The bottleneck lies before the prompt: when AI fails, it fails because of the input

Large language models such as ChatGPT have seemingly radically simplified the use of AI: one prompt is all it takes to generate fluid responses in seconds. However, this convenience masks the real bottleneck of successful AI applications in companies. The article published in INDUSTR by Prof. Dr. Heiko Beier shows that the actual benefit of AI is decided long before the prompt. It is not the choice or size of the model that is decisive, but the quality, structure and contextualization of the underlying data.

The article makes it clear why AI projects often fail, even though powerful LLMs are used: Corporate knowledge is often fragmented, historically grown and semantically ambiguous. Without clear terms, comprehensible sources, metadata, glossaries and consistent knowledge structures, even the most modern AI systems deliver linguistically plausible but technically unreliable results. The focus is therefore on the concept of “AI-Ready Data”, which expands data access to include comprehensibility, trustworthiness and context.

Prof. Dr. Beier shows why scaling AI applications in particular require clear reference structures and the early integration of specialist knowledge. Implicit empirical knowledge, which functions naturally in everyday working life, must be made explicit for AI in order to enable reproducible, explainable and measurable results. The article shifts the focus away from rapid AI output towards strategic groundwork on the knowledge foundation – and explains why this is precisely where the decisive lever for sustainable AI added value lies.