The first generation of industrial AI pilots is behind us. Concepts have been proven. Early adopters are reporting real gains. But for many operations, that’s exactly where progress stops.

Even when success is clear—a working proof of concept, measurable ROI, or buy-in from key stakeholders—many organizations can’t move beyond it. Call it “pilot purgatory,” a state in which AI wins remain concentrated in a handful of use cases, isolated to one or two sites, or entirely dependent on a single internal champion.

The barriers to scaling are rarely technical. Based on direct observation of industrial organizations of varying size and sector, five patterns consistently emerge that keep capable AI programs from graduating beyond the pilot phase.

Here’s a clear approach for breaking through each.

1. The ‘second site’ problem: Why does a successful pilot struggle at the next location?

After a limited initial rollout, many organizations struggle to replicate that success at a second location.

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