The term “use case” is widely used in software development, but not always consistently. Teams often mix different abstraction levels when writing them, which leads to confusion and weak traceability.
To clarify the discussion, it helps to start with the formal idea of a use case and then distinguish two important variants:
- Business use cases
- System use cases
Understanding the difference improves communication and creates a clear path from domain understanding to implementation.
What a Use Case Actually Is
In software and systems engineering, a use case captures requirements by describing interactions between actors and a system to achieve a goal. It represents a sequence of actions that produces an observable result of value for an actor.
This definition highlights a few key properties:
- Use cases capture functional requirements
- They are actor-goal oriented
- They focus on externally visible behavior
- They are independent of implementation
Use cases are therefore not design descriptions. They describe behavior and outcomes.
Business Use Cases
A business use case focuses on the organization rather than a software system. It describes how the business achieves value through workflows involving people, processes, and possibly multiple systems.
It captures:
- Business goals
- Organizational activities
- Roles and responsibilities
- Value creation
It is typically technology-agnostic and written in domain language.
Example
Process Customer Order
A customer places an order.
The organization verifies availability, prepares shipment, invoices the customer, and delivers the product.
This explains how the business operates.
It does not describe system behavior.
Purpose
Business use cases help with:
- Understanding the domain
- Aligning stakeholders
- Exploring requirements
- Identifying system responsibilities
They anchor the discussion in real business intent before solutions are considered.
System Use Cases
A system use case specifies the behavior of a system when interacting with an actor. It defines functional requirements in terms of observable system responses.
It captures:
- Interaction between actors and system
- System responsibilities
- Normal and alternative flows
- Preconditions and outcomes
Example
Create Order
Actor submits order data
System validates input
System stores order
System confirms creation
Alternative flows describe validation failures or unavailable products.
Purpose
System use cases support:
- Requirements specification
- Architecture and design
- Implementation guidance
- Acceptance testing
They translate intent into precise, testable behavior.
Relationship Between Them
The key distinction is scope.
Business use case
Focuses on organizational behavior
System use case
Focuses on system behavior
They are connected but not interchangeable.
Business use cases guide discovery of system responsibilities.
System use cases formalize those responsibilities as interactions.
One business use case often leads to multiple system use cases.
Example:
Business goal
Process customer order
Derived system use cases
- Create order
- Check availability
- Generate invoice
- Track shipment
This progression moves from intent to specification.
Testability and Precision
Both types are useful, but in different ways.
Business use cases provide context and understanding.
They are not directly testable.
System use cases define verifiable workflows:
- Observable
- Repeatable
- Measurable
They can be transformed into:
- Acceptance tests
- Integration tests
- AI-generated scenarios
This makes them a strong bridge between requirements and implementation.
How Business and System Use Cases Are Used in AIUP
In the AI Unified Process (AIUP), the distinction between business and system use cases becomes operational rather than theoretical. Each plays a specific role in moving from domain understanding to executable software.
Business Use Cases in AIUP
Business use cases are used during discovery and alignment. They capture intent before system behavior is specified and are typically developed through workshops and domain exploration.
They help clarify:
- Business goals
- Organizational workflows
- Actor responsibilities
- Expected outcomes
They are not used directly for code or model generation. Their role is to ensure shared understanding and prevent premature solution design.
This step is especially important in AI-supported development. AI amplifies ambiguity. If intent is unclear, generated artifacts reflect that confusion.
Business use cases answer:
Why does this capability exist?
System Use Cases as the Central Artifact
System use cases are the core behavioral specification in AIUP.
They act as the stable contract describing system behavior.
From them, AI-assisted workflows derive:
- Entity models
- Interaction diagrams
- Test scenarios
- Code structures or updates
They connect intent and implementation and support incremental evolution.
AIUP does not regenerate systems wholesale.
Changes occur use case by use case, enabling:
- Controlled updates
- Traceability
- Small reviewable changes
System use cases answer:
How must the system behave?
Human Review and Iteration
Human validation remains essential.
Business use cases are reviewed for correctness of intent.
System use cases are reviewed for precision and completeness.
AI accelerates structuring and generation, but humans ensure:
- Domain accuracy
- Clarity
- Consistency
- Alignment with goals
This feedback loop prevents specification drift and maintains quality.
Summary
Within AIUP:
- Business use cases anchor domain understanding
- System use cases define behavioral contracts
- AI operates primarily on system use cases
- Humans ensure correctness and alignment
Maintaining this separation allows AIUP to connect business intent and software evolution in a controlled and transparent way.
Conclusion
A use case describes goal-oriented interaction that produces value for an actor.
Within this concept, two levels are essential:
Business use cases
Describe how the organization achieves goals
System use cases
Describe how the system behaves to support those goals
They serve different audiences and purposes, but together they create a clear path from domain understanding to software implementation.
In modern development, and especially in AI-assisted workflows, maintaining this distinction is not an academic detail. It is a practical mechanism for keeping intent, specification, and implementation aligned over time.


