Use cases

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.

Discuss a clinical AI decision
Financial services

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.

See the assurance process
Government and public services

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.

Explore engagement options
Higher education

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.

Discuss an education AI use
Enterprise technology and SaaS

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.

See supplier assurance
Enterprise AI portfolios

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.

Explore the platform approach
Across the assurance lifecycle

Three recurring jobs, from first review to continuous assurance.

Establish a defensible baseline

Turn a discovered or proposed AI use into a documented assurance position.

  1. 1Register the AI use
  2. 2Assess inherent risk
  3. 3Identify controls
  4. 4Set evidence needs
See how the baseline is built

Reach an accountable decision

Move from collected evidence to an independent judgement and accountable approval.

  1. 1Collect evidence
  2. 2Independent review
  3. 3Resolve findings
  4. 4Record the decision
Discuss an assurance decision

Keep the position current

Bring material change back through assurance before the original basis goes stale.

  1. 1Monitor change
  2. 2Triage the signal
  3. 3Reassess impact
  4. 4Refresh the position
See continuous assurance
See the complete five-stage assurance process
When organisations use RAIReady

Assurance is most valuable at a decision point or when the basis of an earlier decision has changed.

01

Before an AI system goes live

Establish scope, inherent risk, required controls, evidence and the approval path before operational exposure begins.

02

When an existing tool is discovered

Bring unregistered AI into the inventory, establish its current use and reconstruct the evidence behind the operating decision.

03

When a supplier or model changes

Assess whether a product release, changed terms, incident or public disclosure makes the existing assurance position stale.

04

When evidence or acceptance expires

Return the item to its owner, refresh the evidence and require a new dated judgement rather than silently carrying the old one forward.

05

When a rating is challenged

Record the executive's challenge, the assurance response and the outcome without overwriting the original assessment history.

06

When a board, regulator or customer asks

Produce a versioned report that states the conclusion, supporting evidence, open gaps, limitations and accountable decision-makers.

Discuss the trigger affecting your organisation
Start with your situation

Tell us the AI decision, supplier change or assurance deadline currently in view.