Blockchain + AI: The Future of Healthcare Data

Speaker A: When you put blockchain in the mix, you change the game altogether. Because now you're talking about blockchain being the base layer where you have all of that data sitting and AI that sits on top. That actually gives you what you're looking for. When it comes to AI models, yes, you can have co pilots and you can have ChatGPT, but you're looking at it more from a Gen AI perspective. The minute you get into Agent AI, the minute you get into more details where you're looking at actual strategic decision making, you need more layers of data. And if you don't have that interoperability, it's going to get harder and harder for us to do.

Speaker B: Hello and welcome to Pharma Sessions. I'm your host, Jonathan Caskey. This is a place where I have interesting conversations, hopefully with interesting people. And I'm joined today by my guest, Archita. What's your full title? Arjita.

Speaker A: It's actually data strategy and analytics. I actually lead that for vaccines from work. Um, more m on the global side.

Speaker B: Perfect as always. The views here are ours and don't reflect those of our respective companies. So with that, um, thank you for joining me. It's a pleasure talking to you today.

Speaker A: Thank you so much, Jonathan. I really do welcome this opportunity. It's always interesting to get like minds together, have discussions on what's new trends, et cetera within the healthcare sector. I think it's an ever evolving area. I think we flip our head and there's something new, always waking. So it's really interesting and I'm really glad we have this opportunity.

Speaker B: 100%. Yeah. Before we get into all that, it's always nice to get to know the guests a little bit. So I've got two questions that I use as an icebreaker and I'll let you decide which one you want to answer because I don't want to put anybody on the spot. But the first one is what have you had to eat so far today? It's now 12, 12, 20. And the second one is, do you have a go to karaoke song and if so, what is it? So you can choose which one of those you'd like.

Speaker A: Okay, I'm, I'm gonna, I'm gonna do the karaoke song because with the eating thing I honestly don't remember. I just know that my, my three month old has had something to eat and that's pretty much it. The rest of the day is pretty much a blank. But my go to karaoke song and that actually is believe it or not frozen. It says let it go. I have so many nieces and I have so many close friends with baby girls that they got me so well versed with that song that it is on the tip of my tongue all the time. And that's the one I know I won't miss any words on. So I would definitely let it go.

Speaker B: I love it. That's a challenging, that's a challenging tune. There's a lot of big vocals in that one. Can you do it justice or do you just give it a lot of enthusiasm?

Speaker A: Honestly, my competition is eight year old girls, so I don't need this way to say it. But here's the thing with the sing along. I've heard it so many times in car rides and everywhere that I think I, I don't tend to miss the words on it.

Speaker B: Perfect. Perfect. All right. So as we were going back and forth, the way I always kick these off with guests is to say what are you most excited about in work? And we were going back and forth on LinkedIn and your topic that you suggested, I'm actually really excited about because it's something I don't know a lot about or maybe almost anything about. So you had said we can talk about AI and blockchain in healthcare if that works. I've done a lot of stuff in AI, but blockchain is new to me and where the applicability is there. And I know you, I was looking up you've actually founded a uh, blockchain company. So do you want to share a little bit of your background and where your interest lies?

Speaker A: Absolutely. I call myself a little bit of a crayon box. You'll see a box of M&Ms. Different colors, different flavors, if you will. So my background is I'm a pharmacist by training. I have a master's and PhD in health outcomes. Started my career as uh, working in oncology. Worked with GSK, MD, Anderson Cancer Center, Post that. Within Philips doing healthcare devices. Post that. I was at Tama Pharmaceuticals launching a digital inhaler. So, so just predictive algorithms, trying to predict asthma five days before it happens, things like that. So basically a lot with data and I would say different applications of data starting with health outcomes to market access to corporate strategy, et cetera. My current role is focusing on global vaccines from a data and analytics perspective and it's a very interesting world that I see within pharma at this point. My background really is, I would say very highly strategic but, but also the ability to view data from a microscopic as well as a macroscopic lens and what we can do with that data outside of that. Yes. I have founded a company which actually helps a lot of charity organizations. Basically, when folks want to donate art, I would convert that into NFT and support charities with anything that comes up on that. I'll, um, use multiple platforms to build that. Again, it is more to help charity organizations. When art gets donated, photos get donated, et cetera. So which is where we found one area where a blockchain can be of huge help. And anywhere I sit in the world, I could still help charities within India or any of the other countries, if you will.

Speaker B: And what's been the trend on that recently? Because it was everything, right? It was all whatever five years, four or five years ago.

