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Structured vs. unstructured problems.

Many people have the feeling of being overwhelmed in today’s world. To a large extent, this has to do with the increase in unstructured problems we face in many areas of daily life. These challenges require a different approach than structured problems. Therefore, I would like to show the differences and describe possible solutions.

The differences between structured and unstructured problems

Here you will find three differences between structured and unstructured problems, each described with appropriate examples:

Difference 1: Clarity of the problem

Structured problems are clearly defined and usually well understood. They have a clear problem, known variables, and can usually be well described. Structured problems are often easier to solve because they are based on established rules, processes, or algorithms.

Example: A company wants to calculate the break-even point for a certain product. It knows the variable costs, the fixed costs and the selling price of the product. The problem is clearly defined and the calculation of the break-even point can be performed using a known formula.

Unstructured problems , on the other hand, are often unclear, vague, or poorly defined. They contain many unknown or difficult-to-measure variables and may change over time. Unstructured problems are usually more difficult to solve because they are not based on established rules or processes and often require creative or innovative approaches.

Example: A company notices that employee motivation has dropped recently, but cannot identify the exact reasons. This problem is unclear and requires in-depth study to identify the factors contributing to declining motivation and to develop appropriate measures to improve the situation.

Difference 2: Complexity

Structured problems are usually less complex because they are based on known parameters and a fixed solution path. They can usually be broken down into smaller, more easily detachable parts. Example: A company has a limited number of advertising spaces in various media and wants to allocate the optimal advertising budget among these media to achieve the highest possible reach. This problem can be solved using linear programming techniques based on known data and goals.

Unstructured problems are often more complex because they are influenced by multiple, interrelated factors and do not have a unique solution. They require a high level of critical thinking, analysis, and creative problem solving to arrive at a solution. Example: A company wants to develop a completely new business idea in order to survive in a competitive market. This problem is complex because there are many unknown variables and factors that can affect the success of the business idea. It takes creativity, market research and experimentation to develop a promising business idea.

Difference 3: Solution approaches

Structured problems can usually be solved by standardized, linear solution approaches based on established rules or algorithms. These include mathematical models, process diagrams, and other systematic approaches. Example: A company wants to optimize its inventories to avoid both overstocks and delivery bottlenecks. There are standardized solution approaches that can be applied to optimize inventories.

Unstructured problems , on the other hand, require creative, innovative and often unconventional approaches to solving them. These include brainstorming, design thinking, and other collaborative, experimental methods. Unstructured problems may also require a combination of different approaches and interdisciplinary thinking to find a successful solution. Example: A company wants to improve its corporate culture in order to increase employee satisfaction and retention. There is no set solution or formula to solve this problem. Instead, different approaches need to be tried, such as improving internal communication, introducing flexible working hours or creating opportunities for professional development.

Reasons for the increase in unstructured problems

There are different reasons for the increase of unstructured problems, especially in the business context. Some of them are:

Growing complexity

The world is becoming increasingly interconnected and complex, and companies must deal with a variety of factors such as global markets, new technologies and changing customer needs. These factors can lead to unstructured problems as it becomes more difficult to find clear solutions to complex challenges.

Faster change

Technological advances and globalization have led to an increased pace of change in many industries. Companies need to adapt quickly to new trends and developments, which can lead to unstructured problems for which there are no defined solutions.

Competitive pressure

Increasing competition in global markets is forcing companies to constantly evolve and find new ways to stand out from the competition. This can lead to companies facing unstructured problems when trying to develop innovative products, services or business models.

Information overload

The abundance of information available in today’s world can make it difficult to distinguish relevant information from unimportant information and make informed decisions based on that information. This can exacerbate unstructured problems, as solving such problems often requires the ability to effectively analyze and use a variety of information.

Increasing customer demands

Customers today are better informed and have higher expectations of products and services. Companies must meet these increasing demands and develop innovative solutions to satisfy their customers. This can create unstructured problems as there are no set solutions to meet the different needs and wants of all customers.

