Building Intelligent Health Systems in the Age of AI

Stewart Gandolf (Healthcare Success): Welcome to the Healthcare Success Podcast. Today I’m pleased to welcome our guest, Tom Lawry Lawry, who is both a keynote speaker and the author of the second book on AI in Health. Welcome, Tom Lawry.

Tom Lawry (Author, AI in Health): Hey, it’s great to be with you, Stewart.

Stewart Gandolf (Healthcare Success): I’m so excited about today, Tom. As I told you before, we were laughing about how much fun it is to talk to smart people in formats like this. You had a comment before we started that I’d love you to share.

Tom Lawry (Author, AI in Health): I hoped that was going in my direction on the “smart” comment. I often get credit for being smarter than I really am. My big thing is that I have impatience with things that are status quo that aren’t working—for anything in life.

You look at my specialty, which is healthcare, and there’s a whole lot we could talk about when it comes to the status quo not working in health and medicine. It’s all about asking, “How do we fix those things?” I don’t know that it takes smart people to do that.

Stewart Gandolf (Healthcare Success): That’s a good point. I would just have to say that I feel like we’re kindred spirits on this specific topic. I have a saying to my team that they’ve never, in the 20 years I’ve owned this business, heard me say, “We do this because we’ve always done it that way.” That just isn’t something that’s part of our culture. It’s not something I want.

But you know what, Tom? People default to that. There’s something about it—there’s a magnet. Even if I explicitly say we’re all about innovation here and new ideas, that doesn’t mean chaos. You don’t just throw out ideas and change everything on a whim. But to shut down new ideas before you even start, especially in a creative business or in healthcare, seems like a crazy way to go.

Tom Lawry (Author, AI in Health): I don’t know that I’ve ever met a leader in healthcare who says anything other than, “We’re innovative.” Your body language and your chuckle just revealed your reaction.

Everyone loves to think they’re innovative, but most humans hate change. Somewhere in there are all the impediments to doing things better. Again, my focus is on how AI, done right, is going to empower workers instead of replacing them. That’s the promise. The technology is the easy part. It’s the change and the mindset that get people to truly rethink and reimagine how things are done.

Stewart Gandolf (Healthcare Success): This is something I talk about fairly regularly on my podcast, and it’s so relevant to what you just said that I’ll bring it up again, Tom. Earlier in my career, I gave a couple hundred speeches to physicians and healthcare professionals. Periodically, I’d ask the audience, “How many of you would reinvent healthcare and design it exactly the way it is today?” Especially when you think about reimbursement and all the steps involved.

I’ve never had a single hand go up. Not one. You would think, then, that people would be more open to change. I don’t know. What do you think, Tom?

Tom Lawry (Author, AI in Health): First of all, in fairness, particularly for physicians, nurses, and clinicians, think about going through medical school or nursing school. You learn a lot of best practices that have been developed over time through history and science to say, “In this moment, for this situation, here is the best approach to take.”

When it comes to innovation and change and trying new things, you don’t want your neurosurgeon going into the operating room saying, “Hey, today I think I’m going to freestyle and just see what happens.” But there’s a balance between relying on best practices and recognizing that AI and digital technologies now give us the ability to ask, within the parameters of “do no harm,” what can we start doing that makes those practices better?

Whether it’s improving clinical outcomes or improving the patient experience, do you know anyone who thinks the American healthcare system provides a great consumer experience right now? I’m not trying to criticize healthcare providers. They’re all operating within enormous challenges. Done right, AI doesn’t solve every problem, but it starts moving us toward asking, “What if we could rethink, reimagine, and redesign these things to be better?”

Stewart Gandolf (Healthcare Success): I totally agree. Nobody is purposefully trying to keep us trapped in the same box. It’s just difficult to think creatively. That’s why I think today is such an exciting time because of the promise of AI.

As we dive deeper into this topic, though, I’d love to go back to something you mentioned before we started recording. You were telling me about your first book, when you wrote it, and some of the predictions you made back then. I think that’s great context for getting us started.

Tom Lawry (Author, AI in Health): I’ve got the second edition of my first book, AI in Health, coming out this fall. The original was published by Taylor & Francis in 2020, which means I actually wrote it in 2019. That’s almost seven years ago now.

When it came out in 2020, a lot of people rolled their eyes at some of my predictions, particularly the idea that AI would eventually become the new infrastructure driving healthcare. Today, if you look around, everything is moving toward becoming digitized and intelligent. It took almost six years to get here, but that’s exactly the direction we’re headed.

My focus back then was that there would continue to be traditional health systems, but we’d also see the emergence of what I called intelligent health systems. These are organizations some of which are AI- and digital-first. They’re not simply asking, “How do we make our existing processes more efficient?” Instead, they’re asking, “How do we use AI to completely reimagine a healthcare process and make it better across every channel, every experience, and every touchpoint?”

