The manufacturing data space represents multi-modal and multi-structural data. Over the past years, the majority of research has been focusing on analysis of structured data captured from IT or operational technology (OT) systems. However, the industrial knowledge-base encompasses unstructured data sources, in particular text, which features documented human and organizational knowledge. Yet, the body of knowledge in manufacturing research and also industrial innovation lag behind in untapping the potentials of textual data. This essay aims at deepening insights into technical language processing (TLP), as an emerging field of research in manufacturing. TLP is a human-in-the-loop and AI-enhanced approach to tailor methods and tools in natural language processing (NLP) using manufacturing data. The essay discusses the foundations of TLP and NLP, with a special attention to the emergence of large language models (LLMs) and associated challenges for industrial applications.