Most AI strategies fail the same way: a list of ideas with no owner, no data check and no first step.
Why most AI strategies stall
The typical AI strategy is a slide with twelve use cases, colour-coded by department. It feels like progress and produces nothing, because nobody checked whether the data exists, who would own the result, or which idea to do first. Six months later the same slide is presented again, with newer model names.
What a useful strategy contains
A short list, ranked by impact, feasibility and strategic fit. For each item: the data it needs and whether you have it, build or buy with the reasoning, what it costs to find out, and how you will know it worked. And a 90-day plan with names next to the milestones.
A strategy is a decision about what not to do.
How we get there
We interview the people who run the processes, not only the people who sponsor them. We run one discovery workshop, score every candidate against the same criteria in front of you, and decide together. The output is small on purpose: two or three bets, one of them started before we leave.