I-CAR

Fortune partnered with Great Place to Work to analyze survey feedback representing more than 60,000 U.S. employees.

GPTW_100BestCompaniesToWorkForByRegion_2026-Chicago_SMB-I-CAR_US1__69b3fd205504e0abfadc0513_1

Employees say:"This is the first company I've worked for that truly makes me feel like I can prioritize both my family life and my work life and succeed at all of it. The flexibility in scheduling (flex Fridays) and the holiday schedule truly gives me the time I need personally so that I can recharge and be all-in professionally while at work. The balance makes me more focused and better at what I do! This culture makes me feel valued and trusted."

Best Small and Medium Workplaces in Chicago

Fortune partnered with Great Place to Work to analyze survey feedback representing nearly 62,000 U.S. employees.

Best Workplaces_Chicago_SmallMedium_2025-I-CAR_US1__67fd627ad94556dd4465c987_7

Employees say:"Working here is very unique, in the way that our organization has a wide range of departments that have different task and skillsets. Unlike many other organizations we are able to actually bring all departments together every day year over year without fail to come together to accomplish our core business models with expertise and knowledge and with great passion with healthy functioning TEAMWORK!"

Best Workplaces In Chicago

Founded

Sites

Industry Vertical

Get insights into I-CAR including business profile, stock information, news, and rankings from Fortune.

I-CAR Company Profile, Stock Price, News, Rankings

legacy

i-car

Companies

I-CAR | Fortune

I-CAR Company Profile, Stock Price, News, Rankings | Fortune

Agentic AI has officially graduated from basic decision trees to unscripted autonomous agents, turning customer service into a massive $15 billion market. Startups like Decagon AI are pulling in a $4.5B valuation because of high enterprise demand. Hertz is already routing 50% of its phone interactions through AI voice, dropping its per-call costs by more than half. And the car rental giant is hardly alone. Companies are fast adopting AI customer service agents. However, about 80% of consumers still crave a human on the other line, and 40% of us would literally pay a premium just to bypass the bot. Fortune’s AI Editor Jeremy Kahn takes us behind the scenes to break down the delicate balance between instant resolutions and human connection.

Almost 80% of Americans Prefer Human Agents, but Why Is AI Growing in Customer Service?

