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Microsoft Copilot and AI

Generative AI Use Cases

Identifying and prioritising the scenarios where generative AI genuinely reduces effort, scored on value and feasibility.

Technologies

  • Microsoft 365 Copilot
  • Copilot Studio
  • Power Platform
  • Azure AI Foundry

Scenario selection determines whether an AI programme succeeds. We work at task level, establish what the work costs today, and rank candidates on value against feasibility.

Business outcomes

A ranked shortlist

Candidates scored on value and feasibility, not on novelty.

A measurable baseline

What the task costs today, so improvement can be shown.

Realistic sequencing

Prerequisites made visible before commitment.

Capabilities

Opportunity discovery

Workshops and process review to surface candidate tasks.

Value and feasibility scoring

A consistent frame applied across candidates.

Baseline measurement

Current effort and quality established before build.

Roadmap

Sequenced delivery with dependencies and effort ranges.

How we deliver it

  1. Assess

    Evaluate data readiness, permissions, licensing and candidate scenarios against value and feasibility.

  2. Prepare

    Curate content, review permissions, assign ownership and establish the governance position.

  3. Pilot

    Build and evaluate with a cohort whose daily work matches the scenarios, measured against a baseline.

  4. Scale

    Extend to further scenarios with champions, enablement and a standing content backlog.

  5. Operate

    Monitor quality, review gaps weekly and maintain grounding sources on a fixed cadence.

Next step

Considering Generative AI Use Cases?

We will give you an honest view of the effort involved, the prerequisites and the risks, before anyone signs anything.

Talk to an expert Solutions