AI in industry in 2026
Industry has always dealt with complex technical documents: CAD drawings, BOMs, supplier spec sheets, production orders, quality sheets, incident reports. Historically, this data stays in silos, read by hand, re-keyed from tool to tool. Multimodal LLMs shift the picture: reading a drawing, extracting a BOM from a PDF, summarizing an incident, querying a technical manual in natural language have become reliable operations.
On operations, plants and workshops hold obvious goldmines: predictive maintenance, mobile technician support, shift summaries, automation of ERP/MES/SCM workflows that today demand expensive manual re-entry.
Typical use cases
- Automatic BOM and technical specs extraction from supplier PDFs, with human validation
- Tablet/mobile technician assistant that answers on machine docs, procedures and failure history
- Automatic intervention and end-of-shift report summarization to feed reporting
- Semantic search across quality, safety, ISO and regulatory reference bases
- Smart routing of customer or supplier requests to the right responders, with pre-drafted replies
Stack and specific constraints
Network is often limited on the shop floor: plan for offline mode, deferred sync and edge-runnable models. ERP integration (SAP, Dynamics, Sage), MES and PLM are critical — often through connectors or flat files, sometimes on aging systems. Sensitive technical data (IP, patents): I strictly compartmentalize and favor self-hosted models for critical content. Typical stack: TypeScript, Python for document pipelines, Postgres, pgvector, Cloudflare or on-prem depending on the constraint, multimodal models like Claude or Qwen-VL for blueprint reading.
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