Key Takeaways
- AI in Manufacturing is becoming a core part of manufacturing execution systems (MES), with industry-specific models expected to gain ground over general-purpose AI.
- Production-ready AI remains uncommon, only about a third of MES vendors can demonstrate AI operating in real manufacturing environments.
- Effective manufacturing AI depends on a strong MES foundation with live, contextual production data rather than a chatbot layered over dashboards.
- Manufacturers measure success by how well AI performs everyday tasks like maintenance support, shift handovers, document comparison, and scrap-trend detection.
- Trust is essential for manufacturers who need transparency, governance, explainability, data protection, and control as AI becomes a tool for better and faster shop-floor decisions.
AI in Manufacturing: Moving From Hype to the Shop Floor
Sometime in the last year or two, a lot of us changed how we look things up. Instead of typing keywords into Google, scanning several blue links and piecing the answer together ourselves, we started just…asking, in plain language. And getting an answer back.
It’s a small change in habit, but one that makes all the difference in who gets the answer. You no longer need the right keywords, the right site, or the right person to ask. You need a clear question and that’s within reach for most of us.
That shift is only now reaching the factory floor, and it’s the part of Gartner’s latest Market Guide for Manufacturing Execution Systems that stuck with me most. The message is clear: AI assistants aren’t an optional add-on to MES anymore. They’re becoming part of what MES is.
Gartner expects that by 2027, more than half of enterprise generative AI models will be built for a specific industry or function, not general-purpose, which favors MES providers who actually know manufacturing, not just AI.
But Gartner is just as clear about the catch: only about a third of MES vendors can show AI actually running in a real production environment today. Talking about AI is easy. Getting it onto the shop floor, in a way operators trust and use every day, is the hard part.
So, I was glad to see Critical Manufacturing recognized again this year in the Market Guide among the most relevant MES providers in the world, and among the most advanced on AI. But the recognition isn’t really the point. What it comes down to, simply put: we built the platform so AI has real manufacturing context to work with, not just a chatbot sitting on top of a few dashboards.
AI in Manufacturing Needs a Strong MES Foundation
Manufacturers are interested in AI, and they’re right to be cautious about it. What they need is AI they can trust and actually use day-to-day, not a demo. It has to solve real problems for the people on the floor: operators, engineers, supervisors, and production managers. If it doesn’t, it doesn’t belong there.
That’s the bar we’ve built toward, and most of the work is underneath the AI, not in it. Our MES already handles genuinely complex production environments, tracking what’s happening on the floor down to the individual step, catching deviations as they occur, and working off live data rather than a snapshot from an hour ago.
The AI sits on top of that. And we’ve added it in steps rather than all at once: first machine learning and generative AI to help people make sense of their data, then more agentic AI that can take on parts of a task itself, reasoning over what’s happening, not just fetching it. That’s roughly the direction Gartner sees the market taking, which is reassuring, because it’s the one we’ve been on for a while.
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True Innovation Lies in Data Access, Not AI
The most interesting shift I’ve seen isn’t what AI can compute. It’s who gets to ask the question in the first place. For years, getting an answer out of MES meant knowing your way around a complex interface, waiting on a dashboard someone had built in advance, or asking a specialist to pull the data for you. The information was there. Getting to it wasn’t.
That’s what AI Copilots are for: not by bolting a chat window onto MES, but by letting people interact with it, using plain language. With the Analytics Copilot, users can query production data and build charts and dashboards themselves, no specialized technical skill required. A production manager can ask “What were the main causes of downtime yesterday?” and get a real answer in seconds instead of raising a ticket and waiting in a queue.
The MES Copilot does the same for operators inside the MES they already use, helping them find information, follow procedures, and get on with the job on their own – working from real, trusted data, not guesswork. The value isn’t the AI. It’s what stops being a bottleneck once the AI is there.
The Real Test Comes after the launch of AI
The hardest test for AI in manufacturing isn’t the proof of concept. It’s whether it’s still being used three months later, on an ordinary shift, with no one watching.
That’s the bar we hold our own use cases to: summarizing documents, helping with maintenance by digging through manuals, building shift handover summaries that pull together performance data and logbook notes, comparing document and BOM versions automatically, spotting scrap trends.
None of these will win an award for ambition. But every one of them saves real time, for real people, every day. When AI works this way, it doesn’t replace expertise on the floor. It removes the friction around it.
None of It Works Without Trust
None of this matters if manufacturers can’t trust it, and Gartner is right to put transparency, governance, and observability at the center of the discussion. Manufacturers need to know how AI is being used, where their data goes, and whether a recommendation can be explained and checked, especially in regulated, high-stakes environments.
For us, that’s built into how the system works, not written into a policy and left there. Customer data is never used to train third-party models, and it’s only ever processed at the moment an AI request is actually made. Manufacturers shouldn’t have to give up control to get innovation. They should have both.
Where AI in Manufacturing Is Heading
The Gartner recognition is a nice marker, but it’s not really the point. The point is that MES has outgrown its old job description. Execution, visibility, and control, those still matter, but they’re the floor now, not the ceiling. What’s being built on top of that floor is, in the end, the same small shift we started with, from hunting for an answer to just asking for it, finally reaching the shop floor.
The win isn’t just a cleverer system. It’s the operator, the engineer, the production manager who gets the answer they’d have waited days for before, and who can make a better decision because of it, not just a faster one. That’s the shift worth caring about. Not smarter software for its own sake, but more people on the floor getting the answer they need, when they need it.
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