AI Governance
Define ownership, policy, evaluation, evidence and human oversight for AI systems that matter to business operations.
Trust model
Turn principles into operating controls
Reliability improves when teams can define acceptable behaviour, test it repeatedly, and investigate deviations.
Teams cannot evaluate quality, failure modes, or regression risk before or after launch.
Production AI lacks monitoring, permissions, or escalation paths.
Define escalation, ownership, auditability, and monitoring before AI becomes operationally important.
Controls
Reliability requires evidence
Governance depth should match the application impact, data sensitivity, degree of autonomy, and consequences of error.
Test outputs against representative tasks, expected facts, edge cases, and unacceptable failure modes.
Measure task completion, tool use, policy adherence, and failure behaviour before expanding autonomy.
Place review, approval, escalation, and permission checks where business impact justifies them.
Create operational evidence around who used AI, what it did, what it cost, and how it performed.
Control architecture
Governance that reaches the system
Governance becomes operational when ownership, evaluation, permissions, approvals, evidence, and change control are built into the system.
technology
Discuss a Project
Share the AI system, its users, data, decisions and level of autonomy. Governance should match the actual consequence of error or misuse.