Get Your AI Terminology Straight: A Manufacturing Leader's Guide

Walk into almost any operations meeting today and you’ll hear it:

“We should use AI for this.”
“Can we plug in an LLM?”
“Let’s add a copilot.”

In a recent internal meeting, one of our business partners—someone with deep technology and operations experience—paused and said:

“This might be a stupid question… what is an LLM?”

It stopped the room—not because it was a bad question, but because it was exactly the right one.

Later that same week, sitting in an Irish pub for dinner, I overheard multiple tables talking about AI. The terms were familiar—but often misused or misunderstood.

That’s when it clicked: This misinformation isn’t just happening inside companies—it’s everywhere.

At the same time, two billionaire entrepreneurs with very different worldviews, Mark Cuban and Elon Musk, are both emphasizing how significant this shift is.

Cuban has warned that companies will split into those that are great at AI and those that aren’t. Musk has described the pace of change as a “supersonic tsunami.”

In some ways, this moment feels familiar. I can remember sitting in an executive meeting in the late 1990s discussing a then-small startup called Google—and suggesting it would fundamentally reshape search and the internet. A few experienced leaders in the room laughed it off at the time.

Looking back, it’s a reminder that early signals often sound uncertain—or even unlikely—until they’re obvious in hindsight.

The pressure is real—but so is the confusion.

I present here a practical, 101-level guide to the AI vocabulary showing up in manufacturing and supply chain environments—so leaders can better understand what’s real, what’s useful and how to lean in and apply these capabilities to create real operational advantage across ERP systems, supplier communications and operational data.

Start Here: AI Is Not One Thing

One of the biggest misconceptions is that “AI” is a single capability. In reality, it’s a collection of tools.

In manufacturing environments, most of what teams are referring to today falls into a few categories:

• Language-driven systems (reading, writing, summarizing)
• Pattern recognition (forecasting, anomaly detection)
• Decision support (recommendations, next steps)

Understanding which category you’re actually dealing with is the first step to cutting through the noise.

Machine Learning: the Part Many Companies Already Use

Before copilots, agents and LLMs, many manufacturers were already benefiting from machine learning.

In plain English, machine learning uses historical data to detect patterns, improve predictions and support decisions.

In real-world operations, that includes:

• Demand forecasting
• Inventory optimization
• Predictive maintenance
• Quality and anomaly detection

This matters because it separates today’s AI conversation into two tracks: what’s new and what’s already delivering value. Both matter—but they are not the same.

LLM (Large Language Model)

When your team says “LLM,” they’re referring to systems that can read and generate human-like text.

What it actually does: Processes and generates language based on patterns learned from large datasets.

Where it shows up:

• Summarizing supplier emails or RFQs
• Drafting customer responses
• Translating ERP data into plain language

What to watch for: LLMs don’t “know” your business unless connected to your data. Without that context, they can sound confident—but be wrong.

Copilots

“Copilot” is one of the most overused—and misunderstood—terms right now.

What it actually means: A layer that sits on top of your systems (ERP, CRM, email) to assist users in real time.

In practice, it’s useful for:

• Suggesting responses inside email
• Helping navigate ERP workflows
• Recommending next steps

A copilot doesn’t replace your system—it improves how people interact with it.

Agents

Agents move from assisting to acting.

What they are: Systems that can take a goal and execute steps to achieve it.

Examples:

• Monitoring inventory
• Detecting shortages
• Reaching out to suppliers
• Proposing or initiating reorders

Reality check: Most agent-based systems are still early. They require strong guardrails and tight integration to work reliably in production environments.

Embeddings (The Quiet Connectors)

Embeddings convert your company’s data into a format AI systems can understand and search.

That’s what allows AI to:

• Reference ERP data
• Search internal documents
• Provide context-aware responses

If LLMs are the “brain,” embeddings are how that brain connects to your business.

Where This Actually Helps

The goal isn’t to understand AI—it’s to apply it.

In manufacturing and supply chain environments, early value tends to come from:

• Reducing communication friction
• Making systems easier to use
• Improving access to operational data

These are practical improvements—not massive transformations.

Final Thought: Lean In—This Is a New Set of Tools

For supply chain leaders, the goal is not to become AI experts overnight. It’s to understand the language well enough to ask better questions and identify where these tools can create real advantage.

In many ways, we’ve seen this kind of shift before. Just as the internet and modems expanded communication beyond landlines, AI—what I increasingly think of as digital intelligence—represents the next evolution of tools in the toolbox: faster, broader and more deeply integrated into daily operations.

Not as a replacement for people or systems, but as a new layer of capability that helps both work smarter together.

The opportunity here is not to resist the change, but to lean into it—experiment, learn and find practical ways to use these tools as force multipliers across your existing systems and workflows.

The companies that benefit most won’t be the ones chasing every new label. They’ll be the ones that apply the right tools deliberately, in the right places.

And that starts with understanding what your team means when they say the words.