3. AI Strategy and Business Value Creation (28%)
Two companies in the same industry each spent roughly the same amount on AI last year. The first funded forty pilots chosen by enthusiasm, built most of them from scratch, and measured success by how impressive the demos looked. At year end it could point to a handful of tools in production and no number the CFO believed. The second funded eight initiatives chosen against a value-and-feasibility screen, bought commodity capabilities and built only the one that used its proprietary data, measured baselines before launch, and terminated two projects mid-year. At year end it reported a defensible return and had a data asset competitors could not copy.
The difference was not technology. It was strategy: which problems to pursue, how to source the solution, how to prove value, and where to seek advantage. This is the heaviest domain on the exam (28%, roughly 24 questions) and it covers three tasks: developing AI strategies aligned with business objectives (Task 2.1), measuring and demonstrating business value (Task 2.2), and positioning AI for competitive advantage (Task 2.3).
Domain 1 gave you the vocabulary; this domain asks you to spend money with it. Every decision here builds on Phase 2: build-versus-buy depends on what foundation models and adaptation techniques can do, cost planning depends on token and instance pricing, and "when AI is not the answer" extends the rules-versus-AI test.
⚠️ Exam Trap: Strategy questions reward balanced, staged, evidence-based answers. Options that commit everything at once ("migrate all processes to AI," "build a custom model to maximize advantage," "scale the pilot company-wide immediately") are usually distractors. Look for answers that prioritize, measure, and decide at defined checkpoints.
