Evaluating AI use cases.A grid for the next decision

Many organisations have more AI ideas on the table than they can sensibly test. Some come from business teams, others from IT, a few from a presentation that is doing the rounds. Almost all of them sound plausible.

So the key question is rarely whether AI could handle a task in principle. It is this: which use case deserves the next step, and which does not? A lean evaluation framework helps you answer that question in a way others can follow.

We have more AI ideas than we can pursue. How do we tell which ones are worth the effort?

Why is technical feasibility not enough?

Putting together a convincing demo is no longer hard. It does not show whether a solution will hold up in everyday work. For that, it has to run on real data, fit into existing processes and be overseen by people who have the time and the responsibility to do so.

Many initiatives stall not on the technology but on questions asked too late. May we use this data? Who checks the output? What happens if it is wrong? A good framework asks these questions before the first prototype.

How do you make use cases comparable?

Before you assess anything, describe each idea in the same short format. One page is enough. It answers the following questions:

• Which task should be supported, and who does it today?
• What inputs are available, such as documents, emails or data from existing systems?
• What output is expected, and who works with it next?
• What happens if the output is wrong or incomplete?
• Who owns the process on the business side?

If an idea cannot be described this way, it is not ready for assessment. That is a result too: the idea needs clarifying within the business before it needs a technical review.

Which criteria should you use?

Five criteria cover what decides the way forward. They apply equally to every case.

• Value. What becomes better, faster or more reliable, and for whom? The benchmark is today’s process, not an ideal. A task that comes up often and ties up noticeable time weighs more than a rare exception.
• Feasibility. Is the task suited to AI, or would conventional automation do? Can the systems involved be reached, and does the organisation have the know-how to run the solution?
• Risk. What are the consequences of a wrong result, for the business, legally and for customers? How quickly would an error be noticed? The lower the tolerance for errors, the closer the oversight needs to be.
• Data access. Is the required data available, of sufficient quality, and may it be used for this purpose? Information security and privacy belong in the conversation here, not just before launch.
• Human oversight. Where does a person check, confirm or correct the output? Is there someone who can and will take on that role?

How do you weigh the criteria without getting lost in scores?

Detailed scoring models suggest a precision that does not exist at this early stage. A simple rating per criterion is more practical: met, open or critical.

Not all criteria carry the same weight. Data access and risk are thresholds. If the data may not be used, or an error would be unacceptable, even high value will not help. Among the cases that clear these thresholds, value and feasibility set the order. Human oversight shapes how the pilot is designed.

The reasoning matters more than the rating. Record one sentence per criterion. That keeps the decision understandable for people who were not in the room.

Who should take part in the assessment?

The assessment works best in a small group that brings business and technical perspectives together:

• the process owners, who judge value and practicality,
• people who do the task today and know the exceptions,
• IT and architecture, for systems, integration and operations,
• information security and privacy, for what is permissible,
• someone who can decide on budget and capacity.

With the descriptions prepared in advance, one focused workshop can be enough to agree on an initial ranking.

What follows from the assessment?

Every case assessed ends in one of three decisions:

• Move to a pilot. The case clears the thresholds, and enough value can reasonably be expected. Success criteria, data access and human oversight are defined before work begins.
• Rework. The idea has promise but is not ready. The task may need narrowing, the data situation clarifying, or a simpler form of automation may be worth checking.
• Deliberately stop. Value, risk and effort do not fit together at present. Record the reason, so the idea does not return unexamined at every planning round.

The same logic applies at the end of the pilot. The outcome may be scaling, reworking the approach or deliberately stopping. Stopping is not failure. It protects capacity for the cases that deserve it.

When should you repeat the assessment?

A ranking is a snapshot. New data sources, changed conditions or the results of a pilot can shift the picture. Review the list at fixed intervals, for example as part of regular planning, and whenever a key assumption changes.

The framework itself stays the same. That is what makes decisions comparable over time, and it shows which ideas have genuinely matured.