Many organizations have started to write AI principles, policies and review processes. Fewer have considered whether people can use them to make consistent decisions about real systems and real situations.
A useful governance approach connects expectations to decisions, evidence, accountability and follow-through. It gives people enough structure to act without treating every AI use case as identical.
Start with the decision
Governance should help an organization make decisions.
Those decisions may include whether to explore a use case, approve a system, change its scope, introduce additional controls, continue using it or retire it.
A policy that lists general expectations may be necessary, but it does not answer every practical question. People also need to know:
- What decision is being made?
- Who has authority to make it?
- Who needs to be consulted?
- What evidence is required?
- What conditions would change the decision?
Clear decision rights reduce the risk that important issues remain unresolved because everyone assumes someone else is responsible.
Make accountability visible
An organization may assign overall responsibility for AI governance to a committee or executive. That does not remove the need for ownership at the portfolio, use-case and operational levels.
Someone needs to own the intended outcome. Someone needs to be accountable for the system’s operation. Someone needs to ensure that risks, impacts, controls and evidence are reviewed when circumstances change.
The names and roles will differ between organizations. The need for visible accountability does not.
Define the evidence
Evidence supports both the decision and the ability to review it later.
Depending on the use case, evidence may include the intended purpose, affected groups, data sources, testing results, risk and impact assessments, control decisions, approvals, monitoring results, incidents and reassessments.
The goal is not to create records for their own sake, but rather to make the reasoning behind important decisions available to the people who need to rely on it.
Evidence also has to be proportionate. A low-impact use does not require the same depth of review as a system that affects access to services, employment decisions, financial outcomes or other important interests.
Connect risks, impacts and controls
Risk and impact questions should lead to decisions and controls rather than remain separate exercises.
The organization needs to understand what could go wrong, who or what could be affected, how significant the consequences could be and what can be done about them. Controls may involve technical measures, process changes, human oversight, training, restrictions on use, monitoring or a decision not to proceed. The appropriate response depends on the use case and its context.
Governance is more useful when the connection between the concern, the control, the owner and the evidence is visible.
Plan for exceptions and change
Real operating environments produce exceptions. Data changes. Suppliers change their systems. A model is updated. Users find new ways to use a tool. Performance changes over time. A governance process should make clear what happens when the original conditions no longer apply.
This includes who can approve an exception, how the exception is recorded, when it expires and what triggers reassessment. It also includes a process for revisiting decisions rather than assuming that an initial approval remains appropriate indefinitely.
Monitor what matters
Monitoring should connect to the reasons the system was approved. That may include performance, reliability, incidents, user behaviour, data quality, changes in risk or impacts, achievement of intended outcomes and the effectiveness of controls.
The organization does not need to monitor every possible measure. It needs to identify which changes would indicate that the system, its use or its governance needs attention.
Monitoring without an owner and a response process creates information without follow-through.
Build an operating rhythm
Useful governance is not a single approval meeting. It is a rhythm of decisions, evidence, monitoring and review across the AI lifecycle. The rhythm should be proportionate to the organization’s use of AI and the consequences of getting a decision wrong. It should also be workable for the people expected to follow it.
SCG’s approach connects AI governance with risk management, impact analysis, accountability, evidence, controls, capability development and continual improvement. The purpose is to help organizations make and sustain decisions, not to add a separate layer of paperwork.
A practical test
Ask whether a person responsible for an AI use case could answer these questions without relying on informal knowledge held by one or two colleagues:
- What are we using the system for?
- Who owns the outcome?
- Who could be affected?
- What risks and impacts have we considered?
- What evidence supports the decision?
- What controls and oversight apply?
- What would cause us to review the decision?
If the answers depend on individual memory, the organization may have policy content without a repeatable governance capability.
Useful AI governance makes decisions clearer, responsibilities visible and follow-through possible.
Where does AI governance become difficult to apply in your organization?
