AI Use Case Discovery
The process of identifying which problems in your organization are actually good fits for AI — and which ones aren't. Not every manual process should be automated. Good AI use cases tend to have: clear inputs and outputs, large volume of repetitive work, tolerance for occasional errors, and available data or context for Claude to work with. Poor candidates tend to require deep relationship judgment, physical presence, or extremely high accuracy with no room for error.
In practice
You sit down with your ops team and ask: where do you spend time on repetitive, text-heavy work? They mention weekly status reports, vendor email summaries, and meeting notes. Those are AI use cases — and the process of finding them is use case discovery. The best ones are high-volume, rule-following tasks where mistakes are recoverable.
Related concepts
Where AI Use Case Discovery shows up
4 articlesNot every idea that works with AI makes a good business. How to filter the options, spot the structural advantages, and choose the problem worth building for.
The agencies making the most from AI aren't charging for AI expertise — they're delivering better work faster and pricing the outcome, not the time. Here's how to make that shift without losing current clients.
Claude can't tell you if your idea is good. It can help you figure out whether your assumptions are wrong — before you spend three months building something nobody wants. Here's how to use it for that.
Customer discovery is the one job where Claude is most dangerous if used wrong. Here's how to use it to prepare better, synthesize faster, and avoid the trap of letting it replace the conversations.