Start with the risk framework
Do not evaluate an AI governance consultant through a software demo. Start with how they classify use cases, data sensitivity, human review, and exception handling. NIST AI Risk Management Framework is relevant because it provides a durable structure for AI risk management. A credible consultant should be able to map that framework into decisions your teams can actually use.
PwC Responsible AI survey adds a practical responsible-AI lens: leadership, controls, accountability, and adoption matter as much as technology choice.
Ask how governance reaches the workflow
Microsoft 365 Copilot data protection architecture is useful when evaluating governance around enterprise data because permissions, auditing, and data protection are central to any internal AI workflow. Ask how the consultant will handle identity, restricted content, audit trails, and user training.
IBM Institute for Business Value AI capabilities research helps evaluate whether the consultant is building capabilities or merely writing policies. The consultant should define owners, metrics, review routines, and workflow-specific controls.
Look for artifacts you can operate
Ask for examples of acceptable-use policies, risk-tier rubrics, workflow review checklists, incident response routines, and adoption scorecards. Avoid vendors who cannot explain how governance changes day-to-day work.
Use AI Governance and Training to compare consultants against the operating model your business needs.
