operating-model

Almost every organisation has AI principles and almost none can point to a shipping decision they changed. This lesson covers why abstract commitments fail to bind, the specific gap between a value and a decision rule, ethics washing, and what a principle needs before it can affect anything.

AI changes tasks rather than jobs, which means it redistributes work inside a role instead of removing the role. This lesson covers what that does to a manager: where review load lands, why self-reported productivity is unreliable, the skill-formation problem for junior staff, and which management assumptions stop holding.

An AI QA function has to be built while features are already shipping. This lesson covers the order of work that produces something useful fast, where the function should sit, how to handle a team that resists a new gate, what to measure about the function itself, and the failure modes that quietly end it.

AI governance is a body of work before it is a job title, and most of it is done by people whose title says something else. This lesson sets out what the work consists of, how it splits across legal, risk, data protection and engineering, why a dedicated role appears at some scales and not others, and what the data protection officer precedent does and does not tell you.

With an inventory in place, governance becomes a question of who decides what. This lesson covers the AI policy and what actually belongs in it, acceptable-use rules people can follow, decision rights mapped with RACI, the approval gate a new system passes through, an AI risk register with risks specific to these systems, and escalation that works when something goes wrong at eleven at night.

An AI governance framework that starts with a policy is built on nothing. This lesson covers the artefact everything else depends on: finding the AI systems you actually run, including the ones inside software nobody bought as AI, recording the fields that make the inventory usable, classifying each system, assigning real ownership, and binding the whole thing to triggers so it stays true.

Governance is accountability, and accountability needs names. Learn the core data governance roles, owner, steward, and custodian, and exactly who does what, then the operating models (centralized, decentralized, federated), why heavy committees fail, and how domain ownership and data-as-a-product push accountability to the teams closest to the data.

The model is the least of your problems. This lesson examines how to structure the AI function (centralised, federated, hub-and-spoke), make defensible build-vs-buy decisions, design for actual adoption, and understand why change management — not the model — is the dominant failure mode in enterprise AI programs.