AI approvals that keep leaders in control

Control starts with role clarity

The question is not whether AI is allowed. The better question is what role the AI is playing in a given workflow.

There is a meaningful difference between a model that drafts a response, a model that classifies a ticket, and a model that initiates a consequential action. The control model should match that difference.

Define the approval points explicitly

A practical approval structure often includes:

• AI can prepare or recommend
• A named person approves where customer, financial, or people impact is material
• Exceptions route to a clearer escalation path rather than being forced through

This keeps AI useful without making the accountability ambiguous.

Logging and measurement matter

Leaders need to know:

• Which steps were AI-assisted
• Which approvals were required
• Where exceptions occurred
• Whether the workflow improved cycle time or quality afterward

Without those basics, AI use is difficult to govern and difficult to improve.

Keep the scope honest

The strongest operating model usually starts with one workflow, one bounded use case, and one set of measurements. That is easier to control and easier to review than a broad automation programme launched all at once.