Stop starting with the model. Start with the mirror.
Pick one process your business actually runs. Record people doing it. Write down the handoffs, the bottlenecks, the edge cases, the moments where someone exercises judgment nobody documented. Now you have something an agent can act on, a foundation for measuring whether automation is working, and the artifact that lets you route work to a reviewable, auditable, cost-controlled agent fabric instead of a single developer's CLI.
I've made this case at greater length elsewhere — that most enterprises cannot deploy agents because they cannot describe their own processes. The mirror is the prerequisite. Skip it and you spend a year and a budget cycle proving the obvious: that a model trained on the internet does not, in fact, know how your accounts-receivable team works.
The companies that win the next phase of enterprise AI will not be the ones with the best coding agents or the smartest models. They will be the ones that close the gap between what an AI demo can do and what an AI system in production has to do — observably, reliably, recoverably, at a cost that does not implode when the CFO sees the bill.
The gap between AI demo and AI deployment is called software engineering. The technology is moving fast. The organizations are moving slow. That gap is where all the value is. The move on Monday is small and unglamorous: one process, one recording, one document. Then build outward from there.
Sources
Related Essays
The Mirror Problem
95% of enterprise AI projects fail not because the models are weak. They fail because the company cannot describe its own processes well enough for an agent to act.
The Operationalization Gap: Where AI Demos Go to Die
The gap between an AI demo and an AI deployment is called software engineering. Most organizations are not equipped to close it, and that is where all the value lives.
Vibe Code Has No Production Strategy
A coding agent generates a working Python service. Someone says deploy it. Now what? Speed of creation without speed of operationalization is just faster debt.
Key takeaways
- Stop starting with the model. Start with the mirror.
- Pick one process. Record it. Write down handoffs, bottlenecks, edge cases, and the moments of unrecorded judgment.
- Now you have something an agent can act on, a foundation for measuring success, and the artifact that lets you route to a reviewable agent fabric.
FAQ
What is the first move on Monday?
Pick one process your business actually runs and record people doing it. Capture the handoffs, the bottlenecks, the edge cases, and the moments where someone exercises judgment nobody documented. That artifact is the input.
Who will win the next phase of enterprise AI?
Not the companies with the best coding agents or the smartest models. The ones that close the gap between what an AI demo can do and what an AI system in production has to do — observably, reliably, and at a cost that does not implode when the CFO sees the bill.