Organizations often start AI initiatives with the use case that seems most obvious or most exciting. But obvious use cases aren't always the highest-value ones. Sometimes they're the hardest to execute well. Sometimes the real ROI lives in less glamorous workflows that nobody thought to automate.
The Six Dimensions of High-ROI AI Use Cases
1. Volume and Repetition
AI creates value through repetition. The more times a workflow runs, the more the AI investment pays off. A workflow that runs 10,000 times per month has dramatically more potential ROI than one that runs 10 times. Look for high-frequency workflows first.
2. Data Availability and Quality
AI needs data to work. The best use cases have structured or semi-structured data that's accessible, consistent, and reasonably clean. If the data exists in disconnected systems, requires extensive manual preparation, or is highly variable in quality, the AI project will require significant data engineering work before it can deliver value.
3. Decision Value and Stakes
AI creates the most value when it helps with high-stakes decisions—or when it removes low-stakes decisions from people's plates entirely. Classifying an email might seem low-value, but if that email routing affects customer satisfaction, contract terms, or compliance requirements, the value multiplies.
4. Current Manual Cost
What does it cost in labor to process one instance of this workflow? If it takes 30 minutes of analyst time per 100 documents, and you process 10,000 documents per month, that's significant cost—until you recognize that some of those 30 minutes are spent on tasks that require judgment, and some are spent on repetitive extraction that could be automated.
5. Error Cost and Risk
When humans make errors in this workflow, what does it cost? Errors in data extraction might cause downstream reporting problems. Errors in document classification might cause compliance violations. Errors in contract review might lead to missed obligations. The cost of errors is often hidden until something goes wrong.
6. Workflow Integration
A use case that can be automated in isolation is easier to pilot. But a use case that connects into existing systems and workflows—extracting data that feeds into a database, routing documents that trigger approvals, generating reports that go to specific stakeholders—creates more lasting value because it becomes part of how work actually gets done.
The Use Case Scoring Framework
Score each potential use case on a 1-5 scale for each dimension:
- Volume: How many times does this workflow run per month?
- Data: How accessible, structured, and clean is the data?
- Decision Value: How important are the decisions made in this workflow?
- Manual Cost: How much labor does one instance require?
- Error Risk: What happens when errors occur?
- Integration: How well does this connect to existing systems?
Use cases scoring 4+ across most dimensions are candidates for immediate evaluation. Those scoring 2-3 require work before they're ready. Those scoring below 2 should be deprioritized unless there's a strategic reason.
Common Mistakes in Use Case Selection
Starting with the most complex use case. Organizations often want to tackle their biggest, most visible problem first. But complex workflows often have complex data, complex stakeholders, and complex governance requirements. Start smaller to build capability and confidence.
Focusing on technology rather than outcomes. "We should use AI for X" is not the same as "AI would help us achieve Y faster, cheaper, or more consistently." Always define the outcome first.
Ignoring data readiness. The AI tools exist and the use case is clear—but the data isn't accessible, isn't structured, or isn't clean enough. This is where most AI projects stall. Assess data readiness early.
When This Article is Relevant
- ✓ You're evaluating AI opportunities and need a framework for prioritization
- ✓ You have multiple use cases and need to decide where to start
- ✓ You've struggled with AI projects that seemed promising but didn't deliver ROI