Unstructured problems as a challenge in the daily work routine

Many people have difficulty coping with unstructured problems. Especially when they occur in high-pressure situations, the following behaviors can be observed:

Individuals who have difficulty tackling unstructured problems tend to be indecisive. They have a hard time making decisions. This leads them to endlessly collect and analyze information without ever arriving at a solution or decision (also known as “analysis paralysis”).

Inflexibility

Some people are less open to change and new ideas and have difficulty adapting their thinking and approaches in complex situations. This inflexibility can make it difficult to effectively manage unstructured problems, as such problems often require creative and unconventional solutions. Here, brainstorming methods often help to expand the corridor of thought.

Frustration and stress

Unstructured problems can be overwhelming for some people because they bring uncertainty and complexity. This often leads to stress and frustration when confronted with such problems, which in turn can further impair their problem-solving abilities. A spiral of frustration can develop.

Quick judgment

Some people are quick to jump to conclusions and commit to a particular solution without thoroughly analyzing the problem and considering different approaches. This behavior can lead them to find less effective solutions to unstructured problems.

Some people prefer to work on problems alone rather than seek collaboration and exchange with others. However, this can be less effective for unstructured problems, as sharing knowledge and experience often helps to find better solutions. While there are ideation methods like the Lateral Map that produce good results on their own, the quality of the solutions is always improved by the different perspectives in a multidisciplinary team.

Focus on details

Some people tend to focus on details and particulars instead of seeing the big picture. For unstructured problems, this focus on detail can cause them to lose track of the underlying problem and have difficulty prioritizing. Helpful here is often a method such as the well-known mind mapping , which brings information coherently into an overview.

Risk aversion

Some people shy away from experimentation and unconventional approaches that can be helpful in dealing with unstructured problems. This risk aversion can lead them to focus on proven and known solutions that may be less effective at solving complex and unstructured problems.

Thinking and behavioral approaches to solving unstructured problems.

Problem analysis: At the beginning, try to analyze the problem in detail and identify all relevant factors and variables. Use methods like the mind map to make all information visible. Write down questions, gather information, and look for connections to better understand the problem. The Ishikawa diagram can help to understand causes and effects. If you don’t like the problem, use the Problem Tackler , which turns difficult problems into exciting challenges.

Creative thinking: When working with unstructured problems, it helps to think creatively and innovatively to find new solutions. Unstructured problems require a great openness to unconventional ideas and approaches that go beyond the traditional framework. Exciting methods for your team here are the stimulus image method , SCAMPER or even the Force Fit Game . Often referred to in this context is lateral thinking, an approach that aims to find unusual or unconventional solutions to problems by exploring new perspectives and ways of thinking. Lateral thinking encourages thinking outside the box and discovering unexpected connections or ideas that can lead to innovative solutions.

Critical Thinking: Critical thinking helps to think logically and in a structured way about a problem, questioning hypotheses, assumptions, and conclusions. This approach encourages a thorough analysis of the problem and evaluation of alternative solutions based on facts and evidence.

Hypothesis-based thinking: This approach involves making hypotheses or assumptions about the problem and then systematically testing them to determine their validity. This can help reduce uncertainty in unstructured problems and speed up problem solving.

Adaptability: In the face of unstructured problems, flexibility and adaptability is an important personality trait. Your willingness is needed to change strategies and approaches as new information or circumstances arise. Be open to learning from mistakes and setbacks, thinking approaches like the Lean Startup Method can help.

Collaboration: When faced with unstructured problems, many people seek collaboration and exchange with others. Working together and sharing knowledge and experience can lead to better solutions in many cases. Methods such as the team thermometer help to understand team dynamics and make them visible.

Perseverance: Unstructured problems can be frustrating and difficult. A high level of perseverance and persistence is necessary for the solution. You must be willing to continue to deal with the problem even if you encounter difficulties or do not find an immediate solution.

