Pilots impress. Portfolios pay back.
Around half of organisations have run AI pilots in the last two years, yet fewer than one in five get every project into day-to-day operations. The technology works. The operating model doesn't. Ownership is fuzzy, value isn't measured, and the next use case starts from zero every single time.
Only about a quarter define how success will be measured before a pilot begins — which is why so many end up with a shelf of promising experiments and no evidence to justify scaling them.
Scaling isn't a technology problem — it's an operating problem. Without a repeatable model for how automations are built, run, measured and improved, you end up paying for AI without cashing in on it.