AI Use Case

Short answer: An AI use case describes one concrete application of AI, such as sorting incoming support emails or forecasting weekly demand for a product line. It is narrower than a general aim to use AI in the business. A well-defined use case names the process, the users, the inputs and the output. It also names who acts on that output and how the team will measure success. Companies use the use case as the unit for choosing, funding, testing and overseeing AI work.

Candidate use cases usually come from known bottlenecks. These include repetitive, high-volume work, tasks that involve reading or writing a lot of text, and decisions where past data shows a pattern. The team compares ideas on expected value, feasibility, risk and how quickly each can show a measurable result. Feasibility covers three questions. Does the data exist, and is it reliable? Can the team connect the systems involved? Does the team have time to test? A careful assessment often finds that some problems need no AI at all. Simple rules, standard automation, a process change or better reporting may solve them better.

The team writes up each use case in a short, standard template. It records the current process and a starting point to measure against: volume, time, error rate and cost. It defines what the AI will do and what form its output takes. It lists the data and systems the AI needs, the success measures and the acceptable error rate. The team then assesses the risks and decides where a person must review the output. Next, it tests the use case on real examples in a small trial. Based on the results, the company decides to scale it, adjust it or stop it. After launch, a named owner monitors how well it performs.

An operations team lists 10 AI ideas and scores each from 1 to 5 on value and feasibility. The top idea is routing inbound customer emails to the right team. Today the team handles 2,500 emails a month, at 3 minutes each to read and route, which is 125 hours. The target is to route 80%, or 2,000 emails, automatically. That saves 100 hours, less 10 hours of spot checks, for a net saving of 90 hours a month. The team sets aside an idea to predict late supplier deliveries, because staff record delivery dates inconsistently.

Framing AI work as use cases turns a broad ambition into decisions the company can cost, compare, test and stop. Each use case needs a measured starting point and a success measure before it gets funding. Leaders should review all the use cases together. That way, effort goes to the ones with the clearest value and the fewest blockers.

Value estimates made before a test are assumptions. Real accuracy, adoption and connection work often differ from the estimate. Hours saved become a real saving only when staff move to other work or the company avoids a cost. Running costs such as usage fees, monitoring and maintenance belong in the estimate. Simple 1-to-5 scores hide how uncertain each estimate is. Write down the assumptions behind the top-ranked ideas and check them during testing.