If you’ve ever found yourself watching late-night TV, chances are you’ve stumbled into a commercial break that felt more like a rollercoaster of persuasive shouting than actual advertising. You know the type: “Just wait, there’s more!” “Call now!” “Revolutionize your life—for four easy payments of $19.99!”
At their core, these infomercials thrive on hype. They dangle promises of life-changing convenience without offering much insight into how their miracle gadget actually works.
These days, conversations around artificial intelligence (AI) and machine learning (ML) in manufacturing are starting to feel a little like those infomercials. While there’s no shortage of buzzwords and predictions, there are very few grounded conversations about what AI can reliably do for you right now. And—more importantly—what it really takes to see value and benefit from these technologies.
So. To help cut through the noise, I wrote this blog. No fluff, no gimmicks—just a practical look at where AI is delivering real value on the shop floor, what kind of data infrastructure it needs to succeed, and how to prepare your operations for what’s coming next.
Table of Contents
Predictive Maintenance
Predictive maintenance (PM) has been a staple in manufacturing for years. Our MES platform, TrakSYS™, for example, has long helped teams monitor equipment performance and manage maintenance tasks through real-time data collection and alerting.
AI takes things a step further. It enables more accurate failure predictions by analyzing subtle patterns in historical and real-time data. While TrakSYS tracks the “what” and “when” of equipment activity, AI models can detect anomalies, recommend maintenance before failure occurs, and refine schedules over time by learning from evolving patterns.
Quality Control and Inspection
Using AI-powered computer vision, manufacturers can replace manual quality checks with smart, camera-driven oversight. These systems learn what a product is supposed to look like—its color, shape, dimensions, and texture—and instantly flag deviations on the line.
The result? Continuous, real-time inspection that complements operator input and ensures consistent product quality without slowing production. Integrated with TrakSYS, this layer of visual intelligence supports a closed-loop quality control system that alerts, responds, and adjusts on the fly.
Supply Chain Forecasting
Supply chain complexity isn’t going away. According to Parsec’s 2024 State of Manufacturing Survey, 71% of North American manufacturers cited supply chain disruptions as “very” or “extremely” challenging.
AI-powered demand forecasting helps teams mitigate those risks. By analyzing historical sales, supplier performance, macroeconomic data, and more, AI models can generate dynamic forecasts that adapt to change—helping you plan inventory, anticipate bottlenecks, and prepare for volatility before it disrupts production.
Digital Twins
Digital twins create real-time, virtual replicas of physical systems. Powered by AI and MES, these replicas allow teams to simulate process changes, visualize workflows, and identify inefficiencies—all without disrupting operations. Think of it as your factory’s shadow: everything your plant is doing, mapped digitally and fed by real-time data, so you can test, learn, and improve before committing to change.
The combination of MES aggregation and AI-driven simulation unlocks smarter, faster decision-making and continuous improvement with significantly lower risk.
Robot and Cobot Automation
Industrial robots and collaborative robots (cobots) are becoming more adaptive thanks to AI integration. No longer limited to rigid programming, AI-enabled bots can learn from their environments and from human collaborators, adjusting movements, responding to variability, and avoiding downtime with minimal intervention.
Computer vision can enhance cobot capability even further, allowing them to handle quality control tasks or detect safety hazards. With AI, these machines shift from repetitive workhorses to intelligent partners on the shop floor.
Laying the Groundwork for AI Adoption
While everything I’ve outlined above is indeed possible today, let’s pause for a second. At a foundational level, AI is software. Sophisticated software, to be sure, but software nonetheless. Now, what is the singular element that any viable software solution needs to execute its functions consistently and reliably? Data.
However, not all data is created equal. AI doesn’t just need numbers—it needs meaning. And meaning only comes from context.
In practical terms, raw machine data—say, a temperature reading—only becomes valuable when it’s associated with which machine, on what line, during which shift, in what conditions, and what it was producing. This is contextualized data: structured, tagged, and understood in the broader narrative of your operation. It’s the difference between “the oven was hot” and “Oven #3 on Line 5 exceeded its upper temperature limit during Batch 204 while producing SKUA220, 15 minutes after a maintenance event.”
That’s the kind of clarity AI needs to make decisions. And it’s the kind of data structure TrakSYS is built to create—collecting, labeling, storing, and distributing high-integrity data that’s usable at every level of your operation.
Without that context, AI is flying blind. With it, AI becomes a powerful extension of your team’s decision-making.
Preparing for Future AI Capabilities
What we’re seeing now is just the beginning. The next wave of AI in manufacturing will include natural language interfaces, real-time predictive models, and intelligent decision-support tools.
TrakSYS IQ is a perfect example of what’s next. Designed to integrate AI seamlessly into your MES environment, TrakSYS IQ will allow users to interact with operational data using natural language, surface contextualized insights, and even generate dynamic visuals on demand. These are features designed to make AI not just more powerful, but more accessible. The future of AI in manufacturing will belong to those who can democratize it across their teams, and that begins with a strong data foundation.
With all of this said, it’s important to remember that AI isn’t a shortcut: It’s an accelerator. An accelerator that is wholly dependent on the quality of its foundation.
Have more questions about how to set your organization up for success with AI? Contact us today.
FAQ
Can I implement AI in my facility if I’m still using legacy systems?
What’s the difference between raw and contextualized data?
Raw data is unprocessed: think numbers, readings, or logs. Contextualized data adds meaning by linking it to equipment, time, process steps, and outcomes. AI needs that context to analyze patterns, deliver insights, and make decisions you can actually trust.
Where should I start if I want to prepare for AI adoption?
Start with your data architecture. Make sure you’re collecting clean, consistent data and organizing it in a way that’s accessible across systems. From there, an MES can help centralize and structure it, setting the stage for AI integration.
Is AI in manufacturing overhyped?
Only when it’s pitched as a plug-and-play miracle. AI is powerful, but it’s not magic. When paired with strong data practices and a connected infrastructure, it becomes a highly effective tool for improving maintenance, quality, forecasting, and more.