Introduction
Knowing where AI can be used is only the beginning of business AI planning. The next challenge is connecting an AI capability to a specific business problem.
A business may know that AI can summarize documents, classify information, generate text, analyze data, retrieve knowledge, or automate workflows. None of these capabilities automatically represents a useful business use case. A useful use case exists when a capability is applied to a real activity in order to improve a meaningful business outcome.
This distinction helps organizations avoid building AI projects simply because the technology is available.
Business Problem vs AI Capability vs Use Case
These three concepts should be kept separate.
Business Problem
A business problem is an undesirable situation that affects performance, cost, customers, employees, revenue, quality, risk, or another meaningful outcome.
Examples include:
- Customer requests take too long to process.
- Employees spend excessive time searching for internal information.
- Invoices require significant manual data entry.
- Managers receive reports too late to respond effectively.
- Sales representatives spend too much time preparing meeting information.
AI Capability
An AI capability is something an AI system can do.
Examples include:
- Summarization
- Classification
- Information extraction
- Text generation
- Question answering
- Translation
- Pattern identification
- Prediction support
AI Use Case
An AI use case connects an AI capability to a specific business activity and expected outcome.
For example, “AI summarization” is a capability. “Automatically prepare a short summary of each customer support conversation for the support representative” is a business use case.
The use case becomes more useful when its expected outcome is also defined, such as reducing the time required to review previous conversations.
Start With the Problem
A strong use case begins with a problem rather than a technology.
Consider two approaches:
Technology-first: “We want to use generative AI. What can we build?”
Problem-first: “Employees spend two hours each day searching several internal systems for information. Can AI reduce this effort while preserving access controls?”
The second approach provides a much clearer basis for evaluation.
Ask Why the Problem Matters
Not every inconvenience is important enough to justify an AI initiative. After identifying a problem, determine why it matters.
Ask:
- Does it consume significant employee time?
- Does it delay customers?
- Does it create avoidable errors?
- Does it increase operating cost?
- Does it limit business capacity?
- Does it reduce quality?
- Does it create unnecessary risk?
- Does it prevent employees from focusing on higher-value work?
This helps distinguish a meaningful business problem from a minor annoyance.
Match the Problem to an AI Capability
Once the problem is understood, consider which capability could address it.
| Business problem | Potential AI capability | Example use case |
|---|---|---|
| Employees spend too long reading lengthy reports | Summarization | Generate concise report summaries for initial review |
| Large numbers of requests must be routed | Classification | Classify incoming requests and suggest routing |
| Information must be copied from documents | Information extraction | Extract selected fields from invoices |
| Employees repeatedly draft similar communications | Generation | Prepare first drafts using approved information |
| Employees cannot quickly find internal information | Knowledge retrieval | Provide answers from approved internal sources |
| Large amounts of feedback are difficult to review | Classification and analysis | Group feedback into recurring themes |
The important point is that the capability should follow the problem. The same AI capability can be useful for many problems, while some problems may require no AI at all.
Do Not Confuse Automation With AI
Some business problems can be solved through ordinary automation.
For example, if a company always sends a predefined email when a particular database condition is met, a normal workflow rule may be sufficient. An AI model may add unnecessary complexity.
AI becomes more useful when the workflow involves information that is difficult to handle with fixed rules, such as natural language, documents, ambiguous classification, complex summarization, or pattern recognition.
The objective is to choose the appropriate technology rather than automatically choosing AI.
Define the User
A good use case identifies who will actually use the AI output.
The user could be:
- A customer service representative
- A salesperson
- A finance employee
- An HR professional
- A manager
- An operations employee
- An analyst
- A customer
Understanding the user helps determine what information is needed, how the output should be presented, and where human review should occur.
Define the Workflow
The AI use case should describe where the AI capability fits into the existing process.
Consider:
- What starts the workflow?
- What information enters the workflow?
- What does the AI system do?
- What output does it produce?
- Who reviews the output?
- What happens after review?
- What happens when the AI cannot produce a reliable result?
This turns an abstract AI idea into an actual business process.
Define the Inputs
AI systems require inputs. These may include documents, messages, structured business data, customer requests, product information, or internal knowledge.
The quality and availability of these inputs can strongly affect the usefulness of the system.
For each use case, identify:
- What information is required
- Where the information comes from
- Who is allowed to access it
- How current it needs to be
- Whether it contains confidential or personal information
Define the Output
The output should be specific enough to evaluate.
