AI Automation Checklist
A checklist for finding, scoping, and shipping AI automations that save real hours, with guardrails to keep workflows reliable and safe.
Automation pays off when you target the right tasks and build in the right safeguards. This checklist helps you spot high-value candidates, scope them clearly, and ship with guardrails like human checkpoints and error handling. It is designed to keep automations reliable long after launch.
What's included
- A task discovery worksheet to surface automation candidates
- A scoring method to rank tasks by effort and time saved
- A scoping checklist for inputs, outputs, and edge cases
- Guardrail checks for human review, logging, and rollback
- A monitoring routine to catch failures before they spread
Template preview
PreviewFind Automation Candidates
- List repetitive tasks done more than weekly
- Flag tasks that follow clear, written rules
- Note where data is copied between two tools by hand
- Mark tasks where delays cause downstream problems
- Estimate hours spent on each task per month
Scope and Guardrails
- Define the trigger and the expected output clearly
- List edge cases and how the automation should handle them
- Add a human review step for anything irreversible
- Set up logging so every run can be audited
- Document a rollback plan if the automation misbehaves
Test and Monitor
- Run the automation on sample data before going live
- Compare automated output against a manual baseline
- Add alerts for failures and unexpected volumes
- Schedule a monthly review of accuracy and time saved
Frequently asked questions
No. The checklist is tool-agnostic and works with no-code platforms, though it also flags where a developer can extend an automation safely.
Start with tasks that are frequent, rule-based, and low-risk. The scoping section includes a quick scoring method to rank candidates by effort and payoff.
It covers human-in-the-loop checkpoints, error handling, logging, and a rollback plan so an automation never quietly does the wrong thing at scale.
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