Experimenting and testing: When tackling unstructured problems, experimenting and testing different approaches and solutions is an important activity. To do this, it is important to be willing to take risks and analyze the results of experiments in order to continuously learn and adapt your strategies. Retrospectives or techniques like the AAR review are helpful here.

Focus on the big picture: try to see the big picture and look at the problem in the context of its environment and its long-term impact. Prioritize and focus on the most important aspects of the problem without getting lost in details.

Design Thinking: Design thinking is a user-centered approach that aims to solve problems by understanding the needs and wants of users or customers. This approach includes various phases such as empathy, problem definition, ideation, prototyping, and testing to iteratively arrive at solutions that meet user needs.

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Structured Problem-Solving

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Description

Structured Problem-Solving gives students a process for solving a complex, content-based problem within a specific time limit. All students must agree to a solution and be able to explain the answer and strategy used to solve the problem. The activity will help identify where students need to develop and/or improve their problem-solving skills.

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Use it when you want...

• To break a problem-solving process into specific steps,
• To have students identify, analyze, and solve problems in an organized manner,
• To give students a structured format — preventing them from being overwhelmed by the magnitude of a problem or from engaging in irrelevant steps by providing manageable steps.

What students will need

• There are no special requirements for this approach.

The following workflow is meant to guide you on how you can facilitate a Structured Problem-Solving learning activity within a classroom.

• Create a problem that is complex enough to require students to use sophisticated problem-solving skills. Use research and current questions in the field as a resource.
• Choose an identification and solving procedure appropriate to the selected problem type.
• Solve the problem using the identified problem-solving procedure to uncover any difficulties or errors.
• Create a handout that includes both the problem and the problem-solving steps.
• Determine how groups will be formed.
• Organize students into teams and assign them a complex problem to solve. 
• Ask students to use the specific steps you have identified as a problem-solving technique: (a) identify the problem; (b) generate possible solutions; (c) evaluate and test the various solutions; (d) decide on a mutually acceptable solution; (e) implement a plan, and (f) evaluate the results.
• Teams report the steps they took and the solutions they developed.
• Review reports.
• Provide feedback/grades to group participants.
• Discuss the results of the activity at the next class meeting.

Accessibility and Room Considerations

Technical documentation.

An Environmental Sciences professor wanted to use Structured Problem-Solving to consider the issue of air quality and air pollution. He divided students into teams and asked them to evaluate the air quality directly near the college. He asked them to consider, " What do you know? " and " Where can you go to find out? ". He then asked them to assess the air quality (recommending the environmental pollution scorecard) and to identify major and minor local and regional polluters. He also asked students to develop a presentation for community stakeholders that explained the problem and recommended specific actions (Barkley 245).

In  General Chemistry , the professor developed a series of online modules to introduce concepts for the week. As an introductory course, she knew that few students would see how chemistry could be helpful in their everyday lives. She decided to use a Structured Problem-Solving activity in class to help them recognize where and how they interact with chemistry. She broke students into groups and asked them, " Select the best antacid from the list on the screen ." To help them get started, she gave them a problem-solving strategy in which they would respond to various questions that would help them identify the effectiveness of the antacid. The questions guided them to review the list of active ingredients. The questions also guided them to apply information on acids and bases found in the online modules. From the answers to these questions, groups designed an experiment to test the antacid. Each group presented their ideas to the class, the procedure they would follow, and the data they would collect. At the next class session, the professor had the materials to conduct their experiment to solve the problem and identify the best antacid available over the counter (Barkley 245-246).

Citation/Source

Barkley, Elizabeth F. et al. Collaborative Learning Techniques: A Handbook For College Faculty . Wiley, 2014. pp. 244-248.

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The Basics of Structured Problem-Solving Methodologies: DMAIC & 8D

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When it comes to solving a problem, organizations want to get to the root cause of the problem, as quickly as possible. They also want to ensure that they find the most effective solution to that problem, make sure the solution is implemented fully, and is sustained into the future so that the problem no longer occurs. The best way to do this is by implementing structured problem-solving. In this blog post, we’ll briefly cover structured problem-solving and the best improvement methodologies to achieve operational excellence. Before we dive into ways Minitab can help, let’s first cover the basics of problem-solving.

