AI in Manufacturing: From Curiosity to Capability

Most manufacturing leaders don’t wake up thinking, “We need AI.” They wake up thinking about late orders, stretched teams, disconnected systems, and how to scale without burning people out.

AI usually enters the conversation quietly — as a way to reduce friction, improve visibility, or give teams better information faster. The companies that get the most value don’t jump straight into complex automation. They build confidence step by step, using AI where it fits naturally into how work already gets done.

Below are the different implementations of AI and how they typically unfold…

LLMs (Large Language Models): The Brain

It often starts with information overload or unanswered questions.

A manufacturing company may have years of documentation, SOPs, maintenance logs, customer emails, and internal reports — all valuable, but hard to use when people need answers quickly. Searching through folders or asking the same questions repeatedly slows everything down.

This is where an LLM comes into play. LLMs make AI conversational and expressive.

An LLM can read through large volumes of text and make sense of it. It can summarize a long procedure, pull key points from reports, or help draft communications that used to take hours. Instead of hunting for information, teams can ask a question and get a clear, usable response.

At this stage, AI isn’t making decisions or running processes. It’s helping people think faster and work smarter — like adding an extra analyst who never gets tired.

Bots: The Conversation Layer

As teams get comfortable using AI to find information, the next challenge becomes access. Bots allow for easy access to LLMs.

Not everyone wants to open another system or dig through files. People want answers in the flow of their day — on a portal, inside a tool they already use, or through a simple interface. You might commonly see bots used in a customer service capacity as pop up bots on websites. 

This is where bots shine.

A bot allows employees, customers, or partners to ask questions and get immediate answers without leaving their workflow. For example, a technician might ask how to complete a task, a customer might check the status of an order, or a partner might look up a service offering — all without emailing back and forth.

Bots don’t run the business. They simply talk with LLMs to reduce interruptions, speed up communication, and free teams from answering the same questions repeatedly.

Agents: The Doer

As confidence grows, some organizations start asking a different question: Can AI help us do the work, not just talk about it?

This is where AI agents come into the picture.

An agent can work toward a goal. Instead of just explaining how to compile a report, it can gather data from multiple systems, organize it, and present it in a usable format. Instead of pointing out a problem, it can monitor conditions and flag issues before they escalate, and even suggest solutions you may not have thought of.

For manufacturers, agents might support tasks like preparing operational summaries, monitoring inventory levels, coordinating information across departments, or provide alternatives to problems you are facing.

Because agents can take action, they require clear boundaries, strong oversight, time to train, and a little more trust. Not every situation calls for this level of autonomy, and many businesses intentionally wait until they’ve built trust in simpler AI tools first.

AI Apps: The “Product”

With an AI enabled app, AI stops feeling like a separate initiative and begins to feel like part of the system. All companies will eventually adopt building or using AI apps.

AI apps are complete software solutions that use AI behind the scenes to improve how work gets done. They’re designed around specific business needs — quality control, maintenance planning, forecasting, customer support — and integrate to existing systems.

From the user’s perspective, they’re just software tools that work better than what came before. The complexity of AI stays under the hood, while the benefits show up in faster decisions, smoother processes, and fewer surprises.

This is often where manufacturers see the most value: AI that supports the business without forcing teams to change how they think about their jobs.

How These Pieces Come Together Over Time

These AI capabilities aren’t a checklist — they’re a progression.

Many manufacturing companies start with AI that helps people find and understand information. Over time, they add conversational access, targeted automation, and eventually purpose-built applications that support growth.

Not every company needs every layer. The most successful implementations focus on solving real problems, not chasing advanced technology for its own sake.

The right question isn’t, “How advanced is our AI?” It’s, “Is this helping our people and our processes work better?”

The Bottom Line…Adopt or Get Left Behind?

As we move into 2026, AI is becoming less about experimentation and more about execution.

Our approach when it comes to assisting our clients with AI adoption is to start small, integrate with intention, and build on what already works. This leaves companies better positioned to scale, adapt, and stay competitive. 

Understanding how different types of AI fit into the bigger picture helps leaders:

• Set realistic expectations
• Reduce risk
• Build momentum over time
• Invest wisely
• Integrate AI into their tech strategy

At Swip Systems, we see AI as one part of a broader technology strategy, not a silver bullet, but a powerful tool when applied with purpose. The goal isn’t to replace people or processes. It’s to support them, strengthen them, and help businesses move forward with confidence.