Skip to main content

Seven-stage lifecycle for a governed AI initiative

An initiative should produce evidence and a decision at every stage. Progress is not measured by how much agent or workflow configuration exists; it is measured by whether the team can justify continuing.

Seven Stages

StageMinimum outputDecision
IdentifyProblem, users, expected value, initial ownerIs discovery worthwhile?
DiscoverFeasibility, scope, success criteria, risks, dependenciesIs a pilot safe and useful?
PilotLimited setup, realistic cases, user feedbackContinue, adjust, pause, or stop?
ConfigurePrompts, data, tools, access, approvals, and documentationIs the operating boundary complete?
TestAcceptance evidence, issues, fixes, and go/no-go recommendationIs rollout justified?
Roll outOnboarding, support, monitoring, and feedback loopIs adoption healthy?
OptimizeUsage and quality review, updated roadmapImprove, expand, hold, or retire?

Assign Governance Roles

  • Business owner owns the outcome and prioritization.
  • Admin or delivery owner owns configuration and coordination.
  • Data and system owners approve sources and integrations.
  • Security reviewer approves access, controls, and residual risk.
  • Pilot users test real scenarios and report usability gaps.
  • Release approver makes the go-live decision.

One person may hold more than one role in a small pilot, but the responsibilities must remain explicit.

Keep a Decision Record

At each gate, record the evidence reviewed, open risks, accepted limitations, decision, approver, and next review date. Link the record to relevant agent versions, workflow versions, evaluation results, tool executions, and configuration changes captured in the Audit Log.

Use AI Go-Live Checklist before production rollout. When the initiative enters delivery, continue to Enterprise Kick-off; after launch, use Deployment Reporting and Monthly Operations Review.