Nearly every enterprise I talk to now has AI pilots running somewhere: an analyst team drafting with it, a data science group running a proof of concept, a scattering of individuals quietly using consumer AI tools for work the organization hasn't sanctioned. What almost none of them have is a credible, repeatable path from "this pilot works" to "this is running in production, and risk has signed off on it."
That gap is rarely technical. The pilots that stall usually already prove the underlying capability works. What's missing is everything around it: data governance sign-off, a model risk framework the second line of defense recognizes, and a change management plan that treats adoption as its own workstream instead of an afterthought bolted onto the technical rollout.
Two Different Questions, Answered as One
"Can this work technically" and "can this be adopted safely at scale" are different questions, but most AI pilots only ever answer the first one. A proof of concept demonstrates the model produces useful output; it says nothing about whether the data pipeline feeding it meets the organization's governance standards, or whether the output can be trusted enough to remove a human check without creating unacceptable risk.
Teams that get stuck are usually the ones that treated the second question as something to figure out later, after the pilot proved the concept. By the time they bring it to risk or compliance, the pilot has already accumulated technical debt and organizational momentum around an approach that wasn't built with those functions' requirements in mind, which is exactly when the friction shows up.
Build the Adoption Path Around Risk, Not Against It
The organizations that actually get AI use cases into production involve their risk and compliance functions from the start of the prioritization exercise, not at the end of the pilot. That doesn't mean every use case needs a lengthy sign-off process before anyone tries anything; it means use cases get scored on adoption readiness (data governance status, model risk classification, change complexity) alongside expected value, so the organization isn't surprised later by a governance blocker on its highest-value candidate.
In practice, this often means sequencing a few lower-risk, high-adoption-readiness use cases first, not because they're the most valuable on paper, but because getting them through production establishes the reusable governance pattern the harder use cases will need later.
Change Management Is a Workstream, Not an Afterthought
The last blocker is the most human one: even a fully governance-approved AI tool fails to get adopted if the people meant to use it don't trust it, weren't consulted on the workflow change, or were handed a tool with no clear guidance on where it's genuinely reliable versus where their own judgment still needs to override it.
Giving change management its own budget and owner, rather than treating it as a training session bolted onto go-live, is usually the difference between a use case that gets used six months later and one that quietly reverts to the old manual process the moment nobody's watching adoption metrics anymore.