What is a Use Case ?
Definition and benefits in the context of Data Analytics and AI.
Imagine you are tasked with advancing AI in your company—there are ideas everywhere, but no one knows exactly where the greatest benefits lie. This is where use cases come in: they create clarity, priorities, and a common language between strategy, business units, data science, and IT. Instead of planning abstract AI initiatives, use cases reveal which applications create real added value and what data and resources are needed to achieve this.
In the following article, you will learn what a use case is in the context of data & AI, why it is so effective, and how you can successfully implement it in your organization.
Read time: 6-7 min.
Definition
What is a use case?
Before any Data & AI initiative can create value, it needs clarity about what problem it solves, for whom, and how success will be measured. This is exactly what a Use Case provides.
Origin of the term “Use Case”
The concept originated in software engineering, where Ivar Jacobson introduced it in the late 1980s as a way to make system design more understandable and user-centered. His often-quoted definition reads:
“A use case is a specific sequence of transactions performed by a user and a system in a dialogue.”
Jacobson deliberately established Use Cases instead of more fragmented, task-oriented descriptions (like today’s User Stories).
His goal was to ensure that requirements always reflected the user’s intent and the business context, not just isolated technical features.
This “outside-in” approach remains the reason why Use Cases are so effective in Data & AI initiatives today – they prevent tech-first thinking and consistently focus on value creation and impact.
What is a use case in the context of data & AI?
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 create measurable value.
It shows not only what the system delivers – such as a prediction, recommendation, or generated output – but also why this is relevant (business outcome) and how users interact with it (process context).
A Data & AI Use Case creates clarity about what value AI should deliver, how that value is generated, and where it becomes effective within the process.
It evolves into a living artifact that is continuously refined throughout the lifecycle – from idea to scoping, PoC, pilot, production, and scaling.
As such, it serves as both a shared reference point between business, IT, data science, and compliance and a central steering instrument between strategy, execution, and operations.
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.
Use Case vs. User Story
What is the difference between a use case and a user story?
Although both concepts, Use Case and User Story, originate from software engineering, they operate on different levels of abstraction.
Together, they help bridge the gap between strategic intent and hands-on execution in Data & AI initiatives.
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.
Comparison: Use Case vs. User Story in the Data & AI Context
| Aspect | Use Case | User Story |
| Level | Strategic / Business-oriented | Operational / Technical |
| Purpose | Lifecycle artifact that manages an AI initiative from idea to production. Provides a common framework linking business, data, and technology. | Short-term work item describing a concrete development or integration task. |
| Scope | Covers the full lifecycle (Ideation → Scoping → PoC → Pilot → Operation → Scale). | Exists within a specific phase (e.g., PoC or sprint). |
| Focus | Business objective, process context, data, KPIs, governance, and value creation. | Implementation detail: functionality, feature, or interaction. |
| Duration | Long-term, continuously updated and versioned (living artifact). | Time-bound and completed once delivered. |
| Relationship | Framework from which multiple user stories are derived. | Contributes to the realization of a Use Case. |
| Responsibility | Product/Business Owner, Data Science Lead, Governance. | Product Owner, Development or Data Team. |
| Output | Documented Data & AI application (e.g., Use Case Card) that evolves across the lifecycle. | Completed feature or task contributing to the Use Case’s implementation. |
| Example | “Predictive Maintenance to reduce unplanned downtime by 10%.” | “As a technician, I want to receive an alert when a sensor threshold is exceeded.” |
Agile Perspektive:
From an agile standpoint, a Use Case often corresponds to an Epic – a broader, higher-level unit of work that can include multiple User Stories and Tasks. It serves as a living artifact, evolving throughout the lifecycle as insights, requirements, and data mature.
While User Stories primarily guide execution within the development team, the Use Case acts as a shared reference point between business and tech – from the initial idea to production and beyond.
Use Cases in Action
Why are use cases so helpful for data & AI initiatives?
