5. Business Readiness, Leadership, and AI Transformation (24%)
A global manufacturer announced an ambitious AI program. Within a year it had fourteen pilots, a talented data science team, and a cloud platform ready to go. Two years in, only one pilot had reached production. The post-mortem was blunt: plant data lived in eleven disconnected systems that nobody owned; frontline supervisors had heard about AI mainly through rumors of job cuts and quietly ignored the tools; every pilot was governed differently; and the executive sponsor had moved on after the launch event. The technology had been ready for two years. The organization had not.
This domain is about the organization. It covers four tasks: assessing business readiness and maturity (Task 4.1), establishing data and infrastructure foundations (Task 4.2), leading enterprise-wide change and building an AI-ready workforce (Task 4.3), and scaling AI from pilots to enterprise-wide deployments (Task 4.4). It has more skills (16) than any other domain, and its questions are among the most scenario-heavy on the exam.
Everything you have learned comes together here. Readiness assessment draws on the CAF perspectives from Phase 1, data foundations on the data quality principles from Phase 2, scaling on the prioritization and measurement disciplines from Phase 3, and production-grade operation on the governance and monitoring requirements from Phase 4.
⚠️ Exam Trap: When a Domain 4 scenario describes an AI program that is stalling, the root cause is usually organizational (sponsorship, data ownership, culture, skills, governance, operating model), not technical. Options that respond to an organizational problem with more technology, a bigger model, or more pilots are typically distractors.
