Why read this
Read this when the team has many AI ideas and needs to choose use cases that can produce measurable business value without creating unmanaged legal, cyber, or operational risk.
Choose use cases with measurable ownership
Prioritize AI use cases that have a named business owner, a known baseline, reviewable outputs, and a clear escalation path. Strong early candidates include knowledge retrieval, customer-service triage, software support, compliance intake, fraud review assistance, clinical documentation support, and workforce help desks.
Evidence: NIST AI Risk Management Framework, NIST Generative AI Profile
Match controls to consequence
The same capability can carry different risk in public sector, health, financial services, software, or customer operations. Match the use case to data sensitivity, affected people, cybersecurity exposure, recourse, human review, and whether the workflow could affect safety, access, money, employment, or service quality.
Evidence: NIST AI Risk Management Framework, CISA AI cybersecurity guidance
Keep a portfolio stop rule
A healthy use-case portfolio records what would stop, change, or expand each pilot. Poor accuracy, hidden manual effort, cyber exposure, privacy uncertainty, cost growth, weak adoption, or unclear accountability should change the investment decision before more teams are added.
Evidence: NIST Generative AI Profile, FTC guidance on AI claims
Questions for the buying team
- Which AI use case has a baseline, owner, and review path?
- What business, legal, cyber, or customer consequence changes the control set?
- What evidence would stop, change, or expand the pilot?
Local evidence boundary: this guide organises questions and sources. It is not a legal, security, clinical, financial, procurement, or implementation approval.
Sources and further reading
- NIST AI Risk Management Framework standards guidance
- NIST Generative AI Profile standards guidance
- FTC guidance on AI claims standards guidance
- CISA AI cybersecurity guidance standards guidance