Deploying AI from pilot to production
A successful AI pilot often fails to scale because enterprise deployment requires clear ownership, measurable outcomes, cost accountability, and risk-based oversight, so this guide offers a practical blueprint for moving AI programs from pilot to production.
MAIN POINTS
- Only 23% of C-suite leaders report sustained, enterprise-wide AI impact.
- Pilots are artificially favorable, with handpicked teams, narrow scope, and protected budgets.
- Shared IT and finance accountability often leaves no single owner for AI costs or outcomes.
- The guide provides a chronological blueprint, including ownership decisions, TCO modeling, and oversight tiers.
TAKEAWAYS
- Pilot success should not be mistaken for production readiness.
- Scaling AI requires explicit accountability before deployment expands.
- Defining the AI job clearly helps align users, tasks, outputs, and quality thresholds.
- Human review should be matched to output risk through a structured oversight model.