What Is an AI Use Case?
A clear definition and practical guide to structuring AI opportunities
Simple definition
An AI use case is a specific, well-defined application of AI to solve a particular problem or achieve a particular outcome in your organisation.
It describes: what the AI would do, who would use it, what problem it solves, and what value it creates. A good use case is specific enough to evaluate feasibility, assess ROI, and plan implementation.
Examples
Document processing
Extract structured data from invoices, contracts, or forms to reduce manual data entry, improve accuracy, and speed up processing.
Customer service automation
Answer common customer questions automatically, route complex queries to human agents, and provide 24/7 support to reduce response times and costs.
Decision support
Help analysts or managers make faster, more consistent decisions by providing recommendations, risk assessments, or predictions based on historical data.
Predictive maintenance
Predict when equipment will fail based on sensor data and usage patterns, enabling proactive maintenance to reduce downtime and costs.
Good vs bad use cases
Good use cases
- Specific problem or task
- Clear value proposition
- Defined scope and boundaries
- Identified users and owners
- Feasible with current AI capabilities
- Can be evaluated and measured
Example: "Extract key fields (amount, date, vendor, line items) from PDF invoices to populate our accounting system, reducing manual entry from 2 hours per day to 15 minutes."
Bad use cases
- Vague or overly broad
- No clear value or problem
- Unclear scope
- No identified owners
- Requires capabilities that don't exist
- Can't be evaluated or measured
Example: "Use AI to improve our business" or "Automate everything with AI."
Why specificity matters
Vague use cases can't be evaluated, prioritized, or implemented. Specificity enables:
- Feasibility assessment: You can evaluate whether AI can actually do this given your data, systems, and constraints
- ROI estimation: You can estimate value in terms of time saved, errors reduced, or revenue opportunity
- Implementation planning: You can plan data needs, system integration, and team capability requirements
- Comparison and prioritization: You can compare use cases and prioritize based on value, feasibility, and strategic alignment
- Governance and risk: You can assess risks, compliance requirements, and guardrails needed
Specificity is the foundation for confident AI decision-making.
Why AI fails without structure
Without structured use cases, AI initiatives fail because:
- You can't compare options or prioritize
- You can't assess feasibility or ROI
- You can't plan implementation or manage risks
- You lack stakeholder buy-in (value isn't clear)
- Projects stall or fail because they lack foundation
Structure enables evaluation, comparison, prioritization, and governance—the foundation for successful AI adoption.
How to evaluate feasibility
Feasibility assessment answers: Can AI actually do this given your data, systems, constraints, and capabilities?
Data requirements
What data is needed? Do you have it? Is it accessible? Is it of sufficient quality? Do you have enough examples for training or validation?
System integration
What systems need to integrate? Is integration feasible? What's the complexity? What's the timeline?
Team capability
What skills are needed? Does your team have them? What training is required? Can you hire or partner?
Constraints
What are your budget, timeline, and compliance constraints? Do they allow this use case?
AI capabilities
Can current AI capabilities actually do this? Is it proven technology or experimental? What's the reliability?
How ROI is different with AI
AI ROI is often different from traditional technology ROI because:
- Value may be indirect: Time saved, errors reduced, risk mitigated—not just revenue increased
- Accuracy and reliability matter: 95% accuracy might be valuable, or it might be insufficient—it depends on the use case
- Adoption is critical: Value depends on team adoption, not just technical capability
- Iteration is expected: AI systems improve over time with more data and refinement
- Risk and compliance have costs: Governance, monitoring, and guardrails add cost but reduce risk
Frame ROI in terms relevant to your use case: time saved, errors reduced, risk mitigated, or revenue opportunity. Be realistic about accuracy, adoption, and iteration.
Why governance belongs early
Governance isn't an afterthought. Consider it early in use case definition:
- Risk assessment: What are the privacy, security, bias, operational, and change risks?
- Compliance requirements: What regulations apply? What approvals are needed?
- Guardrails: What controls and monitoring are needed?
- Oversight: Who approves? Who monitors? Who owns compliance?
Governance shapes feasibility, implementation, and value. Consider it early, not late.
How Clairable helps structure use cases
Clairable provides a structured framework for defining, evaluating, and comparing AI use cases:
- Guided prompts to define specific use cases with clear scope and value
- Structured fields: what, who, feasibility, prerequisites, risks, governance, ROI
- Feasibility assessment grounded in your Organisation Profile
- Risk assessment and governance considerations built in
- Comparison and prioritization tools to evaluate multiple use cases
- Decision-ready briefs you can share with stakeholders
Clairable turns ideas into structured use cases you can evaluate, compare, and implement with confidence.
Frequently Asked Questions
What's the difference between a use case and a project?
A use case defines what you want to do and why. A project is the implementation plan. Use cases come first—they define the scope, value, and feasibility. Projects come after—they plan how to implement.
How many use cases should I start with?
Start with 3-5 use cases. This gives you enough to compare and prioritize without overwhelming your team. You can always add more as you build confidence and capability.
Can one use case have multiple AI applications?
Yes. A use case might involve multiple AI components (e.g., document extraction + decision support). Structure them as separate but related use cases, or as one use case with multiple components. The key is clarity and structure.
What if my use case isn't feasible?
That's valuable information. Feasibility assessment helps you avoid costly mistakes. If a use case isn't feasible now, identify what needs to change (data, systems, capabilities) and whether that's worth pursuing.
How do I know if a use case is good?
A good use case is specific, valuable, feasible, and aligned with your strategy. Use Clairable's structured framework to evaluate: is it specific enough? Is the value clear? Is it feasible? Does it fit your goals and constraints?