Informatica World 2024 | Austin Walsh | Microsoft & Richard Cramer | Informatica

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Austin Walsh

Managing Director, US Healthcare, Data and AI

Microsoft

Richard Cramer

Chief Strategist Healthcare & Life Sciences

Informatica

Gen AI for healthcare: Unlocking data insights, improving patient management and enabling personalized medicine

Healthcare is a crucial metric in any quality-of-life discussion. Summarily, cross-sector efforts are underway to bring generative artificial intelligence capabilities to health-related use cases.What tangible results have come of those efforts to alleviate pain points in medical data analysis, patient care and overall health service delivery?Live at Informatica World with Informatica’s Richard Cramer and Microsoft’s Austin Walsh.“More than 80% of the meaningful healthcare data is captured in free text,” said Richard Cramer (pictured, right), chief strategist of healthcare and life sciences at Informatica Inc. “It’s very inaccessible and very difficult for people to process

play_circle_outline Harnessing AI's Transformative Potential to Overcome Staffing Shortages and Improve Healthcare Access

play_circle_outline Use cases and examples of AI implementation in healthcare, such as improving patient triage and personalized medicine

play_circle_outline Collaboration between Informatica and Microsoft to address data challenges in healthcare AI

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Austin Walsh | Microsoft & Richard Cramer | Informatica

Austin Walsh

Managing Director, US Healthcare, Data and AI Microsoft

Richard Cramer

Chief Strategist Healthcare & Life Sciences Informatica

search

Rebecca Knight

>> Hello, everyone. And welcome back to theCube's live coverage of Informatica World. I am your host, Rebecca Knight, sitting alongside my co-host and analyst, Rob Strechay. Rob, the party has started.

>> Yeah, I saw that. We got to the club a little bit early. I mean, this is unbelievable. But the energy is awesome-

Rebecca Knight

>> For sure.

>> To put it mildly.

Rebecca Knight

>> Off the hook.

>> And I think what's going to be fun is we don't have shots up here-

Rebecca Knight

>> No.

>> But we're going to talk about something else that has to do with shots, and we'll try to not go that way.

Rebecca Knight

>> Ladies and gentlemen, Rob Strechay. He's so punny.

>> Please tip your waitress, and I'll be here all week.

Rebecca Knight

>> I would like to introduce our next two guests. We have Austin Walsh, managing director of US healthcare, data and AI at Microsoft. Thank you so much for coming on theCube, Austin.

Austin Walsh

>> Thank you for having me. It's great to be here.

Rebecca Knight

>> And Richard Kramer, chief healthcare strategist at Informatica. Thank you so much for coming on theCube.

>> Thanks for having me as well.

Rebecca Knight

>> So before the cameras were rolling, we were talking about the enormous potential of AI to transform how we live and work, and really the industry that has the most really life-changing potential in my eyes is healthcare. Why don't you just start very, in broad strokes, Austin, to talk about how you see its potential to transform the healthcare industry?

Austin Walsh

>> Yeah. Well, I mean, first off, it touches all of our lives as you'd mentioned. We're all patients, we're caregivers, parents, and we have... Last time you tried to go see your doctor, how long did it take to get an appointment? We've got a real challenge on our hands. 20% of GDP is healthcare-related in the ecosystem. And we have huge staffing shortages. We're expecting 100,000 open positions for nurses, 50,000 clinicians that shortfall in 10 years. So we're in this golden age of healthcare right now where the care we're getting is about as good as it gets. And that's a little scary to me because last time I checked, it's a little hard for me to get access to the kind of care I really want. So how are we going to solve that problem?

>> And it's not like this is all that brand new to healthcare either, right? I mean, again, we were talking beforehand, there was a lot of auto-generated text, what people really associate with gen AI and some of the power of gen AI, but maybe it got out of control a little bit. And we were talking about this. What are some of the things that you see that gen AI can actually help? And even before we get to gen AI, there's a lot of data there as well.

Austin Walsh

>> Well, healthcare is an incredibly data-intensive business, but it's also true that more than 80% of the meaningful healthcare data is captured in free text. And so it's very inaccessible and very difficult for people to process. And if you think about the power of gen AI, one of the things that does incredibly well is summarize. And so the opportunity to take those huge volumes of text and create meaningful, consumable clinical summaries for a clinician or to take all of those different textual documents across a large number of patients and summarize and find insights that you otherwise couldn't have described. And so that's just one very simple example of the power of generative AI in healthcare.

