Amar Drawid [
00:00:00]:
If you are doing generative AI, you need the data in place for you to do that. Otherwise, you're just doing your one right. The importance of data and the amount of time that people actually spend in putting the data together, that still has not changed. Now, the last aspect of this, which is analytics, right? So you're doing different type of analytics now here you're doing generative AI. Of course that's a bit different, but still have to have the data in one place and it needs to be clean, it needs to be governed, it needs to be a high quality. All of those still are the same.
Steve Swan [
00:00:32]:
Welcome to Biotech Bytes, where we discuss technology trends with leaders within our industry. We discuss their thoughts and feelings about what's going on in the industry, what's new, what's coming, what's happening now, I'm your host, Steve Swan, and today I have the pleasure being joined by Amar Drawid. He is the chief AI and business consulting officer at Agilisium Consulting. Did I get that right? Agilisium.
Amar Drawid [
00:00:54]:
Agilissium.
Steve Swan [
00:00:56]:
Agilissium consulting. There we go. Whoo. So I didn't get it. Now I got it. Agilisium Consulting. Well, welcome, welcome, Amar.
Amar Drawid [
00:01:05]:
Thank you for having me here.
Steve Swan [
00:01:07]:
How are you today?
Amar Drawid [
00:01:08]:
I'm doing pretty well. Good.
Steve Swan [
00:01:10]:
Well, good. So what I usually like to do at the onset is just give folks an opportunity to give us a little bit of your background. What kind of led you to being the chief AI and business consulting officer at Agilisium? So tell me a little bit about what's led to this led to today.
Amar Drawid [
00:01:29]:
Sure. So my background has been in heavy and AI. So I started out at Yale doing my bsms in four years, and that was combining machine learning and AI with computer science and biochemistry. And then my first role at Sanofi was doing a lot of bioinformatics, a lot of machine learning analytics. I did my PhD, actually, in machine learning, really focused on developing two algorithms for human immunology and oncology. And then I moved around a bit doing, I would say, some translational medicine in clinical development, where I did a lot of biomarker strategy work, also doing some clinical trials. Moved to commercial with ZS Associates first, where I developed the first model for predicting overall survival of a blood cancer. Then I moved into finance, where I did a lot of financial valuation for companies, and then came back to data analytics, leading, really, AI and data analytics.
Amar Drawid [
00:02:38]:
First as the us head of data analytics at Celgene, later on as the global head of data analytics and infrastructure at Novartis. And this is my latest role, which is I'm the chief AI officer at Agilycm. And so as you see, I've covered research, development, finance, medical affairs, as well as commercial. And so this role actually allows me to work across the entire pharma value chain from research to commercial. So that's, I really love it.
Steve Swan [
00:03:09]:
That's great. So how long have you been with the company with now? And how many years do you have in biotech pharma total?
Amar Drawid [
00:03:15]:
I've been with the company for over a year now and we have done a lot of transformation of the company during that time. But I've been in the industry for almost 25 years. My entire career I've been in the pharma industry.
Steve Swan [
00:03:28]:
Great, great. Wow. Thank you for that explanation there. Focusing on AI has been a big, big component for everybody in the world. Right. But in our field, you know, as it pertains to us, you know, a lot of my podcasts that I've been doing are with the technology leaders within different biotech companies and such. Right. And, you know, AI certainly is on the front burner for everybody.
Steve Swan [
00:03:56]:
So as you go around, because you interact with more than one organization, you interact with different folks. Right. So what are some of the common trends you're seeing today? And I guess I could dovetail off that. How do you help them get to wherever they're going or not getting to that they want to get to? But I guess first, what are some of the trends you're seeing?
Amar Drawid [
00:04:18]:
Yeah, so one of the biggest trends I've seen now is with the whole organizations, both it as well as the business organizations, is to move into generative AI. See, AI as a term has been, it's been used in a lot of different ways, and it was by a lot of people, it was being used more to mean more like the machine learning aspect before even automation, a lot of that right before, and I would say a couple of years ago, a lot of the AI related projects were more the predicted analytics, the prescriptive analytics, and using machine learning to basically to figure out what to do next in your business. More of the focus. And I also saw with it a lot of big infrastructure projects, but also some of these AI projects. This trend that I'm seeing now over the last year is a move away from a lot of these big infrastructure projects and also from a lot of these machine learning projects into generative AI. And everyone, every client that I talk to, they are talking about generative AI. They want to get into it. Some of them are in it but then a lot of them, I see, are more on the PoC, stranger proof of concept stage.
