For mid-size manufacturers navigating automation, AI deployment, and the discipline required to make both of them work.
Most AI decisions in a plant workflow are multiple choice. A classifier can only answer from your list, and it returns a probability you can set a review threshold on. An LLM still does the work whose answer has to be written.
Read →An AI workflow reads your supplier pricing, your customer’s drawings and your nonconformance history. A hosted model guards them with a contract. A model on your own hardware guards them with a firewall, and my bench shows what that costs on a desktop PC with a 12 GB graphics card.
Read →Your ERP vendor is shipping AI agents into the system of record, and the twice-yearly meeting where you used to approve changes is being retired. The control that matters is signing authority, and it doesn’t cover software that acts.
Read →Twenty-one of twenty-one checks passed and the approval link was dead. The automation failures that cost you are the ones that report success, and you will not find them by reading the design.
Read →When operators route around a system that works, they aren’t resisting it. They’re reporting a defect in it. Most rollouts have nowhere for that report to go.
Read →Waiting to clean the data before you automate is the loop that keeps mid-size manufacturers stuck in pilot. Automate the one workflow that matters, and it tells you which data was ever worth fixing.
Read →Manufacturers spent 2026 pointing AI agents at whole processes and watching them fail. The problem was never the model. It was automating a process that was never made reliable first.
Read →Enterprise AI budgets are blowing up. The root cause isn’t the token price. It’s the same failure this practice was built to prevent.
Read →Procurement’s GenAI adoption gap isn’t a technology failure. It’s what happens when individual productivity gains skip the process redesign that would make them organizational.
Read →Automation didn’t fail. Your operating model did. Three shifts changed what automation can do in 2026, but the fundamentals of data, process, and ownership still decide whether it works.
Read →The most effective problem solvers share a set of habits with professional artists. None of those habits involve waiting for inspiration.
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