A practical guide to semantic search and AI-supported knowledge utilisation
AI only becomes productive when context is taken into account.
In this white paper you will learn…
01
Why existing IT systems do not automatically make knowledge accessible.
ERP, PLM, QMS, MES, DMS and SharePoint store information – but they do not yet provide a reliable context for operational decisions.
02
Why traditional filing systems are reaching their limits
Folders, libraries and versions create order, but not meaning: what matters is which information applies to which machine, which plant or which product variant.
03
How a lack of contextual maturity hinders productivity and AI projects
Long search times, abandoned GenAI projects and poor data quality show that, without a structured knowledge base, the scalability of industrial AI will remain a challenge.
04
Why the context layer is key to productive industrial AI
Successful AI does not start with the model, but with semantically structured, verified and accessible knowledge components
From AI pilot to productiv knowledge architecture
Many AI projects fail not because of the technology, but because of a lack of context. Let us analyse together where your greatest potential for improvement lies.