Not a brainstorm. Not a strategy document. A structured process that moves your cross-functional team from scattered AI ideas to one use case — specific enough to hand to engineering, clear enough to defend to leadership.
Engineering wants to build what's technically interesting. Business wants what sounds strategic. Product is somewhere in between. By the time anyone commits to something, the problem behind it has never been properly examined — and six months later, nobody can defend why that use case was chosen over the others.
The cost isn't just the failed pilot or stalled adoption. It's the six months of engineering time that went with it — and the credibility you spent defending it.
AI Problem Framing is built for the moment before the wrong bet gets made.
Your team has more AI ideas than capacity to pursue them, and no structured way to filter.
Engineering, product, and business are each defining the problem differently— and nobody has resolved it.
Money and time are being spent on pilots that never make it to production
There's no clear line of sight between your AI efforts and the business goals you're accountable for
Each use case that survives the day gets documented as an AI Use Case Card — a single-page decision that captures what to build, why it's worth building, who it's for, and what success looks like.
For [Minimum Viable Segment] who experience [specific problem] that is [painful], we believe there's an opportunity to use [this AI capability] to help them achieve [desired outcome or pain relief]. This opportunity is aligned with our business goal of [specific KPI, OKR, or SMART goal].
The card also makes it possible to kill ideas early — not because someone didn't like them, but because they couldn't survive the process. Every idea that doesn't make it, is one your team won't spend the next six months building.
You bring the AI ideas and the business context. DSA facilitates the full day — moving your team through prioritization, customer definition, and use case stress-testing. By end of day, you have a prioritized list of AI opportunities — specific enough to brief engineering, clear enough to take to leadership. The fastest path from scattered AI ideas to a decision your team owns.
One day. Up to 7 participants. On-site or remote
A one-day training for up to 15 people. Your team learns the full AI Problem Framing methodology and leaves with everything needed to run sessions independently — the Playbook, Facilitation Slides, Agendas, and the AI Copilot. One investment.
Every AI decision conversation your team has from this point forward runs through a method, not improv. For teams who've been asked to lead AI initiatives and want to stop depending on external support to define which problems are worth solving.
Up to 15 participants. In-person at your organization
Not ready to commit to an internal engagement? The public session lets you experience the full AI Problem Framing method before deciding how to bring it into your organisation. One day alongside practitioners and facilitators from other companies — you run the method in real conditions and leave with the full Facilitation Toolkit. A low-commitment way to test whether this is the right approach for your team.
How AI Problem Framing works in practice - and what we've learned running it with teams at Google, HSBC, Adidas and beyond.
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