Speaker A: Exactly.

Speaker B: How are things looking in that world now?

Speaker A: I would say there's been a dip. But if you think about it, anything and everything helps. Right? A little bit goes a long way. So I would definitely say that NFT raise has gone down significantly. I think it was everything at one point in Covid where everyone, oh, I'm going to spend X amount in there. And I think that's what you would see with trends in a lot of different areas. But I feel like the, the concept behind it makes so much sense, right? Art, uh, that you cannot steal, art could technically own, but you also protect rights of artists. And I really do believe that very significantly because I see that makes so much sense. Right. You're not looking at a traditional sense of a Monet in a med museum. You're looking at it right here on your computer and in some form, the human ability to own that little part of art of beauty you can using NFTs. So the concept behind it made so much sense to me. So I think for some of us, we were in it for what it means rather than the hype and the trend behind it.

Speaker B: Yeah, yeah, it's almost. I always thought it was almost an interesting thought experiment or something where if you. Starry Night is priceless.

Speaker A: Yes.

Speaker B: At the most impeccable forgery of that might be brushstroke by brushstroke. The same is comparatively worthless. Right. And like, why is that you don't get a piece of Van Gogh when you, if you were to own Starry Night, but yet there's still some magic that we believe that that comes through. And I feel like NFT is some version of that right there. You own whatever the asset is. But it makes a lot of sense to do it for art, for these nonprofits who might have access To I always thought of universities. Harvard has a, uh, Gutenberg Bible or something right there. These classic examples of things that you could never own but make having an nf, the NFT version of that I could see being appealing to certain people 100%.

Speaker A: I think that's where we started from. And the intent was always to help. I think that's. And again, it was more of something where we wanted to make sure that no matter where you're in the world, you're able to give your two cents to it. Right? We have folks who wanted to donate pictures. Now if that's something that speaks to you, it could be worth something more to somebody else. So it's just donating that form of art while still owning your own art. I think it's a very good segue into something. I did want to talk about data.

Speaker B: Bring it back to bring it back to healthcare. I was just very curious about this project. So how does. What is the applicability to healthcare for blockchain?

Speaker A: So to me, the way I think about it is currently our data, you and me, patients, all of our data is actually owned by big institutions. What really comes in is ownership of your own data. Right. That does come into themes of web3etc. How do we have ownership of our own data? Where you own your data. But it also does speak to the greater good, whether it is patient outcomes, whether it is real world evidence, whether it is clinical trials, biomarkers, et cetera. The way currently what we do see within an AI world is there is an issue with interoperability, with privacy that you see there's just different segments of data which don't speak to each other in a certain sense. Or they could be HIPAA regulations that you're worried about, et cetera, which kind of limits the use of AI. And I would say almost over promise under deliver situation is something we are dealing with. However, when you put blockchain in the mix, you change the game altogether. Because now we are talking about blockchain being the base layer where you have all of that data sitting and AI that sits on top that actually gives you what you're looking for. So think of AI as your agent that does the analysis. But blockchain is where the data actually sits. I know it sounds a little bit more simpler than it is, but it is a more neater solution to what we're facing right now because we will have a huge limitation. We are already starting to see that when it comes to AI models, yes, you can have co pilots and you can have chatgpt but you're looking at it more from a gen AI perspective. The minute you get into agent AI, the minute you get into more details where you're looking at actual strategic decision making, you need more layers of data and if you don't have that interoperability, it's going to get harder and harder for us to do that.

Speaker B: Yeah, no, I'm totally with you on that. Where I've, the places where I've seen AI be actually exciting more so than the generative LLM. Um, nature is in this idea of tying together multiple, sometimes 10, 20 data sources. Because if you think about it, there's even if you just take the commercial, forget about the clinical part of the organization, but just the commercial part of the organization. Right? Like you're, you don't even own your sales data, right? That might come from IQB or.

Speaker A: Exactly, exactly.

Speaker B: And then your CRM has your call data and your marketing, whatever you're using for marketing has your non personal promotion and then the formulary status lives somewhere else. And it's like you could just go on and on all day, let alone patient data. That's not even talking about patient data. But that's who, who, which patients are seen by which. Maybe it's the EMR data, right? There's all sorts of stuff from different sources and to me that's the natural application for AI to start to query one place and have IT access all these different data streams and pull them together. I guess where I don't understand is where does blockchain fit into that?