Austin Walsh

>> No doubt about it. If you think about the good old days of how did we do natural language processing, it was stemming words and lemmatization and texturing matching. It was terribly complicated. It wasn't really efficient. And now we're kind of breaking down the barriers of getting value out of that free text. It's pretty amazing.

>> And for example, my dad was a physician, and one of his favorite sayings was, "A diagnosis not thought of is difficult to make." Well, if all of that insightful information is buried in free text and gen AI can help you find the signal in that noise, you can make diagnoses you had not previously thought of.

Rebecca Knight

>> Exactly. That's always what it's so striking to me is how doctors, you see these different specialists and they're not talking to each other and connecting the dots of our health and getting this bigger picture. Talk a little bit about... I mean, the tagline of this show is, "Everybody is ready for AI except your data." Talk, Richard, a little bit about the state of data readiness as you see it right now.

Austin Walsh

>> So we have struggled mightily as an industry before AI was the fashionable thing to get meaningful insights from our data. It's fragmented. It's of inconsistent quality. There's no common understanding of what it means. So all of those pre-existing conditions, if you will, to use a healthcare term, are all present when you then say, "I now want to use that data to train my AI algorithm." And all you're going to do is take really poor data and magnify the effect of it by putting it into an AI algorithm. And so that desire, that need, the urgency to actually make sure you get your data right that you use to train the algorithm that then delivers the value. And so when we talk about everybody is ready for AI except your data ChatGPT has supercharged the enthusiasm and the imagination of everybody about what's possible, but it's only possible if your data is good to start with.

>> And I think one of the things that people think of when they think healthcare, they get very sensitive about PII and their information and things of that nature. Talk to that a little bit about how really preparing the data and what use cases actually fit because maybe not every use case that has PII or how things are trained and how you control that really fits into healthcare and AI and how we get there.

Austin Walsh

>> Well, I mean, one thing that comes to mind is, at least at Microsoft, we've all of a sudden been thrust into the spotlight as an AI company. It's been really, really exciting because there's some massive investments we made in the coveted resource of these GPUs that train these big models like OpenAI. But of course, what we're trying to do is take these large language models and make them available to customers in a secure way where your data is your data, that it's not going to be used to train the foundational model, where you can use certainly the OpenAI models but also the open-source models like Mistral or Llama 3 for Meta, Cohere but also applying all of these ethical considerations, responsible use of AI so you're not inadvertently disclosing information to the wrong people. And what we've learned over the past 18 months is that this concept of grounding LLMs on this data that you've already curated and made sure is the right set of information is tremendously helpful. And so each week that goes by, we're finding new techniques around how we prompt those LLMs, how we ground them and how we're now starting to create agents where you have multiple steps reinforcing, checking each other's work. So the quality of the outcome is just that much better, and I think that's why we're seeing these just incredible breakthroughs month after month.

Rebecca Knight

>> Austin, can you describe for our viewers some of the use cases that you're seeing in terms of the customers that you're using that are implementing this in real life hospitals and clinicians and nurses that are using these kinds of products and how it is changing the way they practice medicine?

Austin Walsh

>> Yeah, I would love to. I mean, what's one place we never want to go? You never want to end up in the emergency department, right? Because it's chaotic. And so once you're there and they determine you need to be admitted, the nurses need to help triage you into where you're going to be placed. And one organization that I think is really impressive that embraced gen AI early is a Mercy Hospital in St. Louis. And just at HIMSS a couple weeks back, I learned about their chief nursing officer saying, "Hey, if we only were able to consume all of this information, our nurses have to go to 16 different screens and try to summarize this." Oh, wow. That's what we were just talking about, the ability to summarize information. So in healthcare, we talk about SBARs, right? The ability to look at the situation and go through the process to have a recommendation. It's a great example of how they went from ideation with a chief nursing officer to putting a tool in the hands of caregivers in 90 days. And that's the promise of cloud and gen AI being made real in a secure and responsible way. And for me, that's really exciting because now patients going from the emergency department being triaged into a critical care unit or something like that, we know they're getting the right type of care.

>> And Richard, I mean, the data has been there, but like Austin was just talking about, it's in different systems of record. And the cognitive load on RNs and the physicians is just so great. How does Informatica and Microsoft come together to really help bring that together and really deal with the data because the data is really the core of AI?

