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Problem Solving and Decision Making: Key Differences & Applications

Explore the nuances of solving complex problems and making intricate decisions in this insightful blog. Gain a deeper understanding of the key distinctions between them. In this blog, explore Problem Solving and Decision Making, their key differences and how to apply these abilities in the workplace. Let's dive in!

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Have you ever faced the trouble of deciding what is right or wrong? In our daily lives, we often come across situations that require us to confront challenges and make choices. This is why two critical cognitive processes are involved in addressing these situations: Problem Solving and Decision Making. While the terms are frequently used interchangeably, they represent distinct mental activities with specific objectives. Problem Solving involves identifying and resolving issues using critical thinking and creativity. On the other hand, Decision Making entails choosing the best course of action among alternatives and considering risks and rewards. In this blog, we will Learn the differences between Problem Solving and Decision Making, how to apply these abilities at work, and some advice on how to improve them.

Table of Contents 

1) What do you understand by Decision Making? 

2) Understanding Problem Solving 

3) What are the differences between Problem Solving and Decision Making?

4) Tips on how to improve Problem-solving and Decision-making skills

5) How can you integrate Decision Making and Problem Solving? 

6) Conclusion 

What do you understand by Decision Making? 

It is a hard choice when we are faced with the question to make important decisions, in organisational setting and personal life as well. Nevertheless, it is not a reason to be afraid, but rather, to master these tasks through comprehensive knowledge of their consequences. First, we should define Decision Making before going on to the difference between Decision Making and Problem Solving.

It is an intellectual process that has a direct impact on our everyday and work-life matters. It is the process of analysing different options to find the best one in line with various factors and the one that is going to meet the objectives.

Effective Decision Making combines Critical Thinking, analysis, and judgment, and it can be the determinant of outcome and consequences. Let's uncover the important steps to Decision -making and some real-life examples:

1) Evaluation of alternatives: The first step in Decision Making requires the identification of problems and conceptualisation of possible alternatives that can help to deal with the given situation or problem.

2) Rationality and objectivity: The correct Decision Making process consists of a detailed analysis of all the data that is accessible, assessing the pros and cons of each scenario, and selecting a logical and beneficial option. 

3) Heuristics and biases: Sometimes, it is possible that you may have mental heuristics to be quick in the decision process. However, biases may be introduced by shortcuts and suboptimal choices could become inevitable for you.

4) Decision Making under uncertainty: Some times, you have to make important decisions based on the information that is not complete or with determined assumptions. The risk is directly connected and making risk assessment is considered to be the answer to this question. You must enhance on your flexibility to address the unpredictable.

5) Group Decision Making: In collaborative contexts, people may arrive at a decision together having discussed, brainstormed and found a common consensus with one another. Such a method taps into the different perceptions and skills.

6) Strategic Decision Making: In organisations, Strategic Decision Making requires being concerned with the possible long-term implications, aligning decisions with organisational goals, and trying to anticipate potential impacts on stakeholders.  

7) Ethical considerations:  This involves assessing the moral implications of choices, decisions, and actions. It revolves around making the right and just choices, guided by one's ethical values and principles.

8) Learning from outcomes: As an effective decision-maker, one should have the audacity to learn from both successful and unsuccessful outcomes because learning from these will only enhance future Decision Making processes. 

Here are some real-life examples that may require you to make some justified decisions: 

a) Choosing between two job offers based on salary, benefits, and career prospects. 

b) Deciding which college or university to attend, considering factors like location, courses offered, and campus culture. 

c) Selecting an investment option after analysing risk, return potential, and financial goals. 

d) Determining the best marketing strategy for a new product launch, considering target audience, budget, and competition. 

e) Making a medical treatment choice for a patient after weighing the benefits, risks, and patient preferences.   

Understanding Problem Solving  

You're now aware of how you can make effective Decision Making. Let us now learn how to effectively carry out Problem Solving tasks in our daily life. Problem Solving is a fundamental cognitive process that entails identifying challenges, finding solutions, and accompliching the set goals. 

It is a logical process aimed at knowing the problem, looking for possible solutions, and choosing the most efficient solution. This helps you to navigate complexities and arrive at successful conclusions. Let us now look at some tips that can help you in Problem Solving effectively:  

1) Problem identification: As a first step towards Problem Solving, effectively carry out tasks. Also, recognise and define the issue or challenge that needs to be addressed.  

2) Data gathering: Gathering relevant information and data related to the problem is essential for understanding its root causes and implications. This helps you become a good problem solver. 

3) Analysis and diagnosis: Analyse the gathered information to identify the underlying causes of the problem. This helps you in devising targeted solutions. 

4) Solution generation: Brainstorming and generating multiple potential solutions is crucial for you when you are exploring diverse approaches to resolve the problem. 

5) Evaluation of alternatives: Carefully evaluate the pros and cons of each solution. This helps you in selecting the most feasible and effective one. 

6) Implementation: After choosing a solution, you have to put the chosen solution into action. This requires planning, coordination, and effective execution. 

7) Creative thinking: Sometimes adopting an open-minded view towards finding a solution to the challenging situations will encourage you to be creative.

8) Root cause analysis: Finding and tackling the cause behind the problem in itself can make a change that lasts and you will get a much better, sustainable solution to your problem.

