A 2024 survey found that 72% of organisations had adopted AI in at least one business function. Adoption, though, does not show whether the risks are being managed. Boards need clear priorities, accountable owners across the business and evidence that controls work. The practical test is whether safeguards shape everyday decisions—and whether leaders know which warning signs require board attention.
Set Board Priorities for AI Governance
Where should the board begin? Set priorities by linking AI governance to business strategy, risk appetite and measurable outcomes. Ask management to identify the uses of AI that matter most to the organisation, assess the potential harms and explain how these systems affect customers, employees and operations.
Link AI governance to business priorities, assess potential harms and understand how systems affect people and operations.
Require evidence that models have been tested for bias across relevant groups, and that management has a plan to address any disparities. Ask for clear data lineage: the sources and changes behind the information used in consequential decisions.
The board should agree what level of risk is acceptable, when issues must be escalated and how often controls should be reviewed. Revisit these decisions when systems, business needs or regulations change.
In my experience, good governance starts with clear questions and evidence, not assurances. Keep the focus on whether controls protect people, preserve trust and support sustainable value—not on technology for its own sake.
Assign AI Oversight Roles Across the Business
Once the board has set priorities and risk thresholds, it should assign clear oversight roles across the organisation. Name owners in technology, legal, compliance, operations and human resources, and give each the authority to match their responsibilities.
Data stewards should be accountable for data lineage.
Product leaders should consider AI ethics when making design and deployment decisions.
Business-unit heads should turn the board’s risk appetite into practical decision rights and escalation routes.
Be explicit about who approves exceptions, resolves conflicts and keeps executives informed. Record these responsibilities in a shared governance charter, then make sure teams understand how it applies to their work.
In my experience, clear ownership helps prevent gaps without pushing every decision up to the board. The board should set direction and oversee the framework; business leaders should make decisions within it and escalate issues when needed.
Review AI Risks, Controls, and Results
Review AI risks, controls and results on a regular schedule. Use audit evidence, incident reports, performance measures and user feedback to decide whether safeguards are working and outcomes remain acceptable.
- Refresh each risk assessment when models, data or business exposure change. Trace data lineage to find weak points, and confirm who’s accountable for the quality of each data source.
- Examine evidence that controls are operating as intended, including access logs, escalation records and model validation results. Challenge exceptions and ask owners to explain unresolved gaps and set out how they’ll address them.
- Compare governance measures with approved thresholds. Track errors, bias, drift and the time taken to resolve problems. Decide whether the evidence supports continued use, tighter controls or suspension.
I advise growing businesses to treat these reviews as decisions, not paperwork. Record the findings, decisions, owners and follow-up dates. At the next review, test whether agreed actions have been completed and whether the evidence shows that risks are under control.
Make AI Governance Part of Daily Decisions
Regular reviews can show whether AI safeguards are working. But it’s the decisions made each day that determine whether those safeguards shape how your organisation operates.
Build AI governance into investment approvals, procurement, product launches and workforce planning. Before a system is deployed, ask teams to record its risks, the controls they’ll apply and how they’ll assess results. Repeat these checks after any material change.
Make accountability clear. Decide who oversees each system, when concerns must be escalated and who’s authority to pause its use. Link these responsibilities to board priorities, then monitor whether they’re followed through consistent measures, incident reporting and sampled reviews.
I have found that governance is most useful when it informs choices, not just documents them. Ask managers to use the evidence to adjust decisions as risks change. That’s how policy becomes part of daily practice and strengthens accountability across the business.
Conclusion
AI governance cannot sit with the audit committee alone. The board should set priorities that reflect the organisation’s risk appetite, then assign clear responsibilities across technology, legal, compliance and HR.
Ask management to provide evidence on how AI systems perform, whether they produce biased outcomes, and where their data comes from. Agree in advance what findings require escalation, who must act and when the board will review progress.
These checks should inform everyday decisions, not just periodic reports. In my experience, accountability works best when it is built into strategy, operations and culture. That gives leaders a practical way to oversee AI and pursue its value responsibly.