Tech

fortuneaifeatures

AI is making customer service even more unbearable. Three second delay and it goes ha ha ha, and I couldn't talk to a representative. How often does someone come in and they're just like, 000 agent agent agent. In 2026, AI and autonomous agents have taken over customer service. A recent report says 88% of contact centers are now leveraging AI, with a quarter of them running on fully integrated automation. Most of the inquiries that happened today in five years, there's no question that AI. will be able to handle all of them, but there's a catch. Another study reveals that nearly 80% of Americans still prefer talking to a living, breathing human being. And the hidden costs of this automation, it's staggering. Research shows that simply disclosing a chatbot's identity before a chat even begins can plummet purchase conversion rates by 80%. There's always that little human touch that that we always need. Yet we're a generation obsessed with instant gratification. How do we square that? As the AI editor at Fortune, I focus on how AI is shaping business and the wider economy. With AI poised to transform customer experience, I wanted to talk with business leaders on the front lines of this shift to find out how they're adapting and what the future of business really looks like. Despite our collective craving for human connection, the AI customer service market has ballooned to over $15 billion in 2026. So agentic AI or AI systems that actually take actions in the world, whereas previously you had automated agents that had to follow a set decision tree. With the current systems, they actually are able to have much less structured flows of how they operate. One of the front runners leading the charge is Decagon AI. Founded in 2024, they've already hauled in $481 million in funding, commanding a $4.5 billion post-money valuation. That is a major indicator of just how eager enterprises are to adopt this tech. Decagon's mission building faster, smarter AI without damaging the customer experience, and it's working. They're maintaining a 93% customer satisfaction score while driving revenue from fully automated interactions with. Some individual contracts now clearing $1 million. What do you think that looks like going forward in, in 5 years? Most of the inquiries that happened today, I would say in 5 years there's no question that an AI agent will be able to handle all of them. In a lot of these mass, like huge volume use cases, the human touch doesn't really matter, right? It's, it's not like you need the human touch to book a new flight or to, you know, reorder a lost credit card. All you want in that situation is just like get it done. It's, it's a little bit more mundane. These are not canned responses. This is something way more personalized. It's very different from what you've tried before. You're kind of changing the customer's behavior. But get this, over 40% of consumers say they'd actually pay a premium just to guarantee they can speak to a real human representative. Around the time of the industrial revolution, people wanted to buy a Ford Model T because the presumption was the quality was higher because it was built in a factory. Now today, curated, handmade experiences. Seem to have more of a premium association with them. Look across the industry and you'll see giants leveraging AI in different ways. Take Chipotle, a behemoth with over 100,000 employees, while some, like Decagon, are adapting to customers' changing behavior, others are doubling down on the interpersonal interaction. What kind of customer experience do you really want people to have going forward? One of the things that I think sets us apart and makes us special is the human interaction. Hi, how are you? Those curated moments of human connection and service. Are always going to be a fundamental part of what the Chipotle experience is. Back in 2024, they automated their hiring process with an administrative AI system called Avocado. And what was the idea behind that AI system? The average restaurant manager was spending more than 10 hours a week on recruiting activity, 20% of their week just on that one task. It's a fraction of that now. And how has AI changed sort of what your daily routine looks like? It allows me to, you know, be in front with the guest, serve them, going over recipe cards, you know, tasting food, a lot of training, people development for Chipotle, you know, we always need that human touch, you know, we always need, uh, people tasting the food because that's what we sell at the end of the day. We definitely always gonna need that human aspect. I don't know what kind of day they might be having before they come here, you know, we give them a burrito, a bowl, you know, we serve them with gratitude, with a smile, you know. Thank you. That could make anybody's day. But to see the customer facing side of this revolution, I synced up with Hertz. As an international car rental giant, they handle massive call volumes. By deploying Decagon's tech since early 2025, they're now resolving 75% of customer inquiries autonomously. We want to take every single one of those calls that comes our way, and we want that to be as seamless for our customers as possible. To see this AI agent interaction, I tried giving Hertz's AI, Haley, a call. Hi, this is Haley. I see you have a rental scheduled for pickup. How can I help you today? Oh hey Hayley, I'd like to change the drop off location. Let me see, where would you like to drop off the car instead? Yeah, if I could drop it off at LaGuardia Airport, is that possible? Great, you're all set with the new drop off at LaGuardia. Yeah, that was pretty good. I mean, I think that's, you know, that's was smooth, and it definitely didn't have to wait, which is nice not to have to queue at all. That's the new expectation. They're going to expect faster. Response times, better experiences. When we look at the future of contact centers, we're gonna see more and more AI agents taking on this workload. Every business will have an AI agent that's kind of like the front door to me that actually isn't to say that the human agents will go away because there's gonna be a lot more things that can be handled. Like why are people doing more of the mundane conversations because they have to, um, like those are the bread and butter. But if the AI can take that away, it frees them up to do more conversations that are not being had today. In fact, Hertz told us that AI voice is now live across 50% of their total phone interactions. In 2026, the cost of a customer interaction dropped by more than half to under $1.50. That shift means companies like Hertz are now seeing roughly $3.5 back for every $1 they put into AI customer service. Yet, depending on the issue, Hertz still reroutes calls to human agents. Hi, this is Hayley. How can I help you today? Hi, Haley, I've got a Hertz rental car right now and I have a flat tire. I'll connect you to a live agent now. Thanks for your patience. Thank you. They've got a flat tire or maybe they're on the side of the road and they're at risk. They absolutely want to hear a human voice and have that level of connection where someone understands that they may be at risk and is doing everything they can to support them in that scenario. Earlier you mentioned that billing is an area that still reverts to the human agent. Why is that? Why, why is the billing so important to be handled by a human? Yes, we think that the billing conversation is one about trust and the customers looking to understand why maybe a charge is not what they believe. It should be. So that's one where we very much default to someone being able to explain those pieces to them, and we found customers much prefer that. But even with those undeniable margins, the trust gap persists. Studies show that only 13% of consumers completely trust AI. Generally speaking, people will say I would be a little bit nervous about that, or in some cases a lot nervous or being very careful to consider PII or other information that might be in those systems and make sure that the models are both protected and secure from that perspective. that were responsibly handling people's information. How do you make sure there aren't hallucinations, because there have been these cases, I mean this famous Air Canada case where the judge said Air Canada was sort of liable for that incorrect information that its AI system had provided as part of a customer support experience. The only way to prevent that in a nondeterministic system is to have all these checks around. We call them supervisor models, and you kind of think of it as like the main agent is executing and you have these like, you know, small models around it that are running in parallel. There's all these models around it to make sure it stays on track and stays within the guard rails. Data proves we want more human interaction and will actively penalize businesses that deprive us of it. But as agentic AI gets increasingly good, will that sentiment hold? Are we staring at a future where consumers are forced to choose between instant resolutions or the human touch? Is there anything you think AI agents? We never really be able to automate when it comes to the customer contact, honestly, no, I think at a certain point it's almost indistinguishable. You'll get into a situation where if you didn't tell, inform the user that you might not know, consumers will now learn the other side, which they're, they're now used to interacting with great AI agents that they'll be disappointed if they're waiting on hold. We have very high conviction that that's where, where the world is moving towards.

