What a good pilot looks like
- Defined metric — what number must move, by how much, by when.
- Bounded scope — one workflow, one user group, one channel.
- Real data — production traffic, not staged test cases.
- Real users — actual employees or customers, not internal testers.
- Eval suite — automated quality checks running throughout.
- Pre-defined go/no-go criteria — what counts as success before you start.
- Production plan — how you would scale if the pilot succeeds.
Why pilots stall
Deloitte 2024 reports 70% of enterprise AI pilots never graduate to production. The pattern: pilot is scoped to look good in a demo, not to be operated. Success criteria are vague. The infrastructure required to ship at scale was never built. Stakeholders for the broader rollout were not part of the pilot. Budget for production was never allocated. The pilot becomes a permanent fixture — running, not generating value, not killed.
How to scope a pilot that actually ships
- Choose a workflow with a measurable bottleneck and a known owner.
- Use production data and users from day one.
- Define success as a measurable change in a real business metric (not "users like it").
- Plan the production rollout before starting the pilot.
- Get the production budget approved contingent on pilot success.
- Set a kill date — if criteria are not met by date X, the pilot ends.
SMB pilots vs enterprise pilots
SMB "pilots" are usually just the first deployment. There is no separate procurement, no production rollout decision — if it works in the pilot, it stays. This is healthier than enterprise pilot theatre. The SMB version of a pilot is a 30-day deploy with a measurable success metric and a no-fault termination clause if the metric does not move. The agency keeps the engagement only if the value is real.
What it means for your business
For SMBs, "pilot" should not mean "ceremony before the real thing." It should mean "30-day first deploy with a metric and a termination clause." That keeps the engagement honest.
Related terms
- AI Readiness — AI readiness is whether an organization can actually deploy AI safely and usefully. Definition, dimensions, and a practical SMB checklist.
- AI Implementation — AI implementation is the end-to-end process of deploying an AI workflow from scoping through production. Phases, timeline, and SMB common pitfalls.
- AI ROI — AI ROI is the measurable financial return from an AI deployment. Definition, calculation, and the common traps that fake the numbers.
- AI Evaluation — AI evaluation is how you measure whether an AI system actually works. Definition, methods, and why evals are the bottleneck in production AI.
- AI Vendor Selection — AI vendor selection is how SMBs evaluate AI vendors on capability, cost, and risk. A practical 12-question checklist and decision framework.