Let us now see some real-life examples where you need to apply your Problem Solving skills: 

a) Resolving a technical issue with a computer by identifying and troubleshooting the actual cause of the problem. 

b) Finding an alternative transportation route when faced with unexpected road closures. 

c) Addressing a communication breakdown within a team by facilitating open discussions and conflict resolution. 

d) Solving a math problem by applying various Problem Solving Techniques and mathematical principles. 

e)  Fixing a malfunctioning appliance by diagnosing the issue and performing necessary repairs. 

Learn to be more Mindful when you are applying your Problem Solving skills with our Conflict Management Training .  

What are the differences between Problem Solving and Decision Making?

Let us now have a look how Problem Solving and Decision Making skills are different from each other:

1) Definition  

Problem Solving is a step-by-step approach that one uses to identify, analyse, and finally come up with the solution to the issues or challenges they face. It seeks to find the origin of a problem, generate possible ideas or solutions, and choose the best alternative to be implemented. In most researches and practices, the primary aim of Problem Solving is reducing or overcoming the negative impacts of the problem.

On the other hand, the Decision Making process gives the choice, which can be taken from different alternatives. Every process of Decision Making produces a choice like taking action, a strategy, or making a resolution. There is not necessarily a problem but it is applicable in any situation which requires making a choice.

2) Objective 

Problem Solving is an effort to overcome a given obstacle or challenge. Its basic aim is to produce a solution that would change the current situation from less desirable to more desirable. On the other hand, Decision Making aims at selecting the best possible choice from among several alternatives. It could be proactive, such as deciding on an expansion strategy for the market, or it could be reactive, such as deciding on a course of action in response to the moves of a competitor.

3) Nature 

In the Problem Solving process, a problem often arises as a response to a discrepancy between what was expected and what is actually experienced, necessitating a solution. This process is typically reactive. On the other hand, Decision Making can be both proactive and reactive. Proactive Decision Making involves making choices based on anticipation of future events, while reactive Decision Making involves selecting courses of action in response to an immediate situation or problem.

4) Process 

The process of Problem Solving usually starts with understanding and diagnosing the problem. This is followed by brainstorming various solutions and analysing the suitability of each before finally implementing the most fitting one.

On the other hand, the Decision Making process typically begins with identifying a need, often through gathering information. This leads to the search for alternatives and compiling a list of these options. The alternatives are then weighed against criteria such as risks, benefits, and implications before making a choice.

5) Tools and techniques 

In Problem Solving, commonly used tools include root cause analysis, brainstorming, SWOT analysis, and fishbone diagrams (Ishikawa). These tools help in pinpointing the origin of a problem and exploring all possible solutions.

On the other hand, Decision Making often utilises techniques such as decision trees, cost-benefit analysis, pros and cons lists, and grid analysis. These methods assist in evaluating the implications of each available choice.

6) Skills required 

The major skills required in Problem Solving include critical thinking, analytical skills, creativity, and resilience. It is crucial to have the ability to persevere and not be overwhelmed by challenges.

However, Decision Making requires analytical skills, risk assessment, intuition, and foresight. The essential capability here is to be accountable for decisions, which involves predicting the outcomes of every choice

7) Duration and finality  

Problem Solving is time-consuming. It requires a deep dive into understanding the problem before moving on to solutions. The process concludes once a solution is implemented, and the problem is resolved. 

On the other hand, Decision Making can be swift (like everyday decisions) or prolonged (strategic decisions) depending on the complexity of the problems. Once a decision is made, the next step is to implement it, but decisions can sometimes be revisited based on outcomes or changing scenarios. 

Gain a deeper understanding of yourself to take more effective Decision Making with our Decision Making Course .

Tips on how to improve Problem Solving and Decision Making skills

Decision Making and Problem Solving are two most important skills that every individual must possess to excel in their career and in their personal life. There are multiple ways which can be used to improve these skills. Let’s have a look at some of these tips to improve these skills:

Developing skills related to Decision Making and Problem Solving

You can improve your Decision-making and Problem-solving skills by developing other skills such as analytical thinking, creativity and critical thinking. These allied skills will help you boost your analytical thinking skills, will help you think creatively and outside the box. Moreover, honing these skills will help you understand the problems deeply and analyse them without getting partial with your decisions.

Effective communication

Communication is the one of the major keys to success. Effective communication helps in solving problems, miscommunications and helps you understand different perspectives to the same problem. By practicing effective communication, you can convey an information or tasks seamlessly to you team members or colleagues. It helps you understand the root cause of any problem and helps you take an informed decision.

Think about past decisions

It may seem unrelated to you in this context, however, thinking back on your decisions that you made previously can help you not repeat the mistakes, or save you the time that you previously took to make a small decision. Reflecting on past decisions helpin analysing the current problems impartially and help you learn more about your own methods to decide or solve a problem.

Research your industry

Before you make any important decision, or solve out a problem, you need to know about your industry in detail. Since not all situations are same, neither are the industries. Every industry, company or business have their own set of goals, requirements, ideologies, and policies. Whenever you are a part of that specific industry, you should keep in mind, their framework. If you are going beyond their framework or their principles, while solving a problem, there may not be any significant impact taken by your decisions.

Keep yourself updated

It is necessary that you keep yourself updated. As you know that our world is going through many technological advancements. Hence you need to know and update yourself so that you can incorporate all these inventions and discoveries in your industry.

Crack Your Interview with Management Interview Questions and Answers .

