Operating Model
TLDR
An operating model is the concrete specification of how work happens. For enterprise AI to deliver value, this specification must change.
Definition (in scope)
An operating model is not a strategy document or an org chart. It is the precise answer to: who does what, who decides, what the handoffs are, and what the controls check. It includes roles with named accountability, workflows that specify where humans act and where systems act, decision rights that clarify authority at each step, and controls that measure quality and catch failure.
For AI to integrate into enterprise operations, the operating model must change. A model can automate a task, but if no one has authority to change the task, if the workflow still assumes a human at every step, if controls are designed to catch manual errors rather than algorithmic ones, the technology remains peripheral.
Out of scope
An operating model is not a vision statement, a set of principles, or a technology architecture. It is not "how we think about work" but "how work actually runs." Strategy sets direction. The operating model specifies execution.
Why it matters for enterprise AI
Most enterprises acquire AI tools and wait for transformation. It does not come. The gap is not technological. It is structural. Without changes to roles, workflows, decision rights, and controls, even the most powerful tools remain disconnected from operations. Pilots multiply. Nothing scales. The enterprises that redesign the operating model will win. The ones that treat AI as a technology purchase will not.
Challenge
Pick one AI pilot your organisation is running. Then answer:
- Who owns the output when the model participates in the workflow?
- What changes to the workflow on Monday, not on a roadmap slide?
- What does quality mean when the process is hybrid, and who checks it?
If you cannot answer those three, the pilot is technology experimentation. It is not an operating model change.
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