AI Workflow Approval Guide

An approval point is a moment in a workflow where a human must review and confirm before anything happens. Building the right approval points into an AI system is what separates a safe implementation from a risky one.

• —The system may take incorrect action based on bad input or a misread document
• —Mistakes are harder to catch before they affect customers, suppliers, or internal teams
• —Audit trails become difficult to produce when something goes wrong
• —Staff lose trust in the system quickly

Approval points do not slow a system down significantly. They protect the business while the system earns confidence.

These actions are low consequence if wrong and easy to review quickly. AI can generate a suggestion and present it for one-click approval:

• —Drafting a reply to a routine enquiry
• —Categorising an incoming email or document
• —Flagging an item as requiring follow-up
• —Generating a summary of a document or email thread
• —Logging a completed task
• —Sending an internal notification

Even for these actions, logging what the AI suggested and what the human confirmed is good practice.

These actions have more consequence if wrong. AI should do the preparation work but not execute without a human confirming:

• —Sending an external email to a customer or supplier
• —Updating a CRM record or status
• —Extracting and recording data from a document into a system
• —Generating a report or briefing that will be shared externally
• —Closing or archiving a case or record

The AI prepares the action. A human reviews it and approves or edits before it runs.

These actions should never run automatically. The risk of an error is too high or the action is too hard to reverse:

• —Sending payment instructions or financial data
• —Deleting or archiving data permanently
• —Making a decision that affects a contract or legal document
• —Taking action on a complaint that could affect a customer relationship
• —Any action where a mistake would take significant time or money to correct

For these actions, the AI may provide a recommendation and relevant context. The decision and the action must stay with a human.

Three real-world patterns to illustrate how this works:

An approval queue is the interface where a human reviews what the AI has prepared. A well-designed queue:

• —Shows the original input (email, document, request) alongside the AI output
• —Highlights what the AI was uncertain about
• —Allows one-click approval for straightforward cases
• —Allows editing before approval for anything that needs adjustment
• —Logs the reviewer, the timestamp, and the decision
• —Escalates items that have been waiting too long

The simpler the approval interface, the faster decisions will be made.

Every AI system that takes action on behalf of a business should maintain an audit trail. The trail should record:

• —What input the system received
• —What the AI decided or suggested
• —What action was taken
• —Who approved it and when
• —What the outcome was

This log is important for debugging errors, demonstrating compliance, and reviewing system performance over time.

Some items the AI cannot classify or handle confidently. Escalation rules define what happens when:

• —Confidence in the AI output falls below a threshold
• —A required approval is not given within a set time
• —An item is flagged as high-risk
• —The system encounters an input type it was not trained on

Escalation should route the item to a named person or team, not to a generic inbox. The escalation itself should be logged.