The right use case doesn't emerge on a whiteboard — it comes from engaging with real processes and data. The starting point is always the question: Where does our company lose time, money, or quality today — and where are the biggest levers?

In practice, four criteria help:

Business relevance: How significant is the value if the problem is solved? A use case that produces only marginal effects rarely justifies the effort.

Technical feasibility: Is data available, are interfaces accessible, is the problem algorithmically solvable? Not every challenge can be addressed with AI — and that's not a weakness, it's honesty.

Strategic fit: Does the use case solve a problem that matters to the company's strategy? AI projects that miss the core business rarely generate the support they need for successful implementation.

Operational viability: Who uses the solution? Who maintains it? Which processes need to change? A use case that works technically but isn't supported organizationally remains a prototype.