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Governance and risk

Build the AI governance model the organisation can sustain and demonstrate: policy, inventory, risk classification, roles and evidence proportional to its context.

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The problem

AI systems already in use with no approved policy, no inventory, no risk classification and no evidence to answer an audit, a funder or an oversight body.

How we intervene

We build the governance model the organisation can actually sustain: policy, inventory, risk classification, roles and evidence, selecting controls proportional to its context.

How the work unfolds

  1. Inventory of the AI systems and uses in operation.
  2. Risk classification and selection of controls proportional to the context.
  3. Drafting of the policy and the responsibility matrix for approval.
  4. Definition of evidence per control and of the review cadence.

Every project runs on the AGORA Delivery Framework —Qualify, Discover, Design, Deliver and Sustain—, which defines how the work is carried out. It is distinct from the commercial journey, which describes what can be engaged. See the methodology →

Deliverables

  • An AI usage policy and data handling rules, drafted for approval.
  • Inventory and risk classification of the systems in use.
  • A responsibility matrix and review cadence.
  • Defined evidence per control and the observed maturity state.

Client participation

The organisation approves the policy and takes on the defined roles. AGORA accompanies implementation and preparation for external reviews.

Where it fits in the commercial journey

  1. GovernBuild policies, accountability and controls.
  2. MonitorMaintain control as AI adoption evolves.

The commercial journey describes what can be engaged. No organisation needs all four stages.

Next step

The exploratory assessment shows which controls are relevant before committing to a scope.

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Sector references correspond to the professional trajectory of the firm's members, not to AGORA Advisory contracts.

  • AI readiness and strategy

    Bring order to the AI initiatives that already exist and turn them into a prioritisation leadership can defend, before committing resources to pilots that never scale.

  • Data and operations

    Make sure the data AI needs is available, owned and reliable, and that the processes using it are defined end to end.

  • AI operating model

    Turn governance into execution: approved rules applied to how each AI use is bought, built and approved, with every step leaving evidence.