Traditional healthcare organizations today are using AI, but most are using it to improve existing processes. At the same time, there’s a growing group—including major companies like Amazon and many small startups—that are saying, “No. We’re going to use AI’s native capabilities to fundamentally reinvent the experience.”

It’s not just about making the experience better. It’s about improving quality, improving outcomes, and making healthcare accessible to more people.

Stewart Gandolf (Healthcare Success): That’s really insightful. Going back to something you said earlier about best practices, I’m a big believer in best practices. But if you only follow best practices and never innovate, eventually you stop moving forward.

I’m curious about this idea of reimagining healthcare. Was that something you expected when you wrote your first book, or is that something you’ve seen emerge over the past several years? Has that surprised you?

Tom Lawry (Author, AI in Health): I’m always asking, “How do we make things better?” Then the question becomes, “How do you use technology—or anything else—to accomplish that?”

A big part of my work is helping healthcare leaders around the world do what I think is the blinding statement of the obvious. We start by looking at the challenges and problems they face. Then we examine the underlying processes they’re using. From there, we begin teasing out how technologies like AI might make those processes better, eventually improving the healthcare system itself.

You and I talked about this before we started recording. When it comes to innovation and technology in healthcare—or really in any industry—the technology is almost the easy part. It’s not necessarily easy, but it’s much easier than getting the human side aligned, changing mindsets, and moving people in a different direction.

Stewart Gandolf (Healthcare Success): That’s a really great point, and yes, we talked about that on our pre-call.

Let’s talk about leadership. AI has gone from being a niche technology to becoming a boardroom priority seemingly overnight. Has all of the excitement been overblown? Has it actually made it harder for healthcare leaders to make good decisions? How do we wrap our minds around the human element of all this?

Tom Lawry (Author, AI in Health): Well… how much time is the podcast?

Seriously, though, this isn’t an indictment of leaders. It’s simply recognizing that we now have extraordinary new opportunities.

Let me back up and give you a broader perspective before drilling into the leadership question. I’m a geeky AI and data guy, but I’m also a history buff. When you study history, you see technologies emerge that improve the way we live and work. Economists talk about something called general-purpose technologies—technologies that come along once every 50 to 100 years.

Think about the printing press. Think about the internal combustion engine. Think about electricity. These innovations didn’t simply make existing work a little more efficient. They fundamentally changed how people lived, how they worked, and how society itself was organized.

I believe AI is one of those general-purpose technologies, and many economists agree. We’re still in the very early stages of seeing how it’s going to reshape the world—not just how we work and live, but eventually the very fabric of society itself.

So just look at things like pervasive use of your smartphones. And they call it a smartphone because pretty much every app you use has some form of AI under the hood. Think about everything you do in your life, business-wise, personally, with that smart device. If all of a sudden, everybody listening, we said, “okay, we’re going to confiscate your smart device for a week,” “You’re going to half to pry it from my fingers.” And it’s an example of how it’s already becoming pervasive. It’s not like everybody’s thinking about, “Oh, AI’s becoming pervasive.” It just is.

And again, we’re in the very early stages. But I want you to imagine, and in my new book I tell a story of when internal combustion engines, which had been around for decades, all of a sudden became mainstream, and just looking at how that changed living in New York.

And the point is, when you’re early in this revolution, and it happens day by day week by week, it’s not like all of a sudden there’s this massive change, but we’re in the very early stages of that. And so in the early stages of the internal combustion engine, there are many entrepreneurs and businesspeople. You know, it was an equine-driven economy. And there were people thinking, well that’s the way it was for a century and it’s going to continue. So they kept doing things like building bigger buildings to store hay and things in New York. The smart money was on things like building gas stations.

So that’s where we are. All industry. Healthcare especially. And my point is if you understand the long view of what happens with a general-purpose technology, your approach is very different than if you just treat this as another cycle of innovation with technology.

You now, the smart money’s on saying, hey, this is a major shift. Over time we’re gonna figure out not just how to make things more efficient, but how we’re gonna use this to reimagine and reinvent whatever business you’re in, including health and medicine.

Stewart Gandolf (Healthcare Success): So I don’t know if you remember “Future Shock,” where the idea was in Toffler that the change of pace in humans would accelerate over time and that has turned out to be suppression.

Tom Lawry (Author, AI in Health): I could turn if he gave me 30 seconds, I could pull that book off the shelf, 1982. You read what he wrote in 1982 today, and I swear I think he was an alien because no human could be that smart in 1982.

Stewart Gandolf (Healthcare Success): Wasn’t it amazing? Like I just still like I remember that book specifically and how powerful that is. And if you look at it’s just it’s like a linear thing, it’s all the way through—8-tracks to cassettes to you know, audiophiles, and how much faster it just keeps going. So I love the idea and I remember your keynote, and by the way, as you remember, Tom, right after your keynote I came up and asked you to be on the podcast. I thought the number-one thing I remember was that idea of there’s such a big, pervasive change, right? It’s not like, you know, Internet 2.0; it’s a whole new thing, right?

