AI governance is an operating discipline, not just a policy
Organizations exploring AI governance consulting often need more than a set of principles. They need clarity about who makes decisions, which uses are acceptable, how teams assess risk, and how leaders know whether AI is creating measurable value.
What should an AI governance engagement cover?
Business priorities and use-case inventory
Begin with the outcomes the organization needs. Inventory existing and proposed AI use cases, their owners, affected workflows and expected value. Distinguish experimentation from production use.
Decision rights and accountability
Define the responsibilities of executive sponsors, business owners, data leaders, technology teams, risk functions and human reviewers. Establish how exceptions are approved and escalated.
Data and organizational readiness
Evaluate information quality, stewardship, access, workforce capability, adoption barriers and cross-functional coordination. Governance that ignores how people work rarely survives implementation.
Practical controls and evidence
Document risk assessment criteria, vendor review, testing, monitoring, change management and incident response. Evidence should be proportionate to the significance of each use case.
Measurement and sustained adoption
Agree on leading indicators, operating metrics and a review cadence. Track not only tool usage but also quality, time saved, rework, exceptions and business outcomes.
Questions to ask a prospective advisor
- How will governance connect to our business strategy?
- Who will own decisions after the engagement?
- How will employees and leaders be prepared to use the process?
- What tangible artifacts and operating routines will remain?
- How will we measure progress and adjust?
Explore the next step
Dr. Christina Sandema-Sombe provides strategic consulting and team training, framework licensing discussions, and executive speaking and workshops. Request a conversation about the governance challenges facing your organization.