The Design of Insight: How to Solve Any Business Problem

The Design of Insight: How to Solve Any Business Problem

by Mihnea Moldoveanu, Olivier Leclerc

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Familiar modes of problem solving may be efficient, but they often prevent us from discovering innovative solutions to more complex problems. To create meaningful change, we must train ourselves to discover previously unseen variables in day-to-day challenges. The Design of Insight is intended to be a personal problem-solving platform for decision makers and advisors who seek answers to critical business questions. It introduces an approach that uses multiple "problem-solving languages" to systematically expand our understanding of problem framing and high quality problem solving. Useful as a critical thinking approach or a think-out-loud document for strategic teams, this brief is a resource for enriching and implementing thoughtful management practices.

Product Details

ISBN-13: 9780804795791
Publisher: Stanford University Press
Publication date: 05/06/2015
Sold by: Barnes & Noble
Format: NOOK Book
Pages: 160
File size: 6 MB

About the Author

Mihnea Moldoveanu is Professor of Business Economics, Desautels Professor of Integrative Thinking, Director of the Desautels Centre of Integrative Thinking, and Founder and Director of the Mind Brain Behavior Hive at the University of Toronto's Rotman School of Management, where he is also Associate Dean of the MBA Program. He is Founder and ex-CEO of Redline Communication, Inc. Olivier Leclerc is a Director (Senior Partner) in McKinsey's West Coast office.

Read an Excerpt

The Design of Insight

How to Solve Any Business Problem

By Mihnea C. Moldoveanu, Olivier Leclerc


Copyright © 2015 McKinsey & Company, Inc., and Mihnea C. Moldoveanu
All rights reserved.
ISBN: 978-0-8047-9579-1



Innovative solutions to complex business problems are like works of art: they elicit emotions similar to the ones we feel in front of great paintings or photographs. They transform our views of the world by showing it to us from different perspectives. When Yann Arthus-Bertrand shows us a photograph of the earth from the sky, it not only expands our horizons in the literal sense but reveals unsuspected patterns in the landscapes we thought we knew well.

A new theory, like the special theory of relativity, does even more for us. Einstein's insight that light moves at the same speed in any inertial frame implies that space, time, and the mass of a moving object contract or expand as a function of the speed with which the object moves relative to a nonaccelerating frame. It is a lens that lets us make a new and valid prediction: the rate at which particles' energy states will decay as a function of the speed with which they move, something the previous picture of space-time did not predict. The new theory does not give us new information. It adds new insight to existing information—to the facts we knew when looking through the old lens—and sets our gaze on critical new information we can seek out.

A new way of looking at a business can also make unseen patterns visible and reveal innovative levers we can use to drive changes that have impact. When Coca Cola describes itself as a conditioned reflex business rather than a "soft drinks business," its marketing department begins to think about how to engineer stimuli that will trigger customers' buy-and-consume routine so reliably that it becomes a reflex. Then the company sets to work on optimizing the color of the drink, the shape of the container, and the sound patterns of the commercials to trigger consumers' desire to "have a Coke and a smile" as a matter of reflex rather than a considered choice.

Google understands itself as a company constantly searching for the best search process. Its approach to research, development, and deployment has taken on a vinelike characteristic—one that is adaptive and expanding. "Search," writ large, is its raison d'être, which encourages it to stay at the forefront of a human activity—search and re-search—that has been addictive since before we knew what the Internet was. Google Earth, Google Books, Google Scholar, Google Patents, and Google Code are all exploration vehicles that expand users' search repertoire while furthering the company's own search for better ways to search.

The business world offers up challenges involving large sets of variables on consumer behavior, trends, regulatory uncertainties, shifting competition, new technologies, and more. Finding a way to see these variables in ways that illuminate an insightful course of action is not so different from seeing the world through Bertrand's photo lens or seeing time and space through Einstein's model. The trick is to discover a lens that focuses us on the variables that matter—those we can observe and control to bring about useful change. To do so, we need to look and see differently. We get to insight by building and using a new lens, not just by collecting more data and analyzing it. That is how we design insight.

Shedding new light on a predicament is hard because we tend to gravitate toward familiar ways of seeing. Our existing models, metaphors, and frames shape our ways of looking, and precedents constrain what we end up looking for. They can provide useful shortcuts for transferring insight from one field of practice to another, but they are our enemies in producing new insight: they are the pictures we took using yesterday's lenses.

This book is an exercise regime for the business mind that seeks insight—a personal problem-framing and problem-solving assistant for business problem solvers. It can be used as a think-out-loud document for strategists, advisors, and executives, alone or in teams, and it answers the questions that should guide all insight seekers—for example:

• How can we harness thinking deeply and precisely to seeing more clearly?

• How can we broaden our line of sight into possible solutions—or narrow our focus to avoid getting sidetracked without losing perspective?

