Most manufacturers have run at least one Gen AI pilot by now – a copilot for engineers, a chatbot for shop-floor queries, an experiment with generative design. Very few have anything running in production at scale. The gap isn’t ambition or budget. It’s that manufacturing Gen AI has different constraints than the office-productivity use cases…
A manufacturing Gen AI pilot that impresses in a conference room demo and a Gen AI pilot that technicians actually use on shift are, more often than not, two different systems wearing the same name. The demo runs on curated examples. The shop floor runs on messy, real, contradictory documentation – and that gap is…
Both get pitched as “Gen AI for manufacturing.” Both can involve the same underlying language models. But RAG (retrieval-augmented generation against your own documentation) and Copilot (an AI assistant embedded in existing tools like Microsoft 365 or engineering software) solve different problems, and manufacturers who treat them as interchangeable options in the same budget line…
Generative AI entered most enterprise conversations as a productivity tool – a way to draft emails faster or summarise documents. Agentic AI is a different proposition entirely. Rather than assisting human tasks, agentic AI executes multi-step business processes autonomously, making decisions, using tools, and completing work across systems that previously required human coordination. For enterprise…
