Approach
Optimize before automate. Govern so teams can move faster.
The principle came from a Six Sigma engagement over twenty years ago and still holds for AI: fix the process, then add the technology, and keep the controls proportionate to the risk.
See what exists
Inventory current, planned and shadow AI, and the processes and data around it. You cannot govern what you cannot see.
Right-size the governance
Assess risk and maturity, then tailor policies, roles and controls to the organization instead of importing a canned framework.
Pilot on one system or issue
Prove the approach where it is cheapest to learn. Triggers for deeper assessment are defined here, not guessed.
Expand gradually
Roll out to the next system once the pilot shows controls that integrate with existing business and technology operations.
Hand it over
Transition ownership to your people, with monitoring and documentation that make audits and regulation routine.
Governance as enabler
Good governance is how teams get to say yes to AI with confidence. Lightweight, proportionate controls let adoption move faster, because people know where the lines are.
Design as a capability
A human-centered design background shows up in the details: disclosures users understand, workflows people follow, and controls that fit how work is done.
For fractional AI governance and focused engagements, see Flowhaus.ai.