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A good pilot proves something can work under friendly conditions. Scaling it means dealing with everyone, everything and every awkward exception that was conveniently missing from the demo.
By Jamie Randall, Director at INTAI · Published 16 June 2026
Start ReadingThe research behind that figure was not a quick survey. MIT's team reviewed over 300 publicly disclosed AI initiatives, conducted 52 organisational interviews and gathered 153 executive survey responses across four major industry conferences. Only around 5% of pilots made it into production with measurable value attached. The other 95% did not fail because the model was wrong or the regulation got in the way, the study is explicit that neither explains the gap. They failed because of how they were approached.
They designed for friction rather than avoiding it, embedding the tool into a genuinely high-value workflow rather than a low-stakes, easy-to-demo one, integrating it deeply into existing systems rather than running it alongside them, and building in memory and learning loops so the tool improved with use instead of staying static. The 95% that stalled, by contrast, tended to lean on generic off-the-shelf tools that looked impressive in a demo and turned out to be brittle the moment they met a real workflow’s exceptions, edge cases and awkward legacy systems, landing in a mode the report describes as high adoption but low transformation: widely used, narrowly valuable.
A pilot proves a capability can work under conditions you selected, with users who volunteered, on a problem you deliberately kept narrow. Scaling proves something different: whether it still works for the colleague who does the task differently, the system that was never quite documented, and the exception that happens every third Tuesday. Most pilots are never actually tested against that second, harder question before someone decides to scale them.
A pilot that worked has told you the concept is sound for the narrow slice of the organisation that tried it. It has not told you whether your data is clean enough to support it more broadly, whether the workflow it touches has the same shape in every team that will use it, or whether anyone has actually redesigned the surrounding process rather than simply bolting the AI tool onto the existing one. Answering those three questions, deliberately and before the scale-up begins, is the difference between joining the 5% and quietly becoming one more line in next year's version of the same MIT study.
If this raised more questions than it answered, that is usually the sign of a conversation worth having. Book a Consultation