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AI strategy and operating model assurance

  • AI strategy
  • Governance
  • Assurance

I reviewed a draft AI strategy against the operating arrangements needed to put it into practice. The review identified where governance, prioritisation and measures needed clearer mechanisms before the strategy could reliably guide work.

Organisational context

A national public-sector regulator with 1,000+ employees, operating across multiple jurisdictions and developing its AI direction alongside an evolving technology operating model.

The challenge

The strategy set out ambition and principles, but important connections to day-to-day operation were missing. Governance labels did not establish who could make decisions or how issues would escalate. Prioritisation was not clearly connected to work allocation, and the maturity ambition lacked a measurable baseline.

My contribution

I tested the strategy against the evolving operating model and work-management arrangements, examining accountability, decision rights, data prerequisites and how proposed work would be assessed and shaped before delivery commitment. I identified gaps between the stated principles and the arrangements needed to apply them.

What the review produced

I recommended named accountability and escalation routes, measurable baselines, evidence-based data prerequisites and an explicit link between AI priorities and the organisation's work-management process. The review made unresolved operating questions visible and set out practical changes for the next iteration of the strategy.

Why this matters

An AI strategy needs to guide real choices about work, authority and capacity. This example demonstrates how I assess whether the proposed direction has the operating arrangements needed to support it.

Takeaway

An AI strategy needs to guide real choices about work, authority and capacity.

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