Executive summary

A diagnostic for governance, risk and assurance teams who need to show who is accountable for each AI system. It measures how much of that accountability rests on evidence, and turns the gaps into owned exceptions before leadership accepts a baseline.

What is inside

What this diagnostic covers

The asset sets out:<ul><li>the decision it supports and when not to use it;</li><li>an inputs table with an evidence grading hierarchy;</li><li>a step-by-step method with written-out formulas and scoring anchors for seven governance domains;</li><li>approval gates and escalation triggers, plus KPI definitions with a named measurement owner;</li><li>a fully worked synthetic case, a failure case, an alternate scenario and a practice exercise;</li><li>editable working tools: a questionnaire, an evidence inventory, an exception register, a clinic challenge log, a baseline summary and a review record.</li></ul>

  • Purpose and decision
  • Context and applicability
  • Inputs and evidence
  • Definitions and assumptions
  • Method and analytical logic
  • Current-state assessment
  • Options and business case
  • Recommended design
  • Execution and activation
  • Governance and risk
  • Measurement and benefits
  • Worked example and practice
  • Working tools and references