements of each workstream can fit together.

The problem-solving sessions mentioned earlier tend to focus on the top-down mode. The structuring, the storylining, the overall messages that they want to convey. In those meetings, often the individual consultants prepare to come into the meeting to “pitch” their various parts, pretending their teammates are the client.

Check out this video for a deeper dive.

This process is intense, but over time it gets easier

The back-and-forth process can feel like whiplash and endless. For a three-month project, you might spend the first month a bit confused. Ask yourself, “Where is this going?” Or “Are we missing something?”

But this is where “trusting the process” is so vital. At first, this is hard but with more experience, you start to get a feel for a project like this. Oh, this is messy right now but it always feels like this, I just need to keep moving forward.

Some types of situations that seem to recur include:

• Wanting to abandon the problem statement immediately: You start digging into the data and find something that is counter to what you had thought when you designed the problem. The key here is not to overreact. If you meet with the team and decide that your initial approach is not going to work, you should focus on defining the problem again 
• Feeling like the hypotheses are overlapping and not MECE: This is common because at first you are simply asking a lot of questions. Typically, you need to go into the bottom-up mode once or twice after tweaking your hypotheses to validate and verify your questions and direction. After that, you can fine-tune the hypotheses a bit more, hopefully making sure they are MECE. Remember though, MECE is never perfect, it is just a directional aspiration.
• Having “too much”: You are near the end of the project and you have hundreds of pieces of information. It feels overwhelming. This is where structuring and storylining is vital. Working with your team, you need to spend probably 4-5 iterations going through everything and reorganizing it. This may feel time-consuming but getting everything right so that it “flows” is vital.

You can check out this video of how the consulting process maps to the pyramid principle.

Okay, but what makes for effective problem-solving? 

In a 2019 episode of the McKinsey Podcast , Hugo Sarrazin, a senior partner at McKinsey, said,

“The most powerful thing is to step back and ask the basic questions: What are we trying to solve? What are the constraints that exist? What are the dependencies?…It’s not like “Can we grow in Japan?” That’s interesting, but [instead] it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?”  When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.”

This is a great perspective.

When I first joined McKinsey I thought the labeling of most meetings as “problem-solving sessions” was just a facade. 

“Why not call them meetings, or discussions?” I asked. 

A colleague shared an interesting hypothesis based on a study she had seen about meeting participant engagement. If meetings were called “problem-solving sessions” or something similar that elicited action, participants would be more engaged and effective. 

Internal meetings at McKinsey were far more engaging than those in my prior job (or those I observed with my clients). 

Part of that could be the name, sure. But it speaks to the broader mindset that most consultants have: that we are here to solve X problem. 

A micro example: if I were leading a problem-solving session, I would send out the agenda beforehand (as is common across companies). Each topic usually included a specific question we needed to answer. If we got off course, it was always helpful to step back and say, “What is the question we are trying to answer today?” 

On a more macro level, the questions laddered to the broader problem statement (what we were trying to solve for/with our client). 

This mindset, this constant referencing back to the most important question or problem statement, is what sets McKinsey and other leading consulting firms apart. 

As you know, the problem-solving approaches of top consulting firms go far beyond simple frameworks or step-by-step guides. The real power lies in the mindset and meta-process that surrounds these methods and in the constant focus on  “what are we trying to answer.”  

Whether you’re leading a team, starting a business, or simply trying to improve your decision-making, this meta-process can offer you something. Remember, effective problem-solving isn’t about having all the answers – it’s about asking the right questions.

Do you have a toolkit for business problem solving? I created Think Like a Strategy Consultant as an online course to make the tools of strategy consultants accessible to driven professionals, executives, and consultants. This course teaches you how to synthesize information into compelling insights, structure your information in ways that help you solve problems, and develop presentations that resonate at the C-Level. Click here to learn more or if you are interested in getting started now, enroll in the self-paced version ($497) or hands-on coaching version ($997). Both versions include lifetime access and all future updates.