How can you integrate Decision Making and Problem Solving? 

Even though Decision Making and Problem-solving have their differences, there are still instances where you need to integrate these two special skills so that you can carry out any challenging tasks or situations, whether it be in the workplace or in your personal life. The following tips will help you show how you can take effective decisions and simultaneously solve problems: 

1) Foster a systematic approach: You can start by adopting a systematic approach to Problem Solving. It involves defining the issue, gathering relevant information, analysing data, generating potential solutions, and evaluating alternatives. Then, you can implement your structured Problem Solving process, which provides a solid foundation for your informed Decision Making. 

2) Identify decision points: You can recognise the key decision points within the Problem-solving process. Then you have to determine which factors require choices and weigh the consequences of each decision on the overall Problem Solving outcome.  

3) Incorporate critical thinking: You can emphasise your critical thinking throughout both Problem Solving and Decision Making. Engage in objective analysis so that you can consider multiple perspectives and challenge assumptions to arrive at well-rounded solutions and decisions.  

4) Utilise data-driven decisions: Ensure that the decisions made during the Problem Solving process are backed by relevant data and evidence. Your data-driven Decision-making minimises biases and increases the chances of arriving at the most suitable solutions. 

Gain a deeper understanding of yourself to take more effective Decision-mking with our Personal & Organisational Development Training . 

Conclusion 

If you integrate both Problem Solving and Decision Making, you can have a more potent approach toward various challenges or tasks. This will help you in making well-informed choices in those circumstances. Moreover, this synergy will empower you to have a Problem -solving mindset to navigate complexities with clarity and achieve effective outcomes. 

Enhance your remote leadership skills with our Managing Remote Teams Course .

Frequently Asked Questions

Problem Solving is both a skill and a competency. It involves the ability to analyse situations, identify issues, generate solutions, and implement them effectively. Developing this capability enhances decision-making, creativity, and adaptability in various personal and professional contexts.

The five steps for Problem Solving and decision-making are: 

1) Define the problem

2) Identify possible solutions 

3) Evaluate alternatives

4) Make a decision 

5) Implement and monitor the chosen solution.

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Decision Making vs. Problem Solving

What's the difference.

Decision making and problem solving are two closely related concepts that are essential in both personal and professional settings. While decision making refers to the process of selecting the best course of action among various alternatives, problem solving involves identifying and resolving issues or obstacles that hinder progress towards a desired outcome. Decision making often involves evaluating different options based on their potential outcomes and consequences, while problem solving requires analyzing the root causes of a problem and developing effective strategies to overcome it. Both skills require critical thinking, creativity, and the ability to weigh pros and cons. Ultimately, decision making and problem solving are interconnected and complementary processes that enable individuals to navigate complex situations and achieve desired goals.

Further Detail

Introduction.

Decision making and problem solving are two essential cognitive processes that individuals and organizations engage in to navigate through various challenges and achieve desired outcomes. While they are distinct processes, decision making and problem solving share several attributes and are often interconnected. In this article, we will explore the similarities and differences between decision making and problem solving, highlighting their key attributes and how they contribute to effective problem-solving and decision-making processes.

Definition and Purpose

Decision making involves selecting a course of action from multiple alternatives based on available information, preferences, and goals. It is a cognitive process that individuals use to make choices and reach conclusions. On the other hand, problem solving refers to the process of finding solutions to specific issues or challenges. It involves identifying, analyzing, and resolving problems to achieve desired outcomes.

Both decision making and problem solving share the purpose of achieving a desired outcome or resolving a particular situation. They require individuals to think critically, evaluate options, and consider potential consequences. While decision making focuses on choosing the best course of action, problem solving emphasizes finding effective solutions to specific problems or challenges.

Attributes of Decision Making

Decision making involves several key attributes that contribute to its effectiveness:

• Rationality: Decision making is often based on rational thinking, where individuals evaluate available information, weigh pros and cons, and make logical choices.
• Subjectivity: Decision making is influenced by personal preferences, values, and biases. Individuals may prioritize certain factors or options based on their subjective judgment.
• Uncertainty: Many decisions are made under conditions of uncertainty, where individuals lack complete information or face unpredictable outcomes. Decision makers must assess risks and make informed judgments.
• Time Constraints: Decision making often occurs within time constraints, requiring individuals to make choices efficiently and effectively.
• Trade-offs: Decision making involves considering trade-offs between different options, as individuals must prioritize certain factors or outcomes over others.

Attributes of Problem Solving

Problem solving also encompasses several key attributes that contribute to its effectiveness:

• Analytical Thinking: Problem solving requires individuals to analyze and break down complex problems into smaller components, facilitating a deeper understanding of the issue at hand.
• Creativity: Effective problem solving often involves thinking outside the box and generating innovative solutions. It requires individuals to explore alternative perspectives and consider unconventional approaches.
• Collaboration: Problem solving can benefit from collaboration and teamwork, as diverse perspectives and expertise can contribute to more comprehensive and effective solutions.
• Iterative Process: Problem solving is often an iterative process, where individuals continuously evaluate and refine their solutions based on feedback and new information.
• Implementation: Problem solving is not complete without implementing the chosen solution. Individuals must take action and monitor the outcomes to ensure the problem is effectively resolved.