Speaker A: That's actually a fantastic question. The way, okay, so think about this, right? When you're having an AI agent actually try and talk across EMR M systems, think of EPIC and Oracle Health. Now you know that these two EMR systems actually don't talk to each other. That is the interoperability. If these two systems are actually hosted on a blockchain, you don't have to worry about the interoperability piece because that gets hosted. You don't have to worry about safety. Privacy is no longer an issue because it is constantly audited. It is completely decentralized. So not one person actually owns it, not one organization actually owns it. It's all on a decentralized. Now think about this is where you add an AI agent on top of this base layer so you can have your AI analysis and the data is actually getting pulled from the blockchain layer. So the issue that you're currently facing, the reason why we're not able to be like hey, I Need iq, I need CRM, I need these hundred other things where a company is trying to figure and trust me, we are trying to figure that right. How do I get these different data sets to sit together? Maybe on a databricks or any of that, but it's a constant effort that you have to make. Now if this was done industry wide where you host everything on a blockchain, you don't ever have to worry about this. You're just going to have to deploy AI agents and actually run that data set across. Because you're not talking about just an Oracle Health or an EPIC or you're not talking about Salesforce data or iqva. You're talking about everything sitting on a blockchain across the spectrum and you're running AI agents. Depending on the question you have. It's, that's more I would say your utopia if you. Right.

Speaker B: Uh, and again maybe I'm just don't have a complete understanding but how does taking it from these different places and then moving the data to a decentralized location, how does that solve privacy issues? I'm not following.

Speaker A: That's actually fantastic. So the reason is because when something is decentralized, nobody owns it. So you and I actually cannot look at the data from a specific person perspective. Right now it is being audited. So the way blockchain works is it's individually owned, your data is owned by you. Okay, but um, it's your choice, it's your consent to share it not just with insurers, but with claims, with iqvr, with et cetera.

Speaker B: If you walk me through an example, what type of data would I be saying yes, I can share my data here.

Speaker A: Say for example, a person gets detected with high HVA1C. Okay, now if you're looking at a clinical course of action, you consent to the fact that yes, my data can be utilized for personalized medication for myself. Now when this data actually sits on a decentralized platform, it doesn't say that this data is belongs to Jonathan. It's a data of person X. Right. You know, it is Jonathan's data to patient X. Now think of multiple patient X's that are sitting on that decentralized platform. You look at different case studies, you look at different ways of trends that what worked for HbA1c lowering given XYZ statistics and boom, you have some kind of patient outcomes that you would be looking at. So now you have a real world example if you will. Now let's spin this as a use case within a doctor's office. The doctor never sees that blockchain. The doctor never sees that decentralized piece of data. We just have an AI agent with patient X who's consented to share their data to get more personalized medication and be like, okay, HBA1C, X, Y, Z. These are the trends that we're seeing with Jonathan. Based on research, this is what we're seeing is most likely to help him. Whether it's drug X versus Y, or it could be even better, maybe this person is eligible for experimental drug X, Y, Z. So this is where you would have more of a real case example. Again, utopia. Er.

Speaker B: What I liked about that example is there you could see the motivation for the patient to then share right ties directly to me as the patient. Hey, if I put this out there, I get this agentic support and it can help enrollment in a clinical trial or give me the best possible treatment or something that I or my doctor might not otherwise be aware of. Is that, or are you seeing, is that the mechanism, or is there a different mechanism?

Speaker A: M. I would say that's the hope, right? You eventually want to get to personalized medicine, if you will, because we know that the shoe doesn't fit every size. It's different for everybody. I think that's where, you know, biomarkers have come into place. But again, it is so limited in terms of clinical trials, et cetera, that it's never come to everyday practice. One of the things, and I think this is where the part where I actually saw this as a huge use case in my previous world with Teva, uh, we actually had a digital. It's interesting, it was a digital inhaler where you have these actual sensors. It was called the digihaler, where you have these actual sensors within the inhaler. So every time a patient takes an inhalation, you get their peak inspiratory flow rate, you get their expiratory volume, and you get 500 data points in real time that you could see on your end. Of course, you don't know who the patient is, but that would help us improve the algorithm into understanding what are the different precursors that gets somebody to take their rescue inhaler. So you're. If you're taking a rescue inhaler, you have an imminent asthma attack. And the concept of personalized medicine became more and more stronger. In my head, the challenge that we were constantly looking at is we didn't have the entire picture. We didn't have the EMR data. We did not know the history of this patient et cetera and that's where we tried to, I would say, weave that channel together by doing partnerships again. Another challenge, interoperability. Right. Think of this as two people speaking different languages. Somebody is speaking Spanish versus another person. Hey, I'm Portuguese. We can't seem to communicate. That's where you need a translator in the middle. And it just adds more and more, I would say, of confusion to a certain extent, which is where having that more neater solution comes into picture. AI cannot solve for interoperability.

