How to Identify a Good AI Use Case Before You Invest

In short: a good AI use case passes six tests

• Clear business problem: there is a decision, process or customer outcome to improve.
• Measurable value: success can be defined in operational or business terms.
• Relevant data: the necessary information exists or can realistically be collected.
• Feasible prediction or output: the target is something AI can reasonably support.
• Usable workflow: people or systems can act on the model’s output.
• Acceptable risk: the cost of errors, privacy and operational constraints are understood.

1. Start with a business decision, not a technology

A weak use case often begins with a tool: “we should use an LLM,” “we need a chatbot,” or “we want machine learning.” A stronger starting point is a decision or process: “we need to forecast demand more accurately,” “we want to prioritize leads,” or “we need to detect unusual transactions earlier.”

The more clearly the business question is defined, the easier it becomes to decide whether AI is relevant and which modelling family may fit. If the problem itself is vague, modelling will not fix that ambiguity.

2. Define what success would look like

Before discussing accuracy, define the business outcome. Would a useful solution reduce manual review, improve forecast reliability, increase relevance of recommendations, shorten response time or help teams prioritize better? The metric does not need to be perfect on day one, but there should be a clear way to judge whether the project is helping.

3. Check whether the data matches the question

Having a database does not mean the data is suitable. Ask whether the relevant signals are captured, whether historical outcomes exist when supervised learning is required, whether timestamps are reliable for forecasting, whether text or images are stored consistently, and whether the data reflects the population or process the model will face in production.

Data gaps do not automatically kill a project, but they may change the roadmap. Sometimes the right first step is improving data collection rather than training a model.

4. Ask whether the output can actually be used

A model can perform well and still create little value if nobody can act on its output. Who receives the prediction? At what point in the workflow? How quickly must the result arrive? What action follows? Does the team trust and understand the recommendation? These integration questions are part of the use case, not an afterthought.

5. Consider the cost of being wrong

Not all errors are equal. A false positive in a low-stakes recommendation is very different from a wrong decision in a regulated or safety-critical process. The acceptable model, level of human review and evaluation method depend on the consequences of mistakes.

6. Compare AI with simpler alternatives

Sometimes a rule, dashboard, better search function or process change solves most of the problem with less complexity. AI is most useful when patterns are difficult to encode manually, the scale is large enough, personalization matters, or predictions must be made repeatedly from complex data.

A simple prioritization matrix

When several ideas compete for attention, score them on business value, data readiness, technical feasibility, integration effort and risk. The best first project is often not the most ambitious one. It is the use case where value is meaningful, data is sufficiently mature and the organization can actually use the result.

What happens after a use case passes the test?

The next step is usually a more detailed assessment of the target, available data, baseline, evaluation method and deployment context. That is the purpose of the Data2Profit AI Opportunity Diagnostic. If the use case is already validated, the AI Solutions page explains what development and integration can include.

About Data2Profit

Data2Profit is an applied AI consultancy founded by Shahaf Klein in Grenoble, France. It helps organizations move from business questions to realistic AI opportunities and, when justified, to working solutions. The focus is practical value rather than adopting AI for its own sake.