• How should we design the process by which we design business solutions?


In business, we never begin our work with a well-defined problem. We start from a difficulty, an issue, a challenge, or a predicament:

• Quarterly sales have suddenly plummeted. What now?

• Manufacturing costs have skyrocketed over the past two quarters. What do we do about it?

• Our arch-competitor has announced a new product we'd not even conceived possible a quarter ago, and it looks like a market beater. How do we respond?

• The client's top management team has gone into a motivational slump according to the chairman of the board: How do we change their behavior?

These are not problems. They are vaguely articulated predicaments, or challenges. As business problem solvers, we never "solve problems" already posed. The work we do creates most of its value through defining problems: turning predicaments into precisely articulated problems we can solve.

What does that mean? What is a well-defined problem? It is a difference between the way things are and the way we want them to be. "Precisely articulated" means just that: we want to be able to measure the most relevant variables pertaining to where we are (the current conditions) and where want to be (the desired conditions); define the time frame in which we will get there from here; and map out the space of possible solutions, that is, the permutations and combinations of all possible changes in the variables we can influence to take us from where we are to where we want to be.

The prototypical well-defined problem is a jigsaw puzzle: you have a stack of nine square tiles, each with some pattern on it. These are the current conditions. You know that the solution to the puzzle is an arrangement of the nine tiles in a square three-by-three tile array such that the patterns fit together, that is, they produce a coherent image (the desired conditions). The space of possible solutions—the solution search space—is all of the possible three-by-three arrays of tiles you can create using the nine tiles at your disposal.

This problem is not simple, but, it is well defined. You know what the solution should look like: you have some hint in or on the box of the tile package. You can verify whether any configuration of tiles fits the bill. You have the means to alter the position or rotation of any tile to get closer to the solution to the puzzle. You may also know that the solution is unique. That will help because you will be aiming to solve for one thing, not for any one of 10, or 100, or 1,000.

Once you have a well-defined problem, you can write down the full solution search space and the rules for searching for a solution. If you are rushed or derive no enjoyment from solving jigsaw puzzles other than the satisfaction of having solved one, you can hire a good Python or C++ programmer who will, for a hundred dollars or less, write code that finds the solution to the puzzle (and all similar puzzles, to boot) in no more than an hour. Clearly the problem definition step is where the gold lies. It is where we add most value when we are engaged to solve problems. The rest is code.

How do you best turn a loosely, fuzzily, tentatively articulated predicament into a well-defined problem? How do you turn hunches and intimations about difficulties like, "We have an accountability problem around here?" into a problem that is defined in terms of actionable levers and observable inputs and outputs, like, "How do we allocate decision rights over order fulfillment decisions to top management team members to achieve a 20 percent improvement in an accountability metric defined in terms of promises made, kept, and broken, and by the end of six months or sooner?"


Language is the key to defining problems. Language matters to problem solving because it supplies the basis for posing problems, that is, for defining them.

At the core, as businesspeople, we end up truly solving only two kinds of problems: prediction and optimization problems. Prediction problems like these: How will competitors respond to our new product? How will this budget cut affect our ability to ship product next quarter? How will the new management team respond to this new ownership structure? And optimization problems like these: How do we most efficiently increase top-line revenue by 20 percent without making additional investments in sales and marketing? How do we achieve the minimal-cost R&D organization for achieving the desired target for earnings before income, taxes, depreciation, and amortization? How do we most effectively aggregate new client information for maximum informativeness to the top management team so as to cut decision time by 20 percent?

Prediction feels intuitive to us, whereas the concept of optimization is worth unpacking because its name and the formalisms that economists and engineers use to represent it often make it sound mysterious and opaque. In fact, it is a natural process that all living creatures engage in at various levels of sophistication, using four elementary steps:

1. Enumerate, or, list, the alternative options for solving a problem. For instance, list all the possible ways to allocate 3 different kinds of incentives to each of 4 different people–already a hefty list of 34, or 81, different reward structures.

2. Evaluate the net benefits of each of the alternatives. For instance, evaluate the benefits of the higher motivation induced by the incentives, net of the costs of the side effects of people pursuing their own incentives at the cost of the firm's benefit for each allocation;

3. Compare the net benefits of the different options among them so as to be able to rank them from highest to lowest in terms of the net benefits they will bring.

4. Select the option with the highest net benefit. This is the optimal solution.

Optimization is rarely easy to do. But it is easy to understand and can stay that way if we remember its foundations.

All well-defined business problem are combinations of prediction and optimization problems. The catch-all problem, "What should we do about X?" can always be decomposed into this prediction problem, "What happens if we do a, b, c, and d?" and an optimization problem, "What is the best way to get from X to Y?"