AI is no longer just generating insights. It is shaping decisions that carry real consequences. In high-stakes environments, being “mostly right” is not enough. Every output must withstand scrutiny from regulators, clients, and internal stakeholders. This roundtable examines what it takes to operate under that pressure, where AI systems break when forced to explain themselves, and how leaders are building technology and organizations that can stand behind every decision. 

Caitlin Halferty, Head of Data and Analytics, Thomson Reuters 
Gregor Stewart, Chief AI Officer, SentinelOne 

Hosted by Thomson Reuters

The Cost of Being Wrong

Conferences

AI is pushing the pace of business beyond what most organizations are used to and beyond what many feel ready for. Decisions are happening faster, expectations are rising, and the margin for error hasn’t changed. Leaders aren’t just asking how to move quickly; they’re asking how to move in ways that don’t create problems later. We’ll bring technology leaders together to share how they’re managing that tension and what they’re accelerating, what they’re scrapping, and how they’re staying focused as everything speeds up.

Sean Bruich, Senior Vice President, AI and Data, Amgen 
Dan Gill, Chief Product Officer, Carvana 
Bret Greenstein, Chief AI Officer, West Monroe 
Nizar Trigui, Chief Technology Officer, GXO 

Hosted by West Monroe

Timing, Trade-Offs, and Speed

AI has proven that it can save time, improve outcomes, and free clinicians to focus on patients, not processes — but most of those healthcare gains have come at the point of care, in individual departments, one tool at a time. The next frontier is building the platform that makes AI foundational across the entire system, so insights compound rather than accumulate in silos. In this intimate dinner in partnership with Philips, we will hear from healthcare leaders about what it takes to get there: the governance frameworks, the workflow integration, and the organizational conditions that turn AI from a collection of promising tools into the infrastructure that powers better care for more people.
Speakers:
Kevin Mahoney, Chief Executive Officer University of Pennsylvania Health System
Jeff DiLullo, Chief Region Leader, North America, Philips
Brian Donley, President & CEO, NewYork-Presbyterian