AI projects rarely fail due to poor model quality alone, but rather due to a lack of clarity about the goal, users, and integration. A clearly formulated use case can help here:
- 💰 Focus on business value: Every data processing operation is linked to clear KPIs and hypotheses (e.g., “−20% processing time,” “+8% conversion”).
- 💎 Prioritization & transparency: Benefits, feasibility, risks, and maturity become comparable—ideal for portfolios and roadmaps.
- 🛠️ Deliverability: From scoping to implementation, expectations, processes (happy/edge cases), and proof of success are documented.
- ⚖️ Governance & Compliance: Data origin, rights, bias/safety risks, GDPR/industry rules, and operation (monitoring, drift) are addressed.
- 📑 Common ground: Use cases create a common language between business, data science, and IT.
What is an use case-driven AI transformation.
In many organizations today, working with use cases is part of what is known as use case-driven AI transformation. This means that strategic goals are not pursued top-down via large technology programs, but bottom-up via concrete, measurable use cases. The basic idea: Instead of first building expensive data platforms or AI infrastructures “in reserve,” the question is asked from the outset:
- Which use cases create the greatest strategic added value?
- What data and technologies are necessary for this – and can then be reused for other use cases?
This results in incremental, value-oriented technological development. Infrastructure, data access, and expertise are created where they contribute directly to implementation – and step by step form a solid basis for further use cases.
This approach prevents long waterfall projects without tangible results and ensures that investments are aligned with real business outcomes. At the same time, it promotes a learning organization that continuously prioritizes, evaluates, and scales use cases.
In short, strategy, technology, and organization develop in a use case-driven manner rather than a technology-driven one, always with an eye on operational added value.
Use Case Categories
Types of 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.
See how Use Cases come to life.
Discover how Casebase helps teams bridge strategy and execution across the AI lifecycle.
LIVING ARTEFACT
The process of creating a data & AI use case
A data & AI use case is not a static document, but a living artifact that evolves as the project progresses. From the initial idea to productive implementation, the use case is gradually specified, evaluated, and documented. The goal is to align business value, data requirements, and technical feasibility – along clearly defined maturity levels and quality gates.
Use Case Funnel Management – From Idea to Implementation.
The path of a data & AI use case usually follows a structured funnel or maturity model: From the initial idea (ideation) to scoping & evaluation and discovery/prototyping to pilot, operation, and scaling, the use case is gradually specified, evaluated, and implemented. Each phase has clear quality gates – this keeps the focus on business value, feasibility, and risk.
Building a Use Case Artifact
Traditionally, a use case consists of a specification (text) and, where appropriate, a diagram (e.g., UML) that visualizes actors, system boundaries, and relationships.
In the context of data and AI, additional dimensions are added: data, models, operation, and governance.
A practical Data Analytics & AI Use Case includes, among other things:
📘 Data & AI Use Case – Recommended Structure
| Category | Field / Element | Description / Purpose |
| Business Context | Problem Definition | Initial situation, business pain, or opportunity addressed by the Use Case. |
| Goal / Job-to-be-Done | What should be improved, automated, or decided? | |
| Value Contribution Assessment | Expected KPIs, value drivers, benefits, or quantified potential (€ / %). | |
| Value Driver Description | Explanation of how value is created (internal vs. external). | |
| Added Value Summary | Short narrative of the improvement or business impact. | |
| Target Vision / Desired Outcome | Description of the future state once the Use Case is implemented. | |
| Strategic Alignment | Link to strategic initiatives, products, or corporate priorities. | |
| Organisational Ownership | Responsible business unit or department. | |
| Feasibility & Scope | Feasibility Assessment | Data availability, technological maturity, and skill/resource requirements. |
| Actors & Target Groups | Who uses or benefits from the solution? | |
| Process Context & Trigger | Where and when does the Use Case occur in the business process? | |
| Process Flow & Logic | Normal flow, alternative paths, and edge cases. | |
| Data Requirements | Required data sources, availability, quality, timeliness, and access rights. | |
| Output & Integration Point | Score, decision, text, API, workflow, or dashboard output. | |
| Use Case Type | e.g., Personalization & Recommendation / NLP / Computer Vision / GenAI. | |
| Technical & Operational | Solution Approach | Short description of the envisioned method, model, or architecture. |
| Technology Mapping | Link to technologies, platforms, and toolchains used. | |
| Non-Functional Requirements | Latency, scalability, cost limits, explainability, and security. | |
| Success Measurement | ML metrics and business KPIs, including experiment or A/B test plan. | |
| Risks & Compliance | Bias, IP/copyright, GDPR, hallucinations, AI Act considerations. | |
| Operation & Monitoring | MLOps / LLMOps setup, drift detection, guardrails, feedback loops. | |
| Linked Artefacts | References to Model Cards, Data Cards, and (future) Agent Cards. |
Standardized Artifacts with Casebase
In Casebase, each use case is documented as a use case card – a structured, versionable artifact that combines business and technical perspectives.
