Use Case Selection

Use case selection means choosing the right work for AI. Not every task needs AI. Some tasks are simple, stable, and already handled well. Other tasks are slow, repetitive, messy, or hard to scale. Those are better candidates. The goal is not to use AI everywhere. The goal is to find the few places where AI can clearly help people work faster, think better, or reduce friction.

Many teams waste time because they start with the tool, not the work. They ask, where can we use this model? That is backwards. A better question is, where is work slow, inconsistent, expensive, or hard to review? AI should be aimed at a real business pain. If the use case is weak, even a strong AI tool becomes a distraction.

Think of AI like a very fast intern. You would not ask an intern to approve a merger or fire an employee. But you might ask them to summarize documents, prepare first drafts, compare options, or collect questions for a meeting. Use case selection is deciding what work is safe and useful to delegate first.

A customer support team might use AI to draft answers from approved help articles. A finance team might use it to summarize expense patterns. A product manager might use it to turn meeting notes into action items. These are better starting points than asking AI to make final decisions with no review.

Use a simple filter before starting. Is the task repeated often? Is the input available? Is the expected output clear? Can a human check the result? Would a first draft save real time? If the answer is mostly yes, the use case may be worth testing.

The common mistake is choosing AI use cases because they sound impressive. Impressive does not mean useful. A small use case that saves one hour every week may be better than a large idea that nobody can safely operate.