Digital & AI-Enabled Commercial Transformation Playbook

Start with the data readiness audit, not a tool evaluation. Most organizations discover the highest-leverage first move is actually a CRM hygiene project, which sounds unglamorous but is the prerequisite that determines whether any subsequent AI use case can succeed.

For most commercial use cases, buying is the right default — the data and integration work is the hard part, not the model itself, and few commercial organizations have a durable reason to own that infrastructure. Consider building only for a genuinely differentiating use case tied to proprietary data no vendor tool can access.

Ninety days is a practical default — long enough to see a real trend in the target metric, short enough to keep organizational attention and to kill it quickly if it's not working. Extend only with a specific, agreed reason, not because the result was ambiguous and no one wants to make the call.

Move to Stage 3 only once Stage 2 has run long enough to show the AI's suggestions are consistently accurate and trusted by the humans using them — usually evidenced by high acceptance rates of its recommendations. Moving to draft-and-approve before that trust is established just adds review friction without saving real time.

Address it directly and early, before the pilot launches — explain specifically what the tool does and doesn't decide, and who does and doesn't see its outputs. Silence on this question is what generates the workaround behavior that quietly kills adoption, far more than the technology itself.