From AI Prototype to Production: An Enterprise Readiness Checklist
A practical review of value, data, controls, evaluation and operations before an AI system crosses the production boundary.
Start with a production definition
A prototype proves that a model can produce an interesting response. Production requires a defined user, bounded purpose, accountable owner, measurable service level and an acceptable failure envelope. Write these down before choosing more technology.
Readiness checklist
Confirm the business decision the system improves; the source and permission model for its data; expected answer quality; adversarial and failure tests; human escalation; audit evidence; latency and cost budgets; rollback; monitoring; and named operational ownership.
Design the control plane
Treat prompts, models, tools, policies and evaluations as versioned system assets. Separate experimentation from approved runtime configuration. Every action-capable tool should use least privilege, explicit identity and auditable inputs and results.
Make scaling a decision, not momentum
A pilot should end with evidence against acceptance criteria. Scale only when the operational model, risk controls and economics are credible—not merely because the demonstration was persuasive.