Podcast: Unlocking the future of manufacturing with generative and agentic AI

Key takeaways

AI is transforming manufacturing, with machine learning, generative, and agentic AI driving efficiency, automation, and smarter decision-making on the factory floor.
Generative AI creates innovative solutions, while agentic AI can act autonomously, making real-time decisions to optimize manufacturing processes and workflows.
AI-powered predictive maintenance is revolutionizing equipment monitoring, enabling manufacturers to prevent failures and improve overall system reliability.
As AI continues to evolve, ethical considerations and accountability in autonomous decision-making will become critical for manufacturers implementing advanced technologies.

You've heard the buzzwords: Generative AI, Agentic AI. But what do these terms really mean for manufacturing operations, and how are they different from the machine learning many are already familiar with? This episode of Great Question: A Manufacturing Podcast cuts through the hype. Join New Equipment Digest Editor-in-Chief Laura Davis as she breaks down the distinctions between key AI types, explores how each is impacting areas like product design, quality control, smart factories, and logistics, and addresses the critical questions around implementation challenges and strategic adoption. Listen now to understand the real potential beyond the buzzwords.

Below is an excerpt from the podcast:

There's no doubt about it, artificial intelligence is really shaping this decade. It's popping up everywhere, across practically every industry, making things more convenient and opening doors we probably haven't even thought of yet. Now, for manufacturing, we've already seen huge leaps in efficiency thanks to Industry 4.0 and automation, right? Well, AI is really the next big wave. In fact, Tom Coshow over at Gartner predicts that by 2028, AI agents—not people—will be making about 15% of routine business decisions all on their own.

That's a huge shift, especially for manufacturing. But to really grasp how AI will achieve this on the factory floor, we first need to get clear on what we mean by 'AI'. It's not just one thing. You've probably heard terms like 'Generative AI' and 'Agentic AI' thrown around, alongside the machine learning many are already familiar with. Let's break down what these different types are and how they fit into the manufacturing picture.

So the big terms you keep hearing are Generative AI and Agentic AI. And it's important to know they're not the same thing.

Before these newer types hit the headlines, many in manufacturing were already using AI, specifically machine learning. Think of machine learning as being focused mostly on prediction—it looks at data, finds patterns, and uses rules we give it to solve specific problems. You see it in predictive analytics, understanding language—that's natural language processing or NLP—and even some basic autonomous systems.

Then you have Generative AI. It also learns from data, but its main job is to create something new based on those patterns. This is the AI that writes text, generates images, maybe even composes music, or designs product models. Think ChatGPT, Claude, DALL-E, Midjourney—those tools that exploded onto the scene? That's Generative AI. It's great for mimicking human-like creative work and can be a real time-saver.

Tom Coshow over at Gartner predicts that by 2028, AI agents—not people—will be making about 15% of routine business decisions all on their own.

Now, Agentic AI is built to interact with the world around it, make decisions, and actually do things to reach a goal—all by itself, no human constantly telling it what to do. That's a key difference from Generative AI, which usually needs a human prompt to get started.

For Agentic AI, think about a self-driving car figuring out traffic based on its sensors, or a robot arm in a warehouse picking and sorting items on its own, or even a smart assistant that manages your calendar without you asking. These systems use sensors to 'see' or 'feel' their surroundings, algorithms to 'think,' and actuators to 'act'.

The core idea with Agentic AI is that it has a goal. It doesn't just react; it considers its objectives and makes choices to get there. This is the type of AI people sometimes joke about taking over because it can act independently. That independence naturally brings up serious questions about ethics and accountability. Like, who's responsible if it messes up? How do we ensure it stays aligned with what we want it to do? Developers are definitely grappling with this, building in safeguards to keep things on track.

So, another way to think about the difference is scope. Generative AI is usually focused on specific, well-defined creative tasks. Agentic AI often tackles bigger, more complex goals that might require multiple steps and constant adjustments along the way. The simple takeaway? Generative AI generates, Agentic AI acts.

So, remember that key difference: Generative AI generates, Agentic AI acts. And often, as we'll see, they can even work together. With that foundation laid, let's explore the interesting part: what does this look like in practice today? Where is AI already making a tangible impact on the manufacturing floor?

When you start combining AI—whether it's machine learning, generative, or agentic—with things like computer vision or natural language processing, you can speed up process improvements like never before. One of its superpowers is crunching massive amounts of data. And I don't just mean looking at numbers—I mean digging through everything, finding hidden patterns, learning constantly, figuring out what it all means, and then telling you what to do next, or even doing it itself, often in seconds. Plus, it's adaptable. Because it's based on algorithms, it can react to changes happening right now—data pouring in from sensors, machines, maybe your whole production line.

About the Podcast
Great Question: A Manufacturing Podcast offers news and information for the people who make, store and move things and those who manage and maintain the facilities where that work gets done. Manufacturers from chemical producers to automakers to machine shops can listen for critical insights into the technologies, economic conditions and best practices that can influence how to best run facilities to reach operational excellence.