When Australian entrepreneur Dr Michelle Perugini co-founded her first AI company, she couldn’t write a line of code. Two decades later, her technology is outperforming human specialists in IVF clinics across 13 countries. Her edge wasn’t technical skills. It was knowing her field well enough to know what needed fixing.

Dr Perugini’s path into AI started in medical research before taking a sharp turn in 2007, when she co-founded ISD Analytics with her partner, who was commercialising AI technology out of the Department of Defence. Their company applied behavioural AI to model how consumers might respond to new products and government policies across a wide range of industries.

“I was initially doing more customer-facing and general business roles,” she says. “But co-founding that company gave me a lot of on-the-job learning. By applying AI across so many different sectors, I developed a really good understanding of where it was likely to have an impact.”

Founder, CEO, and Chair: Dr Michelle Perugini brings two decades of experience in medical research and AI commercialisation to the global stage

After selling ISD Analytics, Dr Perugini founded Presagen and built Life Whisperer, an AI tool that helps IVF specialists identify which embryos are most likely to result in a successful pregnancy. It sounds deceptively simple. But embryos look remarkably similar under a microscope, and even the most experienced embryologists are working with imperfect information. Trained on more than 100,000 patient cases across 13 countries, Life Whisperer proved 25 per cent more effective than human inspection alone.

The breakthrough didn’t come from mastering AI. It came from understanding the problem clearly enough to know AI could solve it.

“People think AI means you need to be a technical expert,” says Dr Perugini. “You don’t. It’s a really multidisciplinary field. You need understanding of the industry problem just as much as you need the technical experts. Anyone can play.”

She built her knowledge by surrounding herself with people who knew what she didn’t. “Through hiring AI experts and running companies in that space, I became more knowledgeable over time.” Not before. During.

That distinction matters. You don’t need to wait until you feel ready.

Dr Perugini’s approach can be distilled into five steps for anyone looking to bring AI into their work:

A: Audit your workflow

Start by looking at where your time actually goes. Identify tasks that are repetitive, slow or prone to human error. Where are decisions being made based on pattern recognition? Where is data sitting unused? Those are usually the highest-value places to start.

P: Problem first, tool second

Before you open a single app, define what you’re actually trying to fix. What does a good outcome look like? What information currently drives this process? A clear problem statement will do more for you than any tool.

P: Partner with experts

You don’t have to figure this out alone. Most workplaces have people in data, analytics or innovation who are waiting to be approached with a real problem. Find them. Come with a specific question, not a vague brief to “use AI somehow.”

L: Launch small

The best AI projects start as small pilots, not company-wide rollouts. Pick one workflow. Use a test dataset. Set a measurable goal. Prove it works before you scale it.

Y: Yield and iterate

Run the pilot, measure what happened, and adjust. Did it save time? Improve accuracy? Make things better for the people using it? Then go again. AI improves through repetition and refinement. So does your instinct for applying it.

The professionals who get the most out of AI aren’t always those who understand it technically. They’ll be the ones who see where it’s needed.

Let your industry knowledge be the foundation of your AI capability, using what you know to build fluency in what you don’t.