- AI pilots rarely fail because of the model; they stall on data, integration, cost, risk approval and ownership.
- Define production criteria before the pilot starts, not after the demo.
- Seven gates (owner and KPI, data, security, evaluation, cost, operability, adoption) turn pilots into deployments.
The pilot trap
Generative AI made prototypes cheap. A capable team can build an impressive demo in days. That's both the opportunity and the trap: demos are built on curated data, with no integration, no access controls, no cost constraints and no one accountable for the result. When the pilot ends, the real work begins, and many organizations discover they never planned for it.
The pattern is familiar to most technology leaders: a portfolio of promising pilots, enthusiastic sponsors, and very little running in production a year later.
Five reasons pilots stall
- No business owner or baseline. Without a named owner and a measured starting point, no one can prove value or champion the rollout.
- Data that only works in the demo. Curated samples hide quality, access and permission problems that surface at scale.
- Risk teams can't say yes. No evaluation evidence, no security review, no policy to approve against.
- Unknown run costs. Token, GPU and platform costs per transaction are discovered after launch, sometimes exceeding the value created.
- No one to run it. Monitoring, incident response and continuous improvement are unassigned.
The 7 gates to production
We use seven gates as the definition of "production-ready." Agree them with your business, risk and technology leaders at the start of the pilot:
| Gate | Question it answers |
|---|---|
| 1. Owner & KPI | Who owns the outcome, and what metric moves by how much? |
| 2. Data readiness | Is the required data accessible, permissioned and good enough? |
| 3. Security review | Have threats such as prompt injection, data leakage and excessive agency been addressed? |
| 4. Evaluation | Does the system meet agreed accuracy and safety thresholds on a representative test set? |
| 5. Cost model | What does each transaction cost to run, and does the value exceed it? |
| 6. Operability | Is it monitored, supportable and reversible? |
| 7. Adoption | Will people actually use it, and how will we know? |
Design the pilot for production
The fix is not more pilots; it's better-designed ones. Run pilots on real data, inside your security boundary, integrated with at least one system of record, with an evaluation suite and a cost model from week one. A pilot designed this way takes slightly longer than a demo, and dramatically less time to reach production.
Build shared foundations as you go: an AI gateway, evaluation tooling, retrieval services and governance workflows. The second use case should take half the time of the first.
What leaders should ask this quarter
- How many AI use cases are in production, and what KPIs have they moved?
- Which pilots have a named business owner and a baseline?
- What does each production use case cost per transaction?
- Do we have a tiered approval path, or does every use case start from zero with risk and legal?
Frequently asked questions
What percentage of AI pilots reach production?
Estimates vary widely by study and definition, but industry surveys consistently report that a large share of generative AI pilots do not progress to production. The causes are usually organizational and foundational rather than model-related.
How long should an AI pilot take?
A well-scoped pilot on real data typically takes four to six weeks, with a further six to ten weeks to productionize, about 12 weeks end to end for a first use case.