Problem Framing vs. AI Problem Framing: What’s the Difference

For the last 8 years at Design Sprint Academy, we’ve been pioneering Problem Framing to help teams make smarter strategic decisions. We’ve done it with companies around the world — from banks and telcos to global retailers and healthcare innovators.

Back then, the biggest challenge wasn’t finding ideas.
It was money, time and executive support.

Problem Framing fixed that. It got senior decision-makers on the same page. It turned fuzzy mandates into clear problems. And it gave product teams enough direction to move fast — without guessing.

But in the AI era, everything has flipped.

Resources are unlocked. Projects get approved fast. Executives don’t need convincing — they’re already sold.

All it takes is two letters: AI.

The pressure to “do something with AI” is so high that many leaders skip the thinking, set a vague vision, wrap it in a mandate, and toss it downstream — straight into the hands of the doers: product teams, AI engineers, and designers.

“Here’s the AI opportunity. Make it happen.”

Those teams are eager. Solution mode kicks in. Tools open. Prototypes appear. Something — anything — starts to move.

But strategy?
Cross-functional thinking?
Space to ask “Are we building the right thing?”

That’s often missing.

And in the rush to launch something smart, teams end up shipping:

• AI solutions that solve half-problems
• AI features looking for a problem
• Copycat implementations that don’t move the business

Not because teams aren’t capable — but because they skipped the step that sets direction.

That gap is why we created AI Problem Framing at the beginning of 2025.

It builds on our original Problem Framing work, but it serves a different moment.

Both are alignment workshops, but they run at different levels, involve different people, and lead to different decisions.

Here is how they differ.

Problem Framing

For Big Decisions, Bold Moves, and High-Stakes Alignment

Problem Framing is a 1-day workshop for senior leaders. Its job is to define the strategic problem before teams, budgets and technology are committed.

This work happens at a high strategic altitude. There are no solutions on the table yet. The question is whether a problem deserves attention at all.

When to use it

When you’re about to make a big bet — on a transformation, a new product, a roadmap, or a strategic shift. This is the moment to slow down and ask:

‍“Are we solving the right problem — and is it worth solving?”

Key characteristics

• Based on real context and existing evidence (research data)
• Involves 6–8 senior leaders from different parts of the business
• Focuses on strategy, customer insight, and trade-offs
• Ends with a tight problem statement linked to business impact

Result

Clarity at the top: what matters now, what can wait, and what should be left alone.

AI Problem Framing

For AI Builders, Fast Focus, and Execution-Ready Clarity

AI Problem Framing operates at a lower, tactical-to-operational altitude.

At this stage, the organization has already decided AI matters. Broad AI opportunities have been defined by leadership. What's missing is focus.

AI Problem Framing is a 1-day workshop for cross-functional AI pods to compare AI use cases and decide what to pursue next.

When to use it

When teams are staring at multiple possible AI directions and asking:

‍“Which of these is worth building and which should we drop?”

This is not about leadership alignment.
It's about making the right call.

Team composition: AI Discovery Pods

AI Problem Framing is designed to be run by AI Discovery Pods — small, empowered, cross-functional teams that bring different views into the same room:

• Product or business leads
• Designers
• Engineers or data scientists
• AI specialists
• Domain experts

These pods sit between two problems: business needs that are still fuzzy and AI capabilities that sound impressive but lack shape.

Putting this pod together forces reality checks, early:

• Is it buildable?
• Does it matter to users?
• Does it support the business?
• Can it be done within the constraints we actually have?

Key characteristics

• Requires no upfront research or data — just the right experts in the room
• Focuses on mapping, assessing, and stress-testing AI use cases
• Tests value, effort, risk, and assumptions
• Produces a single decision artifact

The output: the AI Use Case Card

The output of AI Problem Framing is not an idea, a concept, or an opportunity list.

It’s a clear AI Use Case Card.

This card captures:

• The user and context
• The problem to be solved
• Where AI adds value (and where it doesn’t)
• Expected business impact
• Risks, constraints, and open assumptions

This card is meant to be reviewed, ranked, tested, or dropped. It moves straight into validation through an AI Design Sprint.

Result

Clear, AI use cases that teams can confidently prototype, pitch, or deprioritize — without wasting months building the wrong thing.

The difference — in plain terms

• Problem Framing decides which strategic problem deserves attention.
• AI Problem Framing decides how AI could be applied in a specific, workable way.
• The AI Use Case Card connects strategy and delivery.

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Problem Framing vs. AI Problem Framing - at a glance

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Conclusion

These methods solve different problems at different times, at different altitudes.

Problem Framing helps leaders choose direction.
AI Problem Framing helps teams what to build.

Use each where it fits, and you reduce wasted effort while increasing the odds that AI work leads to real business change.