So how can we as leaders in healthcare grapple with that? Like how do you make that a priority? Because healthcare is brutal. It’s so busy and complicated and regulated and the stakes are really high. How, you know, have you… what have you seen the smart oney do to even grapple with this enormous issue? Like how do you predict the change of the world that’s coming at an accelerated pace?

Tom Lawry (Author, AI in Health): Some of that too is, I mean, the answer is somewhat dependent on well, which part of the world are we talking about? So, you know, here in America, you know, we have a system that we spend three times more per consumer on healthcare than any other developed nation in the world, and yet our quality access and efficiency measures are the worst among developed nations.

So that’s not an issue with our top talent. it’s an issue with the economics that drive decision making. And that’s very different than when you go to a place like I’ll be in Australia working in October, I’ll be in France in September. They have very different systems where the economics are, the economic model is different. But in general, you know, when you look at the first wave of, “How do we change current processes to make things more efficient?” That’s usually, you know, if you look at a Maslow triangle, that’s usually where everyone starts. But then it really goes beyond that to say, well, how do we actually start raising the bar on, again, quality and access among people?

And this is where I think there are a few leaders actually going to this level of saying, not only how do we make our current system more efficient, but how is it going to fundamentally change our clinical practice and our fundamental business models going forward? And I can give you an example, but I’ll stop there because I don’t want to be totally long-winded.

Stewart Gandolf (Healthcare Success): I’d love that example. I’ve got another follow-up question, but go ahead and share the example first.

Tom Lawry (Author, AI in Health): Here’s a couple of examples.

The first applies to every industry, but especially healthcare. I recently gave a keynote to one of the largest groups of marketing directors and patient experience leaders in the country. For decades, websites have been a huge investment. Organizations spend tremendous amounts of money on search engine optimization and carefully curating content to attract consumers to their brand and ultimately bring patients into their facilities.

Now we’re seeing the implementation of bots and AI agents. A study that came out just last week found that the majority of traffic hitting websites is no longer coming from humans with their hands on a keyboard—it’s coming from bots and agents.

So when you look at that impact alone, and a study by UCLA showed once someone discovers something like ChatGPT, their web use goes down by 20%. So what’s happening is people are going to general tools and typing in, “I want you to search for and look for and bring me back recommendations” on something specific. They’re not going to the websites, the bots and agents are. They use a very different set of logic. So you can have the best website for humans and be missing a lot of those consumers who are making decisions because they’re using bots and agents now.

The second example is clinical. There’s an emerging field called oculomics, which uses data extracted from retinal images. Today, when you visit your eye doctor, they routinely take a photograph of your retina. AI is already being used to analyze those images for conditions like diabetic retinopathy.

Over the past several years, researchers have discovered that the exact same retinal image can also help assess cardiovascular disease, neurological conditions, and other health risks. Oculomics is moving us toward a future where a single retinal image can predict far more than eye disease.

The FDA has already approved a handheld camera capable of capturing clinical-grade retinal images. My prediction is that we’ll eventually have an app on our phones that allows us to photograph the back of our own eyes and receive clinical-grade information—not only about eye conditions, but about cardiovascular and other systemic diseases.

If I’m right, then five years from now we have to ask an interesting question. What’s the best screening tool for cardiovascular disease? Is it the traditional process of visiting your primary care physician and then a specialist? Or is it simply taking a picture of your retina with your phone?

People in the clinical world sometimes react strongly when I talk about scenarios like that. But I’m not suggesting we replace physicians or diminish their expertise. Imagine instead that 1,000 people in your service area use that technology in a single day, and many of them are flagged as being at elevated risk. Suddenly, specialists have the opportunity to engage those patients much earlier, before serious disease develops.

To me, that’s an incredible business model. Unfortunately, many people either aren’t thinking that way or they become afraid because they assume it will disrupt today’s business model.

Well, that’s exactly what general-purpose technologies do.

Stewart Gandolf (Healthcare Success): There’s so much to unpack there.

Personally, I have to mention that I went to my optometrist about a year and a half ago, and he told me about this. He said, “By the way, I can tell if you have early-onset dementia.”

I thought, My God.

The entire time he was doing my eye exam, I kept thinking, That is the scariest thing I’ve ever heard. Then he said, “No signs of neurological disease.”

Thank God. But it certainly raised the stakes for what I thought was going to be a routine eye exam.

Tom Lawry (Author, AI in Health): It sounds like you’re working with an innovative provider, which is great.

Like I mentioned earlier, I believe that five years from now, if you walk into LensCrafters, you’ll have a greater ability to assess your cardiovascular health than you would in many physician offices, simply because they’re already building the infrastructure for where this is headed. Their parent company is making some very smart investments because they understand what’s coming.

Many physicians still aren’t familiar with the emerging field of oculomics. You can either be afraid of it, or you can lean into it and ask, “How can I leverage this for my specialty and my practice?”

The same is true from the consumer’s perspective. Why do people use smartphones? It’s not because they love AI. It’s because smartphones reduce friction in their lives.

