The AI Automation Audit Checklist

Step 1: Map the repetitive work

Go through a typical week. For every recurring task, ask these four questions:

• How many times a week does it happen?
• How long does each one take?
• What does it cost when it's done late or wrong?
• Does it follow rules a system could learn?

The tasks that score high on all four are your automation candidates.

Step 2: Score each candidate for leverage

Leverage = Volume × Time-per-task × Error-cost. Rank them honestly:

• List every candidate task from Step 1.
• Give each a 1 to 5 on volume, time, and error-cost.
• Multiply the three numbers for a leverage score.
• Sort high to low. The top three are where to start.

Step 3: Run the readiness checks

Before building your top candidate, confirm:

• The data it needs is accessible, not trapped in someone's head or a PDF.
• The tools involved have an API or a real integration.
• The rules are stable, not changing every week.
• There's a clear "good output" you can check the AI against.
• Someone owns the exceptions when the AI is unsure.

Missing two or more? Fix those first. Automating chaos just makes faster chaos.

Step 4: Draw the human-in-the-loop line

Decide where judgment stays human:

• Where must a person approve before action? (money, legal, sensitive replies)
• Where can the AI act on its own and simply log what it did?
• What's the escalation path the moment it's not confident?

Step 5: Sequence the build

Ship in the order that compounds:

• Start with the highest-leverage, highest-readiness task.
• Get one workflow working end-to-end before adding the next.
• Measure the time saved, then reinvest it in the next build.