AI adoption starts with a business process and the people responsible for it. Agree what the system is meant to improve, what information it may use and who remains accountable for the result.
Assess the consequences of an error
Consider the data involved, the people affected and the actions the system can take. An internal draft and a decision affecting a customer need different levels of review. Document the reasoning behind each assessment.
Higher impact uses need stronger testing and approval. Some uses may be inappropriate even with additional controls. Involve the relevant business, security, privacy and legal specialists in that decision.
Establish a shared set of controls
Define data handling, access, human review, change records and escalation. Apply those controls consistently, then add requirements for the individual workflow. Keep exceptions visible and assign an owner to each one.
Record meaningful changes to models, prompts, tools and permissions. Test again when a change could affect the outcome, and make sure the team knows how to stop the process safely.
Measure the actual result
Compare performance with a baseline. Review time saved alongside errors, rework and incidents. Include the effort needed to check outputs and handle exceptions.
Expand gradually
Start with a use case the team can supervise and evaluate. Review the results, close gaps and agree the next step. A smaller organization can keep the documentation proportionate while still making responsibility, approval and recovery clear.