WHAT IS STRUCTURED PROBLEM-SOLVING?

Structured problem-solving is a disciplined approach that breaks down the problem-solving process into discrete steps with clear objectives. This method enables you to tackle complex problems, while ensuring you’re resolving the right ones. It also ensures that you fully understand those problems, you've considered the reasonable solutions, and are effectively implementing and sustaining them.

WHAT IS A STRUCTURED PROBLEM-SOLVING METHODOLOGY?

A structured problem-solving methodology is a technique that consists of a series of phases that a project must pass through before it gets completed. The goal of a methodology is to highlight the intention behind solving a particular problem and offers a strategic way to resolve it. WHAT ARE THE BEST PROBLEM-SOLVING METHODOLOGIES?

That depends on the problem you’re trying to solve for your improvement initiative. The structure and discipline of completing all the steps in each methodology is more important than the specific methodology chosen. To help you easily visualize these methodologies, we’ve created the Periodic Table of Problem-Solving Methodologies. Now let’s cover two important methodologies for successful process improvement and problem prevention: DMAIC and 8D .

8D is known as the Eight Disciplines of problem-solving. It consists of eight steps to solve difficult, recurring, or critical problems. The methodology consists of problem-solving tools to help you identify, correct, and eliminate the source of problems within your organization. If the problem you’re trying to solve is complex and needs to be resolved quickly, 8D might be the right methodology to implement for your organization. Each methodology could be supported with a project template, where its roadmap corresponds to the set of phases in that methodology. It is a best practice to complete each step of a given methodology, before moving on to the next one.

MINITAB ENGAGE, YOUR SOLUTION TO EFFECTIVE PROBLEM-SOLVING

Minitab Engage TM was built to help organizations drive innovation and improvement initiatives. What makes our solution unique is that it combines structured problem-solving methodologies with tools and dashboards to help you plan, execute, and measure your innovation initiatives! There are many problem-solving methodologies and tools to help you get started. We have the ultimate end-to-end improvement solution to help you reach innovation success.

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A3 Thinking: A Structured Approach to Problem Solving

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Also known as A3 Problem Solving.

Variants include 8D and CAPA.

A significant part of a leader’s role involves addressing problems as they arise. Various approaches and tools are available to facilitate problem-solving which is the driving force behind continuous improvement. These methods range from the advanced and more complex methodologies like Six Sigma to the simpler and more straightforward A3 thinking approach.

The power of the A3 approach lies in its systematic and structured approach to problem-solving. Although it appears to be a step-by-step process, A3 is built around the PDCA philosophy. It relies on the principle that it is much better to address the real root-cause rather than trying to find a solution. Hence, it’s important not to jump to the solution when solving a problem as it is likely to be less effective.

A3 thinking provides an effective way to bring together many of the problem-solving tools into one place. For example, techniques such as the 5 Whys and fishbone analysis can be used during the ‘Analysis’ stage to help identifying the root causes. Additionally, visual aids and graphs are highly recommended in the A3 report, as they are more effective than text in communicating ideas and providing concise project updates.

A3 thinking involves the practice of consolidating the problem, analysis, countermeasures, and action plan onto a single sheet of paper, commonly an A3-sized sheet. This brief document serves as a summary of the project at hand and is regarded as a valuable storytelling tool for project communication. Utilizing the A3 approach doesn’t require any specialized software or advanced computer skills. You may however use readily available A3 templates , or rely on basic tools such as paper, pencil and an eraser as you will need to erase and rewrite several times.

One of the characteristics of the A3 approach is that it does not get into specific details. Detailed documents are usually attached to the A3 report to prevent overwhelming the reader with an excess of information.