Amar Drawid [
00:05:35]:
So they're trying out a few things here and there. Everyone seems to have at least 50 or 100 use cases, but they don't know how to actually make progress with those, those use cases. But then there is a lot of effort into it. I don't think anyone has cracked the how to actually have a generative AI as an enterprise solution. People are trying there, but I don't think anyone has gone there at this point in the industry. But of course they'll go there. But I'm also not sure like they're considering all the different aspects of that, especially about cost, etcetera. But the way we are helping them right now is so we actually, I came up with a generative AI framework specific to life sciences and pharmaceuticals about how should people be thinking about their use cases and what is the framework they should be using around that.
Amar Drawid [
00:06:31]:
So talking about not just the use case in the different domains of pharma, but also what is the feasibility of each use case, what should be the value that each use case should be bringing? Because see, the important thing is that I haven't seen anyone having a really good strategy and roadmap for generative AI. And so that's what I'm trying, I'm trying to, when I talk to a lot of our clients, that's what I try to have them do, which is have a strategy. I mean, right now is, okay, you want to get into it, that's fine, but that's more like hype. Okay, well, where you need to get into is what is the business use case that you're solving? What is the value you're adding? Because if you don't do that a year or two years down the road, you're not going to be able to do a whole lot of that because people are going to ask questions. Okay. Well, yeah, it's great. As a technology, what is the value that is added to us? It needs to be very value focused discussion, and that's what I'm trying to do. And also other thing we have at this point developed over 30 generative AI solutions and focused on really the business use cases and the business questions.
Amar Drawid [
00:07:48]:
So these are the business solutions to solve specific areas. So a lot of the work around generative AI is very much I'm focused on right now.
Steve Swan [
00:07:57]:
I would say that, and I don't know what the numbers look like. Right. But you know, there's a lot of organizations out there and I'm not just picking on biotech and pharma. There's a lot of organizations out there that have it as a cost center, right? So they're not strategic a lot of the times and they're not seen as a value add. But so even to do what you're doing in certain cases is a complete mind shift for them, right. Because they've been just, you know, managing their call as a cost center as opposed to, you know, what are we bringing to the bottom line? What's the strategy behind this? What are we doing? Right. So a lot of times I could see that being a big uphill battle for you, right? But, you know, if you can get that mind shift and get them to think about this, boy, they could, they could apply that to even the rest of their, it. Not just Genai, right?
Amar Drawid [
00:08:52]:
They can. It depends on the actual function that each group is playing, right? I mean, here as well, right? When you're looking at generative AI, what is it actually going to do, right? Is it going to save you costs? Is it going to increase your revenue? Is it going to be able to reduce your risks? That needs to be defined in business terms and different teams in it. They have the different functions, right? I mean, yeah, you have the compliance, etcetera functions that are actually reducing the risk. But a lot of times it's a cost function because these are the necessary things to do. What we need to do from generative AI is, yes, there are some of the use cases that are reducing costs, but there are some use cases that can actually increase the revenue. So in that case, it becomes a discussion. Okay, well, where can we increase revenue?
Steve Swan [
00:09:42]:
Yeah. Well, and also know when Amar comes in, right, and Amar sits down with XYZ business group and the technology folks, what's, where's the end zone? Is what Amar needs to know. Where's the end zone? What am I shooting for? What are we doing? You know, can I help you get there? You know, how many times in a month do I have that conversation? Right? Someone will call me and they'll just say, hey, listen, I need X, Y and Z. Well, tell me a little bit more. Why do you need X, Y and Zenith? You know, what level, you know, all that stuff for what I do, you know, sometimes I can't help somebody get there. Either the person doesn't exist or it's just, maybe it's out of my expertise or whatever, you know, but yeah, you got to understand, and I have to understand, where am I going? What am I doing? What's the strategy? Like you said, and then we can come up with a roadmap, you know, you need to do this, do this, do this, do this. Then we can get to that end goal that, that ends up. Right.