To get to a well-defined prediction-optimization problem requires looking at a challenge through the prism of a problem-solving language. It specifies the variables to try to predict and optimize over, the variables to control and observe, and the measures of performance or success. For example, consider this challenge. The CEO of a large manufacturing business is facing an accountability challenge at the level of her top management team: important client information falls through the cracks, critical orders are shipped late or with defects, and promises that team members make to address the shortfalls are not kept. Order fulfillment is currently slow, sloppy, and unreliable. We can measure speed, precision, and reliability and define an objective—a goal we are driving to. We know the CEO believes the root cause of the difficulty is at the level of the top management team—four executive vice presidents and chief X officer-level people whose collective and individual behavior have led us to where we are now. Let's trust her on that (for the time being).

What we do next in this situation depends on the lens we choose, that is, our way of seeing the challenge. It allows us to focus on specific parts of the challenge, which will become the variables of our problem statement. If you look carefully at figure 1.1, you will see that it can turn into a duck head or a rabbit head, depending on where you start off scanning it. Scan it from the left, and it looks like the profile of a duck's head; you will make out the beak, the plumage, and the eye. But scan it from the right, and it looks like the profile of a rabbit's head; you will make out the ears, the fur, and the eye.

Problem-solving languages have the same lensing feature: you can see the challenge as one problem or as another, depending on which language you use. The language is what guides your gaze. For instance, you can think of the team as a network of information flows and trust ties, as figure 1.2 shows. Then you specify the problem in terms of the bottlenecks in the timely and reliable flow of accurate and relevant information about the order fulfillment process. You next consider ways in which to optimize the network to minimize bottlenecks, misunderstandings, and the flow of distorted information. You could do this by increasing the flow of information among team members who trust one another or making public the private exchanges of information between team members who do not trust each other (e.g., using boards that track service levels over time between two production units within a plant), so distortions of information can be monitored more easily.

Now change your lens and think of the team as shown in figure 1.3: as a group of self-interested agents who make decisions on the basis of different levels of authority (their decision rights), different levels of expertise and decision-relevant information, and their own private incentives that may differ from those of the business. You can use this to consider ways in which to reallocate decision rights and incentives to improve the efficiency of the order fulfillment process—for instance, by giving more decision rights to team members who have mission-critical information and aligning the incentives of executives with those of the business.


To practice business problem solving effectively, you need a method—a series of reliable steps that prescribe a set of actions and are guided by a goal.

Our method begins by specifying the variables to focus on. If we look at the executive team as a group of agents with different levels of authority, we focus on the relationship between the decentralization of authority (how many decision rights the CEO has relative to others on the team) and the efficiency of the process (orders logged and shipped, delays, defects, rework requests), shown in figure 1.4A. We then estimate from prior experience and industry data the key relationships. Let's say our case studies show that efficiency increases with decentralization of authority for problems such as ours, shown in figure 1.4B. We are on our way to a solution (decentralize authority) except that we do not know where on the curve the current team lies, so we measure the concentration of decision rights in the order fulfillment process in this case, as in figure 1.4C, which gives us the initial conditions for our problem. To figure out what we could do, we use our initial conditions and the relationship between the variables to predict the range of possible improvements we can make, as in figure 1.4D, and then optimize our solution by choosing the allocation of decision rights between top management team members that will produce the greatest improvement in the process measures (fulfillment delays, reliability) in the executive team, as in figure 1.4E).

This five-step process of specify-estimate-measure-predict-optimize (SEMPO) takes us from an ill-defined accountability challenge to a well defined problem (reallocate decision rights over D-type decisions to increase efficiency of process P to which D-type decisions are relevant), which naturally breaks down into two subproblems, predict and optimize, that are the workhorses of business problem solving.

What we get by being methodical is depth and precision. We end up with a well-defined, well-posed set of problems we can proceed to solve by making the changes to variables we can measure (decision rights) and that we predict will bring about the desired changes in the values of another set of variables we can measure (efficiency of the order fulfillment process).


Depth of understanding is only one of the upshots of our problem-solving languages. The other is breadth: the diversity that arises from putting several different languages to work on the same challenge.

The "Dream Team"

Imagine you have the chance to build a dream team for competing in a sports meet. You can pick from the 2012 medal winners in all of the sports represented in the Summer Olympic Games to build it: Roger Federer or Andy Murray (tennis, singles), Michael Phelps (swimming), Usain Bolt (short sprint), Sandra Izbasa (gymnastics, vault), Gabby Douglas (gymnastics, parallel bars), and so on. Of course, you will need to compensate each of these athletes according to both the value that he or she brings and each person's market price, so you should choose judiciously.

Your first question may be: What sport am I competing in? It might be inefficient to have both Izbasa and Federer around. Suppose, though, that you do not know in advance what you will have to excel at: you will find out only at the last minute but must commit to a roster now. This may seem unfair, but it is what we have to do when we tackle business problems in real time: we do not know what the world will turn up, but will have to deal with whatever it throws at us. It makes sense, then, to assemble a team that will let you draw on Federer, Bolt, Izbasa, Douglas, and however many more megatalents you can afford, depending on the game you will face. Uncertainty about the game increases the value of your options and flexibility to use them—the value of your team's diversity.