From Tools to Platform: AI's Next Frontier in Healthcare

First of all, thank you very much for joining us on, um, this special night. I'm wearing one half of the colors that, uh, I think a lot of New Yorkers have tonight. I'm Diane Brady, executive editorial director at Fortune. Um, first I want to thank Phillips. Uh, for, uh, this is, um, this is my favorite dinner, and I know, I know if you could say, oh, she always says that, but it actually is, this is our 3rd dinner together, and I, what I love as a journalist is it's so interesting the conversations up here but the conversations around the table because I talk, we talk about AI all the time. I do think the use cases in healthcare and in hospital systems are fascinating and the changes that. We've seen just in the space of a year so I'm looking forward to a great conversation. Let me, um, first of all start we're gonna let these gentlemen get seated, but, um, just to let you know everything up here I'm gonna report on. I do a daily newsletter, um, and so that part feel free to heckle and it might get in the story, but everything around the table's Chatham House, so that's just to encourage a free flow of conversation and let me now turn fresh from his daughter's wedding. To, uh, Jeff Delulo, who is executive vice president, chief region leader for Phillips North America, and, um, thanks, and gentlemen, you can get seated and let Jeff give some opening remarks. My opening remarks are very simple. Thank you for being here, Shez. Thank you for being a great partner as we, uh, try to evangelize what we're doing, uh, for the to help you in the industry, but the power of the, the meeting will not be in this panel. The power of tonight's dinner will be at the conversation at the table. So let's get started. Excellent. Well, let, I feel I should have immediately to my left here will be Kevin Mahoney, chief executive officer of the University of Pennsylvania Health System. Kevin, good to see you. So I will confess that I'm a 76ers fan, so getting off of Penny. And everybody wearing blue killed me, killed me, hurt, you know what, it, it, it only happened in Phillips, but it only happens once in a generation. So you'll be, you'll be good on that, um, and then, uh, two generations, and Doctor Brian Donnelly, president and CEO of New York Press. Cafeterion, um, and of course Jeff you together I think you both represent, um, obviously incredible innovation. Let's just get straight to it. I wanna start, um, well, let me, if you don't mind, Jeff, I'm gonna start with you just quickly. I'd like you to level set the, um, if you don't mind the future health index. I was fascinated just to see the progress that has been made and then I wanna go to you two gentlemen to talk about what you're seeing. What have you seen this time last year feels like it's a very different discussion. So if you don't mind just sharing some tidbits. Well, yeah, thanks Adam, and uh while we were sitting here last year talking about the last year's report, it was really a step forward in trust in AI, and it was uh uh we talked about the the difference between practitioners who were gaining trust in AI and how the general population quite, quite didn't have it, and we talked about the. The need for physicians to be the vehicle by which they build confidence. This year, massive difference where we're talking about true adoption and what we call the AI dividend now because we're actually starting to see the return. We're seeing the return in the way people are adopting it into clinical workflow. We're seeing the return in terms of more patients to be seen and more patient time. We're seeing a return in terms of better diagnostic quality and we're seeing returns in terms of. Health and well-being of a clinical work staff, which is a major undercurrent of the problem in healthcare delivery today, is we don't have enough and we're burning people out, and I know you all deal with that every day. Well, and let's take it down to the ground. What are you seeing? Well, let me first say I'm a much newer CEO than Kevin is, so I would never say that I'm not a Knicks fan. But I spent 25 years in Cleveland, Ohio, and I wasn't really happy in the last series. I mean, I would, I also would agree with Jeff though, seriously, I'm, uh, excited to be here actually for the table discussion and actually I'm here, uh, to learn, uh, from all of you. You know, being a new CEO, I'm seeing the same things, uh, that I think we're seeing across US healthcare. I did spend 5 years in London before I came to New York, and I'll tell you it's not just a US healthcare problem, it's actually an international problem. And our 3 big problems that we're working on our workforce, workforce strategy. You allude to that cost has to be a consideration. No one really wants to make that their main point. It has to be if we're going to have a sustainable mission in healthcare, specifically in nonprofit healthcare, and I think the third big problem we're working on