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How to move an AI pilot to production
Design the pilot to end in a decision: agree the exit criteria, the baseline and the metrics before building, evaluate on real cases and wire governance in from the start. Then turn 'go' into a production path with data access, integrations, monitoring and a business owner. Most pilots stall because these were never scoped, not because the model failed.
What AI does
- Runs the use case on real cases and records accuracy, latency, cost and usage as it goes.
- Is measured by an evaluation harness against labelled examples and the business metric, not by impressions from a demo.
- Produces logs and traces that show what it did and why, for audit and debugging.
- Keeps running under monitoring once in production, so drift and failures surface early.
What stays with people
- Choosing the use case and the decision the pilot must support.
- Setting the exit criteria and taking the go or no-go decision.
- Owning the result in the business once it is live.
- Approving the risk, data and security posture.
How to put it in place
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Write the exit criteria before you build
Agree what 'good enough' means: the business metric, the quality threshold, the acceptable cost and the risks that would stop it. Measure the current baseline so the result can be compared with something real.
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Scope the production path at the start
List the data, systems, permissions, integrations and owner the solution will need when it is live. If any of these cannot be obtained, you have found the real blocker before spending on the pilot.
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Bring governance in on day one
Involve risk, security, data protection and audit while the pilot is being designed, not at the demo. Classify the use case against your AI policy and the AI Act, and record decisions as you go.
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Build with evaluation and telemetry
Build the pilot on real data and connect an evaluation harness that scores outputs against labelled cases. Instrument usage, quality, latency and cost so the final review uses evidence, not a slide.
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Decide with a scorecard
Compare the results with the exit criteria in a structured review with the people who fund the next step. Go, no-go or iterate with a clear reason: all three are valid outcomes.
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Hand over to an owner and operate
For a 'go', name the business owner, plan the integrations and roll out in stages with monitoring, alerts and a way back. Keep evaluating in production, because data and usage will change.
What you need
- A use case tied to a business metric and a sponsor who will act on the result.
- Access to real data and the systems the solution must work with.
- Labelled examples or expert reviewers to build the evaluation set.
- Risk, security and data protection people available from the start.
- A named owner in the business for the live solution.
When it is not worth it
- When nobody will take a decision on the result: a pilot without a sponsor is a demo.
- When the data or systems cannot be accessed in production: solve access first.
- Use cases with no measurable benefit, where even a perfect pilot would not justify running it.
Risks and how to control them
- A pilot that ends in a demo and a maybe: fix exit criteria and a decision date before building.
- Governance arriving late and forcing a rebuild: involve risk and security from the design and use a lightweight template from the start.
- Results that do not hold in production because the pilot used clean or narrow data: evaluate on real, representative cases and keep evaluating after launch.
- Hidden maintenance cost of AI systems, such as data dependencies and pipelines nobody owns: plan operations, monitoring and ownership as part of the path to production.
How Thinkia does it
Our pilots are designed to end in a decision. We lock the use case, the decision it must support, the exit criteria, the baseline and the metrics up front, together with the data, governance and ownership the pilot needs to be real. The pilot is built on Synapse with an evaluation harness, telemetry and governance gates wired from the start, and a governance template pre-wired to your AI policy and EU AI Act posture.
The pilot runs against real cases and closes with a go/no-go scorecard reviewed with the people who fund the next step. For a 'go', we hand over a documented production path covering data, governance, ownership and integration, so 'go' actually means go.
The Thinkia solution behind this guide
- Accelerated AI pilotsFrom idea to evidence in 6 weeks
- AI use-case discoveryEnds with a backlog, not a deck
- MLOps & AI infrastructureTurn experiments into production
Thinkia products involved
Frequently asked questions
Why do so many AI pilots never reach production?
Usually because nobody agreed in advance what success looked like, and because data access, integrations, governance and ownership were left for later. The model working on a laptop is the easy part.
What should the exit criteria of an AI pilot include?
The business metric to move, the minimum quality on real cases, the acceptable cost and latency, and the risks that would stop the project. They should be agreed and measured against a baseline before the build starts.
How do you evaluate an AI pilot?
With a set of real, labelled cases and a harness that scores the outputs automatically, complemented by expert review where judgement is needed. User feedback and usage data add the operational view.
When should governance and security get involved?
From the design of the pilot. If they arrive at the demo, what looked like a pilot can turn into a long remediation project before anyone can use it.
What changes between a pilot and production?
Production needs reliable data access, integrations, permissions, monitoring, support, a way to roll back and a business owner. A pilot can live without them; a live system cannot.
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- AI pilot vs production: what actually changes when you scale?
- LLM evals vs human review: how should you assure the quality of enterprise AI?
- Forward deployed engineers or a project-based consultancy: how should you bring in outside AI help?