Possible outputs include:
- A summary
- A classification
- A list of extracted fields
- A draft response
- A recommendation for human consideration
- A report
- A structured record
- A retrieved answer with supporting source information
Vague outputs make it difficult to determine whether the AI system is actually helping.
Define the Expected Business Outcome
The most important part of a use case is often the expected outcome.
Instead of saying “use AI to summarize customer calls,” define the intended business result.
For example:
“Reduce the average time support representatives spend reviewing previous customer conversations while maintaining the accuracy of important customer information.”
This provides a basis for measurement.
Example: Customer Support
Suppose a support team spends significant time reading previous conversations before responding to customers.
Business problem: Support representatives spend too much time reviewing conversation history.
AI capability: Summarization.
Use case: Generate a concise summary of relevant previous interactions before the representative handles the current request.
User: Support representative.
Input: Relevant customer conversation history.
Output: A concise conversation summary.
Human role: Review the summary and consult the original information when necessary.
Expected outcome: Reduced preparation time without reducing response quality.
This is much more specific than saying “use AI in customer service.”
Example: Finance
Consider a finance department that manually extracts information from supplier invoices.
Business problem: Employees spend significant time entering invoice information into a financial system.
AI capability: Information extraction.
Use case: Extract supplier name, invoice number, date, and selected amounts from incoming invoices and prepare the information for employee verification.
User: Finance employee.
Input: Supplier invoices.
Output: Structured invoice fields.
Human role: Verify the extracted information before it is used for financial processing.
Expected outcome: Reduce manual data entry while maintaining appropriate accuracy controls.
Example: Internal Knowledge
Suppose employees frequently ask the same questions about internal procedures.
Business problem: Employees spend time searching through multiple internal documents for procedural information.
AI capability: Knowledge retrieval and question answering.
Use case: Provide answers using approved internal documents and direct employees to relevant source material.
User: Employees.
Input: Approved internal documentation.
Output: A concise answer with relevant source references.
Human role: Employees verify important information and follow official procedures.
Expected outcome: Reduce information-search time while preserving the authority of official business documentation.
Evaluate the Complete Use Case
A use case should be evaluated as a complete system rather than just an AI model.
Consider six dimensions:
| Dimension | Key question |
|---|---|
| Value | What meaningful business problem does it address? |
| Feasibility | Can the workflow realistically be implemented? |
| Data | Are the required inputs available and appropriate? |
| Quality | How reliable does the AI output need to be? |
| Risk | What happens if the system is wrong? |
| Measurement | How will improvement be demonstrated? |
Weak Use Cases
Some proposed use cases appear attractive but have little practical value.
No Clear Problem
“We should use AI to make our business more innovative” does not define a specific problem or measurable outcome.
Unnecessary AI
If a simple database query or workflow rule solves the problem reliably, adding an AI model may create unnecessary cost and complexity.
No Suitable Data
An AI system cannot reliably provide information that the organization does not possess or cannot appropriately access.
Uncontrolled High-Risk Decisions
A use case becomes much more difficult to justify when incorrect output could cause serious harm and no effective human review or control exists.
No Measurement
If the organization cannot determine what improved, it becomes difficult to establish whether the project created value.
AI Use Case Template
A practical business can document each candidate use case using a simple template:
- Business problem: What problem exists?
- Business impact: Why does it matter?
- Users: Who benefits from the solution?
- Current workflow: How is the work performed today?
- AI capability: What could AI contribute?
- Inputs: What information is required?
- AI output: What should the system produce?
- Human role: What must a person review or decide?
- Risk: What could go wrong?
- Success measure: What result would demonstrate value?
This template creates enough structure to compare ideas and decide which ones deserve further investigation.
From Use Case to Business Case
An AI use case describes what the system could do and why it could be useful. A business case goes further by considering investment, expected benefits, implementation effort, operating costs, risks, and alternatives.
Therefore, identifying a promising use case does not mean the organization should immediately build it. The use case is an input into a broader business decision.
Conclusion
Strong AI use cases connect three things: a real business problem, an appropriate AI capability, and a measurable business outcome.
The process should begin by understanding the problem and current workflow. The organization can then determine whether AI is appropriate, identify the required inputs and outputs, define human involvement, evaluate risk, and establish a measurement approach.
This problem-first approach helps businesses avoid technology-driven projects and focus investment on AI applications that have a genuine reason to exist.