DRIMCO at Siemens Polarion4U Days in Milano

DRIMCO at Siemens Polarion4U Days in Milano

DRIMCO at Siemens Polarion4U Days in Milano - Next GenAI for Requirements Assessment

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At our recent workshop, led by our Managing Directors Dr. Pankaj Gupta and Dr. Bernt Andrassy, we showcased how our in-house GenAI platform DRIM is redefining the way industrial knowledge is digitalized.

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One of the key moments of the workshop was an exclusive sneak preview of our upcoming AI-Agent enabling ๐—ฃ๐—ฎ๐—ฟ๐—ฎ๐—บ๐—ฒ๐˜๐—ฒ๐—ฟ ๐—˜๐˜…๐˜๐—ฟ๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป and ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ฎ๐—ฐ๐˜๐—ถ๐˜ƒ๐—ฒ ๐—–๐—ต๐—ฎ๐˜๐˜€ to query requirements, standards and norms. Instead of manually browsing hundreds of pages, users will be able to interact with documents to identify overlapping or conflicting requirements instantly, reducing engineering overhead and compliance risks.

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The strong attendance and active participation once again demonstrated how crucial GenAI has become within the industrial world. As companies continue to digitalize operations, the need for trustworthy and scalable AI solutions is rapidly increasing. For many, GenAI is no longer a curiosity, it has become a competitive imperative.

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Key takeaways from the workshop participants:

  1. Status Quo โ€” Limited Real-World Adoption

Although many organizations experiment with GenAI, only a few currently apply it in a truly productive way. Most use cases are limited to basic document summarization or office productivity tasks. Industrial-grade applications - those involving requirement elicitation and knowledge reuse - remain largely untapped.

  1. Obstacles โ€” Trust, Integration, and ROI

Participants highlighted the challenges preventing broader adoption:

  • Limited trust in output reliability and traceability, unclear return on investment (ROI) or difficulty integrating GenAI into existing IT landscapes and workflows
  • These hurdles emphasize the need for explainability, governance, and action-oriented agents that can interface seamlessly with enterprise ecosystems.
  1. Expectations โ€” Smarter Navigation of Standards

There is a strong demand for AI systems that can:

  • Compare international standards, identify overlapping and conflicting requirements
  • This capability is vital for engineering teams that operate across global markets with increasing regulatory complexity.

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