The CEO of a regulated financial services business asked me how he should scale. He was in the middle of his Series B raise and the board was pushing him on how the next eighteen months were going to look. He had hired well. His product was working. Revenue was moving. He could still tell you everything that was happening in his company because most of it still happened in front of him.
He could not tell me how it all fit together.
That is the question every scale-up CEO is supposed to be able to answer and almost none of them can. Revenue forecasts are easy. Org charts are easy. How the business actually works as a system, what flows into what, who owns each piece, where the work breaks when it breaks, is the question that gets dodged most often and matters most.
The artefact that answers it is a target operating model.
A target operating model is not an org chart. An org chart shows you who reports to whom. An operating model shows you how the work flows. Customer lifecycle on one side, from acquisition through implementation, account management, support, and billing. Product lifecycle in the middle, from build through test, knowledge management, customer training, pricing, and end of life. Supporting functions on the other side, the ones that keep the business running underneath. Management sitting adjacent, driving strategy across all of it.
When I was COO at Lumeon, every component of that map had a named owner, a codified process, a risk score, and a quarterly review. The TOM was not a deck artefact. It was the source of truth for how the company actually worked. New hires found out who owned what by looking at it. Existing staff improved their own working lives by editing and improving the process they ran. The board read the colours on it when they wanted to know where the risks were.
That was useful in 2022. It is essential in 2026.
Every business is being told to deploy AI. Almost no business is being told what AI needs to deploy into.
An AI agent cannot route a sales-to-success handoff if nobody has ever drawn the handoff. An LLM cannot triage a customer escalation if there is no map of how escalations got triaged before. An automated workflow cannot reduce the cost of onboarding if the onboarding flow lives in three different people’s heads. The agent does not infer the shape of your business from the shape of your data. It needs to be told. The TOM is what tells it.
This is the inversion most operators have not caught up with yet.
The TOM used to be a back-office document. Useful, but quiet. Now it is the substrate AI lands on.
If you can describe a process precisely enough to write a codified swim lane diagram, you can describe it precisely enough to put an agent inside it. If you cannot, you cannot. The work of building a clean operating model and the work of preparing the business for AI are the same work. They were always the same work. AI just made the cost of not doing it visible.
The CEO I spoke to in financial services did not need an AI strategy. He needed a target operating model. Once he had one, the AI strategy would write itself, because he would know which boxes had narrow enough scope and clean enough inputs that an agent could take them, and which boxes needed human judgment and would for the foreseeable future. Without the map he was guessing. With the map he was deciding.
The businesses that win the next decade will not be the ones with the biggest AI budget. They will be the ones whose operating model is legible enough for AI to actually do work inside it.
If you cannot describe the business, no model can save it.