AI Strategy and Business Alignment
Framing AI strategy as business strategy
A credible AI strategy starts from a business problem and a measurable outcome, not from a model. Candidates should be able to move between three layers: the enterprise value thesis (where AI changes cost, revenue, risk, or speed), the portfolio of use cases that ladder to that thesis, and the operating model (funding, ownership, decision rights) that makes delivery repeatable. Know the difference between exploratory and exploitative AI investments, and why a portfolio needs both with different success criteria and kill criteria.
Key concepts: value-driver mapping, use-case prioritization (value vs. feasibility vs. data readiness), build/buy/partner logic, total cost of ownership including inference and human-in-the-loop, and the distinction between a pilot and a production capability. Understand why "AI transformation" is a change program with technology inside it, not an IT project with a change plan attached.
Traps in questions
- Treating a compelling demo as evidence of business value; the exam rewards evidence of a baseline, a counterfactual, and an owner.
- Assuming the highest-accuracy model is the right choice; cost, latency, explainability, and integration often dominate.
- Confusing a strategy artifact (roadmap, charter) with governance (who can say no, and on what grounds).
- Ignoring opportunity cost: every funded use case displaces another.
Rule of thumb
The "so what, who cares, who owns it" test. For any proposed use case, state the metric that moves, the person accountable for that metric, and the decision the model informs or automates. If any of the three is missing, it is a science project. Worked example: a claims-triage idea becomes fundable only when framed as "reduce average handling time 12% for tier-1 claims, owned by the claims operations director, with a human decision retained above a value threshold."