AI Use Case

The term comes from software development, where it describes how someone uses a system to reach a specific goal. In the AI context, a use case is a description of the work: which task AI should support, how often it comes up, with which inputs, with what result, who reviews the result and how its quality is measured.

The use case is the smallest unit in which AI's benefit and effort can be weighed honestly. The later steps build on it: the proof of concept, the pilot, the ROI calculation and the order of an AI roadmap. A general idea such as "AI in sales" is not a use case yet. It can turn into many, such as proposal drafts, sorting incoming inquiries or preparing for customer meetings.

For leadership, choosing use cases is the real prioritization of an AI transformation. It decides where budget goes and which workflows change first. In November 2025, McKinsey observed that many companies have rolled out AI tools, but most have not yet turned use cases into running products or redesigned their workflows around AI.

The five parts of a description.

A usable description names five parts.

The starting situation: in which workflow does the task come up, how often, and who handles it today with how much effort? Without this baseline, no benefit can be proven later. The inputs needed: what data does the AI need, where does it live, is it current, and is the system allowed to access it for this purpose? This data readiness often matters more for success than the choice of model.

The desired result: format, scope and recipient, meaning what exactly exists at the end and where it goes. Responsibility: who checks the result before it moves on and stands behind it? Practitioners call this human in the loop, a person in the workflow who approves or corrects. Quality criteria: how does the reviewer tell that a result is usable, and when it falls short?

A simple test: if each of the five parts can be stated in one sentence, a team can work with it. If one is missing, the description isn't done. Without a reviewer and a quality criterion, a use case is still just an idea.

What a good use case looks like.

Good starter use cases share a few traits. The task comes up regularly, weekly or daily; a task that happens once a year rarely justifies the effort for data access, permissions and review. It has a clear start and a clear end, such as from an incoming inquiry to a finished draft reply. The data needed is available and may be used.

The result can be checked with reasonable effort. A summary someone holds up against the original is easy to check. A forecast whose accuracy only shows years later is not. Errors are visible and their damage is limited: a wrong draft caught before it goes out costs minutes, a wrong answer to a customer costs trust.

Finally, the result lands in a workflow that already exists. A use case that first requires inventing a new process carries twice the risk. At the start, a narrow scope counts for more than a big impact, because it lets the team learn about review, costs and acceptance.

Evaluating and ranking use cases.

A described use case is evaluated before any technical test, usually on four dimensions. Impact: how much work goes away or gets better, measured against the baseline? Effort: what do building, integration and operation cost? Data: is the data available, clean and cleared? Risk: what happens if a result is wrong, and who is affected?

Evaluation includes a legal checkpoint. For companies operating in the EU, the GDPR applies whenever personal data is processed, with its requirements on purpose, legal basis and data processing agreements. Under the EU AI Act, the risk category has to be determined; use in hiring or performance evaluation, for example, can count as high-risk. The AI Act also requires measures that build the AI literacy of employees who work with such systems.

The evaluation produces a simple order. Use cases with high impact, low effort and available data move to the front; those with high risk and unclear data move to the back. This order is the core of an AI roadmap. The unit of evaluation is always the individual use case, never "AI" in general.

From use case to operation.

On its way into daily work, a use case usually passes through three stages. The proof of concept, a small feasibility test, shows whether the task can be solved technically with the company's own data. The pilot shows whether the solution holds up under real conditions, with real users, real cases and the later review step. In routine operation, the application is supported, used by everyone involved, and its costs are assigned.

The use case description supplies the yardstick for each stage. The quality criteria become the acceptance criterion for the proof of concept, the baseline becomes the comparison in the pilot, and responsibility becomes support in operation. A proof of concept without a use case described beforehand has no acceptance criterion. It often ends with a good demo and no decision: in the pilot graveyard.

After a few months in operation, it pays to check against the original description. Has the starting situation changed, does quality still hold, has review effort grown? The description stays the yardstick for the application's entire lifetime.

Common misconceptions.

The most common misconception starts with the tool. A company has bought licenses and now looks for tasks to fit them. That produces use cases that make the tool look good but rarely hit the work that takes the most time. The better order starts with the workflow and picks the tool afterward.

A second is scoping too broadly. "AI customer service" or "automate sales" sound like a plan, but they are topic areas holding many possible use cases. A third is lists of generic examples from studies or trade articles. They help in the search for ideas but don't replace a description of the company's own workflow, data and baseline.

Fourth, success is often measured on a single work step. If drafting gets faster but review takes longer, less is left than expected. So the whole workflow is measured, from request to approved result. Fifth, a use case is often equated with a chatbot. Many good use cases have no chat interface: they sort incoming items, fill in fields or prepare a draft inside an existing system.

What a use case costs and what drives it.

A serious cost estimate is only possible once the use case is described, because the costs depend on its parts. The inputs needed determine the effort for data access and integration with existing systems, often a large item in the build. The desired result and the quality criteria determine how much development, testing and refinement is required.

Operation adds running costs. Many providers bill models by units of processed text, called tokens. Model costs therefore depend on text per call and number of calls, and rise and fall with usage. Add support, bug fixing, keeping up with new model versions and training. One item is missing from many estimates: the time people spend checking and correcting results.

The first use case is usually the most expensive, because it also pays for the foundation: users, permissions, access, the connection to models and rules for review. Every further use case shares that foundation. When comparing vendor proposals, it helps to ask which parts are built only for this case and which will also serve the next use case.

Where the term stops working

A general idea such as "AI in sales" is not yet a use case, and neither is picking a tool. The use case describes the task; the AI project delivers it with a schedule and a budget. AI automation means a workflow runs entirely or largely without manual steps, while many use cases support a person who checks and approves the result.