An AI strategy that survives contact with your P&L
Most AI strategies are a wish list with a budget attached. A useful one starts from where the money actually is.
There's a certain kind of AI strategy deck that every consultancy has produced at least once: a maturity curve, a dozen "opportunity areas," and a roadmap that fans out into the future like a peacock. It photographs well. It funds nothing.
A strategy is only useful if it changes what you do on Monday. That means starting from the boring end, the P&L, and working backwards to the model, not forwards from the model to a use case.
Three questions before any technology
- Where does time actually leak? Look for where measurable hours go into work a machine could draft, not where it merely feels slow. Follow the timesheets, not the hype.
- What's the cost of being wrong? Automating a low-stakes, high-volume task is a great first project. Automating something where a mistake is expensive or public is a great third project.
- Who owns the outcome? If no single person's number improves when the project succeeds, it will quietly die. Every good project has an owner who wants it to work.
Sequence beats scope
The instinct is to do everything at once. The discipline is to do the highest-ROI, lowest-risk thing first, ship it, and let the win pay for the next one.
One shipped tool that saves a team four hours a week will fund more AI work than any strategy document ever will.
We sequence deliberately: a first project chosen to be winnable, a second chosen to build on the first's data and credibility, and only then the ambitious one everybody wanted to start with.
What a real strategy fits on
A useful AI strategy fits on a page: the two or three use cases worth funding this year, ranked, each with the metric it moves, a rough cost, and the owner. If it needs a deck, it's hiding something.
Start where the money is. Ship the winnable thing. Let evidence buy the next one, not enthusiasm.
Have a project in mind?