What people should own in an AI workflow

Where should AI stop and people take over?

Separate preparation from execution. A system may prepare a reply without sending it, suggest a category without accepting it or extract a value without approving the record. Write down which actions need review before they touch a customer or a business system.

Example: AI drafts a response from approved service information. A support specialist checks the request, facts and tone. A team lead handles complaints, exceptions and commitments beyond the specialist's authority. This split is the core of AI-assisted customer support done safely.

How should exceptions be reviewed?

Define acceptable work and test the cases most likely to break it: missing information, unusual requests, conflicting documents and questions your approved knowledge doesn't answer.

Create an exception queue with a named owner. Reviewers should see the original input, the proposed output and why it was escalated, and have a way to correct the work and record what needs to change.

Measure rework, not just speed. If the team can't reliably spot an error, narrow the scope or keep a more manual process until that gap is closed.

How do you protect data and access?

Approve information sources and connected accounts before the workflow runs. Give each tool only the access the task needs, since "excessive agency" is one of the top security risks for LLM applications (opens in a new tab).

Keep operating records separate from reporting. The delivery team may need the customer message; analytics usually only needs the event and service. Personal data of people in the Philippines must also be handled in line with the Data Privacy Act of 2012 (opens in a new tab).

Which four roles does every AI workflow need?

• Business owner: approves the scope and accepts the risk.
• Operator: runs the daily work.
• Reviewer: accepts or corrects results.
• Maintainer: manages rules, prompts, integrations and changes.

A small team can combine roles as long as accountability stays clear. People grow into these roles faster with workflow training built around real examples, and outsourced operators and reviewers can fill gaps when your team is stretched.

How do you improve the workflow over time?

Use recurring exceptions to improve the process and the approved knowledge. Look at where people keep correcting the same output, where handoffs stall and whether the workflow still serves its original goal.

Agree a change procedure and a manual fallback. Test updates on real inputs before expanding. Count the full operating cost, including specialist time, tools and maintenance, before you scale, using the NIST AI Risk Management Framework (opens in a new tab) as a structure for those decisions.

Frequently asked questions