Speaker B: Yeah.

Speaker A: AI cannot solve for the fact that you have data fragmentation. That is where blockchain comes into picture.

Speaker B: Yeah. So at one point in my career I worked for Athena Health, and I forget the exact number because it was maybe 10 years ago, but I want to say we had maybe 12 or 13% of the patients in the US were on Athena. I might have that number wrong, but it was roughly that. And I was actually, I was talking with a company, one of your former employers, and what we were trying to do was take guidelines and use that to basically create an algorithm that say a patient has X, Y and Z criteria. That means they're a likely candidate for, for this treatment. You should test them for that. One of the things that prevented them from moving forward was the siloed nature of things where it's, this is, we can do this, but this is actually a lot of work for this thing that doesn't even affect that many people. And you guys only have, call it 10% of the patients population. So, uh, when you did the math, they're like, there's not a lot. There's not a lot of people there. And one of the challenge that they and we ran into was like Epic and Cerner. At least back then they were pretty stingy with opening up their data again. I don't know what the current situation, situation is. I'm not really into it. But it's one of the challenges that sometimes comes up is that there are like financial motivations for some of these companies to keep their data to themselves because it's just, it's so valuable, potentially 100%.

Speaker A: I think one of the main ways we actually got more stakeholders to be a part of it was because this is around Covid time. And that's when you had the remote patient monitoring reimbursement codes that came out. There was actual value to now start Abby, NDC codes, etc. To remotely monitor your patient, especially for a respiratory disorder. Again, having said that, this kind of goes back to the theme of you have to have access, support Right. Data is it. There is a monetary aspect to data, 100%. I think that's the whole premise of how, uh, IQV has come up to be iqvia, if you will. But I feel like, again, the question then goes back is, who owns that data? Is it the patient or organization? I think that's where a lot of These concepts of Web3 are coming up. I think we're going to see more and more of that as data does become a stronger currency as we move ahead.

Speaker B: Are there. So you have in your current role a global remit. Right. Are there any countries that are doing this particularly well or leading the way in this type of stuff?

Speaker A: Honestly, I wish there was an answer to that, but because I'm in Vaccines, you're looking at an even more dated angle because it is at a population level. So now you're no longer talking at an individual level. Everything's more CDC and who the answer is actually, no. I wouldn't say that either of them are doing a great job at getting all of that data together. However, I think a lot more countries are now cognizant of the fact that who owns the data? That is where a lot of the European Union and European countries are getting more mindful about privacy, data safety, sharing of their data, et cetera. So I would say it is definitely a step towards asking the right question of the ownership of data. And I think once you get into that conversation, you very quickly jump into, okay, so if a patient owns the data, how do we actually get to the next step of helping them with that data? And that's where a lot of these concepts of interoperability, et cetera, will start. Coming up.

Speaker B: This episode is sponsored by Structured Meetings. It's a teams app that turns your own Teams instance into the perfect advisory board platform. Save money, get insights faster, and keep your data secure. Check it out at www.structuredmeetings.com. Yeah, that's so interesting. As you're talking, I was remembering this is again, a while ago, I was talking to somebody else at Merck Vaccines. I should look him up to see if he's still there. But I remember the challenge. We were talking about all this tech stuff. And the challenge, the challenge I'm trying to solve right now is my vials are breaking when they're getting driven over bumpy roads in. I forget what country. Somewhere in Africa. We're on different wavelengths right now because they were literally trying to solve the last mile problem about getting medicines to patients. But it's just, it's so Interesting how even a company like Merck, depending on what you're looking at, whether it's vaccines, whether it's us, whether it's global, whether it's whatever, like you are probably dealing with things that are 180 degree different challenges from other parts of the organization.

Speaker A: 100. I think vaccines is a different world altogether. You may have better data in a lot of other therapeutic areas. You will not find that in vaccines for sure. Now think about dengue, right? Yes. You could have an upcoming vaccine in dengue. It is literally impossible to figure out even the current incidence and prevalence in dengue just because it has not been measured as well as we would like it to. So you're looking at, again, like you said, very different worlds out there. And it could spin one, uh, hundred eighty really fast depending on what sector you're talking to.

Speaker B: Yeah. So I'm curious. So you've done all sorts of different things. The value and access, commercial effectiveness, and now in commercial analytics. So that seems somewhat far away from a pharmacy background. Like how did you end up here?

