Here’s the simplest test for whether your enterprise is actually scaling AI: can a team outside the AI group ship a safe, supported AI feature without reinventing the wheel?
If the answer is no, you aren’t scaling. You’re doing pilots.
I see this constantly. The technology isn’t the bottleneck. Models are good enough. The tooling exists. What’s missing is the operating model – the boring work that turns a demo into something that runs in production for years, with clear ownership, predictable costs, and a way to handle failures.
The pilot trap
Every large organization I’ve worked with has successful AI pilots: impressive demos, enthusiastic teams. Then the question comes: “How do we do this across 50 teams?”
The answer is never “give everyone API keys and let them figure it out.” That path leads to duplicated effort, inconsistent security practices, and a support burden that lands on the same three experts who built the original pilot. I’ve watched this happen at telecom companies. I’ve watched it happen at financial services firms. The pattern is remarkably consistent.
What an operating model actually looks like
Separate shared capabilities from local execution. It’s no more complicated than that.
Shared capabilities are the things every team shouldn’t have to reinvent: platform services, security guardrails, eval frameworks, model access, and policy. A small central group owns these. Their job is to make it easy to build safely.
Local execution belongs to the business teams who own use cases and outcomes. They pick the problems. They ship the features. They own the quality.
The balance matters. Too centralized, and you create a bottleneck where every AI idea has to go through a committee. Too distributed, and you get security gaps, wasted spend, and inconsistent quality. The sweet spot is a lightweight forum that resolves cross-team issues and keeps standards current without becoming a gate.
Governance as a lane, not a wall
The word “governance” makes engineers groan. I get it. But governance done right makes you faster, not slower.
The practical version is simple: data access is intentional and documented, model behavior is testable, audit trails exist, incident response has an owner, and rollback is a button, not a project.
If governance is a checklist that lives in a SharePoint nobody reads, teams will work around it. If it’s embedded into the build process – eval gates in CI, prompt versioning in the repo, monitoring that ships with the feature – teams will rely on it because it makes their lives easier.
Enablement, not evangelism
Scaling fails when enablement is treated like a training event. A two-hour workshop on “prompt engineering” doesn’t help a product team ship a reliable feature. What helps: repeatable patterns, starter templates, and a support path that doesn’t depend on cornering the same overworked ML engineer.
Extend the practices you already have. Your teams already know how to run CI pipelines, do code reviews, and deploy behind feature flags. Add eval suites to the pipeline. Add prompt reviews to the PR process. Make AI features fit into the existing delivery workflow instead of inventing a parallel one.
What to measure
Not tool adoption. Not number of pilots. Not “AI maturity scores.”
Track what’s in production and whether it’s maintained. Track support burden. Track which use cases are paused or retired. These signals tell leaders where to invest and what to stop. Everything else is decoration.
The sequence that works
Establish the platform and guardrails first. Prove the model with a small set of high-leverage use cases. Expand to more teams with consistent support. Review outcomes and simplify anything that causes friction.
The order matters. Each step creates the preconditions for the next. Skip ahead and you’re scaling demand faster than capability, which is how you end up with 50 broken pilots instead of 5 working ones.
This is a management problem. Treat it like one.