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Data and AI readiness assessment

An honest view of whether your data can support the AI use cases you have in mind — and exactly what it would take if it cannot.

Typical timing
4–6 weeks
Engagement
Fixed scope
Delivery framework
User researchDiscoveryAlphaBetaLive
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Objective

Most AI programmes stall upstream of the model: data that is incomplete, inaccessible, ungoverned or not trusted. This assessment tests your priority use cases against the data, platform, governance and skills they depend on, and turns the gaps into a costed, sequenced plan.

You may need this if

  • An AI strategy exists but pilots keep stalling
  • Nobody is sure which data is trustworthy enough to use
  • Pressure to “do AI” without a clear starting point
How it runs

Activities, step by step

The plan follows our delivery framework. Steps that do not apply to this kind of work are left out rather than padded.

  1. 01 · User research1–2 weeks

    Use cases and decisions

    • Workshops to surface and score AI use cases by value
    • The decisions each use case would improve, made explicit
    • Stakeholder interviews across business and IT
  2. 02 · Discovery2–3 weeks

    Readiness assessment

    • Data sources profiled for quality, coverage and lineage
    • Platform, governance, security and skills assessed
    • Readiness scored per use case
  3. 04 · Beta1 week

    Roadmap

    • Quick wins and foundation work separated
    • Costed roadmap with the first use case chosen
    • Executive presentation

Deliverables

What you keep at the end.

  • Scored AI use-case portfolio
  • Data and AI readiness assessment
  • Data quality profile for priority sources
  • Costed twelve-month roadmap

Outcomes

What it is built to change.

  • AI investment pointed at use cases the data can support
  • Foundations fixed once, for many use cases
  • A credible plan leadership can fund

Often combined with

Every engagement starts with a 45-minute working session with the SME who would own the work. No deck.

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