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There is no single universally accepted definition of AI. The OECD’s formulation offers a useful starting point, while NIST’s human-centered taxonomy helps describe what AI does in a particular use case.
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What does “AI use case” mean?
The OECD describes an AI system as a machine-based system that infers how to generate outputs—including predictions, content, recommendations, or decisions—that may influence physical or virtual environments. The system can vary in how autonomous it is and whether it adapts after deployment. This description is based on work by the OECD AI Group of Experts and underpins the OECD Framework for Classifying AI Systems; it is not a universally agreed definition of AI. OECD’s explanation of the definition
In practical terms, an AI use case is the particular human or organizational task that the system supports, together with the outcome it is intended to help achieve. It is more specific than naming a technology (“machine learning”) or a broad sector (“health care”). For instance, “help clinicians identify patients who may need follow-up” describes a task and an intended outcome; the algorithm or model used is a separate detail.
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NIST’s glossary includes multiple AI definitions drawn from different source documents. One definition in NIST Special Publication 800-218A describes a machine-based system that can make predictions, recommendations, or decisions for human-defined objectives. When precision matters, identify the framework and source rather than presenting one wording as the only definition. NIST glossary: artificial intelligence
How to describe an AI use case clearly
NIST’s AI Use Taxonomy: A Human-Centered Approach, published in 2024, classifies AI by the activities it performs in relation to people’s goals, rather than by the technical method or industry. Its 16 use activities can be combined within a single task. This makes it possible to compare systems that use different techniques or operate in different domains but support similar work. NIST AI Use Taxonomy (2024)
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A useful description answers these questions:
- Who is doing what? Identify the person or organization and the task they are trying to complete.
- What outcome is intended? State how the AI contributes to the task, not just that AI is involved.
- What goes in, and what comes out? Name relevant inputs and whether the system produces a prediction, recommendation, decision, or generated content.
- What role does a person play? Clarify whether a person reviews, accepts, rejects, or acts on the output.
- What are the consequences of an error? Explain what could happen if the output is wrong, especially when it affects consequential decisions.
For example, “AI in manufacturing” is too broad to explain a use case. “Use equipment sensor readings to predict which machine may need maintenance, then have a technician inspect it” identifies the task, inputs, output, and human role. The same description can also prompt the right question about the consequences of a missed warning or an incorrect alert.
Examples: generative, predictive, and other AI use cases
Generative AI creates content such as text, images, audio, or video. Predictive AI analyzes data to estimate outcomes, trends, or behavior. NIST’s examples of predictive use include recommendations, classification, and improving workflow efficiency through automated decisions. These are different kinds of contribution; generative AI is not synonymous with AI as a whole. NIST AI use cases
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| Task or goal | Possible AI contribution | Output or result |
|---|---|---|
| Draft a first version of a document | Generate content from a prompt or other inputs | Text for a person to review and edit |
| Estimate future demand | Analyze historical data for patterns | A forecast to inform planning |
| Suggest relevant items or information | Rank options based on available information | A recommendation for a user or employee |
| Sort incoming requests | Classify requests by type or priority | A category or routing suggestion, or an automated routing action |
| Support public services or government work | Assist with service delivery, decision support, forecasting, civic participation, justice processes, or fraud detection | Varies with the task and the role assigned to the system |
These examples are illustrative, not an exhaustive inventory. AI applications also appear in areas such as finance, marketing, health, automobiles, manufacturing, entertainment, and education. The relevant use case is still the specific task and intended outcome within the domain.
How to compare two AI use cases
Compare the work the systems support, not just their labels or industries. The following dimensions help make differences visible:
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- Task and outcome: What is the system meant to help accomplish?
- Inputs: What information does it use?
- Output: Does it generate content, forecast an outcome, recommend an option, classify information, or make a decision?
- Autonomy and adaptation: How much can it do without intervention, and can it adapt after deployment?
- Human review: Does a person check or approve the output before it is used?
- Effect of error: What could go wrong, and who may be affected?
These questions help distinguish a system that drafts material for review from one that automatically makes or triggers a consequential decision, even if both are described as using AI.
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There is no established worldwide inventory or agreed total of AI use cases. Counts depend on how a use case is defined and grouped. The OECD reported examining “200 use cases across 11 core functions of government” in its 2025 government-focused research. That figure describes the scope of that analysis; it is not a count of all AI applications worldwide. OECD government AI use cases
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Similarly, NIST’s 2024 taxonomy sets out 16 use activities for classifying how AI contributes to outcomes. Those activities are a framework for description, not a tally of deployed systems or a limit on the number of possible use cases.
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