What the assessment clarifies
Many AI ideas sound plausible at first: automate a manual task, classify images, summarize documents, search internal knowledge, or detect patterns in process data. The difficult question is whether the idea is technically feasible, valuable enough, and realistic with the data and constraints you actually have.
I review the use case from a research-informed and practical perspective. The goal is not to sell a specific platform, but to make the next decision clearer: continue, prototype, simplify, collect better data, or pause.
Inputs
Use case and business context
What should change if the AI system works? Which decisions, costs, quality issues, or manual steps are involved?
Data and systems
Which data exists, where does it live, how reliable is it, and what privacy or integration constraints matter?
Success and risk criteria
What would count as good enough for a prototype? Where would model errors, hallucinations, bias, or explainability become a problem?
Outcome
Stop/go recommendation
A clear view on whether the use case is ready for the next step.
Risk map
Technical, data, evaluation, and organizational risks made explicit.
Prototype path
A small next experiment if the idea is concrete enough.
Alternatives
Simpler workflows or non-AI options when they fit better.
Wann eine KI-Potenzialanalyse sinnvoll ist
Eine KI-Potenzialanalyse lohnt sich, wenn bereits eine konkrete Idee oder ein wiederkehrender Prozess existiert, aber noch unklar ist, ob ein KI-System wirklich die richtige Lösung ist. Das gilt besonders bei internen Wissensprozessen, visueller Inspektion, Dokumenten-Workflows, Computer Vision oder ersten Automatisierungsansätzen.
Wenn die Idee noch sehr offen ist, kann ein kurzer Feasibility Call reichen. Wenn bereits Daten und ein klarer Prozess vorhanden sind, ist oft ein kleiner Proof of Concept der nächste sinnvolle Schritt.