The problem it solves
Most leadership teams navigating AI decisions do the same thing: they evaluate vendors, compare feature sets, build a business case, and get board approval. They produce alignment.
Alignment is not the same as a good decision.
What produces alignment is frequently a room that has stopped being honest with itself — where challenge feels like obstruction, where the optimistic scenario is the one that gets modelled, and where the questions that would change the decision live in the conversations that happen after the meeting, not in it.
The AI Strategy Workshop is not designed to produce alignment. It’s designed to produce clarity — about what your AI strategy actually depends on, and whether those dependencies can hold.
What actually happens
Your leadership team arrives with a real AI situation: an initiative you’re planning, a significant vendor decision, a strategic direction you’ve committed to but haven’t tested.
I run structured adversarial facilitation. Not brainstorming. Not debate. A disciplined process of surfacing the load-bearing assumptions in your current AI thinking, then stress-testing each one against the scenarios where it breaks.
By the end, your team has a documented assumption map — a complete inventory of what your AI strategy depends on being true, ranked by the damage if it isn’t.
Half-day versus full-day
The half-day format works for a single focused AI question — one vendor decision, one proposed initiative, one strategic direction to stress-test.
The full-day format covers a broader AI investment picture and includes working sessions to test high-priority assumptions, not just name them. If your organisation is navigating multiple connected AI decisions, the full-day format gives them space to interact.
Both formats are available remotely or in-person. In-person is strongly preferred for the full-day format.
"The most expensive AI mistake isn't choosing the wrong vendor. It's committing budget to the wrong question."
What you leave with
Not deliverables. Capabilities and clarity.
- A documented AI assumption map Every belief your AI strategy depends on, surfaced and named — including the ones that had stopped being examined.
- Risk-ranked assumptions Which assumptions, if wrong, would cause the most damage — and which are genuinely solid enough to build on.
- Tested vendor and internal claims The specific claims in your AI roadmap or vendor proposal, examined against counter-evidence and the most relevant failure scenarios.
- A replicable process The assumption-testing methodology explained and documented so your team can apply it to future AI decisions independently.
Good fit / not a fit
- Good fit: A major AI investment is on the table A significant budget decision. A vendor shortlist. A proposed transformation initiative. The stakes are high enough that you want the reasoning examined before the commitment is made.
- Good fit: Your team agrees on the AI direction and that makes you uneasy Meetings end in alignment. The roadmap is approved. But you've noticed that the strongest objections to AI initiatives tend to appear in execution — not in the planning sessions where everyone agreed.
- Good fit: An AI initiative is underway and needs a reset You're six months into an AI programme and results aren't matching the pitch. Before you invest in the next phase, you want the underlying assumptions examined rather than the delivery process optimised.
- Not a fit: Not a fit Teams looking for validation of an AI direction already decided. This workshop is designed to challenge the strategy — it is not designed to build conviction in a direction already chosen.