AI Initiatives

AI access isn't AI adoption.

Most teams already have AI tools. What they don't have is a shared way of using them, so a few developers move faster while team delivery stays flat. That's a workflow problem, not a tooling problem. We turn fragmented AI use into a defined operating model the whole team can rely on.

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Free AI Adoption Diagnostic · runs in your own AI assistant

01 — In your team

What this looks like.

// SIGNALS
  • 01Developers using AI differently, with no shared approach.
  • 02AI-generated code that needs rework in review.
  • 03A few individuals gaining, the team unchanged.
  • 04No clear guidance on where AI should, and shouldn't, be used.
  • 05No reliable way to tell whether AI is improving delivery.

This is activity, not progress. Without structure, AI stays an individual advantage instead of an organisational capability.

02 — Our AI engagements

Define → Apply → Sustain.

// LADDER

Each engagement is fixed-scope and built to be applied, not documented and shelved. Not sure where to start? The AI Operating Model Sprint is the default entry point.

Understand · Entry point

Define how AI should actually work across your teams.

For teams already using AI where results vary by developer and team output hasn't moved.

The problem it solves

  • —AI usage is individual, not standardised.
  • —Output quality depends on who's using the tool.
  • —Rework in PR review and repeated QA cycles.
  • —No shared understanding of when AI should be used.

What you get

  • —Defined AI workflow patterns.
  • —Clear guidance on where AI should and shouldn't be used.
  • —A structured model for consistent adoption.
  • —A prioritised view of what needs to change.
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Understand · Can be an entry point

Make AI work inside your engineering workflows.

For engineering teams where AI usage varies across the same codebase and consistency is the main issue.

The problem it solves

  • —Developers using AI differently across one codebase.
  • —AI-generated code that needs rework or extra validation.
  • —Inconsistent quality; unclear when AI should be trusted.
  • —Duplicated effort instead of reused patterns.

What you get

  • —Defined engineering AI workflows.
  • —Consistent patterns of use across the team.
  • —Guidance on where AI should be trusted.
  • —A structured rollout approach.
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Adopt

Make defined AI workflows hold in real delivery.

For teams that have defined AI workflows but find they break down under real delivery pressure.

The problem it solves

  • —Workflows that look clear but fall apart in real use.
  • —Teams reverting to old ways of working.
  • —Inconsistent adoption across developers.
  • —Blockers and edge cases that only appear in delivery.

What you get

  • —Refined workflows proven against real delivery.
  • —Reduced variation across teams.
  • —Fewer blockers to adoption.
  • —Clearer, more consistent patterns of use.
  • How it runsDefined scope · aligned to your real delivery · hands-on
  • Leads toAI Workflow Stewardship
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Scale

Make AI a dependable part of how your teams deliver.

For teams past initial rollout who are starting to see AI usage drift and early gains fade.

The problem it solves

  • —Workflows defined but not consistently followed.
  • —Teams drifting into different ways of working.
  • —Initial gains fading; value becoming uneven.
  • —No clear visibility into whether AI is still delivering.

What you get

  • —Consistent application of AI workflows across teams.
  • —Reduced variation and more reliable output.
  • —Visibility into where value is being created.
  • —A structured way to expand AI usage safely.
  • How it runsDefined scope · structured cadence · a sustain engagement, not a start
  • Leads toOngoing capability: new use cases absorbed without losing consistency
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Start free

See where your AI adoption really stands.

Free, in your own assistant. About ten minutes, no call.

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