Interconnection and Overlap

While decision making and problem solving are distinct processes, they are interconnected and often overlap. Decision making is frequently a part of the problem-solving process, as individuals must make choices and select the most appropriate solution to address a specific problem. Similarly, problem solving is inherent in decision making, as individuals must identify and analyze problems or challenges before making informed choices.

Moreover, both decision making and problem solving require critical thinking skills, the ability to evaluate information, and the consideration of potential consequences. They both involve a systematic approach to gather and analyze relevant data, explore alternatives, and assess the potential risks and benefits of different options.

Decision making and problem solving are fundamental cognitive processes that individuals and organizations engage in to navigate through challenges and achieve desired outcomes. While decision making focuses on selecting the best course of action, problem solving emphasizes finding effective solutions to specific problems or challenges. Both processes share attributes such as rationality, subjectivity, uncertainty, time constraints, and trade-offs (in decision making), as well as analytical thinking, creativity, collaboration, iterative process, and implementation (in problem solving).

Understanding the similarities and differences between decision making and problem solving can enhance our ability to approach complex situations effectively. By leveraging the attributes of both processes, individuals and organizations can make informed choices, address challenges, and achieve desired outcomes.

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• Understanding Strategy...

Understanding Strategy vs. Planning & Team Problem-Solving Tips

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Supermanagers TLDR - Issue 68

“The value is not in having the answers. It’s in being able to facilitate discovery.” – Karen Mangia, VP at Salesforce and Best Selling Author   (episode 112)

Welcome to a new issue of the Supermanagers TLDR, fellow managers and leaders! 

Today, we’re sharing  exclusive interviews  with two best-selling authors:  Roger Martin  (author of  “Playing to Win” ) and  Karen Mangia  (VP at Salesforce and author of  “Success From Anywhere” ):

• The Difference Between Strategy and Planning (with Roger Martin, author of Playing to Win)
• Don’t Hog All the Problems: Why You Need to Include Your Team When Problem Solving (with Karen Mangia, Salesforce Executive)

Here’s a sneak peek at what we covered ⬇

🔵  roger l. martin (author of playing to win) on the difference between strategy and planning.

TLDR:  Most people talk about  strategy , but what they actually mean is  planning . According to Roger Martin,  strategy is the act of making an integrated set of choices, which positions the organization to win ; while planning is the act of laying out projects with timelines, deliverables, budgets, and responsibilities.

“My framework for thinking about strategy says you have the core of strategy, the heart, as I call it, as the  ‘where to play, how to win’  choice,” says Roger Martin. “If we’re Four Seasons, we’re going to play in the luxury hotel space, and we’re going to win on the basis of superior service.”

Four Seasons actively invests in recruiting and training their employees:

• Getting hired requires 4 separate interviews
• There is a clear promotion and development ladder
• Employees get more perks the more senior they become in the organization
• And much more…

Four Seasons deliberately built their operation around answering the question of how to build long-term retention.  They understand that long-term retention allows them to build the best experience for their guests.

Roger was named the “World’s #1 Management Thinker” in 2017, and this episode of the Supermanagers podcast does not disappoint ⬇

🔴  Don’t Hog All the Problems: Why You Need to Include Your Team When Problem Solving

TLDR:  Leadership is lonely. Reality or Myth?

Karen Mangia (VP of Customer Sales & Market Insights at Salesforce) says this is a  myth :

“What I came to understand over time is that leadership is listening. And leadership is about asking great questions and tapping into your curiosity, not going off into a corner, having all the answers or trying to come up with them, and then presenting this perfect-looking package. So very often, I found that I felt stuck because I was trying to operate in isolation and have answers, rather than ask questions, engage those around me, and create possibilities that ultimately lead to shared ownership.”

Karen used to think that being a great leader meant having all the answers. But trying to have all the answers in isolation can be lonely. In  episode 112 , Karen tells us about a time when she decided to be open and honest with her team about a challenge. These were people invested in the outcome, and when given the opportunity to contribute – they rose to the occasion. The eventual outcome was remarkable! Instead of a loss of people and programs, the team grew to 2x the size AND became self-funded. Episode 112 of Supermanagers podcast is a great listen – tune in below 👇

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Email us at  [email protected]  if you’d like to be a part of the conversation! 👇

… and that’s a wrap! We hope that the content we curated inspires you to continue growing as a leader. 

If you enjoyed this issue, please share the newsletter with a colleague or friend –they can  subscribe here !

Thanks for being part of our community,

Manuela & the  Fellow.app  team

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Manuela Bárcenas

Manuela is the Head of Marketing at Fellow, the company building the top-rated AI meeting transcription and management software for remote and hybrid teams. Areas of expertise include Leadership, Meeting Productivity, and Professional Development.

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Making the Difference: Problem Solving vs Decision Making

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Do you ever find yourself stuck between a rock and hard place, unable to decide what the best course of action is?

I have. Whether it’s what desk to put in our new conservatory space (and I’ll tell you what we ended up deciding later), or who to invite to meetings, or managing to order everyone else’s food and then getting so overwhelmed with having made decisions all day for all the people that I couldn’t choose anything for myself.

I left the café with nothing for me and ended up with a couple of slices of peanut butter toast at home.

Making decisions can be difficult for even the most experienced project managers. But before making any decision, it’s important to understand the difference between problem solving and decision making.

Ready to get into it?

Problem solving involves diagnosing issues that arise during projects while decision making requires taking appropriate steps based on those diagnoses. While they may appear similar at first glance, there are key differences in how each process should be approached – understanding these differences will help you make more informed decisions!

