Assess an AI use case from idea to pilot with suitable data, representative tests, human review and an operating plan.
Who is this for? Departments and technical teams that want to use AI in a targeted manner and systematically assess results.
Use cases and context
An AI pilot needs a concrete task. Summarizing, searching, classifying and proposing courses of action have different requirements. Without a benchmark, it is difficult to see whether a solution actually improves the existing process.
In generative systems, plausible formulations are not evidence of correct content. Suitable test cases, access to permissible information and the handling of uncertain results are crucial. Especially in the case of writing actions, the boundary between proposal and authorized execution must be clear.
The approach in detail
- Define the task, user group and acceptable data. Describe a manual initial solution and measurable success criteria.
- Build a representative test set with typical, difficult, and undesirable cases. Evaluate source reference, completeness, error consequences, and required human review.
- Operate the pilot in a limited way. Monitor usage, quality, and effort; Compare changes to the model, data, and instructions with the same test cases.
Expected outcomes
- Limited use case with benchmark
- Comprehensible quality and error criteria
- Checked data and authorization limits
- Basis for decision-making for introduction or adaptation
Prepare for an informed decision
Bring real task examples in anonymized form, known error cases and today's processing time. Determine who assesses results professionally.
Costs, response times, data processing and dependence on the provider are part of the assessment. Legal requirements are checked for the specific application; a technical pilot does not establish a blanket conformity statement.
Questions and answers
Do you have to train your own model immediately?
Not necessarily. Often, the first step is to check whether an existing solution with suitable data and clear workflows is sufficient.
How are incorrect answers handled?
The process requires recognizable uncertainty, professional examination and a safe alternative route. Critical decisions must not be based solely on convincing-sounding outputs.