The A3 process is typically structured in multiple stages based on the PDCA model. The primary focus is on developing understanding of the current situation and defining the desired outcome before thinking about the solution. While the exact number of stages may vary depending on the preference of the company, what truly matters is adhering to a structured approach to problem-solving.

A3 Seven Stages Model

An A3 process is often managed by an individual who should own and maintain the A3 report. This individual takes the lead in steering the process, facilitating team involvement, and preparing the A3 report with team input. One of the most common models for A3 thinking is the seven stages model which is described in the following.

1. Background – The first step is to identify the business reason for choosing this problem or opportunity. In this stage, you need to identify the gap in performance and the extent of the problem.

2. Current situation – The purpose of this stage is to document the current state of the problem. You may need to refer to the process map or go to the Gemba to truly understand the current situation.

3. Target – The purpose of this stage is to define the desired future state. Clearly identify the expected benefits from solving the problem, the scope, and the key metrics that will help measure the success of the project.

4. Analysis – The objective of this stage is to conduct an in-depth analysis of the problem and understand why it’s happening. It might involve tools like the 5 Whys and cause-and-effect analysis, as well as advanced statistical methods.

5. Countermeasures – Countermeasures are the actions to be taken to eliminate root causes or reduce their effects. The team should brainstorm and evaluate possible countermeasures based on the analysis conducted earlier.

6. Implementation Plan – To achieve the target, develop a workable plan to implement the countermeasures. Gantt charts are great ways to manage implementation plans very simply and easily. Once the action plan is finalized, the team should begin working on the activities needed to implement the countermeasures.

7. Follow-up – The final stage involves evaluating the implementation of the plan and the results achieved. Follow-up actions are important to ensure the benefits extend beyond the project’s completion.

A3 thinking is considered to be the practical form of the PDCA model.

There are many online templates that can be used to manage your problem-solving efforts. One of the simplest and most straightforward ways is to use this A3 problem solving template .

Wrapping Up

A3 thinking represents a logical and structured approach for problem solving and continuous improvement. This approach can be used for most kinds of problems and in any part of the business. Originating from the Toyota Production System (TPS), it has been adopted by many Lean organizations around the world.

A3 thinking not only provides a systematic approach for problem-solving. The development of a continuous improvement culture is at the core of A3 thinking. It has become one of the most popular Lean tools today where people and teams work together to solve problems, share results and learn from each other.

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Structured problem solving tools.

Updated: Feb 28, 2023

Structured vs Unstructured Problem Solving

Using a structured approach to solve problems can help to ensure the correct problem is being solved, with the right solution. When an unstructured approach to problem solving is used, which is generally the case, the problem is not well understood and the solution addresses a symptom, rather than the root cause. Another major disadvantage of an unstructured approach is that it is easy to hit a roadblock and convince yourself that the problem cannot be solved or that the solution cannot be implemented for a variety reasons.

Each of the five problem solving tools described below approach problem solving in a different way. They can help you to find solutions that that might not be immediately obvious and compare possible solutions before choosing the best one.

Problem Solving Definition

A problem is the distance between how things currently are and the way they should be. Problem solving forms the ‘bridge’ between these two elements. In order to close the gap, you need to understand the way things are (problem) and the way they ought to be (solution).

The Difference Between Problem Solving and Decision Making

Although there is a clear distinction between problem solving and decision making, the two are often confused. Problem solving differs fundamentally from decision making. A problem occurs when something is not behaving as it should, something is deviating from the norm or something goes wrong. Decision making is a case of choosing between different alternatives. Decision making is required in response to the question: “Which computer shall I buy?” Problem solving is needed in response to the statement: “My computer won’t work”.

Most problem solving methods follow a common pattern, beginning with a definition of the problem, moving on to the consideration of potential solutions, and culminating with the selection, testing and implementation of a chosen course of action. Divergent thinking techniques can be helpful in generating creative ideas, while convergent thinking can assist in structuring and evaluating potential solutions.

