Automation projects fail most often because the wrong process was picked first. A good first candidate is one that is painful, frequent and well understood, not the most complicated process in the company.
Look for four traits
- Repetitive: the same steps happen again and again.
- Rules-based: a person could write down how decisions are made.
- High volume or time-consuming: it adds up over a week or month.
- Clear inputs and outputs: you know what goes in and what “done” looks like.
Good early examples
Routing website inquiries to the right person, sending reminders at defined pipeline stages, extracting standard fields from invoices or forms, and generating routine weekly reports are all common starting points. They touch few systems and are easy to measure.
Keep a person in the loop
Where a step involves judgment, customer-sensitive wording or financial impact, design a review step. AI can draft or classify; a person approves. This keeps quality high and lets you tighten rules as you learn.
Measure before and after
Record how long the process takes today and how often errors occur. After launch, compare. If you cannot measure it, you cannot tell whether the automation helped.
Check feasibility early
Whether something can be automated depends on the tools involved, available APIs and data quality. A short discovery step avoids surprises.