And if there’s anywhere people would love to reduce friction, it’s when they’re trying to navigate today’s healthcare system.

Stewart Gandolf (Healthcare Success): Again, wow. There are about 10 different directions we could go from here.

I’m actually going to come back to that point about reducing friction, especially around making appointments, because that’s probably the most hated part of healthcare. But before we get there, something else occurred to me while you were talking.

You’re really good at pattern recognition, and I enjoy thinking that way, too. Years ago I heard the story about why India adopted cellular technology so quickly. My understanding was that because the country’s landline infrastructure had become such a tangled mess, it was actually easier to leapfrog directly to wireless technology.

Are there similar opportunities in healthcare? Sometimes when people experience enough pain, they’re willing to skip incremental improvements and jump straight to something completely different. Rahm Emanuel famously said, “Never waste a good crisis.”

What areas of healthcare do you think are positioned for that kind of leap—not simply reinventing the current process, but skipping directly to a fundamentally better one?

Tom Lawry (Author, AI in Health): Yeah. I’m going to frame that a little differently by going back to one of your earlier questions.

When you look at America, we have amazing talent and we spend an enormous amount of money on healthcare, yet we’re still falling short for all the wrong reasons. Part of that is because we operate under a break-fix economic model. When you follow the money, AI isn’t going to fix the underlying economics.

I’ll give you a quick example. I work with a startup that’s doing some fascinating work using AI and what’s known as advanced nudge theory. They approached me because they wanted to launch a pilot program in the United States focused on reducing the progression of diabetes.

As much as everyone says they’re committed to reducing diabetes, I told them to forget about starting in America. The economic model is such that if we suddenly became dramatically better at preventing diabetes, many healthcare organizations would actually go out of business.

Instead, we helped put together a partnership in Singapore. Singapore has one of the most significant diabetes challenges in Asia, and the government had essentially declared war on diabetes. Since the government serves as both the payer and the provider, the incentives are aligned.

They launched a program involving a couple hundred thousand Singaporeans. Half of them were enrolled in an AI-driven platform that continuously gathers health information on each participant. Three times a day, each person receives highly personalized nudges tailored specifically to them.

The early data is encouraging. Participants are walking more, exercising more, and it appears the progression from prediabetes to diabetes is slowing.

To your point, the second major agreement this company signed was in India for many of the reasons you just mentioned. India also faces an enormous diabetes challenge. There are relatively few physicians compared to the size of the population, but wearable technology is becoming increasingly common.

They’re using AI to proactively assess people’s health and deliver personalized guidance before serious disease develops. They didn’t start in the United States. They started in Singapore, then expanded into India. They’re now launching work with the NHS in the United Kingdom, and eventually they’ll come to America.

The challenge isn’t the technology. The challenge is the economic infrastructure. It simply doesn’t encourage us to use AI to pursue the radical idea of improving the health of entire populations at scale.

Stewart Gandolf (Healthcare Success): That’s such a common theme on this show, Tom—the incentives are simply misaligned in this country.

There are efforts like value-based care that are trying to move us in that direction, but your example makes so much sense. You went exactly where the pain is greatest and where the incentives are actually aligned. It’s fascinating that this innovation is happening outside the United States.

You also mentioned something earlier that I’d love to explore further.

Another observation I’ve made over the years—and someday I’d love to write a book about it—is what I call the power of easy. Reducing friction changes behavior almost instantly.

I use the TV remote as an example. You can still walk across the room and turn your television on manually, but how many people have actually done that in the past 30 years? The remote changed behavior overnight because it made the experience effortless.

Where do you see AI creating that kind of transformation in healthcare? Are there areas that just scream, “This could be dramatically easier”? Maybe it’s everywhere.

Tom Lawry (Author, AI in Health): Let’s go back to everyday life. Imagine you’re a single parent with a couple of kids at home and everything you’re trying to manage. You use your smartphone for banking, travel planning, school activities, and countless other tasks because it reduces friction. It keeps raising your expectations for what every experience should feel like.

Then you walk into the front door of a hospital or physician practice, and it’s almost as though time stood still.

Think about registration. Think about prior authorizations. Think about everything involved in simply entering the healthcare system. So many of those processes are exactly the same as they were decades ago.

Healthcare is the last industry still supporting fax machines.

Now imagine a completely different experience. Instead of gritting your teeth every time you need healthcare, what if the system already had your information—with your permission—and could intelligently monitor data we already have?

Imagine receiving personalized reminders based on your health goals. Imagine something like the Singapore example, where highly intelligent, individualized nudges help guide your decisions every day.

All of those capabilities already exist. We have the technology today.

The problem is that we eventually run into the realities of the current healthcare system and the economic model that everyone has to operate within.

Walter Cronkite once famously said that the American healthcare system is neither healthy, caring, nor a system.

That observation has nothing to do with the extraordinary people working in healthcare. In my last book, Healthcare Nation, I describe America’s healthcare system as the country’s largest escape room.