Speaker A: It's very interesting. I always say it's following the breadcrumbs to a certain extent. For me, the breadcrumbs have always been. The premise of that has always been data. That's been, I would say, the, the net that ties all of this together. If you think about health outcomes, everything was dependent on data. Even on the oncology side. When I remember, I started actually with an internship at GSK where one of the biggest things they were trying to solve for in oncology is how do we get this EMR data to get connected with claims data. Again, the question just came back to data. They were trying to launch this new product and breast cancer and this is what they were trying to solve for. And again, we used, and it was such a simplistic way, but we used basically three character zip code, last name and age to identify patients. Right. Again, it's very loose, but that's how we were trying to get those two data sets to connect where we could get the longitudinal as well as the EMR angle, if you will. And again, this is so long back, but yeah, I believe, yeah, it's coming back to the same conversation of data. Similarly, while I was at Philips, I was doing sleep apnea copd, where it came back to how can we get more data points for that same patient to give that holistic experience. That led to a lot of acquisitions while we were at Philips, night balance and obstructive sleep apnea products, etc. It's very interesting how the device industry is similar, but yet different from the pharma industry. And again with the regulations, et cetera. It just gives you more leeway, if you will. I would say I had the most fun in that role because there was a lot more freedom, if you will, when it came to the data angle. Coming back to salesforce, effectiveness, commercial effectiveness, solving for similar angles. Just the question is different, but it came down to that same data angle. Iqvr, Salesforce, et cetera, your lens sort of changes, but this whole process does not. I would say that it ties together very neatly. If you look at it more from a macro angle, when you look at it from a microscopic, you're like, ah, this is different. But when you take that lens further ahead, you'll be like, ah, uh, this kind of makes sense now.

Speaker B: Yeah. So there was, as you were talking about the original part, people owning their data. What I was thinking about was, I think it was called Google Health, which ran maybe 20 to 50. It ran for five years and then they, but basically that was what they were saying was that patients are going to own their data. Uh, you're no longer going to have to go get a CD burned and bring it from one hospital to the next. It's all going to be interconnected and interoperable and it didn't work. And I'm just wondering what's your take on why that is and that what you're describing, this utopia, right? Is this coming from pharma or are you seeing this as coming from uh, some type of an outside provider and being used by pharma to support. And the Google help one was free. I actually did use it because I use stuff like that, but I don't know, it just, I guess it didn't get uptake, it didn't get traction and they killed it.

Speaker A: I think it's very interesting. I saw a lot of, I would say, themes of this. I used to visit this conference hlth, uh, and it's fantastic, right? It's basically tech and healthcare. You would have Amazon Health, Google, all of them come in and basically you're talking about more revolutionizing the way you would look at healthcare. Because they were not thinking of healthcare as the patient angle or the clinician angle. They were looking at it more from a technology perspective. And it's a very fresh lens because sometimes you're so deep in the weeds of talking about outcomes and clinical trials that a, uh, newer lens actually helps you think about it in a different way. But having said that, the way I think this shift is going to happen is based on how we try to adopt AI. Because this shift is going to happen because we're investing a significant amount of money into AI. But when we start hitting those blocks, that's when we're like, let's come back to the root cause. It's been ignored for a very long time, especially within health care. But I still feel like we've come a very long way, especially with these mergers. You would see Optum, CVS, Epic Concern or etc. One of the ways they're looking at it is, oh, uh, let me take over one company and get all of that data together. It's not going to solve a problem. Uh, yes, it's a band aid solution for now, but it's not going to solve the broader problem. I would say within the next five years, this is going to be a very different conversation because I don't think in 2020 we would have been talking about AI the way we were talking now. I know I had a hard time convincing people why predictive algorithm is going to be the next big versus right now. It's taken for granted that predictive algorithm is going to be something everyone's talking about. LLMs. Um, at that time, I had to explain what LLM was. I feel like five years from now, we wouldn't be talking, does blockchain make sense in solving interoperability? We're going to be like, okay, which platform and how should we look at it and how should we take that into account?

Speaker B: That's really interesting. Yeah, I was. I, uh, just while you were talking at Google, it was 2008-2012 that the program from Google that I was talking about ran. But if you think about it, from what you're describing, is a much better value proposition for the patient than you don't have to go and get a CD and bring it from one office to the next. Right. So I feel like that is. That's what everybody is doing now. I'm already doing that. Like, oh, man, my knee hurts. Is, do I need to go see the doctor? Pull up Dr. Chat GPT and see what it has to say. And I'm sure the doctors are annoyed because people are like, oh, chatgpt told me this, and then they have to deal with that. But yeah, I love the concept and that's so interesting. I feel like I've learned something new about blockchain. I think I'm getting it now. So I appreciate you educating me on that today.