Problems can be classified into one of two categories: the ‘fix-it’ or the ‘do-it’ scenario:

Fix-it – solving an existing problem, (e.g. a current product range is falling short of its sales targets). An immediate short-term solution could be to increase marketing activity, for example.

Do-it – moving you in the right direction for what you want to achieve, (e.g. a new product range needs to be introduced to compete with market rivals). This type of problem will require longer term planning in order to achieve its objectives.

Irrespective of the severity or complexity of the problem, the process should:

be systematic and thorough

provide evidence to show how the problem was solved

avoid a rush to a solution without first understanding the cause of the problem

enable possible causes to be assessed

1. Six-Step Problem Solving Model

Problem solving models are used extensively in the workplace and the Six Step method is a simple and reliable way to solve problems. This technique uses an analytical and reliable approach to problem solving. Its process keeps the group assigned to solve a problem on track while they carry out their investigation and search for a solution.

2. The Drill Down Technique

In complex organizations problems are inevitable. Successful organizations take the time to identify these complicated problems and plan a practical resolution as soon as possible. The Drill Down Technique is a popular approach to problem solving in complex organizations. As its name suggests you break down a problem into its various components into small practical pieces that enable you to identify a resolution.

3. The Four Frame Model

The Four Frame Model is one of the most popular and in-depth tools that management use to maximize an organization’s potential. The model divides up an organization into ‘four frames’ – Structural, Human Resource, Political and Symbolic – with the objective of better understanding the organization. Each frame must be looked at in a meaningful way; if one or more is discarded the findings will be incomplete.

4. Eight Disciplines of Problem Solving

This problem-solving tool requires that a practical plan is created at the start of this eight step process. It requires a clear definition of the problem, individuals involved in the solution and the resources. Each of the eight disciplines in this process, are designed to move you a step closer to a successful resolution.

5. The Cynefin Framework

The core of the Cynefin framework is the way that it breaks down problems into one of five contexts. You place your problem into one of these specific contexts and it will then help you decide how best to approach it. This problem-solving tool is a level ‘above’ others because it requires you to figure out how you should be thinking about a problem in the first place. The framework is often used in conjunction with other problem-solving tools in finding a solution.

6. The 5-Whys Technique

The 5 Why’s Technique is an easy-to-use method for uncovering the root of an issue. All you need is asking ‘Why?’ five times. Start with an issue and ask why it happened. Make sure your answer is based on unbiased facts. Continue the process of asking ‘Why?’ four more times. Eventually, you reach the root of the issue. Now you can try to find a solution.

Start Using Structured Problem Solving Techniques

Hopefully you find these different techniques useful and they get your imagination rolling with ideas on how to solve different problems. There are other techniques you can use, but when it comes to solving problems from simple to complex, these techniques will work well. And keep in mind that you can combine these techniques as well.

Here are 5 takeaways to use the next time a problem gets you and your team tangled up:

Don’t start by trying to solve the problem (or the symptom). First, aim to understand the root cause of the problem.

Use questions to generate ideas for solving the problem.

Look to previous problems to find the answers to new ones.

Clear your preconceived ideas and past experiences before attempting to tackle the problem.

When you are looking for people to add to your team – look for people that highlight their ability to solve problems!

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McKinsey Problem Solving: Six steps to solve any problem and tell a persuasive story

The McKinsey problem solving process is a series of mindset shifts and structured approaches to thinking about and solving challenging problems. It is a useful approach for anyone working in the knowledge and information economy and needs to communicate ideas to other people.

Over the past several years of creating StrategyU, advising an undergraduates consulting group and running workshops for clients, I have found over and over again that the principles taught on this site and in this guide are a powerful way to improve the type of work and communication you do in a business setting.

When I first set out to teach these skills to the undergraduate consulting group at my alma mater, I was still working at BCG. I was spending my day building compelling presentations, yet was at a loss for how to teach these principles to the students I would talk with at night.

Through many rounds of iteration, I was able to land on a structured process and way of framing some of these principles such that people could immediately apply them to their work.