We’ve taken some of the most talented people in the world, locked them inside with consumers and five trillion dollars of your money, and then challenged everyone to escape from an incredibly convoluted web of policies and economic incentives.

The reality is that physicians, nurses, and healthcare leaders are struggling under many of the same constraints that patients experience.

AI can help. But even if every promise people make about AI actually came true—which it won’t—it would mostly make the existing system more efficient.

There are much bigger conversations we need to have, including one fundamental question: What if our financial incentives actually rewarded keeping people healthy instead of simply fixing them after they become sick?

Today, about 97% of all healthcare spending goes toward break-fix care. Only about 3% of our five trillion dollars in healthcare spending goes toward prevention.

Stewart Gandolf (Healthcare Success): Wow.

That’s a fantastic pull quote for the podcast summary. I knew the numbers were bad, Tom, but I had no idea they were that bad.

Tom Lawry (Author, AI in Health): I’ll send you a copy of Healthcare Nation. My goal with that book was to explain the technical side of healthcare in a way that even your mother could understand. I’m sure your mother’s smart.

Everyone keeps looking at AI as though it’s some kind of magic bullet. It can absolutely do remarkable things.

But it can’t solve the underlying structural problems that clinicians, administrators, and healthcare executives wrestle with every day. They didn’t create the system as it exists today. They’re simply trying to lead organizations within an incredibly difficult environment.

Stewart Gandolf (Healthcare Success): That’s really true.

Once incentives become deeply embedded in a system, they’re incredibly difficult to change. It’s almost like everyone agrees the current system isn’t ideal, yet we’re all committed to preserving it because of how it’s structured.

I want to shift gears to another topic that’s especially important from my perspective as the owner of a marketing agency.

One of the most frustrating parts of my job is that we can generate thousands of phone calls for a hospital, physician group, or health system through SEO, paid search, AI optimization, and every other marketing tactic imaginable—and then they simply don’t answer the phone.

According to my friend Aaron Clifford at Press Ganey, the single most hated part of the healthcare experience is trying to make an appointment. This is such a painful thing for most people.

You mentioned reducing friction earlier. I actually just returned from an AI event hosted by Sword Health. As a company, we’ve identified roughly 30 to 40 organizations working on AI-assisted appointment scheduling and patient access.

I’m incredibly excited about the potential for AI to handle phone calls, online forms, physician scheduling rules, and all of the complexity involved in matching patients with the right providers.

It may sound pedestrian compared to some of the more futuristic topics we’ve discussed today, but this is the number one frustration patients experience.

What have you seen happening in that area so far?

Tom Lawry (Author, AI in Health): Again, I think it comes back to the two types of organizations that are emerging.

There are traditional healthcare organizations that are already using AI, and they’re primarily focused on creating efficiencies. Some of that absolutely includes improving patient experience touchpoints like scheduling.

Then there are organizations taking a very different approach. They’re asking, “How do we completely reinvent scheduling?” I don’t really want to talk about specific vendors, but there are companies doing some very innovative work around scheduling platforms and patient experience applications.

Some of those technologies still need to mature, and healthcare organizations still have to adopt them. Scheduling has always been challenging for a variety of reasons. Some of it has been technology, although that’s improved dramatically.

The other reality is that if you lined up 10 major healthcare organizations and looked at how each one schedules patients, you’d find 10 different approaches. Sooner or later—particularly with surgical scheduling—there’s usually an experienced human who serves as the gatekeeper because they have the judgment to coordinate everything correctly.

I’m not saying it’s easy. But banking and many other highly regulated industries also manage incredibly complex processes, and they’ve figured out how to modernize them. Healthcare continues to be the laggard.

Stewart Gandolf (Healthcare Success): Yep, for sure.

What other areas do you think are especially important for health systems? I want to come back later and talk about life sciences because I know you’ve spent time there as well.

But when you think about hospitals and health systems specifically, what opportunities stand out to you? Radiology? Nephrology? Dermatology? Every specialty seems to have possibilities. Are there areas that you think are particularly exciting?

Tom Lawry (Author, AI in Health): Honestly, I think every specialty has opportunities to innovate.

But since you mentioned marketing, let me come back to that because I actually think marketing is a proxy measure for improving the overall consumer and patient experience.

I recently had this discussion at a conference. In many ways, healthcare marketing leaders are among the smartest people in an organization when it comes to understanding how patients experience healthcare. Yet many of them aren’t even invited to the table when these broader transformation conversations happen—let alone asked to lead them.

I can tell from your body language that you know exactly what I’m talking about. I always want to begin with the patient experience. Obviously, you don’t want an outstanding patient experience paired with poor-quality medicine. You need both.

There is so much we can do. Right now I’m speaking frequently with marketing and patient experience leaders about something I call bot psychology. Going back to websites and digital experiences, I often say, AI and bots don’t care—they compare. That changes how we think about organizing data and creating digital experiences. When we start designing information for both humans and AI systems, we can make those experiences much smarter.

