Anti Pattern

#20 I, graph

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In this episode, Michael and Christian pull back the curtain on the hard truths of applying AI in the industrial domain. It’s not just about flashy demos — it’s about the messy, practical, and often painful hurdles that trip up even the most ambitious initiatives.
We explore five key pain points:

  • IP protection — How do you safeguard proprietary algorithms, data and models when they’re feeding into shared platforms or cloud services?
  • Hallucinations — Why does AI still confidently make stuff up, and what risk does that pose when you’re controlling physical assets instead of just chatting?
  • Reasoning – how far did we come with AI that is not just memorizing, but actually reasoning?
  • Costs — From infrastructure to data preparation to ongoing model governance, the price tag for “just throwing in AI” is far higher than many expect.
  • The need for machine-readable data & knowledge graphs — AI only works when your assets, systems and processes speak a common language. We dig into why building that foundation (a ‘graph’ of knowledge) is arguably more important than the algorithm itself.

    • Together, Michael and Christian unpack stories from the field, call out common anti-patterns, and suggest what must go right before the next generation of industrial AI can deliver real value. Whether you’re an executive, a system engineer, or just curious how AI actually scales in manufacturing, this episode gives you a lens on the un-glamourized part of the journey.

    Tune in and ask yourself: is your data ready to speak graph? Are your IP walls strong enough? And do you really know what your AI is doing when it “thinks”?

    Der Beitrag #20 I, graph erschien zuerst auf Elevating Patterns.

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