>> The data is the core, but one of the truisms is... And I've been doing this for a while. And for decades, we've lamented that data silos are bad, and we must break down the data silos and physically consolidate data and control it in order to get to value. And silos have not gone away. They've gotten worse. And so I would argue that that style of consolidation and control never worked, and it can't possibly work now. And so when you think about the modern data estate that's going to support your AI initiatives, it is fragmented silos, discongruent data everywhere, and that is now a characteristic of the data landscape. It is not a problem to be solved. And so if you think, right, the idea of a data catalog, it knows where all of your data is and everywhere it goes and what it means, the idea of applying consistent data quality rules that are driven by organizational policies, the fact that you have transparency... I go way back that the key to trustworthy data is transparency. If you're transparent, I can disagree with you but still trust you. If you're not transparent, I can agree with you, and I'm not going to necessarily trust you. So all of those very familiar data management principles really are the precursor to having trustworthy data to train your models to deliver the value.

Austin Walsh

>> And I think, if I may, I mean, what's really powerful is when we team up with your expertise in master data management and data catalog and these kind of modern cloud platforms that allow you to leave your data where they are, where it is and provide shortcuts and virtually bring this information together. Our latest product called Microsoft Fabric, it's just incredibly powerful. We're able to do things we were never able to do at a cost that's far lower than it's ever been seen before.

Rebecca Knight

>> How does the product development process work in terms of your collaboration with healthcare professionals to identify their pain points and then come up with innovative solutions using the data experts, the AI experts? What does that look like?

Austin Walsh

>> Well, one thing that comes to mind is we have the benefit of being able to go to all the different health systems, all the different healthcare payors, to the different biopharma companies, and guess what? We see them doing the same thing over and over and again. And some of them do it well, and some of them struggle. Some have resource challenges in terms of human capital and technical expertise. So we look for those opportunities to provide accelerators, to provide standards both through technical product innovation or even the policy lobby where we can support standards like HL7 FHIR. So there's a lingua franca in terms of that health data. So when we bring it all together, things work as expected.

Austin Walsh

>> And it's also true, providers are not shy in letting you know what frustrates them, right? I mean, provider burnout is an enormous problem in healthcare. We take our most gifted, most valuable resources and relegate them to some of the most Byzantine, repetitive tasks that add no value. And you talk about, when do providers actually catch up with all their paperwork and do their correspondence with their patients? At home after they're done working. And so the opportunity for gen AI to actually offload some of that routine communication workload, it makes a disproportionately large impact on the provider. To us, it sounds, well, that sounds pretty basic, but for a provider who's working a 14-hour day and they get to go to bed or spend two hours more with their family, because we've automated that through gen AI, is enormously impactful.

>> Yeah. I look at it and go, the burnout is real. Funny enough, my physician, my primary care left practice and went into IT. I said, "Are you nuts?" I'm like, "You're going from the frying pan into the fire with this." But I think what we've always looked at is how do we bring all of this together to make it easier, to your exact point. Where do you hope we get to in the next year with this? Because to me, I'm a glass half full... Probably of some alcoholic beverage after all this music. But I'm a glass half full looking into where healthcare can benefit from this over the next year. Why don't you both give us your view on where we get to?

>> So I think one area is going to be provider workloads that we just talked about. I think the other area is going to be patient member, consumer satisfaction, the ability to make a much better, more tailored experience with your healthcare consumers that takes advantage of all of those things that you already know about them. You need to be doing a tailored personalization to a population of one, you or me. You know everything about me. It's just not accessible. And so to me, if I were picking the top two, that's where I would go.

Austin Walsh

>> Absolutely. I mean, having that digital experience, that digital front door, critically important. The other side of that is personalized medicines, right? Fundamentally, are there the biopharma companies developing therapies that are unique to my genetic makeup-

Rebecca Knight

>> Genetic makeup.

Austin Walsh

>> To my epigenetic kind of profile, to my proteomic expression? I mean, these are all tremendous opportunities that today, we're seeing people use these new techniques, and it's just we're seeing at a molecular level the relationships that were just really impossible to do before. So it's really inspiring because as a lifetime big tech person, we're seeing the impact in the real world, right? We're kind of making that connection to value, to closing that loop on improving the lives of our communities. And that's what makes it all worthwhile.

