Manufacturing’s Next Leap, Part 1: How AI Is Changing Plant Data Forever

The manufacturing world is standing on the edge of a massive shift. For the past 20 years, we’ve been wiring up our plants: adding sensors, building SCADA systems, implementing MES, and managing obsolescence. All of it has worked together, at least somewhat, to digitize the factory floor.

But if we’re being honest, most of that infrastructure has been built to look backward. It’s designed to generate reports, dashboards, and KPIs that summarize what already happened.

What we’re witnessing now is a fundamental rethinking of how people interact with plant data.

The old way required you to be an analyst. You’d dig through spreadsheets, navigate dashboards, filter charts, and hope you could find the answers in all the noise. That model doesn’t scale, and it can’t keep up with the speed of modern operations.

We’re now shifting from reactive reporting to real-time interrogation. From dashboards you browse to answers you ask for. And more importantly, answers that are delivered in context, on demand.

Whether it’s a shift supervisor on a tablet or a VP in a meeting, the insights they need can now come to them instantly, without digging, filtering, or waiting on someone else to build the report.

This is what makes Agentic AI, MCP (Model Context Protocol), and contextualized data storage so powerful. Together, they turn data into something people can use. Real answers, in plain language, for anyone who needs them.

Ask Your Plant Anything

Imagine this:

“What were the top five causes of downtime last shift? Build me a shift handoff report. What are all the production orders currently running on Line 3? Are there any risks to the schedule? Which assets are running below standard rate right now?”

Now imagine getting those answers instantly, in plain language, without hunting down the right people or opening multiple applications and reports.

That’s the value we’ve seen from building out MCP Servers (Model Context Protocol Server).

It’s an intelligent layer that understands:

• What data exists
• Where it lives
• What it means
• And how to answer operational questions using it

It works by interfacing with your MES and industrial historian. And with the right architecture and digital strategy, those use cases can extend across nearly any service or application within the business through an Industrial DataOps platform.

This isn’t just a new interface. It’s a new way of thinking about data access in manufacturing.

AI Can’t Save You from a Data Mess

Here’s the part no one wants to hear.

If your data foundation is weak, if your tags are scattered, your events lack context, or your plant structure isn’t clearly modeled, then AI won’t help. In fact, it might make things worse.

Natural language models don’t magically understand your business. They need clarity. They need structure. They need context.

That’s why the foundation matters more than ever.

Connect. Collect. Store. (And a Nod to Those Who’ve Led)

We’re proud to be part of a growing community that believes the future of industrial intelligence starts with getting the basics right.

We’ve been applying these principles for years, and we’re grateful to those who’ve helped articulate them clearly, like Walker Reynolds, who popularized the simple but powerful mantra:
Connect. Collect. Store. (Analyze, Visualize, Find Patterns, Report, Solve.)

• Connect your plant to a Unified Namespace grounded in ISA-95
• Collect high-resolution, contextualized data from your processes
• Store it in a way that’s explainable, queryable, and scalable

Without this discipline, no AI solution will live up to the hype. You’ll get biased answers, partial insights, or nothing useful at all.

The Window Is Closing Faster Than You Think

Falling behind used to be a slow process. You’d see it over five, maybe ten years.

Now? It can happen in two.

If your competitor is building a strong data foundation today, and you’re still managing by spreadsheet or tribal knowledge, you won’t just fall behind, you’ll lose your ability to compete.

What’s Next?

In Part 2, we’ll break down exactly how to build the data foundation that makes this transformation possible. We’ll walk through Unified Namespace design, contextual collection, and how to prepare your data for on-demand answers.

If you’re serious about bringing real-time awareness and decision-making to your operations, you won’t want to miss it.

Written by: Bruce Slusser