Expect operating governance, not abstract policy
A growing business usually needs practical governance before it needs a large AI office. NIST AI Risk Management Framework is the right reference point because it frames AI risk around mapping, measuring, managing, and governing systems. An AI governance consultant should translate that into acceptable-use rules, risk tiers, workflow controls, and owner accountability.
PwC Responsible AI survey reinforces that responsible AI needs executive attention and operating discipline. The deliverable should help teams decide what they can use AI for this week, not just describe principles.
Connect governance to workflows
IBM Institute for Business Value AI capabilities research is useful because it links AI return to capabilities. Governance should support those capabilities: trusted data, adoption routines, measurement, and clear operating roles. If governance does not change how people review outputs, protect data, and escalate exceptions, it will not hold.
McKinsey State of AI 2025 adds the scaling context. Many organizations are still early in enterprise AI scaling, so governance has to help teams move from experiments to controlled workflow adoption.
Measure whether governance is used
Measure policy adoption, approved workflow count, exception reviews, training completion, and incidents or near misses. A useful consultant will leave templates, decision rights, and a review cadence the business can keep using.
Use AI Governance and Training to define the governance layer before expanding to higher-risk workflows.
