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An AI use case is a specific task or outcome in which an AI system uses inputs to produce an output—such as a prediction, recommendation, generated content, or decision—that may affect a physical or virtual environment. For example, forecasting equipment failure and drafting a customer-support reply are different AI use cases because they support different tasks and produce different outputs.
There is no single universally accepted definition of AI. The OECD’s description is one useful framework; the examples below also draw on the National Institute of Standards and Technology’s (NIST) human-centered approach to describing AI use.
What does “AI use case” mean?
The phrase combines two ideas: an AI system and the human task it helps accomplish. A useful description names who is doing what, the intended outcome, and how AI contributes. It should also make clear what information goes into the system and what kind of output comes back.
The OECD describes an AI system as a machine-based system that infers how to generate outputs—including predictions, content, recommendations, or decisions—that can influence physical or virtual environments. Its systems may vary in autonomy and in how they adapt after deployment. The OECD notes that there is no universally accepted definition of AI; its description, developed by the OECD AI Group of Experts in 2018, underpins the OECD Framework for Classifying AI Systems. Read the OECD.AI explanation and its 2023 update to the OECD Recommendation.
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NIST’s glossary includes definitions drawn from multiple source documents. One definition in NIST SP 800-218A describes a machine-based system that can make predictions, recommendations, or decisions for human-defined objectives. These formulations are related, but neither should be presented as the one definition accepted everywhere. NIST’s glossary entry identifies the source of its definitions.
How to describe an AI use case clearly
NIST’s 2024 AI Use Taxonomy: A Human-Centered Approach classifies the activities AI performs in relation to human goals. Its 16 use activities are independent of particular AI techniques and industries, and a single task can combine more than one activity. That makes the taxonomy useful for comparing systems that use different methods but help with similar goals. See NIST AI 600-1.
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When documenting a use case, answer these questions:
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- What is the intended outcome? Explain what success looks like and what the AI contributes.
- What goes in and what comes out? Identify the input information and whether the output is a prediction, recommendation, classification, generated content, or another result.
- What happens after the output? State whether a person reviews, accepts, edits, or acts on it, or whether the system takes action automatically.
- What are the consequences of an error? Explain what could happen if the output is wrong, particularly if it affects a consequential decision.
This description keeps attention on the practical use rather than treating a technical method—such as machine learning or a language model—as the use case itself.
Examples of AI use cases
AI use cases span many fields, including finance, marketing, health, automobiles, manufacturing, entertainment, and education. NIST distinguishes generative AI, which creates content such as text, images, audio, or video, from predictive AI, which uses historical data to forecast outcomes, trends, or behavior. Its predictive examples include recommendations, classification, and workflow efficiency through automated decisions. These are broad categories, not a complete inventory of deployments. NIST’s AI resources discuss these kinds of applications.
- Generate a draft: A team provides background information and asks an AI system to draft text. The output is content for a person to review or revise.
- Forecast an outcome: An organization provides historical data to estimate a future event or trend. The output is a prediction that may inform planning.
- Recommend an option: A system analyzes available information to suggest a product, action, or next step. The recommendation can support a person’s choice.
- Classify information: A system sorts incoming items into categories, such as routing a request to a relevant team. The output is a classification that may feed a workflow.
- Support a public service: Government applications can assist with public-service processes and delivery, justice, civic participation, decision support, forecasting, or fraud detection. The OECD’s 2025 analysis covers 200 use cases across 11 core government functions; that figure describes the scope of its government research, not the total number of AI applications worldwide. See the OECD overview of AI in government.
How AI use cases differ
Two systems may both use AI yet serve different purposes; conversely, systems using different techniques can support the same human task. Compare them by the work they support and the role their outputs play.
| Comparison point | What to ask |
|---|---|
| Task and outcome | Who needs help, what are they trying to do, and what result is intended? |
| Inputs | What information does the system use? |
| Output | Does it generate content, predict an outcome, recommend an option, classify information, or make a decision? |
| Autonomy and adaptiveness | How much does the system act independently, and can it adapt after deployment? |
| Human role | Does a person review or approve the result, or does the system act on it directly? |
| Impact of error | What happens if the output is inaccurate or unsuitable? |
These distinctions matter because the label “AI” alone says little about what a system actually does, who relies on it, or how much oversight is appropriate.
Is there a complete list of AI use cases?
No single worldwide inventory is established by the sources cited here. The OECD’s 200 cases concern its analysis of government functions, while NIST’s 16 activities provide a way to classify how AI contributes to tasks; they are not counts of every deployment. Treat any list of examples as illustrative unless its scope, geography, date, and method are specified.
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