Business
Use Case (for AI)
Also known as: application
A specific, real problem where AI is applied to create value; picking the right use cases is what turns AI hype into results.
A use case is a specific job you actually use AI for, "draft first replies to customer emails", "summarise long reports", "help write code". The technology itself is not the point; the value comes from applying it to a real problem where it saves time or improves quality. The skill is spotting which of your tasks AI is genuinely good at, and starting there.
An AI use case is a concrete application of AI to a real task or problem, defined by who it helps, what it does, and what value it creates. Success with AI comes less from the model and more from choosing good use cases and designing the workflow around them.
What makes a strong use case
- Real pain or value: it saves meaningful time, cost or improves quality.
- Good task-AI fit: it plays to strengths (drafting, summarising, analysis) not weaknesses.
- Verifiable: you can check the output, important given hallucination.
- Right mode: decide whether to augment or automate.
How to find them
Start from your actual work, experiment widely (AI literacy grows by doing), and keep the ones that clearly pay off, a practical, portfolio approach rather than one big bet.
A use case operationalises AI value: a bounded application with defined users, inputs, outputs, success metrics and a chosen interaction mode (augment vs automate). Practitioner guidance stresses that returns depend on use-case selection and workflow redesign more than on raw model capability (Woods 2024): identify high-value, well-fit tasks, prototype quickly, measure, and scale what works.
Selection criteria include value density, task-model fit relative to the "jagged frontier", verifiability and reversibility (which set the required oversight and guardrails), data availability, and integration cost. A common failure mode is technology-led adoption ("we need an AI") divorced from a concrete problem, yielding pilots that never reach ROI. Mature programmes maintain a portfolio, cheap experiments to discover fit, disciplined evaluation, and reinvestment in the winners, while treating AI literacy and change management as prerequisites for the workflow changes that actually capture value.