is access. There's a lot of problems we're working on. But we're really trying to focus, uh, on how AI can help us with some major, major problems, uh, and getting to a point where we see the ROI on specific problems. Do you want to add to that? We were talking a little bit at the table. Talk about where you are and what's different from that. No, no, sure, I, so I will confess that I'm an old man. And I've been through this a lot. In my first job, Medicare was still paying its costs. And, and, and then DRGs came on. This is the end of American medicine as we know it. We're not in an AI moment. Like everybody keeps saying AI is gonna fix that. A, this is a moment for AI. We're in a moment where America can't afford its healthcare, and we need to use every tool we can, but if we think it's just. For open AI and F, uh, you know, people to make money on it, that's not what it's about. It's truly changing the way we care for people. Better outcomes and a lower cost. Can you talk a bit about, um, I think I absolutely agree, and I promise if I mention open AI or anthropic, you can, you, I'll put a little tip jar there. I put, but, um, I would love to hear about where is it making a difference, um, then in terms of addressing those needs you talk about labor cost, patient care delivery. Oh, look, uh, the obvious one is ambient listening. 15% reduction in time the doctors spent typing, 17% reduction in pajama time. That's all helping with the burnout. It's helping, it's helping remarkably with getting the data. We put up our, our new building, um, several years ago and we had a retreat on the, What would be the ideal patient experience? Well, wouldn't it be great if you had a cappuccino machine? Wouldn't it be great if we had sofas instead of chairs. You know, it'd be great when you walked in the exam room, the doctor knew who you were, why you were there, and everything in your history, and AI can do. Yeah. And, and it'll make the, the visit much more productive, uh, and, and, and that'll lead to reduced costs. You know, um, one of the things, Brian, last week I was in, uh, California and they're already creating new positions. You know, one company created 13 new positions around AI. I'd love to know, um, especially as a new CEO, are you creating, are you reorganizing in any way to actually try to lean into this? Are you creating new positions, hiring differently? Yeah, I, I would strongly support Kevin's comments. That AI is not an innovation strategy. It's not an IT strategy. It has to be an enterprise strategy, as you say that Kevin, about like you saw before where it's gonna be the end of medicine, you know, this has been going on. What we've been doing is going on for thousands of years. It's not going away to your point. You know what we're doing right now is we see and actually two of our outstanding people I know for sure here, but we're seeing in pockets where we're doing this right now, label the two, yeah, Serena Fay from Wild Cornell, thank you both, uh, for being here. Um, what we're seeing, uh, uh, and what we're focusing on is how we actually improve our AI literacy across the organization and all 45,000 people. And then we want to take the different tools and we've got to figure out how we get them on the platform and get them across the system and how they help the care model that we're designing, not that we change the care model to the AI tool, but we use the AI tools to help change our care model to develop a sustainable way of delivering care, of continuing research, because you know what we do phenomenally in this country is research and discovery and then certainly education. So we're talking about some of the pain points and where we are in this moment. I'm gonna start with you. What, what is, do you see as the pain point in getting to where you wanna go? No, I, I, I think it's too. I'm gonna build on what you said. I have more people coming into my office. I want to be the AI chief. You know, I, I, and no, like you're not gonna be the AI, well, the real chief AI officer, please stand up that, that there's a groundswell of I need positions, and, and that's just gonna be counter to what I'm trying to, uh, achieve. I think the real breakthrough is not going to be in the administrative side. We talk about that a lot. I think the real breakthrough would be on the clinical side. So our our our pathologists have tissue lab cortex. We were talking a little bit with my colleague from, uh, Sloan Kettering. They, they're able to go through the biobank. Epic spectra all all the data quickly and find out that biomarkers that are pointing to a therapeutic response, right? You know, my father-in-law was dying from, uh, uh, collective duct cancer and the pathologist kept coming up with a better reading and the pathologist, or I'm sorry, the oncologist said to me it doesn't matter. I only have one tool. So you, you may have a more precise definition of what your dad's dying from, but we don't have any more tools. What, what lab