Excerpted from The Design of Insight by Mihnea C. Moldoveanu, Olivier Leclerc. Copyright © 2015 McKinsey & Company, Inc., and Mihnea C. Moldoveanu. Excerpted by permission of STANFORD UNIVERSITY PRESS.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

Table of Contents

Contents and Abstracts1What Is This All About? chapter abstract

Business problem solving is hard not only because problems feature a lot of uncertain, volatile elements, but also because they are loosely defined or undefined. We introduce the basic craft of problem shaping and definition, and show how models and modeling languages help us turn ill-defined, ill-structured problems into well-defined problem statements we can solve. We also show how using several different problem solving languages to define the same problem can increase the span of possible solutions and the probability of finding and unexpectedly optimal ('innovative') solution, once it is defined. We introduce five problem solving languages – which we call Flexons - drawn from the natural and social sciences that provide lenses for representing and defining problems, and which are powerful enough to help us frame problems at any level at which we may choose to intervene: individuals, dyads, groups, teams, organizations, markets and institutions.

2The Networks Flexon chapter abstract

We introduce and elaborate the Networks Flexon – a problem solving language that uses graphs to represent entities as varied as social interactions in an executive team, deal flows in an investment syndicate, modules in a software platform or value-linked activity chains in an industry. We show how problems at multiple levels of analysis can be defined, shaped, structured and simplified using this language, which supplies not only a basic representation scheme – nodes and links – but also a set of performance measures at the level of the network as a whole (density, connectivity, clustering, path length) and at the level of individual nodes (centrality measures). The Networks Flexon has been used in fields as varied as statistical mechanics, industrial organization economics, sociology, psychology, neuroscience and information theory to solve problems across many different disciplines.

3The Decision Agent Flexon chapter abstract

We introduce and elaborate the Decision Agent Flexon – a problem solving language that uses agents, decisions, outcomes and payoffs or incentives to define and structure business problems at any level of analysis. The Flexon also uses concepts such as decision rights, and information and expertise to model the complex landscape of relationships in a hierarchical or non-market organization – and offers a set of performance metrics – such as agency costs, communication costs and coordination costs – that problem solvers using the flexon can use to define their objective functions. This flexon – used broadly across the fields of economics - decision sciences, game theory, agency theory – can be flexibly deployed to define business problems at any level of analysis.

4The System Dynamics Flexon chapter abstract

We introduce and elaborate the System Dynamics Flexon – a problem solving language that uses causal maps and stocks and flows of money, matter and information to define and structure business problems at any level of analysis. The Flexon also supplies a set of performance metrics for business problem solving, such as desired transient and steady state responses of a dynamically evolving system – to supply objectives to the problem solver. This modeling language has been broadly used and developwed to datre in fields as diverse as classical mechanics, mechanical, chemical and electrical engineering, psychology, neuroscience and macroeconomic modeling to supply tight and precise problem formulation at any level of analysis.

5The Evolutionary Flexon chapter abstract

We introduce and elaborate the Evolutionary Flexon – a problem solving language that uses the basic concepts of populations and variation, selection and retention mechanisms to supply models of the evolution of entities ranging from deliberations within a group, to the evolution of organizations, product modules, technologies, and markets. The Flexon also supplies a set of outcome measures – like fitness maximization and the speed and accuracy of convergence in variation-selection-retention processes to allow problem solvers to define and sharpen their problem solving objectives. The Evolutionary Flexon has been used in fields as diverse as theoretical and experimental biology, biological archaeology and anthropology, industrial organization economics, sociology, psychology, theoretical computer science and the theory of algorithms to both explain and predict a large number of phenomena in multiple domains of experience.

6The Information Processing Flexon chapter abstract

We introduce and elaborate the Information Processing Flexon – a problem solving language that uses the basic concepts of problems, solutions, solution search spaces, problem solving agents and the complexity of various problem statements to represent business processes at any level. It also supplies a set of performance measures like accuracy, reliability and speed of convergence that will help problem solvers define objective functions for the problems they are solving. The Information Processing Flexon has been used in fields as diverse as information theory, theoretical computer science, the theory of algorithms, organizational theory and statistical mechanics to address a large number of diverse problems.

7Recombinant Problem Solving and the Design of Insight chapter abstract

This chapter shows how the five Flexons can be used, serially and in parallel, to define, structure and solve loosely articulated business difficulties, predicaments and situations. Through examples, it highlights the value of the flexons in both supplying the requisite depth and precision required for solving real business problems in practical amounts of time, and the value of diversity and heterogeneity of models and modeling languages in solving any business problem.

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