Austin Walsh

>> And the other thing I would add is the only thing I'm 100% certain of is we're wrong about what is actually the potential because if you look at a couple of the most prestigious health systems in the country, their charter from the executives on down is to reinvent the healthcare experience, how healthcare is delivered using AI. I mean, that's a pretty daunting mandate. And I think the creativity and innovation that is going to come forth from a mandate like that to rethink everything we do in a new light because of gen AI is pretty exciting. So a year from now, who knows.

Rebecca Knight

>> Very exciting times. Austin and Richard, thank you both so much for joining us on theCUBE. A really interesting conversation.

>> Thanks for having us.

Rebecca Knight

>> I'm Rebecca Knight for Rob Strechay. Come back to us tomorrow for more coverage of Informatica World. You are watching theCUBE, the leader in technology, enterprise news and analysis.

Austin Walsh | Microsoft & Richard Cramer | Informatica

search

Rebecca Knight

>> Hello, everyone. And welcome back to theCube's live coverage of Informatica World. I am your host, Rebecca Knight, sitting alongside my co-host and analyst, Rob Strechay. Rob, the party has started.

>> Yeah, I saw that. We got to the club a little bit early. I mean, this is unbelievable. But the energy is awesome-

Rebecca Knight

>> For sure.

>> To put it mildly.

Rebecca Knight

>> Off the hook.

>> And I think what's going to be fun is we don't have shots up here-

Rebecca Knight

>> No.

>> But we're going to talk about something else that has to do with shots, and we'll try to not go that way.

Rebecca Knight

>> Ladies and gentlemen, Rob Strechay. He's so punny.

>> Please tip your waitress, and I'll be here all week.

Rebecca Knight

>> I would like to introduce our next two guests. We have Austin Walsh, managing director of US healthcare, data and AI at Microsoft. Thank you so much for coming on theCube, Austin.

Austin Walsh

>> Thank you for having me. It's great to be here.

Rebecca Knight

>> And Richard Kramer, chief healthcare strategist at Informatica. Thank you so much for coming on theCube.

>> Thanks for having me as well.

Rebecca Knight

>> So before the cameras were rolling, we were talking about the enormous potential of AI to transform how we live and work, and really the industry that has the most really life-changing potential in my eyes is healthcare. Why don't you just start very, in broad strokes, Austin, to talk about how you see its potential to transform the healthcare industry?

Austin Walsh

>> Yeah. Well, I mean, first off, it touches all of our lives as you'd mentioned. We're all patients, we're caregivers, parents, and we have... Last time you tried to go see your doctor, how long did it take to get an appointment? We've got a real challenge on our hands. 20% of GDP is healthcare-related in the ecosystem. And we have huge staffing shortages. We're expecting 100,000 open positions for nurses, 50,000 clinicians that shortfall in 10 years. So we're in this golden age of healthcare right now where the care we're getting is about as good as it gets. And that's a little scary to me because last time I checked, it's a little hard for me to get access to the kind of care I really want. So how are we going to solve that problem?

>> And it's not like this is all that brand new to healthcare either, right? I mean, again, we were talking beforehand, there was a lot of auto-generated text, what people really associate with gen AI and some of the power of gen AI, but maybe it got out of control a little bit. And we were talking about this. What are some of the things that you see that gen AI can actually help? And even before we get to gen AI, there's a lot of data there as well.

Austin Walsh

>> Well, healthcare is an incredibly data-intensive business, but it's also true that more than 80% of the meaningful healthcare data is captured in free text. And so it's very inaccessible and very difficult for people to process. And if you think about the power of gen AI, one of the things that does incredibly well is summarize. And so the opportunity to take those huge volumes of text and create meaningful, consumable clinical summaries for a clinician or to take all of those different textual documents across a large number of patients and summarize and find insights that you otherwise couldn't have described. And so that's just one very simple example of the power of generative AI in healthcare.

Austin Walsh

>> No doubt about it. If you think about the good old days of how did we do natural language processing, it was stemming words and lemmatization and texturing matching. It was terribly complicated. It wasn't really efficient. And now we're kind of breaking down the barriers of getting value out of that free text. It's pretty amazing.

>> And for example, my dad was a physician, and one of his favorite sayings was, "A diagnosis not thought of is difficult to make." Well, if all of that insightful information is buried in free text and gen AI can help you find the signal in that noise, you can make diagnoses you had not previously thought of.

