AI in Manufacturing — Capability / Function Map
Manufacturers keep asking the same question: AI sounds great, but where does it actually fit in my plant? The answer is scattered across vendor press releases, lighthouse reports, and customer stories that take days to read through.
Open the interactive map — a force-directed graph of 34 real-world AI deployments (BMW, Audi, Foxconn, Bosch, Siemens, Toyota, ABB, DeepMind and others), groupable two ways: by the 13 AI capability blocks (Plan, Review, Search, Generate, Forecast, Simulate, …) or by 8 manufacturing functions (Quality Inspection, Predictive Maintenance, Generative Design, Digital Twin, …). Click any node for the original problem, the AI technique used, the measured benefit, and a link to the source.
Agentic Evaluation
Everyone’s shipping agents. Almost no one agrees on how to tell whether they actually work. If you’re a software engineer or product manager trying to take an LLM agent past a flashy demo, the playbook is being written in real time - and scattered across blog posts, papers, and postmortems you don’t have time to read.
Open the interactive guide It distils 201 pieces of practitioner advice into 11 key challenges judge reliability, observability, the offline-to-production gap, cost and capacity, and more each linking back to the original 2025–2026 source. This is a snapshot as of 12 May 2026.
The 11 challenges aren’t arbitrary buckets. They sit on top of the structural constraints set out in Physics of AI the non-negotiable regularities every AI system has to design around so each piece of advice is anchored to a real engineering force, not just the latest blog post.