Inside the organization, there are opportunities everywhere. Radiology has been one of the earliest specialties to embrace AI. There’s still a lot of discussion suggesting we may eventually need fewer radiologists because AI will replace them. Whenever I hear that, two things immediately occur to me. First, the people making that argument usually don’t understand what AI is actually good at or what humans are uniquely good at. Second, they often don’t really understand what radiologists do.

Done properly, AI is about empowerment. It’s about helping physicians, nurses, and every other healthcare professional become better at the work they already care about. In radiology, for example, AI can perform what I call pre-reads. The goal isn’t to eliminate radiologists. It’s to save them time so they can focus more on the most challenging cases instead of spending as much effort on routine interpretation.

To me, radiologists are among the most important consultants supporting every other medical specialty. Giving them more time, better information, and greater insight only strengthens the entire healthcare system.

There’s another concept I talk about called AI and data whispering. All of the diagnostic tests we perform today have been whispering valuable information to us for years. We simply haven’t had the ability to hear everything they were telling us.

Take a standard chest X-ray. With the right AI models, researchers are discovering that it can help predict future cardiovascular risk. I’m not suggesting we should start ordering chest X-rays simply to assess heart disease. I’m saying that every diagnostic test we already perform contains more intelligence than we’ve historically been able to extract. If AI allows us to uncover that hidden information, everyone benefits. It helps clinicians make better decisions, and it moves healthcare another step forward.

Stewart Gandolf (Healthcare Success): That’s amazing.

I want to ask you the big question. You’ve touched on it throughout our conversation, but I’d like to dig a little deeper.

One of my former mentors used to say that people interpret everything through the lens of, “What does this mean for me personally?” When it comes to AI, that usually translates into questions like, “What does this mean for my career? What does this mean for my job?”

People have invested decades building expertise, so it’s understandable that AI feels threatening. I remember during your keynote you addressed this directly. You talked about whether AI displaces workers or empowers them. For our listeners—whether they’re radiologists, CEOs, marketers, or healthcare executives—what would you say to those who are worried about what AI means for their future?

Tom Lawry (Author, AI in Health): You’re going to have to invite me back for another episode because that could be an entire conversation by itself.

Here’s the thing. The number one issue—and it’s not being addressed well in healthcare—is that every employee, whether they’re the CEO, a physician, a nurse, or someone working in administration, is hearing about AI every day. They’re reading stories saying it’s going to take their jobs, replace them, or somehow make them obsolete.

So the number one question people ask themselves isn’t, “How can AI help me?” It’s, “What does this mean for me and my career?” They’re wondering whether they’ll still have a job, whether they’ll still enjoy their work, whether they’ll have more influence or less. If you’re a physician or a nurse, you’re asking whether AI is somehow going to conflict with your clinical judgment.

The reality is that most people working in healthcare are knowledge workers. These aren’t factory jobs where someone repeats the same task a 1,000 times a day. Those kinds of repetitive jobs are the ones most likely to be automated.

Healthcare is different. The overwhelming majority of healthcare jobs are going to be augmented by AI. AI comes alongside people to make them better at what they already do. That’s why our boot camps for clinicians and healthcare leaders don’t focus on turning people into AI experts. You don’t have to become a technologist or a data scientist. You simply need to understand the fundamentals and recognize that your wisdom, your judgment, your experience, and everything you’ve learned throughout your career remain enormously valuable.

AI isn’t replacing those things. The opportunity is figuring out how AI can eliminate the repetitive, low-value work so you can spend more time doing the things that only you can do. That’s the real value proposition. Whenever I hear someone talking about AI replacing healthcare workers, I think they’re looking at it through the wrong lens. The conversation shouldn’t be about replacement. It should be about empowerment.

That brings us back to what I call the AI leadership imperative. When leaders assume AI is simply another technology initiative and hand it off to the CIO or CTO, the organization follows one path. When leaders recognize that AI is fundamentally about empowering people rather than deploying technology, the entire conversation changes. Leadership then becomes responsible for building a culture that embraces AI, helping employees develop new skills, and addressing exactly the fears you just described.

One of the first things I do when I begin working with a healthcare organization is spend time talking with frontline employees—staff nurses, laboratory personnel, researchers, and others. I’m listening for one thing. When people talk about AI, do they feel AI is being done with them or to them? That distinction is incredibly important. It tells you almost everything you need to know about how effectively leadership is communicating and guiding the organization through change.

Stewart Gandolf (Healthcare Success): That makes total sense to me.

From a business standpoint, it reminds me of a conversation I had just last night. I was invited to dinner with a group of agency owners, and there were several private equity investors there, as there usually are. Someone brought up AI, and the conversation quickly turned into, “What’s the private equity perspective? You invest in marketing agencies. What do you think?”

It was fascinating because some people around the table were saying, “The agency business is finished.”

The reason I bring that up is because I think there are real parallels with healthcare. On the other hand, just like you said earlier, the smart money isn’t saying those businesses disappear. They’re saying the businesses evolve.

