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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.

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The problem

Scattered initiatives, expectations that differ between departments, and difficulty deciding where to start without committing resources to pilots that never scale.

How we intervene

We survey the real use of AI in the organisation, identify use cases with verifiable value, and build a prioritisation that leadership can defend before its own committee.

How the work unfolds

  1. Survey of real AI use with the areas involved, including undeclared uses.
  2. Agreement with leadership on value, risk and feasibility criteria.
  3. Application of those criteria to the surveyed cases and selection of priorities.
  4. A phased roadmap, with owners and the decisions required at each stage.

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 inventory of AI initiatives and uses under way, including undeclared ones.
  • Prioritisation criteria agreed with leadership and applied to the surveyed cases.
  • A phased roadmap, with the capabilities and decisions required at each stage.
  • Defined owners and a decision model to sustain the path.

Client participation

The client provides access to the areas involved and decides the priorities. AGORA facilitates, documents and holds the line on method.

Where it fits in the commercial journey

  1. AssessKnow where you stand.

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

Next step

A free exploratory assessment locates the starting point before defining a scope.

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

  • 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.

  • 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.

  • 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.