Let’s explore both processes as well as their similarities and differences.

What is problem solving?

You’ve been solving problems since you were a baby: how to stand up, how to get your socks off, how to get your parents to bring you your favorite sippy cup.

As an adult, we solve problems every day at work and at home.

So it probably sounds a bit odd to want to define problem solving before we go any further. Surely we all know what we are talking about as we do it all the time?

Humor me. Problem solving is the process of identifying and analyzing a problem, generating potential solutions, and selecting the best solution to address the issue. It involves breaking down complex problems into smaller components and then finding ways to solve them.

The problem solving process

If you think that description sounds linear, then you’d be right. Problem solving fits neatly into a process, one that we don’t even know we’re following most of the time.

The problem solving process typically consists of four steps:

• Identify the problem
• Generate possible solutions
• Evaluate each option
• Select an appropriate course of action.

That does make it sound easy. Wicked problems need a slightly different approach (PMI has a problem solving training course that is brilliant and will help with that).

But for now, let’s stick with a high-level approach that works for most problems.

1. Identify the problem

First, it’s important to understand what caused the issue in order to determine how best to resolve it.

You’d be surprised at how many managers don’t bother to find the root cause of the problem to truly understand it. Use techniques like the 5 Whys or an Ishikawa diagram to dig down into what the problem actually is.

2. Generate possible solutions

Brainstorming is one way to come up with different ideas for potential solutions. You could also interview experts, review lessons learned or innovative solutions from previous projects, research what the rest of your industry is doing or consult customers on what they’d like to see. There are no silly ideas at this point!

Choose the creative approach that gets you a range of options to review.

Read next: How to improve problem solving with lessons learned.

3. Evaluate each option

Once you have several options to consider, you can evaluate each one based on its effectiveness and cost before deciding which one is most suitable for your situation.

Use pairwise prioritisation, multi-criteria decision making or analytical hierarchy process (AHP) to help with the evaluation.

4. Select an appropriate course of action

Now you’ve got all the options for solving your problem, you can actually solve it by choosing a course of action that will sort it out. This is where decision making comes in. in this step you make the decision.

Finally, implement your chosen solution and monitor its progress over time so that any necessary adjustments can be made as needed.

Benefits of problem solving skills

There are many benefits associated with having effective problem solving skills.

These include improved decision making abilities (more on that in a minute), increased creativity, better communication skills, greater confidence when faced with challenging situations, enhanced ability to think critically, more efficient use of resources, improved relationships between colleagues or team members due to shared understanding of goals and increased productivity levels due to fewer mistakes being made during projects or tasks.

(Breathe. That was a long sentence, sorry.)

All these advantages make problem solving an invaluable skill in both personal life and professional life scenarios.

What is decision making?

Basically, decision making is the process of selecting a course of action from a number of alternatives. It involves gathering information, weighing options, and choosing the best option for achieving a desired outcome.

But how is that different to problem solving?

Decision making is the process of doing Step 4 of the problem solving process. It’s the choice making, option selection, conclusion of the analysis and thinking.

It’s decisive (duh), purposeful, specific. It removes the ambiguity of the ‘what do we do?’ and helps the team move towards the ‘OK, how do we do that?’

It brings action to a situation.

The decision making process

There is a simple method for decision making too, although the actual decision itself might be tough to make.

• Identify that a decision is required
• Ensure you have the data to make the decision
• Make the decision
• Tell whoever needs to implement the decision

1. Identify that a decision is required

The decision-making process typically begins with identifying what decision needs to be made. Are you making the right decision, or is there something else, deeper, different that is really what’s required?

In this step you also want to identify who is making the decision. That could be your project sponsor, a panel, you by yourself, a committee or whoever. Getting this step clear saves headaches later.

2. Ensure you have the data to make the decision

Do you have all the info you need to make the decision? If not, get it.

When decisions are made quickly but thoughtfully, they can save time and resources while still producing quality results.

Major decisions need more time spent on this step to make sure you understand all the variables.

3. Make the decision

After considering all potential solutions, it’s time to make a choice based on what will yield the best results for everyone involved.

This is the hard part: make the decision! The person or people responsible for this should weigh up the data and use their professional judgement to choose the right course of action. Decision trees can be useful here to avoid unconscious bias (or conscious bias!).

Obviously this is harder for complex decisions. What vendor to choose for a 3-year outsourcing arrangement is harder to decide than what venue to book for a team away day.

4. Tell whoever needs to implement the decision

Great – you’ve made the call and know what to do, but does the rest of the team? Don’t keep the decision to yourself!

Make sure whoever needs to know the next steps is aware that the decision has been made so they can implement it and take the right action.

Having confidence in decisions leads to greater trust between team members and better collaboration overall, which can lead to improved project outcomes over time. Well-made decisions often create opportunities for growth within teams by allowing them to learn from their mistakes as well as their successes along the way.

Similarities between problem solving and decision making

Problem solving and decision making sound very similar, right?

Well, that is true. Both processes involve gathering information, analyzing it, and coming up with solutions or courses of action. They both require critical thinking skills to identify potential solutions or options that are most likely to be successful.

The processes use a similar flow

Both processes involve identifying a problem or issue, researching possible solutions, evaluating those solutions based on criteria such as cost-effectiveness or feasibility, selecting an option from among the available choices, implementing the chosen solution, and you’d also want to monitor its effectiveness over time.

