From AI pilots to production: closing the value gap

Over the past few years, almost every organisation has experimented with artificial intelligence. Proofs of concept are easy to start and often impressive in a demo. Yet only a fraction reach production — and fewer still deliver measurable value a year later.

Why pilots stall

The pattern is familiar. A pilot is scoped around what the technology can do rather than a business problem worth solving. It runs on a hand-curated data extract. It has no owner on the business side, no plan for integration into real workflows, and no answer to the questions risk and compliance teams will inevitably ask.

Four conditions for scale

1. Start from the decision, not the model. Identify a decision or task that matters, who makes it today, and what “better” would mean in numbers.
2. Treat data as a product. Production AI needs reliable, governed, continuously refreshed data — not a one-off extract.
3. Design for the workflow. Value appears only when people actually use the output. Design the human-in-the-loop experience as carefully as the model.
4. Build trust in from day one. Transparency, evaluation and controls should be part of the design, aligned with regulation such as the EU AI Act.

The question is not “what can AI do?” but “what will we do differently because of it?”

A portfolio, not a lottery

Organisations that scale AI manage it as a portfolio: a few high-value use cases taken all the way to production, shared platforms that make the next use case cheaper, and clear criteria for stopping what is not working. That discipline is what turns experimentation into advantage.