cortex does using all that information, working with. Pharmaceutical, can we come up with new methods of, of treating, uh, the disease that'll have, and we were talking about ResMed and the standing, uh, ovation at, um, ASCO. Hopefully those will happen faster, faster and faster and quicker and. That's because of the advances in cancer research, right? The advances in, uh, the data collection. We have more data. As an industry, you could check a stick out. And we all talk about we've got to use it better. Gathering it at the patient level. And, and really getting in the genetic makeup. We, you can't do that fast enough, but AI, what large language models allow us to do that. Do you see a friction point? I'm gonna go to you, Jeff. Yeah, certainly. I think a big friction point is trust of, uh, clinicians, you know. And if the trust from the clinicians is there, and I think we have to continue, uh, to work on that. I think we have to be really, really hard on these tools to make sure they measure up to that and be careful of how fast we bring them in, knowing that you can't be too slow with it. I think trust is a really big, uh, point with it. go ahead, yeah, so, um. I know this is being taped and I'll probably get charged 4x from Epic for saying this. The number one pension point in my institution is Epic. Because people are inventing, innovating, coming up with the legacy system in place. I, I have one group, mostly in IT, Epic now, Epic forever, that an integrated solution is always better. And then I have physicians and other people. Let's innovate and come up with a better solution. But then you can't put it inside the Epic system, so, uh, it, it is continuing to be a major friction point for us because I have a lot of people wait. Let's just wait for Epic to come up with the tools. Like why are we out in front? Let's just wait and, and I that. That's in my top It's good to know. I, I, what would you say? Oh, I'm gonna take it a slightly different lens as a technology provider. What we're seeing is when they're, when, when the AI tooling comes alongside the clinical workflow and really embeds in the workflow, not the other way around, where you make the workflow meet the technology, we see high adoption. A third of folks have said they're, you know, you, you can read the report, but a third of folks have said I can see 5 more patients a week. Imagine that times hundreds of thousands of clinicians. Uh, people are getting hours and weeks of time back per year because of dictation and ambient and other things, and that's just scratching the surface. So imagine the productivity that we could create for an industry if we just do this effectively. But where I see friction in a lot of point solutions today from a technology lens is, how do you. Take that and learn from it, but no, that's not actual workflow. You really have to think bigger. You have to think enterprise. You have to think systemless platforming how health systems that are winning, winning the early battles here are thinking in terms of a system and how to integrate these into a platform of care along a patient longitudinal journey or service line journey. And they take taking a big picture and that's where we want to play as a as a technology provider because our view is it's always about the end to end workflow. It's not about the quality of a piece of data that you can get to diagnose and I think and productivity is very important in how we scale that, but I think what's also really important will help drive trust also and that to me this gets to the whole workforce problem we're having in healthcare is we've eroded out the meaning and purpose of the work. And so the meaning and the purpose of the work, which I said before has been here for thousands of years, is one person sitting with another at their greatest time of need they've probably ever had. And so I think what we have to do with these tools also is constantly focus how we can expand that time where an exchange of empathy happens between a patient and a provider, and I think it's important that the ROI is also thought in that way. What we look at from clinicians. we look at 4 phases, it's data collection, which is taking history, it's analyzing that data, and then it's interpreting what the analysis was and then it's counseling the patient. And what we find, I think, is that most clinicians are fine with getting rid of history-taking data collection. Analysis can clearly be done better by AI and give me more time for sitting with a patient for interpretation of the results and then counseling. Anything you'd want to add before we I do. One more pain point, which is regulation, whether it's from the state or Medicare and billing. We're putting in ambiance and the number one friction point is with our billing compliance officer. Who said you're going to gather so much more data that the two is going to become three, threes will become fours, fours will become fives, and you're going to get flagged. And, um, our, our