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Six problem-solving mindsets for very uncertain times

Great problem solvers are made, not born. That’s what we’ve found after decades of problem solving with leaders across business, nonprofit, and policy sectors. These leaders learn to adopt a particularly open and curious mindset, and adhere to a systematic process for cracking even the most inscrutable problems. They’re terrific problem solvers under any conditions. And when conditions of uncertainty are at their peak, they’re at their brilliant best.

Six mutually reinforcing approaches underly their success: (1) being ever-curious about every element of a problem; (2) being imperfectionists , with a high tolerance for ambiguity; (3) having a “dragonfly eye” view of the world, to see through multiple lenses; (4) pursuing occurrent behavior and experimenting relentlessly; (5) tapping into the collective intelligence , acknowledging that the smartest people are not in the room; and (6) practicing “show and tell” because storytelling begets action (exhibit).

Here’s how they do it.

1. Be ever-curious

As any parent knows, four-year-olds are unceasing askers. Think of the never-ending “whys” that make little children so delightful—and relentless. For the very young, everything is new and wildly uncertain. But they’re on a mission of discovery, and they’re determined to figure things out. And they’re good at it! That high-energy inquisitiveness is why we have high shelves and childproof bottles.

When you face radical uncertainty, remember your four-year-old or channel the four-year-old within you. Relentlessly ask, “Why is this so?” Unfortunately, somewhere between preschool and the boardroom, we tend to stop asking. Our brains make sense of massive numbers of data points by imposing patterns that have worked for us and other humans in the past. That’s why a simple technique, worth employing at the beginning of problem solving, is simply to pause and ask why conditions or assumptions are so until you arrive at the root of the problem. 1 This approach was originally developed by Sakichi Toyoda, the founder of Toyota.

Natural human biases in decision making, including confirmation, availability, and anchoring biases, often cause us to shut down the range of solutions too early. 2 Daniel Kahneman, Thinking, Fast and Slow , New York, NY: Farrar, Straus and Giroux, 2011. Better—and more creative—solutions come from being curious about the broader range of potential answers.

One simple suggestion from author and economist Caroline Webb to generate more curiosity in team problem solving is to put a question mark behind your initial hypotheses or first-cut answers. This small artifice is surprisingly powerful: it tends to encourage multiple solution paths and puts the focus, correctly, on assembling evidence. We also like thesis/antithesis, or red team/blue team, sessions, in which you divide a group into opposing teams that argue against the early answers—typically, more traditional conclusions that are more likely to come from a conventional pattern. Why is this solution better? Why not that one? We’ve found that better results come from embracing uncertainty. Curiosity is the engine of creativity.

We have to be comfortable with estimating probabilities to make good decisions, even when these guesses are imperfect. Unfortunately, we have truckloads of evidence showing that human beings aren’t good intuitive statisticians.

2. Tolerate ambiguity—and stay humble!

When we think of problem solvers, many of us tend to picture a poised and brilliant engineer. We may imagine a mastermind who knows what she’s doing and approaches a problem with purpose. The reality, though, is that most good problem solving has a lot of trial and error; it’s more like the apparent randomness of rugby than the precision of linear programming. We form hypotheses, porpoise into the data, and then surface and refine (or throw out) our initial guess at the answer. This above all requires an embrace of imperfection and a tolerance for ambiguity—and a gambler’s sense of probabilities.

The real world is highly uncertain. Reality unfolds as the complex product of stochastic events and human reactions. The impact of COVID-19 is but one example: we address the health and economic effects of the disease, and their complex interactions, with almost no prior knowledge. We have to be comfortable with estimating probabilities to make good decisions, even when these guesses are imperfect. Unfortunately, we have truckloads of evidence showing that human beings aren’t good intuitive statisticians. Guesses based on gut instinct can be wildly wrong. That’s why one of the keys to operating in uncertain environments is epistemic humility, which Erik Angner defines as “the realization that our knowledge is always provisional and incomplete—and that it might require revision in light of new evidence.” 3 Erik Angner, “Epistemic humility—knowing your limits in a pandemic,” Behavioral Scientist , April 13, 2020, behavioralscientist.org.

