Why did the pilot work but production did not?
An AI pilot runs on clean samples, friendly users and a small bill. Production meets real data, a risk committee and real volume. We help you decide whether yours deserves to scale, then build what the demo skipped.
Asked by CIOs, and by the business owners who funded the pilot.
DemoProvedThe model does the task on sample data
Four conditions still open before it scales
DataSample onlyReal records, real users, real permissions
EvaluationNot measuredProof it is good enough
ControlsNot designedWhat happens when it is wrong
OperationsNot costedRunning cost and ownership at scale
Decidescale, revise or stop
Demo ready. System not.
A pilot answers one question: can the model do it? Production answers four more. They are not taken in turn: a review left to the end is one of the commonest reasons pilots stall. Any one of the four can hold the launch.
Will it work in our real business?
Ready when it has run on real records, with real users.
How will we know it is good enough?
Ready when it clears the bar the business owner agreed.
What happens when it gets something wrong?
Ready when the risk owner signs against the tests.
Will the economics and operations hold at scale?
Ready when cost and quality hold at real volume.
The decision is not whether the pilot works. It is whether it deserves to scale.
A pilot can work and still not be worth scaling: the benefit is too small, adoption too hard, the running cost too high, or a simpler route gets there for less. Why scale this pilot at all? Start with question 1, Value & Economics
Not sure which of the three yours is? Leaving it undecided costs too: the pilot keeps spending while the people who built it move on. We review one pilot against these four decisions and give you the answer, with the evidence.
Get a scale, revise or stop viewFrom delivered work, one decision at a time. Each figure is an operating result: a change in the work, as the client reported it.
Data
Will it work in our real business?
A global manufacturer built its first forecasting use case on the real systems, not a cleaned sample, so there was no gap to close later.
Forecast accuracy, from CRM, ERP and third-party market data of uneven quality, integrated and cleansed rather than sampled. Forecasts refresh as new data arrives, in the dashboards the planners work from.
Operating result Read the case studyEvaluation
How will we know it is good enough?
One wrong answer in a demo is hundreds a day in production. An error rate that looks fine on fifty questions turns into a queue at real volume, and what each error costs depends on what it touches: a clumsy sentence is cheap, a wrong payment or a wrong instruction is not. Change the numbers and see.
Estimated wrong answers reaching people, per day
750
About 750 wrong answers a day reach people. In the demo it was 1 or 2, which is why it looked fine.
Of documents routed to a reviewer by rule, the nonstandard ones, for a global financial information provider: a bar that decides what the system handles and what a person sees.
Operating result Read the case studyControls
What happens when it gets something wrong?
A hand-off that works: when the assistant cannot solve it, a person takes over with everything it already knows, so nobody is asked twice.
Fewer Tier 1 live-agent contacts on the issue types covered, with 28% higher first-contact resolution, for an electronics manufacturer. Cases it cannot solve go to an agent with the full context.
Operating result Read the case studyOperations
Will the economics and operations hold at scale?
A cost per order that falls at full volume, running inside the systems the business already uses.
Fewer delivery miles per fulfilled order, and 18% fewer stockout incidents at key partners, for a food manufacturer and distributor. Built as a layer on the planning system it already ran, not a replacement.
Operating result Read the case studyWhere most stalled pilots start
A stalled pilot rarely needs another pilot. In two weeks we score the one you have against your systems as it runs, and tell you whether to scale it, revise it or stop. It works the same way for a pilot that disappointed: you learn whether the model, the data or the setup let it down, and whether it deserves another round.
Delivered as the AI & Data Assessment Sprint, pointed at your pilot. Fixed scope, fixed fee agreed in writing first.
What you get
Other ways in
The practices behind this page: Generative AI & AI Agents, AI & Machine Learning, Data Engineering & Platforms and AI Security & Cyber Risk.
Usually on what surrounds the model. The data was clean in the pilot, nobody could prove it was good enough, the review arrived at the end, and the unit cost was never modeled. Sometimes the model itself is the limit, and an honest evaluation shows that early. None of it is visible in a demo.
Usually not. If the pilot proved the use case, the next step is a review of what production needs, then the build. A new pilot makes sense when the use case itself is still unproven.
Usually keep what proved the use case and rebuild what assumed a demo: the data connections, the tests, the controls and the cost model. We say which is which at the start, before anything is built.
Against labeled answers from your own work, with a bar the business owner agrees before launch, stricter where a mistake costs more, and tested again on every change. “It seems to work” does not clear a risk committee.
Whichever clears your bar for quality and security at the lowest unit cost, and we route between models where it pays. We are model-neutral by policy and hold no exclusive vendor incentives.
Then you hear it with the evidence: scale it, revise it or stop. A pilot stopped on evidence is money saved, not money lost.
Thirty minutes with the person who would lead the work: a senior AI and data strategist who has taken programs from strategy to systems running in production, in more than one industry. If it should stop at the pilot, you will hear that first.
Book 30 minutes on your pilotPilot stuck? Thirty minutes on what production needs, with the person who would lead the work.