The process can be iterative if necessary; if one solution does not work out as expected then another may need to be tried until a satisfactory outcome is achieved.

They both produce a satisfactory solution

Problem solving and decision making usually lead to some kind of action being taken in order to address a given issue or situation. Problem solving often involves finding creative solutions for complex problems, while decision making typically entails selecting a course of action from several possibilities after carefully evaluating each option’s advantages and disadvantages.

But ultimately, the goal is for something positive (or at least neutral) to come out of the helpful process so that whatever challenge was initially presented can be effectively addressed.

Despite being so similar you could pretty much interchange them in some circumstances, there are some differences.

Differences between problem solving and decision making

Although they have similarities in terms of the process used to come up with a solution, their goals differ significantly.

Process goals are different

The goal of problem solving is to find a solution to an existing issue. It involves identifying the cause of a problem and then finding ways to address it. Problem solving often requires input from multiple stakeholders who can provide different perspectives on how best to solve the issue at hand.

On the other hand, decision making focuses on choosing the best option from multiple alternatives. This could include selecting between competing products or services or deciding which strategy will be most effective for achieving certain objectives.

In other words: decision making doesn’t necessarily mean there is a problem. We make decisions every day about small things and big things, but they aren’t all problems that need the creative step of solutioning.

Sometimes a decision just needs to be taken and the options are clearly known.

They require different stakeholders

Another key difference between problem solving and decision making is that while problem solving typically requires input from multiple stakeholders, decision making is usually done by one individual or group who has access to all relevant information needed for the decision-making process.

To give you an example. Let’s say on a technical project the development team hit a problem. They have to bring in various subject matter experts to research and identify the parameters involved. They consult, brainstorm and debate. It’s a group effort, and it’s likely to end in a solution.

However, if I need my project sponsor to choose between two risk treatments, I’ll take him my recommendation and a summary of options and he’ll simply choose. Done.

Decisions are made based on what is known about a situation rather than relying on external opinions or advice when trying to make an informed choice about what course of action should be taken next.

They produce different results

The nature of both processes also differs in terms of the types of solutions they produce. Problem solving typically results in creative solutions that can be implemented over time, while decision making produces immediate choices from among existing alternatives without necessarily creating something new or unique.

Both processes involve the identification of a problem or issue, the collection of information to evaluate possible solutions, and an analysis of potential outcomes. The main difference between them is in their goals: problem solving seeks to identify the root cause of an issue and develop a solution that will address it; decision making focuses on selecting from among available options.

Both processes require careful consideration of facts and opinions before any action is taken. Problem solving often involves more people than decision making as it requires collaboration to identify underlying causes and brainstorm potential solutions. Decision makers may consult with others for input but ultimately make decisions independently based on their own judgment.

Still got a question?

What is the difference between decision and decision making.

A decision is the act of making a choice between two or more alternatives. Decision making is the process by which decisions are made. It involves gathering information, analyzing data, evaluating alternatives and choosing a course of action based on this analysis. The outcome of the process is the decision. The decision-making process also includes monitoring progress to ensure that goals are being met and taking corrective action if needed.

What is the importance of problem-solving and decision making?

Problem-solving and decision making are essential skills for project managers and managers in general. The processes keep work moving by making sure problems get solved and decisions get made so team members are not blocked from finishing their tasks.

What are the steps in problem-solving and decision making?

Problem-solving and decision making involve a series of steps that can help ensure the best possible outcome. The first step is to identify the problem or opportunity, then analyze it by gathering relevant information and evaluating potential solutions. After considering all options, select an appropriate solution and develop an action plan for implementation. Finally, monitor progress to ensure success and make necessary adjustments along the way. By following these steps, project managers can effectively manage projects while minimizing risks and maximizing results.

Before you go…

Sometimes there isn’t a right decision – it’s simply important to make a decision. As for the desk, in the end, we used a piece of furniture we already had upstairs and didn’t buy one at all.

I spent a morning measuring and researching options, and I’ll never get that time back, but that’s OK.

As a leader, you should be skilled at solving problems and making decisions, and the processes that support them. However, you don’t have to be doing all the solving and making all the calls yourself. As long as you facilitate the process and get the right people in the room, you can step back and let the experts do their thing.

Let the right people do the work and create an environment where your projects move forward because everyone’s got what they need to keep things moving.

Project manager, author, mentor

Elizabeth Harrin is a Fellow of the Association for Project Management in the UK. She holds degrees from the University of York and Roehampton University, and several project management certifications including APM PMQ. She first took her PRINCE2 Practitioner exam in 2004 and has worked extensively in project delivery for over 20 years. Elizabeth is also the founder of the Project Management Rebels community, a mentoring group for professionals. She's written several books for project managers including Managing Multiple Projects .

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A 5-Step Problem-Solving Strategy

Specify the problem – a first step to solving a problem is to identify it as specifically as possible.  It involves evaluating the present state and determining how it differs from the goal state.

Analyze the problem – analyzing the problem involves learning as much as you can about it.  It may be necessary to look beyond the obvious, surface situation, to stretch your imagination and reach for more creative options.

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research problems for which you lack complete information. Get help.

Formulate possible solutions – identify a wide range of possible solutions.

