The Hidden Knowledge Gaps Holding Back Industrial AI Transformation – with Dr. Nikita Golovko, Siemens

Shownotes

"In 80 percent of cases, you need to stay in the field of classical software architecture and classical machine learning. It's too sexy to go to GenAI, but it's rather easy to make this mistake and to build a rather unstable system."

In this episode, Carsten Bange and co-host Florian Bigelmaier talk with Nikita Golovko from Siemens about the critical gap between data readiness and AI implementation in industrial settings. Nikita shares valuable insights on building a solid data and knowledge infrastructure that supports both classical AI and generative AI approaches. The conversation explores the right balance between traditional machine learning and GenAI, the importance of semantic layers, and the organizational challenges of implementing AI projects successfully.

This episode is part of our DATA Festival series, featuring speakers from our upcoming event in Munich. Stay tuned for more exciting insights from industry leaders sharing their cutting-edge projects and innovations.

Catch Nikita on stage this June at the DATA Festival Munich -- one of Europe's leading events for data, AI, and technology leaders.

Save your spot now:<[https://hubs.li/Q044Z4qG0>](https://hubs.li/Q044Z4qG0>)

Dr. Nikita Golovko on LinkedIn.

Carsten Bange on LinkedIn.

Florian Bigelmaier on LinkedIn

BARC on LinkedIn.

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