Laurence Moroney is one of the world’s leading artificial intelligence speakers, known for making cutting-edge AI accessible, ethical, and impactful. As Lead AI Advocate at Google and the driving force behind TensorFlow’s global education initiatives, Laurence stands at the intersection of technology and transformation—empowering organisations to unlock AI’s full potential.
A celebrated author and educator, he is also a highly regarded voice among leadership speakers and technology speakers, with a mission to bridge the gap between technical advancement and human-centred progress.
In this exclusive interview, Laurence shares powerful insights on the evolving role of AI in business, healthcare, and society—and why strategic leadership will be essential in navigating the future responsibly.
Q: From your work at Google, what have you learned about the practical value of artificial intelligence in driving business efficiency and culture?
Laurence Moroney: “I think, pretty much similar to what I was saying, is allowing people who are already skilled to become more efficient and move up that value chain—allowing us to contribute much better to the business while also potentially giving us a much better experience at work.
“I mean, nowadays it’s pretty common to talk about things like work-life balance—20 years ago it wasn’t. And nowadays, because of better business processes, because of maybe artificial intelligence beginning to find its way into the enterprise and driving those efficiencies, valued staff and valuable staff can be treated better than they were.
“So, you know, that’s one of the things that it’s taught me. And then the second thing I think that it’s taught me was a really interesting scenario.
“At Google, we are famous for the perks that we get, and one of those perks is free food. Now, if you’re a large company and you’re giving free food out to employees, you’re going to be under the magnifying glass for food wastage.
“So it was a decision made at a very early stage that machine learning would be used there to try and figure out how we can be much more efficient and productive and reduce waste in the food that we serve. And that has become a part of it. So the algorithm is like saying, well, if there’s barbecue night, there’s probably going to be more people showing up, you know, and things like that.
“As a result, the food waste that we’ve had—I can’t talk about across the company—but I know in the café in the office that I’ve worked at, which is in Kirkland in Washington, the percentage is like, you know, less than 0.1% wastage, if I remember this stat right.
“And that has been driven by the fact that artificial intelligence and machine learning has been applied to that relatively mundane but very important task. And, you know, if that’s the kind of thing that can be done for that kind of task, what can we do for other, bigger, more important tasks?”
Q: In what ways can AI meaningfully support healthcare systems and sustainability efforts—beyond the usual expectations of automation and diagnostics?
Laurence Moroney: “Great question. So, I’ll start with healthcare.
“I think when people think about AI and healthcare, they generally think about AI doing the healthcare itself first and foremost. That may not be the best way of thinking about it. I mean, it’s still useful there—but let me first talk about, going back to the theme I was talking about earlier on, about driving efficiency.
“Healthcare systems, particularly in the UK—you’re lucky enough to have a National Health Service. It’s a government-run body, it’s funded by taxpayers. As you have an ageing population, and as higher needs for healthcare are needed, it’s political suicide to raise taxes to pay for that. So the government’s in a little bit of a bind.
“One of the things they want to do is drive efficiency. I once spoke with the manager of an A&E department in the UK, and she shared how much effective waste they have to do in order to be prepared for the worst.
“So, Saturday night and Friday night are the nights when you have most people coming into A&E—because of, like, post-bar brawls and stuff like that.
“They have to oversubscribe to the number of doctors that are available. They have to have extra doctors on call just in case there’s a surge. A lot of medical equipment expires, so they have to always have enough on hand for the worst-case scenario. But then, some of that expires and is wasted.
“Here’s an example of where, by driving efficiency—by using AI and machine learning to be able to better predict based on historic data—they might be able to save some of that money and save some of that wastage, while still hitting the service level that they need to be able to hit to provide proper care for people.
“I don’t want to suggest in any way cutting the proper care for people, but I want to suggest maybe here’s a way that the waste could be cut. But of course, it can also be used in healthcare.
“There’s one project that I always like to share called diabetic retinopathy. Diabetic retinopathy is the world’s leading cause of blindness. But it can also be cured or prevented by early screening. The problem is, in many countries, there aren’t enough doctors to do that early screening.
“So, we at Google worked together—we hired a bunch of doctors—and we worked with some folks in India, where we got, I think, about 30,000 retina scans. From that, we had the doctors label those retina scans to be everything from no diabetic retinopathy all the way up to serious diabetic retinopathy.
“Then we trained a computer vision system on that. And what the computer vision system was able to do was replicate how the doctors had labelled this—only more efficiently than a human doctor could. So now there’s a way that the existing human doctors could be augmented and made more efficient by the use of a system like this one.
“I don’t want to suggest in any way replacing the doctor—because the human still needs to be in the loop—but now, given that there’s a shortage of doctors, what if a doctor can be made more efficient by being able to diagnose 30 people in a day instead of 10 people in a day? Then that shortage of doctors, and the damage caused by that shortage of doctors, can be alleviated.
“So I think things like that are where AI and machine learning can be enormously helpful in healthcare—both in the administration side to help it be more efficient and cut costs, as well as to make doctors more efficient in some areas, like where computer vision is possible to help with diagnosis.”