Steve Swan [
00:10:36]:
That goal, right?
Amar Drawid [
00:10:37]:
Yes, yes. And the key here is to have people who understand both it and technology, but as well, business, because we need to be able to tie whatever people are doing in technology to the business outcomes. Right? Because that's the end goal. The end goal is always the business outcome. How are we helping the patients? How are we improving the patient lives? Now, that could be either through better engagement with the patients or providing them better support, or even providing better support and providing the right drugs to the physicians. It could be developing better drugs and doing clinical trials in a much better way and bringing the drugs to market faster way, or it could be even in research where it developed or actually discovering new drugs, it could be in any of those. But then the end game should be tied to what the business outcome should be.
Steve Swan [
00:11:37]:
Now, the name of your organization, is that tied to our framework that we developed? Are we going with an agile mindset here?
Amar Drawid [
00:11:46]:
Yes, absolutely. So it's.
Steve Swan [
00:11:49]:
I had to ask.
Amar Drawid [
00:11:51]:
It's a tech organization and very much big into, started out very much into data and application engineering. Data engineering, that's very much of a forte. But of course, now it has become more of a data analytics organization. So we do a lot of data and analytics, but at the same time now we have also become a very big generative AI player as well. I believe we have one of the largest portfolios of Genai solutions in life sciences at this point. So we have really reinvented ourselves. I mean, of course, the data engineering and data aspect is there. It's very strong.
Amar Drawid [
00:12:29]:
But then this is a new angle that we have developed for the company, which is the generative AI having the.
Steve Swan [
00:12:35]:
Foundation, the data, the data engineering, the data analytics. That's 80% of the battle or more.
Amar Drawid [
00:12:42]:
Yes. Yeah. Because see, even if you're doing generative AI, you need the data in place for you to do that. Otherwise you're just doing a one off. Right? For you to do Gen AI in a sustained manner, you're going to have to have, that hasn't changed. The importance of data and the amount of time that people actually spend in putting the data together, that still has not changed. Now, the next aspect, the last aspect of this, which is analytics, you're doing different type of analytics. Now here you're doing generative AI.
Amar Drawid [
00:13:16]:
Of course that's a bit different, but still have to have the data in one place, and it needs to be clean, it needs to be governed, it needs to be a high quality. All of those still are the same.
Steve Swan [
00:13:25]:
And that's your foundation. Right. So all the leaders that I talk to on my podcast from the past, they all talk about that we can build the greatest thing. We can have Amar come in and build us something that looks great, but if we don't understand the data and we don't have it clean, we don't have it structured enough where we can actually use it. Yeah, doesn't matter.
Amar Drawid [
00:13:43]:
Doesn't.
Steve Swan [
00:13:43]:
We can ask it to do anything. It can't do anything because we're not putting any garbage in, garbage out. Right. So we're not putting anything quality into it, so we can't get anything out of it. Right.
Amar Drawid [
00:13:52]:
Yeah, absolutely.
Steve Swan [
00:13:53]:
So now would you say, I mean, obviously AI and Gen AI, the big picture, right? And then obviously data going in. Are there any other trends that you see tangential to this or in addition to this that we should be thinking about in life sciences that has either helped or do you think may help us now or in the near future? Anything come to mind or anything that you're seeing as a trend that's coming down the road?
Amar Drawid [
00:14:18]:
Well, I'm seeing different trends across the value chain. So, yes, in general, there is better and better recognition about data analytics across the value chain that I'm seeing this. I mean, this was not necessarily the case earlier in commercial, I'm seeing a lot more trend into going into omnichannel marketing. So quite a bit going from just multi channel marketing, which is the channels independent to each other, to really coordinating those and orchestrating that. So that's definitely a big trend that I'm seeing on the commercial side. As a result of that, some of the roles on the commercial side are changing now and becoming more digital digitized. So next best action is one of the big things that I'm seeing there. On the clinical side, I'm definitely seeing a lot more like, you know, how do we actually do the clinical trials in better ways? How can we use real world evidence much more, and how can we even do, like, more analytics in clinical side to actually get more out of the clinical trials we're doing? And on the research side, I'm also seeing a lot of this use of these new technologies, generative AI, and of course, it's based on neural networks, about how we can speed up identifying the targets, how can we speed up identifying the molecules, the drug molecules for these? So I'm seeing these trends, and I think all of these are really good trends to have across the board.