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consider similar problems and how you have solved them

Evaluate possible solutions – weigh the advantages and disadvantages of each solution.  Think through each solution and consider how, when, and where you could accomplish each.  Consider both immediate and long-term results.  Mapping your solutions can be helpful at this stage.

Choose a solution – consider 3 factors:

compatibility with your priorities

amount of risk

practicality

Keys to Problem Solving

Think aloud – problem solving is a cognitive, mental process.  Thinking aloud or talking yourself through the steps of problem solving is useful.  Hearing yourself think can facilitate the process.

Allow time for ideas to "gel" or consolidate.  If time permits, give yourself time for solutions to develop.  Distance from a problem can allow you to clear your mind and get a new perspective.

Talk about the problem – describing the problem to someone else and talking about it can often make a problem become more clear and defined so that a new solution will surface.

Decision Making Strategies

Decision making is a process of identifying and evaluating choices.  We make numerous decisions every day and our decisions may range from routine, every-day types of decisions to those decisions which will have far reaching impacts.  The types of decisions we make are routine, impulsive, and reasoned.  Deciding what to eat for breakfast is a routine decision; deciding to do or buy something at the last minute is considered an impulsive decision; and choosing your college major is, hopefully, a reasoned decision.  College coursework often requires you to make the latter, or reasoned decisions.

Decision making has much in common with problem solving.  In problem solving you identify and evaluate solution paths; in decision making you make a similar discovery and evaluation of alternatives.  The crux of decision making, then, is the careful identification and evaluation of alternatives.  As you weigh alternatives, use the following suggestions:

Consider the outcome each is likely to produce, in both the short term and the long term.

Compare alternatives based on how easily you can accomplish each.

Evaluate possible negative side effects each may produce.

Consider the risk involved in each.

Be creative, original; don't eliminate alternatives because you have not heard or used them before.

An important part of decision making is to predict both short-term and long-term outcomes for each alternative.  You may find that while an alternative seems most desirable at the present, it may pose problems or complications over a longer time period.

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Making decisions and solving problems are two key areas in life, whether you are at home or at work. Whatever you’re doing, and wherever you are, you are faced with countless decisions and problems, both small and large, every day.

Many decisions and problems are so small that we may not even notice them. Even small decisions, however, can be overwhelming to some people. They may come to a halt as they consider their dilemma and try to decide what to do.

Small and Large Decisions

In your day-to-day life you're likely to encounter numerous 'small decisions', including, for example:

Tea or coffee?

What shall I have in my sandwich? Or should I have a salad instead today?

What shall I wear today?

Larger decisions may occur less frequently but may include:

Should we repaint the kitchen? If so, what colour?

Should we relocate?

Should I propose to my partner? Do I really want to spend the rest of my life with him/her?

These decisions, and others like them, may take considerable time and effort to make.

The relationship between decision-making and problem-solving is complex. Decision-making is perhaps best thought of as a key part of problem-solving: one part of the overall process.

Our approach at Skills You Need is to set out a framework to help guide you through the decision-making process. You won’t always need to use the whole framework, or even use it at all, but you may find it useful if you are a bit ‘stuck’ and need something to help you make a difficult decision.

Decision Making

Effective Decision-Making

This page provides information about ways of making a decision, including basing it on logic or emotion (‘gut feeling’). It also explains what can stop you making an effective decision, including too much or too little information, and not really caring about the outcome.

A Decision-Making Framework

This page sets out one possible framework for decision-making.

The framework described is quite extensive, and may seem quite formal. But it is also a helpful process to run through in a briefer form, for smaller problems, as it will help you to make sure that you really do have all the information that you need.

Problem Solving

Introduction to Problem-Solving

This page provides a general introduction to the idea of problem-solving. It explores the idea of goals (things that you want to achieve) and barriers (things that may prevent you from achieving your goals), and explains the problem-solving process at a broad level.

The first stage in solving any problem is to identify it, and then break it down into its component parts. Even the biggest, most intractable-seeming problems, can become much more manageable if they are broken down into smaller parts. This page provides some advice about techniques you can use to do so.

Sometimes, the possible options to address your problem are obvious. At other times, you may need to involve others, or think more laterally to find alternatives. This page explains some principles, and some tools and techniques to help you do so.

Having generated solutions, you need to decide which one to take, which is where decision-making meets problem-solving. But once decided, there is another step: to deliver on your decision, and then see if your chosen solution works. This page helps you through this process.

‘Social’ problems are those that we encounter in everyday life, including money trouble, problems with other people, health problems and crime. These problems, like any others, are best solved using a framework to identify the problem, work out the options for addressing it, and then deciding which option to use.

This page provides more information about the key skills needed for practical problem-solving in real life.

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As always at Skills You Need, our approach to these key skills is to provide practical ways to manage the process, and to develop your skills.

Neither problem-solving nor decision-making is an intrinsically difficult process and we hope you will find our pages useful in developing your skills.

Start with: Decision Making Problem Solving

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What is the difference between search and planning?

I'm reading the book Artificial Intelligence: A Modern Approach (by Stuart Russell and Peter Norvig).

However, I don't understand the difference between search and planning. I was more confused when I saw that some search problems can be determined in a planning way. My professor explained to me in a confusing way that the real difference is that search uses a heuristic function, but my book says that planning uses a heuristic too (in chapter 10.2.3).

I read this Stack Overflow post that says in a certain way what I'm saying.

