How to Identify and Prioritize AI Use Cases in a Business

Prioritize AI use cases by comparing a real problem's value, feasibility, and consequences. Begin with work people already struggle to complete, then ask whether AI offers an appropriate way to improve it. A list of possible tools is less useful than a clear account of where time, quality, or service currently breaks down.

The output of prioritization should be a small set of experiments with owners and decision criteria. A long ranked spreadsheet is only valuable if it leads to a choice about what to investigate next.

Collect opportunities from actual work

Ask team members to describe recurring tasks that involve delay, duplication, searching, or interpretation. Request recent examples. For each opportunity, capture who performs the work, how often it occurs, what inputs are available, and what happens when the task is done poorly.

An illustrative agency might identify proposal drafting, invoice reconciliation, and searching past project documents. These are different opportunities with different requirements. Do not group them under “automate administration,” because that hides the details needed to compare their usefulness and difficulty.

Define the outcome before the solution

For document search, the desired outcome might be that a project manager finds an approved example without interrupting colleagues. For proposals, it might be a consistent first draft that preserves the agreed scope. Describe the current baseline using available observations, and label estimates clearly.

Then ask whether a simpler change could deliver the same outcome. Better document naming, a shared template, or a form may resolve much of the problem. An AI opportunity should survive comparison with those alternatives, especially when they cost less to operate or are easier to verify.

Use three lenses

• Value: frequency, time spent, cost of errors, and importance to the people affected.
• Feasibility: access to appropriate data, a clear task definition, available skills, and the ability to test outputs.
• Consequence: who may be affected by a mistake, how detectable it is, and whether it can be reversed.

You can score each lens using a simple scale, but write the evidence next to the score. Avoid multiplying speculative numbers into a precise-looking business case. Unknown data access or uncertain user demand should remain visible as an uncertainty to investigate.

Choose a bounded first experiment

Suppose the agency's documents are accessible and the search task is frequent, while invoice reconciliation involves inconsistent records and complex approvals. An internal search aid may be the more manageable experiment even if another idea sounds more ambitious.

Limit the test to one document collection and one team. Specify what counts as a correct result, how unsupported answers are handled, and who reviews findings. Choose a short observation period based on how frequently the task occurs. The aim is to reduce uncertainty before making a larger commitment.

Revisit the portfolio with evidence

Maintain a brief record for each use case: problem, audience, baseline, proposed intervention, main uncertainty, owner, and next decision. After a pilot, replace assumptions with observations. A lower-priority idea may become attractive when its data improves; a promising one may be paused after difficult testing.

Prioritization is an ongoing management practice. Reserve capacity to support existing solutions, not just start new ones. A small portfolio of useful systems with clear owners can create more dependable value than a large collection of demonstrations that nobody maintains or measures.

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Connect this exercise with AI Opportunity Thinking. For the underlying concepts, explore the book AI Strategy for Organizations and the THINK Academy. Choose a task from your own work, keep the artifacts you produce, and use the questions in this article to review your decisions as you develop the capability.

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