Amar Drawid [
00:15:51]:
I think the progress you're going to see is probably going to be more incremental than very revolutionary at this point.
Steve Swan [
00:16:00]:
Right. Well, I mean, we got to walk right before we run.
Amar Drawid [
00:16:03]:
Yeah.
Steve Swan [
00:16:04]:
That's what we're learning how to do. You know, people are calling it the Wild west, or this is like when the Internet came out, those kinds of things, you know, I mean, there was a lot of hype. There was a lot of headlines around AI and Gen AI. And did it get ahead of itself? Possibly, you know, I don't know. You know, but it's. It seems like we're, like you said, we're getting there incrementally. Right. In our industry.
Amar Drawid [
00:16:26]:
Yeah, yeah, yeah. And in pharma, the issue is that we have to be very careful about it. Right. Because we. We are a very heavily regulated industry, and then we have to have very strong regulation around Genei as well, especially when we are dealing with patients or anything related to that, because they need to have only the accurate information, and that is key. So I would say, you know, Pharma will adopt this, but I see that more like it's going to be a cautious approach, so that it is. It fits into the framework, the regulatory framework and the compliance framework. But, Jenna, as a technology, I mean, it is.
Amar Drawid [
00:17:08]:
I believe it is historically the fastest adopted technology ever in the world. And a lot of things are just not figured out at this point. And you're seeing every month, every few weeks, there is these new large language models that come in. They change the game. It is very much like a moving target right now, and you have to be very careful about how you can harness that. I mean, it's fantastic. I think it's going to make a lot of big difference in how we do business in pharma. But the idea is going to be how we actually.
Amar Drawid [
00:17:45]:
That's why the strategy and the roadmap are very important in driving this. And it's also not just about, okay, well, here's the latest new OpenAI model, or Gemini. But it's not just like getting. Okay, well, what is the latest and greatest it's about. Okay, well, what value is that bringing to me? Right. That's the main question that needs to be asked.
Steve Swan [
00:18:04]:
Well, yeah. And I also understand that I think the FDA came up with their own, I don't know, internal group handling AI now.
Amar Drawid [
00:18:16]:
Yeah, I believe so. I believe so. Yeah. So I think there is some presentation, there was some meeting around that. So some, some videos there that are out there as well. So FDA is also trying to figure it out. I think everyone is doing the right thing by really going at it, going to basically espouse it, which is fantastic. They're not shying away from the new technology, but I think it's just going to take time to actually just, I'm not talking about years and years and years.
Amar Drawid [
00:18:47]:
I think it's at least some time to just see, okay, what is possible, what is not possible, and then how do we, how do we actually ring fence some of that and how do we get value out of it?
Steve Swan [
00:18:58]:
Well, like anything else, if there's economic incentive potentially on the other side of it, gold rush, let's start with that. And we can go all the way up to today. Right. But if there's potential economic incentive on the other side, it's going to create foot traffic, mental traffic, whatever you want to call it, people investigating it and looking at it to see what we can do or what we should do or what we will do and whether those economic incentives bear fruit. That has yet to be determined. Right. But I think for our industry, the things that we all point at, weve already gone through some of the automations and things like that. But you just talked about the discovery.
Steve Swan [
00:19:35]:
Can we take that? Because when you see these CEO's getting beat up in front of the government on C SPAN for their drug prices, they always talk about their research period, their ten year lead, time to get something up and running or more. If we cut a year off that, if we cut 18 months, that's huge. That's a lot of money. That's economic incentive. That's money and time for everybody, for the patients. I mean, start with the patients, work all the way back to the person that was the chemist in the lab. So it's saving everybody time and energy. So I think that there is, and everybody sees the potential for the economic incentive.
Steve Swan [
00:20:17]:
We just have to get that whole value chain aligned. Right. And that's what I think we're in the process of doing, I think, I hope.
