The Pilot-to-Production Checklist

16 checks to move AI from promising pilot to governed, production-ready operations.

Built on Turbotic research with 1,004 senior UK decision-makers and on our work delivering AI programmes with enterprise clients and transformation leaders — research that found only 16% deploy AI at scale successfully.

The research behind it

This checklist is based on Turbotic research surveying 1,004 senior UK decision-makers involved in setting and assessing AI projects, conducted by Research Without Barriers, 13–21 July 2026, and on Turbotic's experience delivering AI and transformation programmes with enterprise clients and their leadership teams.

ResearchGovernance is the #1 barrier to scaling AI Research88% of UK leaders believe Shadow AI is in use White paperBehavioral drift in AI agents Free toolAutomation Feasibility Check

Stage 1

Discover

Is this worth building, and who says so?

The use case serves a strategic AI objective agreed at leadership level, not a standalone technology test.
A business owner (the P&L owner) is named and accountable for the result.
The business case is validated by the P&L owner and finance before build starts.
The data it needs exists, is accessible and has been consolidated and prepared.

Stage 2

Govern

Is governance designed in from day one?

Risk is classified, including obligations under the EU AI Act.
Guardrails, identity and access management are specified before go-live.
Human accountability and oversight are set according to risk.
Observability and an audit trail capture what the AI did and why.

Stage 3

Build

Will it work in your real systems, repeatably?

The integration path into your existing (often multi-brand) systems landscape is confirmed.
It is tested on real production data, not just a clean sample.
Edge cases and failure handling are defined: what happens when it is wrong?
It runs through a repeatable delivery pipeline, from opportunity to value reporting.

Stage 4

Scale

Who runs it, proves it and grows it?

Value is reported after go-live and re-validated by the business and finance.
A named operator owns day-to-day performance, drift and continuous improvement.
Domain experts can build within central guardrails (hub-and-spoke), with QA before anything scales.
There is a plan to train the wider workforce and grow an internal team, so you are not dependent on outside help.

Your progress

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Frequently asked questions

What is a pilot-to-production checklist?

A structured set of checks that confirms an AI pilot has the ownership, governance, integration and operating model it needs to run in day-to-day operations.

Why do most AI pilots never reach production?

According to Turbotic research, the top barriers are governance and compliance (37%), proving ROI (35%), lack of internal AI skills (34%) and poor data quality (34%).

When should I use this checklist?

Before a pilot starts, and again before deciding whether to scale it.

Does the checklist cover the EU AI Act?

Yes. The Govern stage includes risk classification and the controls needed for regulated use cases.

Ready to move your AI beyond the pilot stage?

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