How to Identify Good AI Use Cases

Start with a business problem

A strong AI use case starts with a problem that matters to the business. Faster response times, lower administrative effort, better customer service, quicker analysis, and easier access to knowledge are examples of outcomes worth investigating.

Starting with a technology instead can produce impressive demonstrations that do not solve an important operational problem.

Look for work that requires understanding

AI is particularly useful when people spend time reading, interpreting, classifying, summarizing, comparing, searching, or deciding what information is relevant. These activities often sit between raw information and a business action.

• Emails that need to be classified or routed.
• Documents that need information extracted or compared.
• Business knowledge that employees repeatedly search for.
• Customer conversations that need summarization or interpretation.
• Research that requires gathering and synthesizing many sources.
• Business data that people want to explore using natural language.

Evaluate the opportunity across five dimensions

• Business value — What measurable improvement could the use case create?
• Volume — How frequently does the work occur?
• Data — Is the information required available and usable?
• Feasibility — Can the workflow be integrated into existing systems?
• Risk — What happens if the AI output is incorrect?

Decide how much human involvement is appropriate

Not every AI workflow should run without human review. For low-risk tasks, AI may be able to complete the workflow directly. For higher-risk decisions, a better design may be AI-assisted work where a person reviews the output before the final action.

The objective is not maximum autonomy. It is an appropriate balance between speed, quality, control, and cost.

A useful scoring exercise

List potential use cases and rate each one from low to high on business value, frequency, feasibility, and risk. This creates a structured conversation about priorities without assuming that the most technically impressive idea is the best first project.

Then choose one contained workflow where success can be measured. A successful pilot should produce evidence that informs the next investment.

Good AI use cases have a clear outcome

A useful AI project should answer a simple question: what will be better after this is implemented? If the answer is faster processing, fewer manual hours, better response quality, improved access to information, or more useful analysis, define how that improvement will be measured before building.