PM Quest learning library

Lesson 7: Design Thinking

Apply the design thinking methodology to solve complex problems creatively.

What is Design Thinking?

Design thinking is a mindset and approach to problem-solving and innovation anchored around human-centered design.

Tim Brown, CEO of IDEO defined design thinking as:

“Design thinking is a human-centered approach to innovation that draws from the designer's toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.”

It is a non-linear and iterative process that teams use to understand users, challenge assumptions, redefine problems and create innovative solutions to prototype and test.

Design thinking is different from other innovation and ideation processes in that it’s solution-based and user-centric rather than problem-based. This means it focuses on the solution to a problem instead of the problem itself.

For example, if a team is struggling with transitioning to remote work, the design thinking methodology encourages them to consider how to increase employee engagement rather than focus on the problem (decreasing productivity).

The essence of design thinking is human-centric and user-specific. It’s about the person behind the problem and solution, and requires asking questions such as “Who will be using this product?” and “How will this solution impact the user?”


The Five Stages of Design Thinking

The design thinking process has five phases: Empathize, Define, Ideate, Prototype, and Test.

Stage 1: Empathize

The first, and arguably most important, step of design thinking is building empathy with users. By understanding the person affected by a problem, you can find a more impactful solution.

Even though we call it empathize, during this stage, the process is typically collecting quantitative and qualitative data through user research. Through this data, we try to identify the feelings and needs of the people which eventually leads to the identification of the user problems and their pain points.

Stage 2: Define

Once you accumulate the information, you analyze the observations and synthesize them to define the core problems. These definitions are called problem statements.

A lot of different methods are used to break down the insights from empathize stage and frame them into a problem statement.

Stage 3: Ideate

This phase of design thinking is developing solutions to the problem. The goal is to ultimately overcome cognitive fixedness and devise new and innovative ideas that solve the problems you identified.

This begins with what most people know as brainstorming. Hold nothing back during brainstorming sessions — except criticism. Infeasible ideas can generate useful solutions, but you’d never get there if you shoot down every impractical idea from the start.

Stage 4: Prototype

This is an experimental phase. In this stage, the aim is to identify the best possible solution for each problem. The team produces inexpensive, scaled-down versions of the product (or specific features found within the product) to investigate the ideas.

Stage 5: Test

The team tests these prototypes with real users to evaluate if they solve the problem. The test might throw up new insights, based on which the team might refine the prototype or even go back to the Define stage to revisit the problem.

Remember: This step isn’t about perfection, but rather experimenting with different ideas and seeing which parts work and which don’t. The five stages are not a one-way checklist; testing can send the team back to redefine the problem or learn more about the user.

Apply design thinking to a product problem

Pick one narrow experience, such as booking a medical appointment. Gather evidence about the people involved, write a point-of-view statement, generate several solutions, and prototype only the riskiest interaction. Test whether a representative user can complete the task and explain what they expect to happen next.

Avoid using the framework as workshop theatre. Each stage should reduce a specific uncertainty. If you already have strong evidence about the problem, spend less time rediscovering it and more time testing the riskiest solution assumption.