Create

Design Thinking × AI

From fuzzy problem to tested prototype — in days.

Problem reformulation, AI-assisted ideation, rapid prototyping, user testing, and a scaling plan — all in one structured engagement. Design Thinking rigour with AI tools applied at every step, turning ambiguous challenges into tangible, validated solutions.

Innovation Teams Business Teams Product Teams
2 to 3 days In-person Hybrid
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Why the combination works

AI accelerates ideation. Design Thinking keeps it human-centred.

Design Thinking without AI is slow in the ideation phase — teams get stuck iterating on the same concepts because the divergence phase runs out of steam. AI without Design Thinking is fast but directionless — lots of ideas, none of them properly validated against a real user problem.

Together, they solve both problems. AI accelerates divergence dramatically — generating, expanding, and combining concepts at a pace no team can match manually. Design Thinking keeps the process anchored to a real user problem, validated empathy data, and tested assumptions. The result: better ideas, faster, tested sooner.

DT without AI
Rigorous but slow. Ideation often plateaus. Prototype fidelity limited by time and resource.
AI without DT
Fast and generative. But ideas are disconnected from user empathy. Hard to know which to pursue.
DT × AI
Human-centred rigour at the speed of AI. Ideas grounded in real user problems, tested before the workshop ends.

The DT × AI framework

How Design Thinking stages map to AI tooling.

Empathise
AI-assisted synthesis
AI synthesises user research, interview transcripts, and existing data to surface patterns and tensions your team may not have spotted. Faster empathy map generation.
Define
Problem reformulation
AI challenges and expands "How Might We" statements. Structured prompt frameworks that push the team past their first-draft problem framing to something sharper and more generative.
Ideate
AI-accelerated divergence
The ideation phase where AI makes the biggest difference. Generate 50 concepts in the time you'd normally generate 10. Then apply human judgment to select, combine, and refine.
Prototype
AI-built prototypes
AI-assisted rapid prototyping. Working, testable prototypes built during the workshop — not wireframes, not slides. Real things users can interact with.
Test
Structured user testing
Prototypes tested with real users before the workshop ends. AI-assisted synthesis of test findings. Go/no-go recommendation per concept, with rationale documented.

The structure

Day by day.

Day 1

Problem reformulation

Deep dive into the challenge. Empathy synthesis, assumption mapping, "How Might We" reformulation. By end of day one, the team has a validated problem statement — sharper than when they arrived.

Day 2

Ideation & prototyping

AI-accelerated ideation: from problem to concept library. Concept selection and prototyping sprint. By end of day two, working prototypes exist for 2–3 concepts, ready for testing.

Day 3

Testing & scaling plan

Structured user testing with real users. AI synthesis of findings. Concept prioritisation and a concrete scaling & implementation roadmap. The team leaves with a decision and a plan.

You walk away with

Four deliverables. Zero ambiguity.

  • Reformulated, validated problem statement — sharper than you arrived with
  • AI-generated and tested solution concepts — with user testing data
  • Working prototype of the priority concept
  • Scaling & implementation roadmap — concrete next steps with ownership

We came in with a problem we'd been debating for six months. Day one alone reformed the question entirely — and we ended up solving a different, much more valuable problem than the one we thought we had. The prototype we built on day two is now in our product roadmap for Q3.

Inno
Head of Innovation
Healthcare · Belgium

Common questions

FAQ

  • No. The workshop is fully facilitated and the methodology is introduced as we go. Teams with prior DT experience will move faster through certain stages, but the facilitation is designed for participants who have never encountered Design Thinking before. If your team has strong DT experience, we focus more heavily on the AI augmentation layers — which are always new to teams regardless of their DT background.
  • A rough problem area is helpful — but you don't need a sharp problem statement. One of the core outputs of Day 1 is a reformulated, validated problem statement. Many of our best DT × AI sessions start with a fuzzy "we know something isn't working" and end with a precise, testable problem statement that the team hadn't articulated before. Come with a challenge, not a solution.
  • Mixed teams produce the best outcomes. Include people who understand the problem (domain experts, customer-facing staff), people who will implement the solution (product, tech, ops), and at least one senior decision-maker who can commit to next steps on Day 3. Optimal group size is 8–16 participants. Larger groups can be accommodated in parallel sub-teams with a shared synthesis session.

Continue the journey

What comes next.

Create

Prototype Sprint

Take the validated concept from the DT × AI workshop and build a production-ready prototype in 10 business days. Fixed scope, guaranteed delivery.

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Learn

AI Innovation Sprint

If you have a team that needs to frame and prototype a use case in a single day rather than 2–3, the AI Innovation Sprint is the faster-format option.

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Every transformation starts with a free AI Maturity Assessment.

30 minutes. Senior consultant. No commitment. Results visible within weeks.

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