While the “official” McKinsey problem solving process is seven steps, I have outline my own spin on things – from experience at McKinsey and Boston Consulting Group. Here are six steps that will help you solve problems like a McKinsey Consultant:

Step #1: School is over, stop worrying about “what” to make and worry about the process, or the “how”

When I reflect back on my first role at McKinsey, I realize that my biggest challenge was unlearning everything I had learned over the previous 23 years. Throughout school you are asked to do specific things. For example, you are asked to write a 5 page paper on Benjamin Franklin — double spaced, 12 font and answering two or three specific questions.

In school, to be successful you follow these rules as close as you can. However, in consulting there are no rules on the “what.” Typically the problem you are asked to solve is ambiguous and complex — exactly why they hire you. In consulting, you are taught the rules around the “how” and have to then fill in the what.

The “how” can be taught and this entire site is founded on that belief. Here are some principles to get started:

Step #2: Thinking like a consultant requires a mindset shift

There are two pre-requisites to thinking like a consultant. Without these two traits you will struggle:

• A healthy obsession looking for a “better way” to do things
• Being open minded to shifting ideas and other approaches

In business school, I was sitting in one class when I noticed that all my classmates were doing the same thing — everyone was coming up with reasons why something should should not be done.

As I’ve spent more time working, I’ve realized this is a common phenomenon. The more you learn, the easier it becomes to come up with reasons to support the current state of affairs — likely driven by the status quo bias — an emotional state that favors not changing things. Even the best consultants will experience this emotion, but they are good at identifying it and pushing forward.

Key point : Creating an effective and persuasive consulting like presentation requires a comfort with uncertainty combined with a slightly delusional belief that you can figure anything out.

Step #3: Define the problem and make sure you are not solving a symptom

Before doing the work, time should be spent on defining the actual problem. Too often, people are solutions focused when they think about fixing something. Let’s say a company is struggling with profitability. Someone might define the problem as “we do not have enough growth.” This is jumping ahead to solutions — the goal may be to drive more growth, but this is not the actual issue. It is a symptom of a deeper problem.

Consider the following information:

• Costs have remained relatively constant and are actually below industry average so revenue must be the issue
• Revenue has been increasing, but at a slowing rate
• This company sells widgets and have had no slowdown on the number of units it has sold over the last five years
• However, the price per widget is actually below where it was five years ago
• There have been new entrants in the market in the last three years that have been backed by Venture Capital money and are aggressively pricing their products below costs

In a real-life project there will definitely be much more information and a team may take a full week coming up with a problem statement . Given the information above, we may come up with the following problem statement:

Problem Statement : The company is struggling to increase profitability due to decreasing prices driven by new entrants in the market. The company does not have a clear strategy to respond to the price pressure from competitors and lacks an overall product strategy to compete in this market.

Step 4: Dive in, make hypotheses and try to figure out how to “solve” the problem

Now the fun starts!

There are generally two approaches to thinking about information in a structured way and going back and forth between the two modes is what the consulting process is founded on.

First is top-down . This is what you should start with, especially for a newer “consultant.” This involves taking the problem statement and structuring an approach. This means developing multiple hypotheses — key questions you can either prove or disprove.

Given our problem statement, you may develop the following three hypotheses:

• Company X has room to improve its pricing strategy to increase profitability
• Company X can explore new market opportunities unlocked by new entrants
• Company X can explore new business models or operating models due to advances in technology

As you can see, these three statements identify different areas you can research and either prove or disprove. In a consulting team, you may have a “workstream leader” for each statement.

Once you establish the structure you you may shift to the second type of analysis: a bottom-up approach . This involves doing deep research around your problem statement, testing your hypotheses, running different analysis and continuing to ask more questions. As you do the analysis, you will begin to see different patterns that may unlock new questions, change your thinking or even confirm your existing hypotheses. You may need to tweak your hypotheses and structure as you learn new information.

