Enterprise AI usually gets discussed as a tool choice. That is too late. The first useful question is whether the use case changes risk in a way the organization must be able to explain later.
NIST’s AI Risk Management Framework, plus its generative AI profile, frames AI across design, development, deployment, and use. Microsoft’s AI strategy guidance adds the same practical direction: identify the use case, define the technology and data strategy, and set responsible AI expectations up front.
That combination points to a simple decision rule: classify the use case by consequence before you judge the model by capability. A meeting-summary assistant, for example, may be acceptable in an internal pilot, but if it writes records that managers rely on, the organization now needs review points, data boundaries, and a support path that can answer who used it, what it saw, and how its output is checked.
The mistake is treating every AI experiment as if it deserves the same approval path. Low-impact drafting tools can move quickly. Anything that touches customer data, employee records, or decisions should be managed like an operational control, not a demo.
That is a more durable way to scale AI: not by asking whether it can produce useful text, but by asking whether the business can operate it responsibly after the pilot ends.