Enterprise AI & decision governance
Move from an AI experiment to a governed workflow, with private deployment, human approvals and traceable execution.
Start with the operating evidence.
Document decision owners, data permissions, model versions, approval limits and system interfaces. Distinguish advice, approved actions and automated execution so that the same recommendation cannot silently acquire wider authority.
The management question
Which decisions can AI support, who remains accountable, and what must be explained before a recommendation is executed?
Compare feasible alternatives.
Design a review record that captures inputs, constraints, alternatives, approver and execution status. Route unusual inputs and infeasible results to accountable people. Define pause, rollback and monitoring before enlarging the permitted scope.
What we deliver
A use-case assessment, decision model, deployment design, approval rules and an auditable recommendation-to-execution workflow.
Agree how value will be measured.
Begin with one decision, a named business owner and a baseline period. Review recommendations alongside the existing process before connecting execution. Agree the data refresh cycle, approval limits, exception handling and measures of service, cost and risk. Outcomes depend on the agreed scope and evidence; illustrative scenarios are not promised customer results.
How value is measured
Decision cycle time · approval adherence · traceability · operational exceptions