A project vacillates many times between these two approaches. Here is a hypothetical timeline of a project:

Step 5: Make a slides like a consultant

The next step is taking the structure and research and turning it into a slide. When people see slides from McKinsey and BCG, they see something that is compelling and unique, but don’t really understand all the work that goes into those slides. Both companies have a healthy obsession (maybe not to some people!) with how things look, how things are structured and how they are presented.

They also don’t understand how much work is spent on telling a compelling “story.” The biggest mistake people make in the business world is mistaking showing a lot of information versus telling a compelling story. This is an easy mistake to make — especially if you are the one that did hours of analysis. It may seem important, but when it comes down to making a slide and a presentation, you end up deleting more information rather than adding. You really need to remember the following:

Data matters, but stories change hearts and minds

Here are four quick ways to improve your presentations:

Tip #1 — Format, format, format

Both McKinsey and BCG had style templates that were obsessively followed. Some key rules I like to follow:

• Make sure all text within your slide body is the same font size (harder than you would think)
• Do not go outside of the margins into the white space on the side
• All titles throughout the presentation should be 2 lines or less and stay the same font size
• Each slide should typically only make one strong point

Tip #2 — Titles are the takeaway

The title of the slide should be the key insight or takeaway and the slide area should prove the point. The below slide is an oversimplification of this:

Even in consulting, I found that people struggled with simplifying a message to one key theme per slide. If something is going to be presented live, the simpler the better. In reality, you are often giving someone presentations that they will read in depth and more information may make sense.

To go deeper, check out these 20 presentation and powerpoint tips .

Tip #3 — Have “MECE” Ideas for max persuasion

“MECE” means mutually exclusive, collectively exhaustive — meaning all points listed cover the entire range of ideas while also being unique and differentiated from each other.

An extreme example would be this:

• Slide title: There are seven continents
• Slide content: The seven continents are North America, South America, Europe, Africa Asia, Antarctica, Australia

The list of continents provides seven distinct points that when taken together are mutually exclusive and collectively exhaustive . The MECE principle is not perfect — it is more of an ideal to push your logic in the right direction. Use it to continually improve and refine your story.

Applying this to a profitability problem at the highest level would look like this:

Goal: Increase profitability

2nd level: We can increase revenue or decrease costs

3rd level: We can increase revenue by selling more or increasing prices

Each level is MECE. It is almost impossible to argue against any of this (unless you are willing to commit accounting fraud!).

Tip #4 — Leveraging the Pyramid Principle

The pyramid principle is an approach popularized by Barbara Minto and essential to the structured problem solving approach I learned at McKinsey. Learning this approach has changed the way I look at any presentation since.

Here is a rough outline of how you can think about the pyramid principle as a way to structure a presentation:

As you build a presentation, you may have three sections for each hypothesis. As you think about the overall story, the three hypothesis (and the supporting evidence) will build on each other as a “story” to answer the defined problem. There are two ways to think about doing this — using inductive or deductive reasoning:

If we go back to our profitability example from above, you would say that increasing profitability was the core issue we developed. Lets assume that through research we found that our three hypotheses were true. Given this, you may start to build a high level presentation around the following three points:

These three ideas not only are distinct but they also build on each other. Combined, they tell a story of what the company should do and how they should react. Each of these three “points” may be a separate section in the presentation followed by several pages of detailed analysis. There may also be a shorter executive summary version of 5–10 pages that gives the high level story without as much data and analysis.

Step 6: The only way to improve is to get feedback and continue to practice

Ultimately, this process is not something you will master overnight. I’ve been consulting, either working for a firm or on my own for more than 10 years and am still looking for ways to make better presentations, become more persuasive and get feedback on individual slides.

The process never ends.

The best way to improve fast is to be working on a great team . Look for people around you that do this well and ask them for feedback. The more feedback, the more iterations and more presentations you make, the better you will become. Good luck!

If you enjoyed this post, you’ll get a kick out of all the free lessons I’ve shared that go a bit deeper. Check them out here .

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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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. 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?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but 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.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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