You can see it coming before the demo ends. The pilot runs. The board nods. Someone says "we need to scale this" — and then nothing happens for nine months. By the time people check in, the pilot has been quietly archived, the use case has a different name, and the budget has moved on.
This isn't an AI capability problem. The models work. The data is fine. The platform team shipped what they said they'd ship. The gap is somewhere else, and it shows up in the same three places every time.
The pattern behind the pattern
Ask ten enterprises why their AI pilot didn't scale and you'll get ten answers. Dig into those answers and three themes repeat. Pilots had no shared runtime, so every team rebuilt from scratch. Governance was written, signed off, and never enforced at inference — so audit asked questions nobody could answer. And nobody agreed on how success was measured, which meant "should we expand?" became a political conversation instead of a data-led decision.
These aren't AI problems. They're operating-model problems. But they only appear once AI is in production, which is why they surprise everyone.
Why tooling doesn't close the gap
Over the last 18 months, the tooling market has matured. Azure Foundry, Bedrock, Vertex AI, Langfuse, LangSmith, and Lakera now expose the signals you need — traces, evaluations, cost attribution, policy enforcement. The ingredients exist.
What's still missing is the operating model that wires them together. A platform that emits traces isn't the same thing as an organisation that uses those traces to make expand/hold/stop decisions. That's the Lastmile, and it's not a tooling purchase — it's a set of agreements, owners, and cadences.
What the Lastmile actually needs
The short version: someone named as owner per live AI service, a policy that's enforced at runtime (not on paper), cost attributed to use case and business unit, evidence retained for every interaction, and a monthly scorecard that makes expand/hold/stop decisions mechanical.
None of this is glamorous. None of it wins hackathons. All of it is what separates AI programs that compound from AI programs that quietly die.
Where to start
You probably don't need more pilots. You need governance for the ones you already have. Pick the most important live use case. Establish the policy, the cost ledger, the evidence retention, and the scorecard. Then — and only then — fund the next one.
That's the Lastmile. It's less exciting than building. It's where the value is.