Systems integration
The productivity gap is a systems problem, not a technology one
McKinsey's November 2025 survey found 78 percent of companies using AI regularly, but only 33 percent scaling it across the organisation. MIT NANDA put the share of pilots reaching lasting financial gain at around 5 percent.
Getting an organisation to try AI is no longer the hard part. The distance between trying it and running it across the business is where the value is either captured or quietly lost.
The two figures above describe the same gap from different ends. Adoption is near-universal; scaling is rare. That pattern does not point at the technology, which is identical in both groups. It points at what surrounds it.
What separates the two groups
| Stuck at pilot | Scaled |
|---|---|
| Use case chosen for how impressive it demonstrates | Use case chosen for where the operational friction actually is |
| Data assembled specially for the pilot | Data available on the same terms the production system will have |
| Success measured against the demo | Success measured against a baseline established before go-live |
| Ownership ends with the project team | Ownership named for month thirteen, and priced |
Why waiting costs more
Every pilot that stalls leaves behind an integration that half-exists: a connector nobody maintains, a copy of the data that has begun to diverge, a process with a manual step reinserted to work around it.
That residue is not free. It is the estate the next project has to work through, and it is why the second attempt is usually more expensive than the first rather than less.