Different AI decisions. The same need for a defensible assurance record.
RAIReady helps organisations answer the practical questions behind AI approval, ongoing use and accountability. These examples are illustrative, not client claims.
Operating contexts
The assurance question changes with the people, decisions and obligations involved.
Health and clinical services
Can this clinical or triage support tool be used safely with patient data, and who accepts the residual risk?
Common situations
Clinical decision support
Patient communication and triage
Sensitive data used by an external model
RAIReady response: Classify the use, connect clinical, privacy and security obligations to evidence, separate clinical ownership from assurance review, and record the accountable decision.
Can AI used in credit, servicing or fraud be explained against prudential and consumer obligations?
Common situations
Customer eligibility or prioritisation
Fraud and anomaly detection
Generative AI in regulated workflows
RAIReady response: Test the assessment basis, controls and evaluation evidence; document limitations and findings; then keep the approved position under review as models and suppliers change.
Is each AI use consistent with public-sector policy, and is the decision record ready for scrutiny?
Common situations
Citizen-facing automation
Operational prioritisation
AI-enabled procurement
RAIReady response: Create a traceable assurance case showing purpose, affected people, evidence, independent judgement, remediation and the authority behind the decision.
What assurance supports AI touching student data, assessment integrity, teaching and research?
Common situations
Student support and retention
Assessment and academic integrity
Research tools handling protected information
RAIReady response: Apply a consistent assessment while distinguishing each context, gather evidence from owners and suppliers, and escalate material gaps before approval or wider use.
What happens to customer or employee data sent to external AI providers, and how can that be evidenced to buyers?
Common situations
AI embedded in a software product
Copilots used by employees
Third-party model and hosting dependencies
RAIReady response: Link product claims, architecture, evaluations and supplier evidence to controls, identify unanswered questions, and produce a current assurance position for customers and governance teams.
Which AI systems need attention now, who owns the next action, and what changed since the last review?
Common situations
Distributed AI adoption
Different business-unit risk thresholds
Board and committee oversight
RAIReady response: Maintain one inventory, prioritise exceptions, assign evidence and remediation, monitor decision dates and give executives a portfolio view with the basis behind every measure.