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AI Strategy
Updated: July 12, 2026 · 6 min read

How Do You Evaluate an AI Use Case?

Between “we could do that with AI” and a viable project lies a structured evaluation. This article shows the four dimensions you can use to compare and prioritize AI use cases with a clear head.

Why a structured evaluation?

In almost every company, ten or more AI ideas appear quickly. At the start, you can implement one or two. Without a structured evaluation, the loudest idea often wins – not the best one. A shared evaluation logic makes ideas comparable and the decision traceable.

Dimension 1: Business benefit

The core question: what changes measurably if the use case works? Hours saved per week, shorter response times, fewer errors, more requests handled. The more concretely the benefit can be quantified, the sturdier the prioritization. “Would be handy” isn't a benefit – “saves inside sales six hours a week” is.

Dimension 2: Data foundation

AI solutions are only as good as the data they work with. Check: does the necessary information exist digitally? In what quality and structure? Who is allowed to access it? A use case with an exciting benefit and no data foundation isn't a project – it's homework on your data first.

Dimension 3: Technical feasibility

This is about an honest assessment: can today's models solve the task reliably enough? Which systems need to be connected, and do interfaces exist for them? How critical are errors – and can human oversight catch them? A short technical proof of concept often answers these questions faster than any discussion.

Dimension 4: Effort and risk

Effort includes development, integration, testing, and ongoing operations – risk includes data protection, compliance, and acceptance within the team. What matters is the ratio: a moderate benefit at minimal effort often beats the spectacular use case that requires six months of integration.

The evaluation in practice

A simple scoring approach has proven itself: rate each dimension from 1 to 5, weight benefit and feasibility double, and rank the candidates. The result isn't mathematical truth, but it's a structured basis for discussion – and it usually becomes clear quickly which use case is the right one to start with.

Incidentally, exactly this evaluation is the core of an AI Discovery Workshop: it ends with a prioritized list and a clear recommendation for the next step.

Frequently asked questions

At the beginning: one. A focused first project delivers results faster, ties up fewer resources, and creates the learning curve that every following project benefits from.

A measurable before-and-after comparison: processing time, response rate, error rate. The criterion is defined before implementation – not adjusted to fit afterwards.

The business side that knows the process, someone with technical judgment, and the decision-makers. If one of those three perspectives is missing, the evaluation comes out skewed.

Let's talk about your situation.

In a free initial consultation, we'll work out which step makes sense for your business.