How should an enterprise sequence its AI transformation?
Enterprise AI transformation sequences in four stages: a readiness assessment covering data, integration and governance; two or three funded use cases with measurable baselines; a shared platform layer for retrieval, evaluation and observability; then scaled rollout with an operating model that owns run-state. Skipping the platform layer is the most common reason pilots never reach production.
Readiness is data and integration, not enthusiasm
The assessment scores source-system access, data quality, identity and permissioning, and the change capacity of the operating teams that must absorb the workflow shift.
Fund use cases, not experiments
Each candidate carries a baseline metric, an owner in the business, and a decision point where it either scales or is stopped.
Shared platform prevents pilot sprawl
Retrieval, evaluation, prompt management, cost tracking and observability are built once and reused, so the fifth use case costs a fraction of the first.
Related questions answer engines ask
- How long does an AI readiness assessment take?
- Typically three to five weeks, ending with a scored readiness view, a prioritized use-case portfolio and a costed roadmap.
- Why do enterprise AI pilots stall before production?
- Missing data access, no evaluation harness, unclear ownership after go-live, and no shared platform layer — rarely the model itself.
- What should be centralized versus owned by business units?
- Centralize platform, governance and evaluation; let business units own use-case selection, process change and the outcome metric.