The following can be added as supplements:
- Model Cards: document ML/LLM models, versions, metrics, and deployment limits.
- Data Cards: describe data sets used, origin, quality, rights, and risks.
- Agent Cards: for AI agents, LLM chains, and autonomous workflows.
These standardized artifacts create transparency, traceability, and governance throughout the entire AI lifecycle – from the initial idea to productive use. This turns the use case into more than just a project document; it becomes a central, living control instrument for data & AI initiatives.
Process implementation
Integrating data & AI use cases into your AI transformation
A use case is always a team artifact – it is aimed at everyone who is jointly responsible for the success of an AI initiative:
Roles that benefit from use cases
👩🚀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.
For a use case to work in practice, it should not be viewed in isolation, but rather as an integrated component of the overall AI transformation.
It helps to align strategy, technology, and organization:
- Strategically through clearly prioritized use case portfolios,
- operationally through shared artifacts and language,
- culturally through continuous learning and adaptation along the maturity levels.
Practical tip: Successful teams see the use case as a “living document” – a central control instrument that evolves with each step of insight and is regularly reviewed.
Divide large projects into deliverable Use Cases
For larger products or programs, several use cases can together form this strategic initiative. This is because individual use cases can in turn be defined within this initiative – separable areas of responsibility that can be implemented separately.
Example: Chatbot development
- One use case covers speech processing (intent recognition, dialogue control).
- Another relates to image interpretation (visual inputs).
- A third generates speech responses or uses generative AI for more natural dialogues.
Each use case has clear hypotheses, metrics, and termination criteria.
This avoids lengthy PoCs with no effect and provides genuine feedback from practice at an early stage.
Practical Do´s & Dont´s
Good use case practice means: “Think big, start small, learn fast” – but always with a documented, verifiable business goal.
✅ Do´s
🟢 Clarify the business outcome first
🟢 Check data quality and compliance early on
🟢 Use dual metrics (ML + business)
🟢 Define slices with clear termination criteria
🟢 Actively support change and adoption
🟢 Keep artifacts (use case, model, data card) up to date
❌ Dont´s
🔴 Technology without a clear business fit
🔴 Endless PoCs without integration
🔴 Only looking at offline metrics
🔴 Ignoring drift, safety, or costs
🔴 Leaving governance and ownership unclear
Data Analytics & AI Use Cases
Conclusion
Data Analytics & AI Use Cases are the common language between business, data/IT, and governance.
In the data & AI environment, they ensure that ideas become effective, measurable solutions – structured across the entire lifecycle, from the initial hypothesis to productive operation.
Their roots in software engineering (Jacobson) – the outside view of interaction – are more relevant today than ever: only those who understand what problem they are solving for whom can anchor data & AI in the long term.
Tools such as Casebase support this approach by combining use cases, data, models, and governance in a consistent structure – so that AI visions can be turned into demonstrable added value.
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