Most AI projects that disappoint were aimed at a process nobody had defined clearly. The ones that work usually start small: one repeated task, one measurable outcome.
Good first candidates
- Answering the same customer or staff questions over and over
- Pulling fields out of invoices, forms or emails
- Sorting and routing incoming requests
- Drafting first-pass summaries that a person then reviews
Poor first candidates
- Decisions with legal or financial consequences and no human review
- Processes that change every week and have no written rules
- Tasks where you have no examples of what "correct" looks like
A useful test: if a new colleague could do the task from a short written guide, it is probably a good candidate. If even your team disagrees about the right answer, define that first.
How to start
Pick one process, collect twenty or thirty real examples, and build a small prototype against them. Measure how often the output is right, where it fails, and how much review time it saves. That evidence tells you whether to expand, adjust or stop.
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