Recent research shows that we are better at solving problems when we think in terms of odds rather than certainties. 4 Annie Duke, Thinking in Terms of Bets: Making Smarter Decisions When You Don’t Have All the Facts , New York, NY: Portfolio/Penguin, 2018. For example, when the Australian research body Commonwealth Scientific and Industrial Research Organisation (CSIRO), which owned a core patent on the wireless internet protocol, sought royalties from major companies, it was initially rebuffed. The CSIRO bet that it could go to court to protect its intellectual property because it estimated that it needed only 10 percent odds of success for this to be a good wager, given the legal costs and likely payoff. It improved its odds by picking the weakest of the IP violators and selecting a legal jurisdiction that favored plaintiffs. This probabilistic thinking paid off and eventually led to settlements to CSIRO exceeding $500 million. 5 CSIRO briefing to US Government, December 5, 2006. A tolerance for ambiguity and a willingness to play the odds helped the organization feel its way to a good solution path.

To embrace imperfectionism with epistemic humility, start by challenging solutions that imply certainty. You can do that in the nicest way by asking questions such as “What would we have to believe for this to be true?” This brings to the surface implicit assumptions about probabilities and makes it easier to assess alternatives. When uncertainty is high, see if you can make small moves or acquire information at a reasonable cost to edge out into a solution set. Perfect knowledge is in short supply, particularly for complex business and societal problems. Embracing imperfection can lead to more effective problem solving. It’s practically a must in situations of high uncertainty, such as the beginning of a problem-solving process or during an emergency.

Good problem solving typically involves designing experiments to reduce key uncertainties. Each move provides additional information and builds capabilities.

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3. take a dragonfly-eye view.

Dragonfly-eye perception is common to great problem solvers. Dragonflies have large, compound eyes, with thousands of lenses and photoreceptors sensitive to different wavelengths of light. Although we don’t know exactly how their insect brains process all this visual information, by analogy they see multiple perspectives not available to humans. The idea of a dragonfly eye taking in 360 degrees of perception 6 Philip Tetlock and Dan Gardner, Superforecasting: The Art and Science of Prediction , New York, NY: Crown, 2015. is an attribute of “superforecasters”—people, often without domain expertise, who are the best at forecasting events.

Think of this as widening the aperture on a problem or viewing it through multiple lenses. The object is to see beyond the familiar tropes into which our pattern-recognizing brains want to assemble perceptions. By widening the aperture, we can identify threats or opportunities beyond the periphery of vision.

Consider the outbreak of HIV in India in the early 1990s—a major public-health threat. Ashok Alexander, director of the Bill & Melinda Gates Foundation’s India Aids Initiative, provided a brilliant example of not just vision but also dragonfly vision. Facing a complex social map with a rapidly increasing infection rate, he widened the problem’s definition, from a traditional epidemiological HIV transmission model at known “hot spots,” to one in which sex workers facing violence were made the centerpiece.

This approach led to the “Avahan solution,” which addressed a broader set of leverage points by including the sociocultural context of sex work. The solution was rolled out to more than 600 communities and eventually credited with preventing 600,000 infections. The narrow medical perspective was sensible and expected, but it didn’t tap into the related issue of violence against sex workers, which yielded a richer solution set. Often, a secret unlocks itself only when one looks at a problem from multiple perspectives, including some that initially seem orthogonal.

The secret to developing a dragonfly-eye view is to “anchor outside” rather than inside when faced with problems of uncertainty and opportunity. Take the broader ecosystem as a starting point. That will encourage you to talk with customers, suppliers, or, better yet, players in a different but related industry or space. Going through the customer journey with design-thinking in mind is another powerful way to get a 360-degree view of a problem. But take note: when decision makers face highly constrained time frames or resources, they may have to narrow the aperture and deliver a tight, conventional answer.