Do you see similar situations in healthcare where people are more afraid than they should be? Areas where the business model will certainly change, but the opportunity is actually much greater than the fear?

Tom Lawry (Author, AI in Health): I think it comes back to recognizing that AI is a general-purpose technology. It’s no longer a question of whether it’s going to change how we work, live, and organize society. It’s already happening. The question is where it’s headed and what that means for your profession and your future.

I remember when Photoshop first became popular. People said it was going to destroy professional photography. It didn’t. What it did was elevate photography. Photographers who had spent years working with Kodachrome film and developing prints in darkrooms suddenly had an entirely new collection of creative tools available to them. Photography didn’t disappear. It evolved.

I think AI is very similar. We’re still early in the journey. If you’re spending all your energy wishing things would stay the same—if you’re still committed to feeding hay to the horse—you’re probably focusing on the wrong future. Instead, lean into it. Understand it. Master it. Recognize that AI is fundamentally about empowerment.

People who do that will have an enormous advantage over those who continue resisting. Whether you’re a physician, a nurse, a CEO, a radiology technologist, or someone working in revenue cycle management, you already possess valuable knowledge, wisdom, and experience. The opportunity is learning how to combine those strengths with AI so you can operate at an entirely new level.

Stewart Gandolf (Healthcare Success): As we look ahead… actually, before we wrap up, I don’t think we’re quite ready yet. I’m having too much fun, so let’s steal another couple of minutes. I wanted to ask about life sciences. We’ve spent most of our conversation talking about health systems, but I know you’re well connected across the industry. Are there developments in life sciences—drug discovery or other areas—that you find especially exciting right now?

Tom Lawry (Author, AI in Health): That’s another topic that could fill an entire episode. The answer is absolutely yes. Life sciences and pharmaceuticals are among the areas where we’re already seeing major breakthroughs. I think this is where we’re going to see a virtuous cycle of innovation continue to accelerate.

Today, bringing a new drug to market typically takes around 10 years, costs several billion dollars, and roughly 90% of potential drug candidates fail somewhere along the development pathway. We’re already seeing companies like Insilico Medicine fundamentally rethinking that process. They’re using AI, synthetic data, and advances in computational biology to accelerate discovery. We’re also seeing remarkable progress in protein research, where AI is helping scientists create entirely new biological structures.

Clinical trials are another area with enormous potential. Here in the United States, recruiting the right participants—and ensuring appropriate representation of women and people of color—has always been a major challenge. AI is becoming dramatically better at identifying appropriate participants, filling trials more efficiently, and improving the diversity of those studies. The upside is tremendous.

If you think back to the COVID-19 vaccines, one company gained a significant competitive advantage because it invested early in cloud computing and AI capabilities. What might otherwise have taken close to a decade reached the market in roughly two years. That transformation is already happening. It’s only going to continue accelerating.

Stewart Gandolf (Healthcare Success): That’s amazing.

Another topic we spend a lot of time discussing with our clients is how AI is changing the way people search for healthcare information. One of the things our company does is help healthcare organizations rethink their websites because, in some ways, everything we used to optimize isn’t nearly as important as it once was. The way content is structured is changing. The technology is changing.

Now we’re talking much more about off-site signals, reputation, and how AI systems evaluate credibility. Consumers are still getting information from hospitals and health systems, but they’re no longer getting it exclusively from them, and that’s creating a lot of challenges.

I don’t know how deeply you’ve explored that topic, but every industry seems to be facing the same realization that much of what we’ve done historically is becoming outdated. At the same time, that’s also an opportunity because everyone else is starting from the same place.

There are probably two or three questions wrapped up in there, Tom, but I’d love to hear your thoughts.

Tom Lawry (Author, AI in Health): I think we can answer that in two ways. The first is from a process standpoint. When I look at what’s happening with bots and agentic AI—and this is only going to accelerate over the next 12 months—the implications are enormous. If the data is correct that websites now receive more traffic from bots and agents than from humans, every marketer should immediately be paying attention.

Optimizing for AI is very different from traditional search engine optimization. If organizations aren’t already focused on that, they’re behind.

The second point is about consumers themselves. Several excellent studies have been published over the past few months showing that consumers around the world are placing increasing trust in AI-generated health information. They’re also placing more confidence in social media and other nontraditional information sources than many clinicians are comfortable with. A lot of healthcare professionals react negatively to that trend, but I can’t control what consumers are doing. I’m simply telling you what the data shows.

Part of what’s happening is that consumers have become so frustrated with the healthcare system that they’re looking elsewhere for answers. That’s actually an opportunity.

How do we better serve consumers when they’re looking for information? How do we become the trusted source when someone sits down at a keyboard—not to search Google—but to ask an AI assistant a question? Someone might type, “I live in Minneapolis. My father is on Medicare. He’s having hip surgery in Seattle in two weeks. Help me find an appropriate step-down facility after he’s discharged.”

If AI agents begin answering those questions and directing even 20% of patients somewhere other than your organization because you haven’t prepared for that future, that’s going to have a significant impact on your business.

