Your company's first AI use case: choosing it with just two criteria
Companies don't need an "AI strategy" as much as they need a first measured win. Two criteria settle the choice: financial impact and data readiness — everything else is noise.
The question we hear in every executive session: "Where do we start with AI?" The honest answer is simpler than the circulating slide decks: start where measurable financial impact meets data that is actually ready.
Measurable financial impact means a baseline exists today: how many inquiries go unanswered? How many hours go into document entry? How many sales opportunities cool off before anyone reaches them? If you can't measure the current state, you can't prove any improvement — and the project dies at the first budget review.
Data readiness does not mean "big data." Handling customer inquiries needs only your documented FAQs and policies. Invoice automation needs a sample of supplier invoices. An internal knowledge assistant needs the operating manuals you already have. The requirement is that knowledge exists and is approved — not that it is enormous.
The practical move: draw a simple matrix — financial impact vertical, readiness horizontal — and place every AI idea raised in your company on it. The top-right quadrant usually holds one or two candidates. That is your first project: scoped within a quarter, with a written success measure before starting.
One final rule from operating production agents ourselves: customer-facing AI must be constrained by approved knowledge, an explicit workflow, and clear human escalation. That discipline isn't a limit on the value — it's what makes the value deliverable.