How to Choose Your First AI Use Case
A practical framework for selecting the right first AI use case - prioritizing for quick wins, avoiding common traps, and setting up for a successful first deployment.
The most common mistake in enterprise AI adoption is choosing the wrong first use case. Teams either pick something too ambitious (which stalls before delivering value), too trivial (which delivers value but builds no capability), or too politically complex (which gets mired in stakeholder disagreements before any code is written).
This guide provides a framework for choosing a first AI use case that builds real capability, ships in a reasonable timeframe, and creates momentum for subsequent projects.
What Makes a Good First Use Case
A first AI use case should score well on four dimensions:
1. Feasibility - The problem is well-suited to current AI capabilities. AI works well on language tasks (extraction, summarization, classification, generation) and pattern recognition tasks with good training data. It works poorly on tasks requiring guaranteed-correct reasoning, real-time data access, or complex multi-step physical interaction.
2. Data availability - The inputs the AI needs exist and are accessible. Documents, structured data, audio/video recordings - whatever the use case requires should be in a format the system can process. If significant data collection or cleansing work is needed before the AI can run, account for that in your timeline.
3. Evaluability - You can measure whether it works. If you cannot define what a correct output looks like, you cannot evaluate quality or improve the system. Good first use cases have clear success criteria: a document is correctly extracted, a classification matches a human label, a summary contains the required information.
4. Low blast radius - If the system makes an error, the consequence is limited. An AI-assisted first draft is recoverable (a human reviews and corrects). An AI system that directly sends customer communications or makes financial transactions has higher stakes and requires more robust validation before deployment.
The Scoring Matrix
Rate candidate use cases on these criteria (1-3 scale):
| Criterion | 1 (Low) | 2 (Medium) | 3 (High) |
|---|---|---|---|
| AI capability fit | Requires reasoning beyond current AI | Possible with current AI but complex | Clear capability match |
| Data availability | Major data work needed | Minor data work | Data ready to use |
| Evaluability | Hard to define success | Can define but expensive to measure | Clear, cheap-to-measure criteria |
| Blast radius | High-stakes errors | Medium-stakes errors | Low-stakes or human-reviewed |
| Stakeholder alignment | Complex politics | Some alignment work needed | Clear champion, minimal politics |
Sum the scores. Pursue use cases scoring 12-15 first. Use cases scoring 8-11 are second-wave candidates after you have built initial capability. Use cases below 8 are not good first projects regardless of their potential value.
Common First Use Case Categories
High-scoring (good first projects):
- Internal document summarization (meeting notes, reports, policies)
- Email classification and routing for internal operations
- FAQ generation from existing documentation
- First-draft generation for internal communications
- Structured data extraction from standardized documents
Mid-scoring (second wave):
- Customer-facing chatbots (requires higher quality bar and more testing)
- Code review assistance (requires integration with development workflow)
- Complex document analysis (contracts, legal documents)
Low-scoring (not first projects):
- Fully automated customer communications without human review
- Financial decision automation
- Medical diagnostic applications
- Anything with significant regulatory obligations before you have AI governance in place
The Quick Pilot Test
Before committing to a full build, run a manual pilot of your top candidate: take 20-30 real examples of the input, run them through the AI manually, and evaluate the outputs. This takes a day or two and tells you whether the AI capability is actually there for your specific data. Many projects that looked good on paper fail this test quickly - which is exactly what you want to discover before spending weeks on infrastructure.
Setting Up for Success
Once you have chosen a use case, define success metrics before building. What is the current manual cost (time per task, error rate)? What threshold of AI performance justifies deployment? Who will review AI output before it is used? These questions are much easier to answer before the system exists than after.
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