Stewart Gandolf (Healthcare Success): One thing we’ve talked about quite a bit is that Google traditionally provided options, whereas ChatGPT, Gemini, Perplexity, and the other AI platforms increasingly provide recommendations.

We don’t have time to explore that in depth because I have one final question, but I wanted to share a quick story. One of the surgeons we work with was applying for a grant. She told me about a patient with an incredibly rare diagnosis and a very unusual combination of medical conditions. The patient entered all of that information into an AI system and asked, “Who’s the best physician in my state for this exact situation?”

She said the AI identified precisely the right specialist. It wasn’t simply looking for a vascular surgeon or a broad specialty. It identified the physician with the exact experience needed for that very unusual case. That’s remarkable.

Tom Lawry (Author, AI in Health): That’s actually an even better example of what I’ve been describing. Now take that one story and multiply it by a 1,000 different situations. As people become accustomed to using generative AI, they’re no longer beginning their searches with Google. They’re beginning with AI assistants. Your example illustrates exactly where this is headed. Today, AI is making recommendations.

If you and I have this conversation a year from now, I believe AI agents won’t simply recommend options—they’ll generate complete action plans. Think about the difference. With Google, people receive a list of websites and then do the work of comparing and deciding for themselves. With AI agents, the intermediary is now making those decisions on the user’s behalf. That’s already happening, and it’s only going to become more sophisticated.

Healthcare organizations have a choice. They can embrace that reality and learn how to participate in it, or they can ignore it. Personally, I’d much rather see those recommendations shaped by organizations with genuine clinical expertise, credibility, and integrity than leave those decisions entirely in the hands of AI agents.

Stewart Gandolf (Healthcare Success): I remember years ago, when physician ratings first became common online, some doctors would say, “I’m not a pizza.” Ironically, a few even sued media organizations or patients over online reviews. Of course, those lawsuits often damaged their reputations far more than the original reviews ever did. The organizations that embraced transparency ultimately adapted to that new reality. I think AI presents a very similar moment.

Before we wrap up, though, I want to come back to leadership. You suggested before we started that we shouldn’t follow a script, and I’m really glad we didn’t. I had 10 or 12 leadership questions prepared, and we’ve gone in much more interesting directions.

So let me consolidate those into one final question. For the healthcare leaders listening today, what are four or five things they should be thinking about right now? You mentioned earlier that AI shouldn’t simply be delegated to IT. What else should leaders be doing to prepare their organizations for what’s coming?

Tom Lawry (Author, AI in Health): I’ve been immersed in this research while finishing my new manuscript, so there’s a tremendous amount of data on this. Every healthcare organization in America—and roughly 70% of healthcare organizations globally—is already using some form of AI today. Very few, however, are generating measurable value at scale.

The obvious question is: Why? The number one reason has very little to do with technology. It’s the human factor. For the past six years, researchers have asked leaders across virtually every industry what the biggest obstacle is to creating value from AI. For six consecutive years, the number one answer has been employee resistance.

To me, employee resistance is really a proxy for leadership. It tells me organizations aren’t doing the things they need to do. Everyone is asking the same question we discussed earlier: What does this mean for me? That’s why every healthcare leader should be investing heavily in upskilling knowledge workers throughout the organization.

The second issue is that roughly 80% of AI projects in healthcare fail. Why? One reason is that organizations fail to invest in developing their people. Another is that they don’t establish clear success metrics before launching AI initiatives. They approve projects and invest money without first asking a very simple question:

How will we define success, and how are we going to measure it?

Another challenge is data. Everyone loves talking about AI, but AI needs data the same way fire needs oxygen. Organizations continue investing in AI while neglecting their underlying data infrastructure. Without a strong data foundation, it’s almost impossible to create value at scale.

Finally, it comes back to change management. None of these responsibilities can simply be delegated to the CIO or CTO. These are leadership responsibilities. Leaders have to own them. They have to model them. And they have to create cultures where people see AI as an opportunity to become better—not something to fear.

Stewart Gandolf (Healthcare Success): Fantastic.

Before we finish, tell us a little about your new book. When is it coming out, and what can readers expect?

Tom Lawry (Author, AI in Health): It’ll be released this fall. It’s really an update to my 2020 book, but it’s not a technical manual. I think of it as a leadership manifesto. It brings together research from many of the world’s leading organizations along with nearly 20 years of my experience working with healthcare leaders around the globe. The focus is on where healthcare is today and what leaders need to do to create value at scale. It’s about moving beyond simply creating efficiencies and instead building what I call intelligent health systems. For leaders who want to help shape that future, that’s who I wrote the book for.

Stewart Gandolf (Healthcare Success): Outstanding, Tom. Thank you so much for your time today. I knew this conversation would be fun, and I hope you enjoyed it as much as I did.

Tom Lawry (Author, AI in Health): I had a blast. Thanks a lot.

Stewart Gandolf (Healthcare Success): Thank you.