What decisions belong in an AI strategy?
Start with the business problem and a small number of candidate workflows. State who owns each outcome, what data and systems are needed, and what failure is unacceptable. A strategy is useful when it makes a decision easier to take or reject. AI use case prioritization ranks individual opportunities, while AI governance sets the controls that apply across them.
It should also name the capabilities that cannot be wished away: delivery capacity, data access, adoption support, evaluation, and operations. An AI operating model turns those choices into responsibilities once work begins.
How is it different from an AI roadmap?
The strategy says why and where to invest. An AI roadmap turns that into a sequence of milestones, dependencies, owners, and decision points. Strategy chooses the work. A roadmap makes the work observable over time.
Each priority needs an AI business case with a measurable outcome and a reason to stop. NIST’s AI RMF is useful as a durable risk-management reference, but no external framework can choose a company’s priorities for it.