So, what is the difference between search and planning?

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2 Answers 2

What is planning.

Planning (aka automated planning or AI planning ) is the process of searching for a plan , which is a sequence of actions that bring the agent/world from an initial state to one or more goal states, or a policy , which a function from states to actions (or probability distributions over actions). Planning is not just the use of a search algorithm (e.g. a state-space search algorithm, such as A*) to find the plan, but planning also involves the modelling of a problem by describing it in some way, e.g. with an action language , such as PDDL or ADL , or even with propositional logic.

There are different planning problems , such as

• classical planning
• planning in Markov Decision Processes (which are mathematical models that describe stochastic environments/problems, i.e. environments where an action $a$ may not always lead to the same outcome)
• temporal planning (i.e. when multiple actions can be taken at the same time and/or their duration may be different/variable)
• hierarchical vs non-hierarchical planning

There are different planning approaches , such as

planning by state-space search (with or without heuristics), i.e. formulate the planning problem so that you can apply a state-space search algorithm (examples of state-space search algorithms are A*, IDA*, WA*, DFS or BFS); this is probably the approach to planning that makes people confuse planning with search (which typically implicitly refers to state-space search)

planning by SAT , i.e. formulate the planning problem so that you can you a SAT solver to solve it (e.g. SATPLAN )

planning by solving constraint satisfaction problems (similar to SAT)

planning by "symbolic" search methods (such as Binary Decision Diagrams)

There are many different planning algorithms or planners (of course, each of these corresponds to some approach or is used to solve a particular planning problem), such as

• policy iteration in the context of Markov decision processes
• STRIPS (which stands for Stanford Research Institute Problem Solver, but note that STRIPS also and maybe more commonly refers to the corresponding description language used to describe the planning problem)

What is search?

In the context of AI, when people use the word search , they can either implicitly refer to state-space search (with algorithms like A*) or, more vaguely/generally, to any type of search, e.g. gradient descent can also be viewed as a search algorithm, typically, in a continuous state space (rather than discrete state space), e.g. the space of real-valued vectors of parameters for a certain model (e.g. a neural network).

What is the difference between planning and search?

The difference should more or less be clear from my first paragraph above that describes planning: search is used for planning , but the planning process/study also includes other things, such as describing the problem with an action language, such as PDDL. You can also view planning as an application of search algorithms of different types. In any case, planning is about searching for plans or policies. You should probably check this course for more info. This definition of (automated) planning is consistent with our usual notion of planning .

Often terminology is vague and if this will be a test item for school I would circle back with teacher with my response before accepting it.

They key difference is prior knowledge.

Search - when one searches they explore without prior knowledge. This would likely be a stochastic process of trial and error. There is no expectation about state transitions. Example is exploring an abandoned house. You have no idea what you'll find next.

Plan - you have a model of state transitions and can make predictions. One step or multi step prediction capabilities allow you to predict an outcome and therefore choose an outcome from several options.

• 1 $\begingroup$ You can search with prior knowledge. This is typically done with heuristics and this is known as informed search. $\endgroup$ –  nbro Commented Mar 19, 2021 at 9:02
• $\begingroup$ Your over thinking this person's question and not directly answering it at the level he is asking it. My answer was purposely simplistic because the question is simplistic $\endgroup$ –  cagney Commented Mar 19, 2021 at 16:28
• 1 $\begingroup$ But it's wrong to say that search cannot use domain knowledge. A* (a search algorithm) is an informed search algorithm that can use domain knowledge. I'm not overthinking anything. My answer is based on my knowledge of the topic and the info that I've found after having researched the topic even further for a few hours. $\endgroup$ –  nbro Commented Mar 19, 2021 at 16:32
• $\begingroup$ Although your answer has correct information and mine leaves out lots of information I think that the person asking it needed a simple answer. If you boiled down your answer into a single qualitative description of the difference im curious what it would be. $\endgroup$ –  cagney Commented Mar 19, 2021 at 16:37
• 1 $\begingroup$ The main difference between planning and search is given in my first paragraph. In other words, I would say that planning is an application of search (or applied search) or a "specialized" search in order to achieve a goal starting from some initial conditions. One thing that needs to be super clear is: planning definitely involves some notion of "search", but (almost) any problem-solving technique involves search anyway (maybe apart from random guessing). $\endgroup$ –  nbro Commented Mar 19, 2021 at 16:39

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Problem-Solving and Decision-Making

• First Online: 17 October 2019

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There are major differences between decision-making and problem-solving. The two entities differ in discrete and subtle ways and should be resolved at different levels within teams or organizations. Decision-making usually involves more experienced higher-order, process-dependent, and non-linear skills. The impact of decisions is usually more global, long-term, and less quantifiable and qualifiable.

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Bibliography

Bosk CL. (2003). Forgive and Remember: Managing Medical Failure . Second Edition. Chicago, IL: University of Chicago Press; 2003.

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Montgomery K. How Doctors Think: Clinical Judgment and the Practice of Medicine . New York, NY: Oxford University Press; 2006.

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Pellegrino ED, Thomasma DC. A Philosophical Basis of Medical Practice. New York, NY: Oxford University Press; 1981.

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Vordermark II, J.S. (2019). Problem-Solving and Decision-Making. In: An Introduction to Medical Decision-Making. Springer, Cham. https://doi.org/10.1007/978-3-030-23147-7_3

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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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