How to Operationalize the Product Operating Model, the Double Diamond, and Design Thinking

Every company today wants to innovate faster — to think like a startup and act like an enterprise. Organizations invest in Design Thinking, The Double Diamond, or The Product Operating Model. These frameworks/methods/set of principles define what good looks like. They describe the ideal journey — from discovering the right problems to delivering the right solutions.

But most organizations stop there. The frameworks/principles stay on slides. And teams are left asking the same question:

“How do we actually make this work?”

The Problem: Great Theory, Poor Execution

These methods, frameworks, principles don’t fail because they’re wrong. They fail because they’re not operationalized — not translated into clear steps, roles, and decisions that teams can repeat.

So, what you get is:

• Design Thinking as sticky-note theatre.
• Double Diamond as a poster on the wall.
• Product Operating Model as a nice PowerPoint — but not a lived system.

Organizations need systems that make frameworks work. That’s exactly what AI Problem Framing and AI Design Sprints do.

They turn the theory into practice — step by step. The image below shows how.

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AI Problem Framing — to explore the problem space

If you map the Double Diamond, the Product Operating Model, and Design Thinking, they all start with the same question:

“How do we decide which problems to solve?”

But how exactly do you do that — especially when seven people from different departments walk into a room with different priorities, different languages, and a vague AI mandate?

That’s where AI Problem Framing shines.

It turns theory into something repeatable and actionable — a structured, one-day workshop that helps teams go from scattered ideas to well-framed, high-value AI opportunities.

Think of it less like a brainstorming session, and more like a recipe. Each step has a purpose:

• Start with context, not technology — map existing initiatives, business goals, and available data.
• Bring customers in early — use guided templates like proto-personas and journey maps to make needs and friction points visible.
• Filter ideas through shared criteria — growth, pragmatism, money, and data readiness — to align the team around what’s both desirable and feasible.
• End with clarity — three validated AI Use Case Cards that summarize the “why,” “who,” and “what next.”
  

Every activity is time-boxed, has a defined outcome, and builds on the one before it. It’s not creative chaos — it’s structured collaboration that produces tangible results. By the end of the day, the team has moved from “we should do something with AI” to “we know exactly which problem is worth solving first — and why.”

That’s how you bring the first half of the Double Diamond, or the discovery stage of Design Thinking (Empathize & Define), to life.

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AI Design Sprint — structure for the solution space

Once the right AI use case is framed, the next challenge is building and validating the solution — and that’s where the AI Design Sprint comes in.

It gives teams a clear, time-boxed structure to move from insight to impact — testing ideas fast, aligning across functions, and learning from real users before committing resources.

Here’s how the AI Design Sprint maps across the Product Operating Model, the Double Diamond, and Design Thinking:

• In the Product Operating Model, it represents the “How to Solve Problems” phase — turning framed opportunities into tested, validated solutions.
• In the Double Diamond, it maps to the second diamond — Develop and Deliver — where teams move from clarity to tangible outcomes.
• In Design Thinking, it brings to life the stages of Ideate, Prototype, and Test — translating ideas into experiments, learning fast from real users, and iterating with evidence.

An AI Design Sprint is a four-day structured process that bridges the gap between concept and validation — combining human-centered design with the rigor AI projects demand. Each day has a clear focus and measurable outcome:

Day 1 – Understand & Define

• Teams align on the AI Use-Case Card from Problem Framing and zoom in on what needs to be solved.
• They use the AI Friction Heat Map to surface enablers and blockers across data, tech, integration, and legal dimensions.
• Then, they map the moment of AI intervention, identifying exactly where in the user journey AI adds value without creating friction.
• The day closes with a Checklist Exercise — defining differentiators, guardrails, success signs, and risks.
• By the end of Day 1, the team knows what success looks like and where to focus.

Day 2 – Ideate & Decide

• Through Lightning Demos, Crazy 8s, and Solution Sketching, the team explores diverse solution paths.
• They use Heat Maps, Speed Critiques, and Super Votes to prioritize, then storyboard the concept they’ll prototype.
• The outcome: one clear solution hypothesis ready for testing.

Day 3 – Prototype & Test

• In the morning, the team plans and builds the prototype, writes the interview script, and runs a dry run.
• By afternoon, they conduct three user interviews, gathering real feedback on the concept.
• Testing happens hours after building — not months later.

Day 4 – Iterate & Test Again

• Teams refine the prototype based on feedback and test again with three new users.
• They synthesize results and extract insights that directly inform the next build.
• Iteration is no longer optional — it’s built into the structure.
• By the end, the team holds a validated AI prototype backed by evidence, not opinion.

Together, the AI Problem Framing Workshop and the AI Design Sprint form a continuous loop:

Problem Validation → Solution Validation

A complete system for discovery and experimentation — fast, collaborative, and repeatable.

From Validated Idea to Build

Together, these two methods bridge the gap between theory and execution. They help teams find the right problems, test the right solutions, and build with clarity — instead of chasing trends or relying on guesswork.

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Why This Matters Now

Most frameworks look good on paper — until people have to use them. That’s when the friction shows up: different interpretations, competing priorities, and meetings that feel productive but lead nowhere.

AI isn’t creating that problem. It’s exposing it.

When things move this fast, clarity becomes a competitive advantage.

You can’t afford frameworks that live in slide decks. You need shared systems that help people think clearly together — under pressure, across functions, and with real data on the table.

That’s the promise of Problem Framing and Design Sprints. They don’t add more theory. They make theory work.

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Want to see this system in action?

Join one of our Agentic AI Bootcamps — where teams go from AI ambition to validated solutions in just five days.