As organisations explore the opportunities presented by AI, many are developing governance frameworks to support its responsible use. This involves establishing policies, defining roles and responsibilities, and putting processes in place to manage risk and oversight.
These developments provide an important foundation as they demonstrate that governance arrangements exist. But when an AI-supported decision is later reviewed or challenged, a different question emerges: how can an organisation demonstrate that those arrangements were applied in practice?
This is where the distinction between governance and evidence becomes important.
Governance on paper versus governance in practice
An organisation that has implemented an AI governance framework may have the following:
- An AI policy
- Defined approval processes
- Risk registers
- Human review requirements
- Oversight committees
- Staff guidance and training
Together, these provide assurance that the organisation has considered the risks associated with AI. However, if a particular decision is later queried, the focus changes to whether the appropriate procedures were followed.
The organisation may be required to show:
- What information was provided by the AI system?
- What information was considered by the decision-maker?
- What concerns or limitations were identified?
- What challenge or scrutiny was applied?
- How was the final judgement reached?
Without evidence, organisations may be left relying on personal recollections or assumptions about what happened rather than being able to demonstrate it.
The evidence gap
This creates what might be described as an ‘evidence gap’. Many governance frameworks focus on defining controls and responsibilities, but fewer address how evidence of those controls will be captured during everyday work.
In practice, many decisions remain difficult to reconstruct after the event. Information is often scattered across emails, meeting discussions, documents and other systems. Over time, the context behind decisions, and the connections between related information, can be lost or become difficult to reconstruct.
Additionally, as staff move roles or organisations, it becomes increasingly difficult to understand whether a decision was underpinned by careful consideration, scrutiny and sound judgement.
Why this matters
When organisations can demonstrate how decisions were reached, they strengthen:
Accountability
Stakeholders can understand who made decisions and why.
Transparency
Reviewers can see what information was considered and how AI influenced the outcome.
Learning and improvement
Teams can evaluate previous decisions, identify patterns and improve future practice.
Trust
Internal and external stakeholders gain greater confidence that governance is not only documented but also actively applied.
In other words, evidence transforms governance from a statement of intent into something that can be verified.
A practical example
A public sector team is using an AI system to summarise consultation responses and identify key themes. The AI-generated analysis is reviewed by an officer before recommendations are prepared.
A few months later, stakeholders raise questions about how a particular conclusion was reached.
The team produces its policy, governance framework and risk assessment, which demonstrate that appropriate controls were designed. However, the stakeholders also ask:
- Which AI outputs informed the recommendation?
- Were any outputs challenged or disregarded?
- What additional evidence was considered?
- What reasoning led to the final conclusion?
This context was not captured when the decision was made, so providing clear answers proves difficult.
Building evidence into decision-making
As AI becomes embedded in everyday work, organisations may need to think differently about governance.
The challenge is not simply creating more policies or more oversight structures. Instead, organisations should consider how evidence is captured as a natural part of the decision-making process.
This involves preserving:
- The information available at the time
- Relevant AI-generated outputs
- Human observations and challenge
- The rationale behind decisions
- The context in which decisions were made
When these elements are recorded together, it becomes much easier to demonstrate not only what decision was reached, but how and why it was reached.
Governance needs evidence
AI governance is often discussed in terms of policies, controls and oversight. Yet when decisions are reviewed, the critical question is usually much simpler: ‘what happened, and why?’
Answering that question requires not just governance documents, but evidence.
This is where tools like VeriNote can help, by preserving the information, context, reasoning and human judgement behind decisions, creating a clear record of how outcomes were reached.