- ·A pilot proves a demo. Production needs live data access, approvals, monitoring, and a security sign-off.
- ·The blocker is rarely model quality. It is integration and trust.
- ·Treat the first workflow as a deployment project, not a science project.
- ·Start where the data already lives. On-prem removes the largest blocker on day one.
Almost every enterprise now has an AI pilot. Almost none have AI in production. The demo dazzles the room, the budget gets approved, and then the project quietly stops at the edge of the real systems. The reason is not that the model got worse. It is that the demo and the deployment are two different problems.
The demo works. The deployment does not.
A pilot runs on a curated sample, a friendly dataset, and one engineer's machine. Production runs on the messy live stream, behind the firewall, under audit, with real money and real consequences attached. The distance between those two worlds is where most AI quietly dies.
The three real blockers
- 1 Data access.
The agent needs live read and write access to systems it is often not allowed to reach from a public cloud at all. - 2 Approvals.
No regulated team will let software act unsupervised. Someone must own every consequential decision. - 3 Observability.
When it acts, someone must be able to see exactly what it did and why, after the fact.
Build for production from day one
Scope the first workflow as a deployment, not an experiment. Decide where it runs, who approves what, and how it is logged before you measure a single accuracy number. Running it on-prem removes the largest blocker, data leaving the building, before you even start, which is why so many regulated deployments end up there.