regulations haven't kept up with, uh, you know, where, where we're at. Brian's an orthopedic surgeon. He knows when somebody comes in pretty much what, what they need. If they put that into medical chat, GPT, you could probably get the labs, the, the imaging you need, except it has to be ordered by a doctor. Um-hum. And you know, in Pennsylvania, the governor is putting in guardrails that AI can't do, do the ordering and so I, I think regulation getting regulations out of the 1970s into um the current environment will be a friction point that's gonna hold us back. And I'm sorry, I just have to add Kevin, Kevin always reminds more and more things. The other thing from a friction point is the pace of governance. And where governance is with innovation and I, and I think that's a really big friction point for us and it seems like it is for others. Well, we've seen incredible advances in, in the software certainly from where I sit and, and to your point, um, that hasn't kept up in most industries. Jeff, I wanna go to you again. What are you seeing or hearing in terms of, um, take us a little bit around the corner because I know we're gonna take this to the table, but where are you seeing the most traction with the people you speak to? Is it, I mean, because there doesn't seem to be this efficiency, there's the augmentation, there's the cognitive burden. I really like what Brian said people join this profession to sit in front of a human being and help them have a healthier life. That's what they, that's what they do. The fact that we can be more productive, we'll never have enough staff, we'll never have enough radiologists, we'll never have enough pathologists, it just will not be the case. So anything we can do to bridge that gap helps people spend more time either with more patients or spend more time with the patients in front of them to actually interact with them, look at their nonverbals, look at their body language, connect with them, and really have a diagnostic you can't get on a on a scan. And I think like what we're seeing, and I love the New York area because I honestly as a as a cohort I think this is one of the more progressive areas where people are really pushing the envelope on AI movement. We're going what I'm excited about is that we're moving away from the dialogue of point solutions and. Am I building some trust with pilots that don't go anywhere? 2, I'm actually seeing adoption because it's being designed around real workflow like end end workflow, and that's going to drive more impact, and impact begets impact, and I, I really, I feel like the flywheel is starting. That's exciting. It, it takes, I love how you talked about data layer, but keep in mind the whole object is to have a physician in front of a human being helping them be a healthier human being. And if that's where our design point is, we, we, uh, we have a lot of progress in the last year. Before we sit down, final thoughts. Yeah. So, I want us to be careful of the rhetoric. Um-hum. So, um, Doctor Zeke Emanuel, who, who's a friend of mine, he's always on TV. Uh, he works down at Penn and, Don't go into radiology. Like AI is going to replace radiologists. I had, I had another idea, which is let radiologists be doctors. OK. They see more lung nodules than anybody, but the way it is, they read it, it goes back to the thoracic surgeon, they schedule an appointment. So, I, I think we have to, don't fall prey to the rhetoric. It's not going to fix everything. Physicians are still going to be needed. But they're gonna have to learn new tools, but we're not. We're not throwing out the, the current healthcare system, but I just worry that everyone's getting a little carried away. If you've seen the New York Times today, University of, uh, California system. Um-hum. Went, went to all AI, and the editorial today is it's tearing it apart. And you know they had a $16.9 million investment with OpenAI and the faculty are like we didn't get a raise, but you got $16.9 million so I think we all just have to. Figure out how to use it in in our system, not change our system for AI. I mean, I probably on a similar thing that Kevin says, you know, first of all, appreciation to all of you because, you know, you're where a lot of our whole discovery is coming from. So thank you, uh, for that. AI has to continue to focus on how it enables our for us 45,000 people. To be better at what they do, they come to work every day and they wanna be better and better at what they do and I think AI the opportunity is for them to be better at what they do. It's not to replace people for what they do. Last word to you before we sit down, Jeff. Uh, human-centered AI enabled healthcare, and I love partnering with the health systems in this room because that's, we're all chasing the same thing. I can't wait to hear the discussion at the table. Great, thank you very much. Please join me in thanking our guests up here. We're gonna go to you now.

I-CAR

I-CAR in Fortune Rankings

Videos