How AI creates real added value

1. AI creates measurable added value – when used correctly.

Dornbach emphasizes that it's not the hype that matters, but concrete, measurable results. Using practical examples, he shows how generative AI is already having a real impact today: “We were able to achieve time savings of around 80% with generative AI in approval procedures for complex investments.” Significant efficiency gains have also been achieved in industry and the banking sector – for example, through automated testing procedures in systems engineering or AI-supported compliance checks in accordance with the new EU DORA standard.

2. Humans remain responsible – “human in the loop” is a design principle.

AI must be explainable and traceable, especially in safety-relevant or regulated areas. Dornbach explains: "It is extremely important to have full transparency – through explainability and traceability. [...] The respective employee is in full control of this entire activity." Only when experts can understand, classify, and actively decide on AI results can trust – and real progress – be achieved.

3. Not every use case is a good AI use case.

A key message from the interview: Organizations must learn to identify the right use cases – and have the courage to reject unsuitable ideas. Dornbach explains: “We have to show that we really have a measurable impact – otherwise it will do more harm than good to the technology.”

Johann Dornbach paints a nuanced but clear picture: Generative AI will fundamentally change the world of work and many industries. It's not just about technology, but about courageous decisions, interdisciplinary collaboration, and a conscious approach to responsibility. His appeal: “We mustn't be afraid of failure. Just get started – no matter where. Small businesses, large companies, government agencies: if we don't get started, the wave will overwhelm us.”