What is a Use Case ?
Definition in the context of Data Analytics and AI.
Developing AI in an organization often starts with a flood of ideas — but no clarity about what actually creates value. Business teams talk about problems, data teams talk about models, IT talks about systems. Everyone uses different language, and priorities are fuzzy.
This is where Data Analytics & AI Use Cases come in. They create the shared understanding, structure, and focus needed to turn AI from scattered initiatives into measurable results.
In this article, you’ll learn what an Use Case is, how it works, and why it’s one of the most effective tools for successful Data & AI initiatives.
Read time: 6-7 min.
Definition
What is a use case? (General Definition)
A Use Case describes what a system should do, for whom, and why it matters before diving into technical implementation.
It outlines:
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a user goal,
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the interaction sequence between user and system,
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and the outcome the user wants to achieve.
This concept originated in the late 1980s, when Ivar Jacobson introduced Use Cases as a more user-centered way to capture system requirements. His well-known definition is:
“A use case is a specific sequence of interactions between a user and a system.”
The key idea: Start from the user’s perspective, not the system’s internals.
This outside-in logic becomes even more crucial in Data & AI – where value, context, and integration matter as much as the model itself.
Definition Update Needed?!
Why the Classical Use Case Definition Isn’t Enough for AI
Traditional Use Cases focus on interaction: user → system → result. AI Use Cases, however, must describe far more:
- how value is created
- what data is required
- which model or logic is applied
- how outcomes integrate into processes
- how risks, compliance, and monitoring are handled
- how the initiative evolves through its lifecycle
AI systems do not behave deterministically; they operate probabilistically. This introduces requirements that classic Use Cases were never designed to capture.
Therefore, organizations need a modernized Use Case concept tailored to Data & AI.
Definition In Context of Data & AI
What Is a Data Analytics & AI Use Case?
In the context of Data & AI, a Use Case describes the targeted application of data and AI to solve a specific business problem for a defined user group and to create measurable value.
It not only clarifies what the system is expected to deliver – for example a prediction, a recommendation, or a generated output – but also why this is relevant from a business perspective and how it fits into the surrounding process. In other words: it connects the expected outcome with the users, decisions, and workflows it should improve.
A Data & AI Use Case therefore establishes clarity about what value AI should deliver, how that value is created, and where it becomes effective within the process.
As projects mature, the Use Case evolves into a living artifact – continuously refined from the initial idea to scoping, discovery, PoC, pilot, production, and scaling. Because of this lifecycle function, an AI Use Case becomes both a shared reference point across business, IT, data science, and compliance, and a central steering instrument that links strategic goals with execution and operational reality.
ℹ️ Use Case Defintion in the Context of Data & AI:
A data & AI use case is the combination of business goals, data, and technology to solve a specific problem in a measurable way. It provides clarity about what value AI should deliver, how this value is created, and where it takes effect in the process – as a central control instrument between strategy, implementation, and operation.
VALUE OF A USE CASE
Why AI Use Cases Matter (Short Overview)
Use Cases are the backbone of successful AI initiatives because they:
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anchor every idea to business value and KPIs,
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make feasibility, risks, and benefits comparable,
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provide a shared language between business, data, and IT,
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reduce friction and misalignment,
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prevent technical experiments without deployment paths.
A Shared Artefact
Roles That Benefits From AI Use Cases?
A use case is always a team artifact – it is aimed at everyone who is jointly responsible for the success of an AI initiative:
👩🚀Product or business owners … who define the technical objectives and expected added value.
👩🚀 IT and architecture teams … who provide infrastructure, data platforms, and integration paths.
👩🚀Compliance & data protection manager … who ensure legal and ethical frameworks are in place.
👩🚀 Data scientists and ML engineers … who design methods, models, and technical feasibility.
👩🚀Managers and decision-makers … who set priorities and evaluate business impact.
Use Case Categories
Types of AI Use Cases
In practice, a distinction is made between two perspectives and—within the black box—two levels. This structure helps to clearly link the goal, benefits, and implementation.
Black Box vs. White Box
- Black Box Use Case (business-focused):
Describes what the system should do and why this creates value (outcome, KPI, user perspective) without revealing how it works internally. These are often expanded by business use cases and KPIs (business objectives at the process level). - White Box Use Case (technology/governance supplement):
Documents how the performance is generated: data flows, models/prompts, components, interfaces, guardrails, MLOps.
Examples of AI-specific use case subtypes
- Analytics/BI: KPI reports, root cause analysis, monitoring.
- Predictive/prescriptive: forecasts, scores, recommendations.
- GenAI/LLM: Knowledge assistance, text/code generation, RAG.
- Automation: document extraction, classification, decision workflows.
Casebase combines these views in a consistent model
A black-box specification for portfolio & stakeholders is supplemented by white-box details for technology & governance – consistently throughout the entire lifecycle (idea → scoping → discovery → PoC → pilot → production → scale).
Goals, data, models/prompts, KPIs, risks, and operations thus remain consistently linked.
Difference
Use Case vs. User Story:
What is the Difference?
A Use Case is a strategic, lifecycle-oriented artifact that spans from the initial idea to operational use and scaling.
It serves as a conceptual and functional reference across this entire journey — connecting business goals, data foundations, technical implementation, and governance.
User Stories, on the other hand, are operational work items created within individual phases — for example, during a PoC — to describe concrete development or integration tasks.
They are time-bound, managed within task or sprint frameworks, and completed once delivered.
ℹ️ In short, the Use Case provides the continuous steering framework across the lifecycle of an AI initiative with big picture view, while User Stories represent the tactical execution steps that bring it to life.
See how Use Cases come to life.
Discover how Casebase helps teams bridge strategy and execution across the AI lifecycle.
Data Analytics & AI Use Cases
Conclusion
AI Use Cases provide the essential foundation for building AI solutions that actually work — and that create measurable business value. By combining business outcomes, data requirements, process context, feasibility, and governance into one coherent artifact, they ensure that AI initiatives stay aligned with strategic goals while remaining operationally realistic.
As organizations mature, Use Cases become the central steering instrument connecting:
- strategy
- portfolio management
- delivery
- compliance
- and ongoing operations
Mastering AI Use Cases means mastering AI transformation.
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