Rebecca Knight

>> Exactly. That's always what it's so striking to me is how doctors, you see these different specialists and they're not talking to each other and connecting the dots of our health and getting this bigger picture. Talk a little bit about... I mean, the tagline of this show is, "Everybody is ready for AI except your data." Talk, Richard, a little bit about the state of data readiness as you see it right now.

Austin Walsh

>> So we have struggled mightily as an industry before AI was the fashionable thing to get meaningful insights from our data. It's fragmented. It's of inconsistent quality. There's no common understanding of what it means. So all of those pre-existing conditions, if you will, to use a healthcare term, are all present when you then say, "I now want to use that data to train my AI algorithm." And all you're going to do is take really poor data and magnify the effect of it by putting it into an AI algorithm. And so that desire, that need, the urgency to actually make sure you get your data right that you use to train the algorithm that then delivers the value. And so when we talk about everybody is ready for AI except your data ChatGPT has supercharged the enthusiasm and the imagination of everybody about what's possible, but it's only possible if your data is good to start with.

>> And I think one of the things that people think of when they think healthcare, they get very sensitive about PII and their information and things of that nature. Talk to that a little bit about how really preparing the data and what use cases actually fit because maybe not every use case that has PII or how things are trained and how you control that really fits into healthcare and AI and how we get there.

Austin Walsh

>> Well, I mean, one thing that comes to mind is, at least at Microsoft, we've all of a sudden been thrust into the spotlight as an AI company. It's been really, really exciting because there's some massive investments we made in the coveted resource of these GPUs that train these big models like OpenAI. But of course, what we're trying to do is take these large language models and make them available to customers in a secure way where your data is your data, that it's not going to be used to train the foundational model, where you can use certainly the OpenAI models but also the open-source models like Mistral or Llama 3 for Meta, Cohere but also applying all of these ethical considerations, responsible use of AI so you're not inadvertently disclosing information to the wrong people. And what we've learned over the past 18 months is that this concept of grounding LLMs on this data that you've already curated and made sure is the right set of information is tremendously helpful. And so each week that goes by, we're finding new techniques around how we prompt those LLMs, how we ground them and how we're now starting to create agents where you have multiple steps reinforcing, checking each other's work. So the quality of the outcome is just that much better, and I think that's why we're seeing these just incredible breakthroughs month after month.

Rebecca Knight

>> Austin, can you describe for our viewers some of the use cases that you're seeing in terms of the customers that you're using that are implementing this in real life hospitals and clinicians and nurses that are using these kinds of products and how it is changing the way they practice medicine?

Austin Walsh

>> Yeah, I would love to. I mean, what's one place we never want to go? You never want to end up in the emergency department, right? Because it's chaotic. And so once you're there and they determine you need to be admitted, the nurses need to help triage you into where you're going to be placed. And one organization that I think is really impressive that embraced gen AI early is a Mercy Hospital in St. Louis. And just at HIMSS a couple weeks back, I learned about their chief nursing officer saying, "Hey, if we only were able to consume all of this information, our nurses have to go to 16 different screens and try to summarize this." Oh, wow. That's what we were just talking about, the ability to summarize information. So in healthcare, we talk about SBARs, right? The ability to look at the situation and go through the process to have a recommendation. It's a great example of how they went from ideation with a chief nursing officer to putting a tool in the hands of caregivers in 90 days. And that's the promise of cloud and gen AI being made real in a secure and responsible way. And for me, that's really exciting because now patients going from the emergency department being triaged into a critical care unit or something like that, we know they're getting the right type of care.

>> And Richard, I mean, the data has been there, but like Austin was just talking about, it's in different systems of record. And the cognitive load on RNs and the physicians is just so great. How does Informatica and Microsoft come together to really help bring that together and really deal with the data because the data is really the core of AI?

>> The data is the core, but one of the truisms is... And I've been doing this for a while. And for decades, we've lamented that data silos are bad, and we must break down the data silos and physically consolidate data and control it in order to get to value. And silos have not gone away. They've gotten worse. And so I would argue that that style of consolidation and control never worked, and it can't possibly work now. And so when you think about the modern data estate that's going to support your AI initiatives, it is fragmented silos, discongruent data everywhere, and that is now a characteristic of the data landscape. It is not a problem to be solved. And so if you think, right, the idea of a data catalog, it knows where all of your data is and everywhere it goes and what it means, the idea of applying consistent data quality rules that are driven by organizational policies, the fact that you have transparency... I go way back that the key to trustworthy data is transparency. If you're transparent, I can disagree with you but still trust you. If you're not transparent, I can agree with you, and I'm not going to necessarily trust you. So all of those very familiar data management principles really are the precursor to having trustworthy data to train your models to deliver the value.