Speaker A: Believe it or not, actually, a lot of this I saw themes of this. I was listening to this Nvidia call, shareholder call. And that's where they said the next big thing in AI within the medical field is going to be radiology. They're like, you're not, you're not going to need radiologists anymore simply because AI is going to do such a fantastic job of reading these different images, if you will. And I think the push for us is, I don't 100% agree that AI replaces that human angle. Just we were like, oh, robot's going to come and take our jobs away years back. That was the conversation. Because it misses that strategic angle. It misses that, I want to say, common sense angle that humans bring to the picture. But I think we have a much bigger thing to solve for right now for AI to really give its full potential, and that is the data problem. So once we look to solve for that and build the right infrastructure, I think we can maximize what AI can do for us.

Speaker B: Yeah. That is, to me, maybe a much more optimistic or interesting perspective of. That's where I've actually seen it be helpful. Right. Like, I've done a bunch of stuff with the AI, use it to summarize notes, and it's fine for that. Right. I have a really annoying task with these podcasts where I have to get Somebody's image from LinkedIn and update it with a title and put it in a certain format for. I'm not a graphic designer. I don't like doing that. Right. So I did. I built a little AI agent that can do.

Speaker A: That's cool.

Speaker B: But some of the stuff that we were talking about before, about connecting systems, it's not actually making the decision for you, but it is coalescing the information and presenting it to you in a way that can help you make a better decision supporting the human rather than trying to replace it. Because I think what we're seeing is a lot of these companies that, whether they even believed it or just using it as an excuse to lay off tons of people on behalf of AI, are now retiring. Right. Because it's not there. I'm, um, on my soap like a soapbox now. But it's like we've been hearing this for three, four years. Can you name one piece of AI art, for example, that has actually made any type of an impact or moved anybody in any way? It's not completely derivative and it's okay, that might be interesting or might feel fool a bunch of grandparents on Facebook, but it doesn't actually do anything positive from that sense. But if you get into what you're talking about, that is an actual real benefit. Like, mhm, looking at data and maybe bringing a drug to market faster. Right. That is something that is very within the realm of.

Speaker A: I agree. For me, it's honestly how it impacts patients. Right. If you have data and AI that even helps one patient, they've done their job. I think that's where I would look at. And a lot of times, again, we all tend to use ChatGPT or Claude and be like, hey, you know what? I have XYZ symptoms, tell me what's going on. And again, sometimes it's a much better version of Google, if you will. But it's never going to replace a physician for us because you need that assurance from your physician saying, hey, you know what, it's just a war and it's nothing scary, it's just showing it to me. Um, and I do that. I do that with my three month old all the time. You know, before going into that panic.

Speaker B: I'm glad I had my babies before ChatGPT like puts your anxiety on steroids

Speaker A: 100% and it will tell you to calm down at the same time. So it's a little bit more crazy there. But having said that, it's not as real yet. I feel there are a lot of real use cases which I absolutely see. But I think there are a few issues that we need to solve for and it to me always goes back to stronger the data, better we understand trends. And that's what AI to me really is, if you will.

Speaker B: You can't build a good house on a bad foundation. Right. And the data is your foundation.

Speaker A: Well, you can build a house with AI. There you go. That is also true.

Speaker B: All right, Arjita, thank you so much. Joining with a, uh, three month old, talking as if you've had a full night of sleep. Thank you for joining.

Speaker A: Thank you so much for having me, Jonathan.

Speaker B: This was a pleasure. This episode is sponsored by structured meetings for listeners who don't know my background. I'm a huge fan of asynchronous ad boards. I used to work for the leading provider of Async ad boards. And not to brag, but my clients did more events than anyone else's. I've literally led thousands from the vendor side, so I can humbly claim some expertise. One of the questions I used to get all the time was, can we just do this in teams? And the answer back then was not really. Teams out of the box is clunky and not fit for purpose. Structured meetings change that by creating an Ms. Teams app that lets you have these discussions in a really smart way. In your own teams instance, it changes teams just enough to check all the boxes for what's needed for great ad boards. And also it's super fast and it saves people a ton of money. Visit www.structuredmeetings.com for more info. Alright, back to the show.