AI transformation is more than a technology problem. People across the organization have to decide where AI creates value, how the work should change, and what's worth building. We give them a structured way to come together and make those judgment calls well.
For AI Enablement teams and AI Champions expected to move beyond individual AI adoption and create measurable business impact.

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FDEs combine engineering with customer discovery and cross-functional decisions. Train them to guide those conversations with confidence.
Equip technical delivery teams with a consistent discovery process to clarify client or internal business requests before committing to a build.

Need to decide where to invest in AI or redesign a business-critical process? We facilitate workshops to tackle those challenges.
If you need to do this repeatedly across your organisation, we help you build an internal AI Lab.
The Design Sprint was originally developed at Google Ventures. Problem Framing was developed by us. We've spent the past decade using and refining both methods with organisations across industries to tackle complex, high-stakes challenges.

John and Dana Vetan have facilitated hundreds of sessions across cultures and industries — not from the stage, but at the whiteboard with the team. Over a decade of working inside large, complex organisations, they adapted the Design Sprint and Problem Framing methodologies for enterprise realities: shorter formats, stronger problem framing, better team dynamics.
When AI changed everything, they built what didn't exist yet — AI Problem Framing and the AI Workflow Sprint — structured methods for helping organisations move from AI hype to real, aligned execution without wasting time on the wrong problems.
The goal has always been outcomes not good vibes, decisions not fake alignment, validation not opinions.
Three days of hands-on training in the full AI facilitation methodology — AI Problem Framing on day one, AI Workflow Sprint on days two and three. You leave with the full toolkit to run AI decision-making sessions independently: playbooks, facilitation slides, agendas and AI capability cards. Max 8 participants per cohort.

Berlin · Dec 9–11, 2026 · 3 days · €2,500
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Berlin · 2027 Dates TBA · 3 days · €2,500
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Turner Construction Company came with a real problem: 11,000 employees, hundreds of active job sites, and decades of operational knowledge locked inside people’s heads. Leadership knew AI was part of the answer — they just didn’t know which part. DSA ran an AI Problem Framing session with Directors and VPs, facilitated AI Workflow Sprints across three teams, and trained Turner’s own people to run the methodology independently.
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ENOC brought together senior leaders from across commercial, retail, industrial, aviation, sustainability, HSE, and operations functions to explore how Design Sprints apply to real enterprise challenges.
Using their own organizational problems, teams practiced defining long-term goals, surfacing assumptions, and framing Sprint Questions before moving toward solutions. They left with a practical way to create alignment before execution begins.

Red Bull’s digital team had a problem: too many feature ideas, no structured way to decide which ones were worth building. In one focused week — a Problem Framing day followed by a four-day Design Sprint — a cross-functional team moved from scattered ideas to a tested prototype validated with real users. They left with a clear direction for their Community platform, a prioritised feature backlog, and a repeatable approach to customer-centric product decisions.
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Which? didn't arrive at Problem Framing through a formal mandate. They were winging it — running sessions without a structured method, trying to filter incoming requests without a shared language. Over three years and three training programs, that changed.
Today Problem Framing isn't something a few people on the UX team know. It's how the organisation makes decisions.

Practical thinking on AI, product decisions, and the methods that make them stick.