A research and business case pack for deciding who is accountable for AI systems and their outcomes. Every claim in the case is either traced to evidence or openly labelled as an assumption still to be tested.
What this asset covers
The pack sets out the decision, the inputs it needs and how to weigh evidence. A repeatable scoring method shows which assumptions to research first. The options structure keeps cash, staff capacity and risk reduction apart and times each benefit to when it actually lands. Governance gates, KPI definitions and execution tables follow. A synthetic worked example compares several costed options, with a failure case and a practice exercise. The editable tools are an assumption register, source log, research questions register, coverage worksheet, challenge checklist and review record.
- 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