Key takeaways
- Pick the repetitive handoff that costs the most time each week.
- Map the process as it really runs, not as the documentation says.
- Automate the common case first and route exceptions to a person.
- Measure success in the same units you used to describe the problem.
Automation projects often fail for a plain reason: they start with a tool instead of a problem. A team hears about an AI assistant, buys a license, and weeks later nobody can say what it saved.
Start with the handoffs
Look for work that moves between people or systems more than it changes. Invoice approvals, lead routing, onboarding checklists and status updates are common examples. The steps are repetitive, easy to describe, and expensive when they stall.
Write down the process as it really runs
Before anything is built, map the steps as they happen today, not as the wiki says they happen. For each step, note who does it, which system they use, and where the work waits. Then answer these four questions:
- How often does this run each week?
- How long does one run take, from start to finish?
- Where does it most often get stuck or go wrong?
- What does a good outcome look like, in one sentence?
Keep a person on the exceptions
Good first automations handle the common case and send the unusual ones to a person, with the context already attached. Trying to automate every edge case on day one is the fastest route to a project that never ships.
Measure it in the units you started with
If the problem was hours lost each week, measure hours each week after launch. A simple before-and-after number is worth more than a dashboard nobody checks.
The best first automation is usually the boring one that runs every day.
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