AI risk is often an operating-model question.
Principles and policies matter, but responsible adoption is not achieved by policy language alone. A use case becomes governable when the organisation can explain what decision or workflow it supports, who owns the outcome, what evidence is acceptable and how exceptions are handled.
Define the human role deliberately.
“Human in the loop” is too vague to be a control. Leaders should specify when a person reviews, approves, overrides, investigates or escalates an AI-supported output—and what competence, authority and information that person needs to act responsibly.
Measure value and risk in the same scorecard.
Efficiency should not be separated from quality, adoption, exceptions, control effectiveness and stakeholder impact. Looking at these measures together gives leaders a more realistic view of whether an AI use case is creating durable value or simply shifting risk elsewhere.
A successful pilot is not the same as enterprise readiness.
Scaling changes the problem. Data quality, process ownership, security, training, monitoring, incident response and governance must mature alongside the technology. The operating model is part of the AI product.
Start with the decision—not the model.
A stronger first question is: which important decision or workflow needs to become more consistent, informed or responsive? That reframing narrows the use case, clarifies accountability and creates a better basis for deciding whether AI is actually the right intervention.