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4. pursue occurrent behavior.

Occurrent behavior is what actually happens in a time and place, not what was potential or predicted behavior. Complex problems don’t give up their secrets easily. But that shouldn’t deter problem solvers from exploring whether evidence on the facets of a solution can be observed, or running experiments to test hypotheses. You can think of this approach as creating data rather than just looking for what has been collected already. It’s critical for new market entry—or new market creation. It also comes in handy should you find that crunching old data is leading to stale solutions.

Most of the problem-solving teams we are involved with have twin dilemmas of uncertainty and complexity, at times combined as truly “wicked problems.” 7 A term coined in a now famous 1973 article: Horst W. J. Rittel and Melvin Webber, “Dilemmas in a general theory of planning,” Policy Sciences , 1973, Number 4, pp. 155–69. For companies ambitious to win in the great unknown in an emerging segment—such as electric cars or autonomous vehicles, where the market isn’t fully established—good problem solving typically involves designing experiments to reduce key uncertainties, not just relying on existing data. Each move (such as buying IP or acquiring a component supplier) and each experiment (including on-road closed tests) not only provides additional information to make decisions but also builds capabilities and assets that support further steps. Over time, their experiments, including alliances and acquisitions, come to resemble staircases that lead to either the goal or to abandonment of the goal. Problem-solving organizations can “bootstrap” themselves into highly uncertain new spaces, building information, foundational assets, and confidence as they take steps forward.

Risk-embracing problem solvers find a solution path by constantly experimenting. Statisticians use the abbreviation EVPI—the expected value of perfect information—to show the value of gaining additional information that typically comes from samples and experiments, such as responses to price changes in particular markets. A/B testing is a powerful tool for experimenting with prices, promotions, and other features and is particularly useful for digital marketplaces and consumer goods. Online marketplaces make A/B testing easy. Yet most conventional markets also offer opportunities to mimic the market’s segmentation and use it to test different approaches.

The mindset required to be a restless experimenter is consistent with the notion in start-ups of “failing fast.” It means that you get product and customer affirmation or rejection quickly through beta tests and trial offerings. Don’t take a lack of external data as an impediment—it may actually be a gift, since purchasable data is almost always from a conventional way of meeting needs, and is available to your competitors too. Your own experiments allow you to generate your own data; this gives you insights that others don’t have. If it is difficult (or unethical) to experiment, look for the “natural experiments” provided by different policies in similar locations. An example would be to compare outcomes in twin cities, such as Minneapolis–St. Paul.

It’s a mistake to think that your team has the smartest people in the room. They aren’t there. They’re invariably somewhere else. Nor do they need to be there if you can access their intelligence via other means.

5. Tap into collective intelligence and the wisdom of the crowd

Chris Bradley, a coauthor of Strategy Beyond the Hockey Stick , 8 Chris Bradley, Marin Hirt, and Sven Smit, Strategy Beyond the Hockey Stick: People, Probabilities, and Big Moves to Beat the Odds , Hoboken, NJ: Wiley, 2018. observed that “it’s a mistake to think that on your team you have the smartest people in the room. They aren’t there. They’re invariably somewhere else.” 9 For more from Chris Bradley, in a conversation with Rob McLean, see “ Want better strategies? Become a bulletproof problem solver ,” August 2019. Nor do they need to be there if you can access their intelligence via other means. In an ever-changing world where conditions can evolve unpredictably, crowdsourcing invites the smartest people in the world to work with you. For example, in seeking a machine-learning algorithm to identify fish catch species and quantities on fishing boats, the Nature Conservancy (TNC) turned to Kaggle and offered a $150,000 prize for the best algorithm. This offer attracted 2,293 teams from all over the world. TNC now uses the winning algorithm to identify fish types and sizes caught on fishing boats in Asia to protect endangered Pacific tuna and other species.

