From pilots to platform: building an AI operating model

Almost every organisation has run an AI pilot. Far fewer have an AI system that more than one team relies on every day. The gap between the two is rarely the technology. It is everything around it.

A pilot is designed to answer one question: can this work? Production has to answer different questions. Who owns it? Who fixes it at 7pm when it gives a strange answer? How does it get better? Where does the budget come from next year? Pilots that never face those questions stay pilots.

Choose pilots that can graduate

The best pilots are chosen with production in mind. Before starting, agree what success looks like in terms the business already measures, which system the pilot would plug into if it worked, and who would own it afterwards. If nobody can answer the last question, the pilot is research, which is fine, but it should be labelled as such.

Separate the platform from the use cases

Every AI project needs the same foundations: access to models, access to data, a way to evaluate outputs, logging, security and cost control. When each project builds these from scratch, the first project is slow and every project after it is just as slow.

Build the foundations once, as a shared platform, even a small one. Then each new use case is mostly about the business problem, not the plumbing. This is the moment AI stops being a series of experiments and starts compounding.

Organise around products, not projects

Projects end. AI systems in production do not. They need someone watching quality, handling feedback and improving them as the business changes. Treat each production system as a product with an owner, a small backlog and a way to hear from the people who use it.

• A product owner from the business, who decides what good looks like.
• Technical owners who keep it running and improve it.
• A feedback loop from the people using it, reviewed regularly.

Measure what the business measures

Model accuracy is a useful engineering number, but it is not why anyone funded the project. Tie every system to the measure it was meant to move, whether that is time to answer a customer, cost per transaction or hours spent re-keying data, and report on that. It keeps the work honest and makes the case for the next investment.

Grow capability on purpose

The goal is an organisation that can identify, build and run AI systems with less outside help over time. Pair external experts with internal staff on real work, write down what you learn as reusable patterns, and train the people who will operate the systems before handover, not after.

An AI operating model sounds grand. In practice it is a handful of decisions: which pilots are allowed to start, which shared foundations exist, who owns systems in production, and how success is measured. Make those decisions deliberately and pilots start turning into platforms.