Austin Walsh

>> And I think, if I may, I mean, what's really powerful is when we team up with your expertise in master data management and data catalog and these kind of modern cloud platforms that allow you to leave your data where they are, where it is and provide shortcuts and virtually bring this information together. Our latest product called Microsoft Fabric, it's just incredibly powerful. We're able to do things we were never able to do at a cost that's far lower than it's ever been seen before.

Rebecca Knight

>> How does the product development process work in terms of your collaboration with healthcare professionals to identify their pain points and then come up with innovative solutions using the data experts, the AI experts? What does that look like?

Austin Walsh

>> Well, one thing that comes to mind is we have the benefit of being able to go to all the different health systems, all the different healthcare payors, to the different biopharma companies, and guess what? We see them doing the same thing over and over and again. And some of them do it well, and some of them struggle. Some have resource challenges in terms of human capital and technical expertise. So we look for those opportunities to provide accelerators, to provide standards both through technical product innovation or even the policy lobby where we can support standards like HL7 FHIR. So there's a lingua franca in terms of that health data. So when we bring it all together, things work as expected.

Austin Walsh

>> And it's also true, providers are not shy in letting you know what frustrates them, right? I mean, provider burnout is an enormous problem in healthcare. We take our most gifted, most valuable resources and relegate them to some of the most Byzantine, repetitive tasks that add no value. And you talk about, when do providers actually catch up with all their paperwork and do their correspondence with their patients? At home after they're done working. And so the opportunity for gen AI to actually offload some of that routine communication workload, it makes a disproportionately large impact on the provider. To us, it sounds, well, that sounds pretty basic, but for a provider who's working a 14-hour day and they get to go to bed or spend two hours more with their family, because we've automated that through gen AI, is enormously impactful.

>> Yeah. I look at it and go, the burnout is real. Funny enough, my physician, my primary care left practice and went into IT. I said, "Are you nuts?" I'm like, "You're going from the frying pan into the fire with this." But I think what we've always looked at is how do we bring all of this together to make it easier, to your exact point. Where do you hope we get to in the next year with this? Because to me, I'm a glass half full... Probably of some alcoholic beverage after all this music. But I'm a glass half full looking into where healthcare can benefit from this over the next year. Why don't you both give us your view on where we get to?

>> So I think one area is going to be provider workloads that we just talked about. I think the other area is going to be patient member, consumer satisfaction, the ability to make a much better, more tailored experience with your healthcare consumers that takes advantage of all of those things that you already know about them. You need to be doing a tailored personalization to a population of one, you or me. You know everything about me. It's just not accessible. And so to me, if I were picking the top two, that's where I would go.

Austin Walsh

>> Absolutely. I mean, having that digital experience, that digital front door, critically important. The other side of that is personalized medicines, right? Fundamentally, are there the biopharma companies developing therapies that are unique to my genetic makeup-

Rebecca Knight

>> Genetic makeup.

Austin Walsh

>> To my epigenetic kind of profile, to my proteomic expression? I mean, these are all tremendous opportunities that today, we're seeing people use these new techniques, and it's just we're seeing at a molecular level the relationships that were just really impossible to do before. So it's really inspiring because as a lifetime big tech person, we're seeing the impact in the real world, right? We're kind of making that connection to value, to closing that loop on improving the lives of our communities. And that's what makes it all worthwhile.

Austin Walsh

>> And the other thing I would add is the only thing I'm 100% certain of is we're wrong about what is actually the potential because if you look at a couple of the most prestigious health systems in the country, their charter from the executives on down is to reinvent the healthcare experience, how healthcare is delivered using AI. I mean, that's a pretty daunting mandate. And I think the creativity and innovation that is going to come forth from a mandate like that to rethink everything we do in a new light because of gen AI is pretty exciting. So a year from now, who knows.

Rebecca Knight

>> Very exciting times. Austin and Richard, thank you both so much for joining us on theCUBE. A really interesting conversation.

>> Thanks for having us.

Rebecca Knight

>> I'm Rebecca Knight for Rob Strechay. Come back to us tomorrow for more coverage of Informatica World. You are watching theCUBE, the leader in technology, enterprise news and analysis.