Crowdsourced problem solving is familiar in another guise: benchmarking. When Sir Rod Carnegie was CEO of Conzinc Riotinto Australia (CRA), he was concerned about the costs of unscheduled downtime with heavy trucks, particularly those requiring tire changes. He asked his management team who was best in the world at changing tires; their answer was Formula One, the auto racing competition. A team traveled to the United Kingdom to learn best practice for tire changes in racetrack pits and then implemented what it learned thousands of miles away, in the Pilbara region of Western Australia. The smartest team for this problem wasn’t in the mining industry at all.

Of course, while crowdsourcing can be useful when conventional thinking yields solutions that are too expensive or incomplete for the challenge at hand, it has its limitations. Good crowdsourcing takes time to set up, can be expensive, and may signal to your competitors what you are up to. Beware of hidden costs, such as inadvertently divulging information and having to sieve through huge volumes of irrelevant, inferior suggestions to find the rare gem of a solution.

Accept that it’s OK to draw on diverse experiences and expertise other than your own. Start with brainstorming sessions that engage people from outside your team. Try broader crowdsourcing competitions to generate ideas. Or bring in deep-learning talent to see what insights exist in your data that conventional approaches haven’t brought to light. The broader the circles of information you access, the more likely it is that your solutions will be novel and creative.

Rookie problem solvers show you their analytic process and math to convince you they are clever. Seasoned problem solvers show you differently.

6. Show and tell to drive action

We started our list of mindsets with a reference to children, and we return to children now, with “show and tell.” As you no doubt remember—back when you were more curious!—show and tell is an elementary-school activity. It’s not usually associated with problem solving, but it probably piqued your interest. In fact, this approach is critical to problem solving. Show and tell is how you connect your audience with the problem and then use combinations of logic and persuasion to get action.

The show-and-tell mindset aims to bring decision makers into a problem-solving domain you have created. A team from the Nature Conservancy, for instance, was presenting a proposal asking a philanthropic foundation to support the restoration of oyster reefs. Before the presentation, the team brought 17 plastic buckets of water into the boardroom and placed them around the perimeter. When the foundation’s staff members entered the room, they immediately wanted to know what the buckets were for. The team explained that oyster-reef restoration massively improves water quality because each oyster filters 17 buckets of water per day. Fish stocks improve, and oysters can also be harvested to help make the economics work. The decision makers were brought into the problem-solving domain through show and tell. They approved the funding requested and loved the physical dimension of the problem they were part of solving.

Rookie problem solvers show you their analytic process and mathematics to convince you that they are clever. That’s sometimes called APK, the anxious parade of knowledge. But seasoned problem solvers show you differently. The most elegant problem solving is that which makes the solution obvious. The late economist Herb Simon put it this way: “Solving a problem simply means representing it so as to make the solution transparent.” 10 Herbert Simon, The Sciences of the Artificial , Cambridge, MA: MIT Press, 1969.

To get better at show and tell, start by being clear about the action that should flow from your problem solving and findings: the governing idea for change. Then find a way to present your logic visually so that the path to answers can be debated and embraced. Present the argument emotionally as well as logically, and show why the preferred action offers an attractive balance between risks and rewards. But don’t stop there. Spell out the risks of inaction, which often have a higher cost than imperfect actions have.

The mindsets of great problem solvers are just as important as the methods they employ. A mindset that encourages curiosity, embraces imperfection, rewards a dragonfly-eye view of the problem, creates new data from experiments and collective intelligence, and drives action through compelling show-and-tell storytelling creates radical new possibilities under high levels of unpredictability. Of course, these approaches can be helpful in a broad range of circumstances, but in times of massive uncertainty, they are essential.

Charles Conn is an alumnus of McKinsey’s Sydney office and is a board member of Patagonia and former CEO of the Rhodes Trust. Robert McLean is an alumnus of the Sydney office and is the advisory-board chair of the Nature Conservancy Australia. They are the authors of Bulletproof Problem Solving: The One Skill That Changes Everything (Wiley, 2018).

This article was edited by David Schwartz, an executive editor in the Tel Aviv office.

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