AI can produce an answer in seconds. The harder question is whether anyone in the organisation can explain why that answer should be trusted, what happens if it is wrong, and who has authority to act on it.
That is a governance conversation. It belongs alongside discussions about the tool’s usefulness, speed and cost.
Start with the decision, then the technology
A useful starting point is to identify the business decision AI is supporting. Is it summarising information, recommending an action or making a judgment that affects someone? Different uses call for different levels of scrutiny.
Then make accountability visible. Identify an owner, clarify who reviews the output and agree when a concern must be escalated. “A human is involved” says very little unless that person has the time, competence and authority to challenge the result.
Three questions for the next leadership discussion
- Who is accountable for the outcome of this use of AI?
- What evidence would help us recognise a poor or harmful result?
- Who can pause the activity when the risk becomes unacceptable?
The human consequences deserve equal attention. Consider whose data is involved, who may be affected by errors, and whether people can question a decision that concerns them. These are practical design questions for the operating model.
Governance should also evolve with experience. A process that works for a limited pilot may need to change when the tool reaches more teams, touches more sensitive information or influences more significant decisions. Set a review point and make sure someone owns it.
Organisations may delegate tasks to AI. They still need people who can own the judgment, ask difficult questions and take responsibility for what happens next.
If your team could not explain an AI-supported decision to the person affected, what would you need to change?