Checklist
The AI Automation Checklist
What to check before, during, and after you build an AI agent or workflow automation — so it actually gets adopted, not shelved.
Most AI automation projects fail from scope, not technology — automating the wrong thing, or shipping an agent nobody trusts enough to use. This checklist is the sequence that avoids both.
Before You Automate
- The process is well-defined enough that two humans doing it reach the same result
- You can name the specific hours or dollars currently lost to doing it manually
- Someone with authority to change the process is involved from day one
- You have ruled out that a simpler fix (a form, a template, a trigger) solves it
Choosing What to Automate First
- Highest-volume, most repetitive task is prioritised over the most technically interesting one
- The task has clear success/failure criteria an agent can be measured against
- Edge cases and exceptions are documented, not just the happy path
- The task does not require judgment calls with legal or financial consequences — yet
Building the Agent
- Agent is trained on your actual content and data, not generic assumptions
- Confidence threshold is defined for when the agent escalates instead of guessing
- Every action the agent can take (book, update, reply) is explicitly scoped and permissioned
- A human can see what the agent did and why, after the fact
Testing & Escalation Rules
- Tested against real historical requests, not just happy-path scripts
- Escalation path is tested — a human actually receives and can act on handoffs
- Agent has been tried by someone who was not involved in building it
- Failure mode is graceful — a confused agent hands off, it does not loop or guess
Launch & Monitor
- First 30 days have a named owner watching outcomes, not just uptime
- There is a way to review a sample of agent conversations or actions weekly
- A feedback loop exists for correcting the agent when it gets something wrong
- Success is measured against the number named before you automated it
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