In 1995 I enrolled in an Associate Diploma in Industrial Engineering at Hunter TAFE. I was 19. I thought I was signing up for something close to Industrial Design. I was wrong.
What I found instead was a discipline obsessed with one question: how do you make a system work better? Industrial engineering isn’t about products. It’s about processes. It’s about eliminating waste … time, energy, effort, cost … and replacing it with flow. With logic. With systems that almost run themselves.
I didn’t know it then but that diploma would become the most relevant qualification I own. Not because manufacturing is back. But because the principles I learned in that classroom are exactly what underpins the agentic AI revolution happening right now.
The technology caught up. The thinking was always there.
What Is Industrial Engineering, Really?
Most people haven’t heard of it. Those who have usually picture factory floors and clipboards. But the Institute of Industrial and Systems Engineers defines it simply: they figure out how to do things better. They engineer processes and systems that improve quality and productivity.
The tools of industrial engineering … process mapping, time-motion analysis, quality control frameworks, continuous improvement philosophies like Kaizen … were built for one purpose. To take a complex, repeatable system and make it faster, leaner and more reliable.
Sound familiar?
That’s precisely what agentic AI does. It takes a process … any process … and automates the steps, removes the friction and lets the system run with minimal human intervention. The agent is, in engineering terms, the optimised workflow made intelligent.
The Principles That Predicted This Moment
1. Process Before Tools
Industrial engineering taught me that you never reach for a tool before you understand the process. You map the steps. You find the bottlenecks. You identify what’s generating value and what’s generating waste. Only then do you decide how to fix it.
Most businesses approaching AI do the opposite. They reach for a tool first … a chatbot, an automation platform, a new piece of software … without ever questioning the underlying process. Then they wonder why it doesn’t work.
Building an effective agentic system starts exactly where industrial engineering starts. With the process.
2. Eliminate Waste First
One of the first things industrial engineering teaches you is the concept of muda … the Japanese term for waste. Wasted time. Wasted motion. Wasted resources. Any step in a process that doesn’t add value is waste.
Before you automate a process, you need to strip it of waste. Because automating a broken process doesn’t fix it. It just makes it break faster.
This is one of the most overlooked steps when businesses set up AI agents. They automate what they’ve always done rather than first asking whether what they’ve always done is actually worth automating.
3. Quality Assurance Is a System, Not a Checkpoint
In industrial engineering, quality isn’t inspected in at the end. It’s built into the process from the start. Quality assurance (QA) is about designing a system where quality is the natural output … not something you check for after the fact.
Agentic AI systems require the same thinking. The quality of output depends on the quality of the inputs, the instructions, the constraints and the feedback loops you design. A well-built agent produces reliable output not because you review everything it does … but because you engineered it to.
4. Continuous Improvement Is the Point
Kaizen … the Japanese philosophy of continuous, incremental improvement … is a cornerstone of industrial engineering. The idea is simple: you don’t have to get from A to B in one leap. You improve a little at a time. Consistently. Relentlessly.
This is exactly how agentic AI should be implemented. Not as a single transformation project with a start and end date. But as an ongoing practice of testing, refining and improving the system … one workflow at a time.
Businesses that treat AI as a one-off implementation will fall behind. Businesses that treat it as a continuous improvement practice will compound their advantage over time.
5. Just in Time Applies to Knowledge Too
Just In Time manufacturing … the idea of having exactly what you need, exactly when you need it … was a revelation in industrial production. It reduced inventory waste and increased responsiveness.
I’ve applied this same concept to learning for years. I call it Just In Time Learning. Rather than trying to know everything in advance, I learn what’s needed when it’s needed. The internet made this possible for individuals. AI is making it possible for entire business operations.
Agentic AI is Just In Time at scale. The right information, the right action, triggered at the right moment … without a human needing to initiate every step.
Why Designers and Creatives Are Uniquely Positioned
There’s an assumption that AI is a technical domain. That you need to be an engineer or a developer to build these systems. I’d push back on that.
Design thinking … the ability to observe a problem from the user’s perspective, map a journey, identify friction and prototype solutions … is structurally identical to what industrial engineers do. The language is different. The methodology is the same.
Designers understand flow. We understand the cost of friction. We understand that if a system is confusing or cumbersome, people find a way around it. These instincts translate directly into building agentic systems that actually work in the real world.
Add 20 years of running a small business … understanding operations, client experience, deadlines, quality control and cash flow … and you have a perspective on AI implementation that no amount of technical training alone can replicate.
The Shift I’m Making … and Why Now
For 20 years, psyborg® has been a design studio. Branding. Web design. Creative communication. The work has always been about solving problems visually … helping businesses connect with their audience through clear, intentional design.
But the tools of design are being automated. Not replaced … but fundamentally shifted. The role of the designer is moving from execution to direction. From making, to orchestrating.
That shift looks a lot like industrial engineering.
And so I’m bringing that part of my background … the part that was always there, quietly informing how I’ve built and run this business … back to the foreground. Because the businesses that thrive in the next five years won’t just be the ones that adopt AI. They’ll be the ones that engineer it properly.
Process before tools. Quality built in. Continuous improvement. Waste eliminated.
It was always part mind | part machine. Now the machine just does a lot more.
Where to Start
If you’re a business owner wondering where AI fits into your operations, start with the same question an industrial engineer would ask:
What are the repeatable steps in your business that consume time but don’t require genuine human judgement?
Map them. Clean them up. Then … and only then … look at how an agent can take them over.
That’s not a technical process. It’s an engineering one. And it starts with a conversation.
Ready to engineer your business for the agentic era? Start your evolution →
Daniel Borg
Creative Director
psyborg® was founded by Daniel Borg, an Honours Graduate in Design from the University of Newcastle, NSW, Australia. Daniel also has an Associate Diploma in Industrial Engineering and has experience from within the Engineering & Advertising Industries.
Daniel has completed over 2800 design projects consisting of branding, content marketing, digital marketing, illustration, web design, and printed projects since psyborg® was first founded. psyborg® is located in Lake Macquarie, Newcastle but services business Nation wide.
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