What is an AI use case analysis?

How to conduct an AI use case analysis

An AI use case analysis starts with a structured review of the organisation’s operations: what work is being done, who is doing it, what information it requires, and where time, quality, or cost problems exist. From this foundation, candidate use cases are identified — areas where AI might plausibly help. Each candidate is then evaluated: does AI genuinely outperform the current approach in this specific context, or is the improvement marginal? What data is required, and is it available in the quality and format needed? What governance and oversight mechanisms does deployment require? What is the EU AI Act risk classification? What are the consequences of AI errors in this use case? This evaluation produces a scored and ranked list of use cases, which feeds directly into the AI strategy prioritisation process.

Common mistakes in AI use case selection

The most common mistake in AI use case selection is identifying use cases based on what AI can theoretically do, rather than on what the organisation actually needs. This produces a long list of technically plausible AI applications, many of which do not address real problems. The second most common mistake is selecting use cases without assessing the data and governance requirements — leading to AI deployments that fail because the underlying data is not in the right state, or because the governance structures needed to operate the AI safely are not present. The third mistake is ignoring EU AI Act classification: selecting use cases that involve high-risk AI without accounting for the compliance requirements creates legal exposure. A rigorous use case analysis avoids all three mistakes by grounding selection in operational reality.