AI strategy as concrete operational decisions, not as vision documents.

What is AI strategy in operational terms?

The framework for deciding where AI investment is operationally justified in a specific business — which use cases warrant the spend, which are premature, which are too peripheral to matter. AI strategy at the operational level is concrete and bounded; it produces specific decisions with rationale, evidence, and triggers for revisiting.

Build, buy, or wait — how is the decision made?

By reading the operation against the maturity of available tools. Build when the use case is core to the business and off-the-shelf options force operational compromises. Buy when mature tools fit the use case at acceptable cost. Wait when the technology is moving fast enough that committing now creates expensive lock-in. The decision happens against the specific operation, with reversibility weighed.

How do you evaluate AI vendors without getting sold?

By running a structured comparison: define the use case in operational terms, set evaluation criteria tied to operational outcomes, request reference customers operating at similar scale, test against real data, and weigh total cost of ownership rather than headline pricing. Vendor sales cycles are designed to defer comparison; the buyer's discipline is to keep the comparison structured.

What does AI readiness actually mean?

Operational readiness is broader than technical readiness. It includes data access and quality, team capacity to supervise AI-assisted work, process maturity that makes the AI's job definable, governance that decides what AI owns and what stays human, and economic readiness to absorb the cost while the system is being built. A team passing the technical readiness check often fails the operational one.