Defining AI use cases is one of the most common challenges businesses looking to adopt AI face. And the problem I see most often right now is that teams are approaching the identification of AI use cases like an exercise in creativity.

But here’s the thing: The best AI use cases aren’t ideas; they’re broken steps in a workflow with measurable pain.

What I mean is that every job is a workflow made of tasks. Every workflow is a sequence of actions people repeat—daily, weekly, monthly—that either produces outcomes or fails to. And it’s inside those steps that your best AI use cases tend to appear.

A Universal Example

Every organization has internal meetings, and almost every organization has a “meeting problem.” Not because meetings are inherently bad, but because meetings create commitments—and commitments are only valuable if they reliably turn into action.

Here’s a familiar meeting-to-execution workflow:

• Someone decides a meeting is needed.

• Someone scrambles for context.

• An agenda is drafted—or not.

• The meeting happens.

• Notes are taken, inconsistently.

• Decisions are made, ambiguously.

• Action items are mentioned, vaguely.

• Ownership is implied.

• Deadlines are aspirational.

• A recap may or may not be sent.

• Systems may or may not be updated.

• Follow-ups slide into Slack threads and side conversations.

• A week later, the same topic returns, in a slightly different form.

Once you can see the workflow, the use cases start to show themselves—because many of these steps are predictable: the same patterns, the same artifacts, the same gaps repeating week after week. This is what “hiding in plain sight” looks like.

A meeting copilot could draft agendas. An AI note-taker could produce a recap that reliably separates discussions from decisions. Action items could be extracted, formatted, and assigned with less manual chasing. Nothing about this requires a huge transformation. It’s simply inserting assistance into the steps where work breaks down.

The Simple Method

If you want a repeatable way to find use cases, you just need a clear process:

1. Pick one workflow you can observe. Choose something that repeats weekly and matters to outcomes.

2. Break it into 10–20 concrete steps. If you can’t watch it happen, it’s too abstract.

3. Go step-by-step and ask: where could AI assist? Not “replace a person.” Assist a step.

And then—this is the part most teams skip—don’t leave the answers as a list.

Once you’ve found a handful of steps where AI clearly helps, systematize it. Turn it into a simple standard your team can repeat: how agendas get drafted, how notes become decisions, how action items get captured, assigned, and tracked. Pick a small group, run it for a week or two, and refine it based on what actually happens in the real world.

That’s the shift: from “we’re experimenting with AI” to “this is how work happens here now.”