Amar Drawid [
00:20:24]:
Right? Absolutely. And that's why we have to find areas where it's going to make a lot of difference. It's going to make a big difference.
Steve Swan [
00:20:33]:
Right.
Amar Drawid [
00:20:33]:
And then the way I see it is that, I mean, I talked about three of these before, like increased revenue, cut costs, have better regulation, but also another thing is get better quality is another one. Can we have something, uh, can we do things with a, with a more better quality and more consistency? Because that is just going to then end up in, whatever you're doing is going to, you're going to be able to do it better. Right. So that's a, that's the thing. So those are some of the things. But we have to, we have to quantify that, which is not a matter. I think the revenue cost is a bit easier, but it's, again, even that is not that usually clear cut because a lot of things affect the revenue, a lot of things affect the cost. These are some tricky aspects to calculate.
Amar Drawid [
00:21:21]:
But I think a lot of times if something is really adding value, your business stakeholders usually do get convinced that, yes, there is value because if you take that away, then they're not able to function well. So that to me is a good.
Steve Swan [
00:21:38]:
Indication you'll hear about it, right?
Amar Drawid [
00:21:41]:
Yes, they'll hear about it. Yes, exactly.
Steve Swan [
00:21:43]:
You definitely hear about it. Now, I think one of the things I like asking folks during the podcast is, you know, what makes you different, what makes you unique, right. And I'm not just talking, you could talk about you, right. But you could also talk about your organization, right. Because there's a few folks like yourself and there's a few other organizations that do what you do. But you know why, you know, when Amar, I know why I would, but I don't want to load the, you know, I don't want to cloud the question why would someone say, you know, hey, Amar's my guy, let's do this. Because Amar's got this. Is there something that you say to folks or something that you would point at that would say, hey, this is my guy.
Steve Swan [
00:22:24]:
This is the one I want to talk to about Genai and this is the guy that's going to get me there.
Amar Drawid [
00:22:27]:
Yeah. So I think one of the biggest differentiators that we have and myself personally as well as our company is bringing both the business and the tech together. So one of the things I've done as the chief AI as well as the business consulting officer at Agileysium is build a team of subject matter experts who are marketers, they are scientists, they are clinicians. So they are not really data analytics experts, but they are experts in the business aspect of pharma. Yeah. And they work very closely with the Genei techies. And so the way we build this, these solutions, we don't build the tech solutions, we build business solutions. And so the idea is that the tech solution gets developed.
Amar Drawid [
00:23:22]:
But then there are multiple reviews with the SME's and this we call the Business Review and we call this iterative business reviews because there are a lot of iterations of these because what we want to develop is a business solution, not a technical solution. And this business solution is something that we then give to the clients. But as a result, because of this, this is the change in paradigm and not a lot of tech companies have this, which is that we have our SME's who actually talk to our clients and this is a business conversation about what is needed and then we will do the translation into what is it that's needed for tech. So that's something that I believe is a pretty big differentiator that we are seeing. And a lot of clients are saying, oh, that's fantastic, right? Like, I mean we don't have to explain to you everything about what we actually want to do. We can talk to our peer who's a subject matter expert in your company. So that's something that we've been able to do. And of course, like, you know me as I described, like, you know, I've worked in research, development, the medical affairs commercial, like all of these, right? So that's how like I'm able to bring in the expertise from any of those.
Amar Drawid [
00:24:33]:
And then, so from whenever in the value chain, the question is I'm able to bring that in and then talk about. But also as a result of that. See, one thing also you have to think about is, see right now, a lot of the companies, they have their different functions who are thinking about it separately, but you can think about like the technical solution for many of these, something in research or something in commercial, the technical solution could actually be the same. Although the business question and the business solution that you're looking at is completely different because you focus on your area. So what we're able to do is say, hey, you know what, you can develop this and this business solution is great, but then the technical solution of that, you can also apply that to that other thing. And then you just have to have like, you know, put that into business terms and then you may need some bells and whistles to make it a business solution. But there is the underlying commonality. So a company should think about this, right? Like they should think about what are some of the common technological solutions they need to build which, which can be applied across the board for a lot of different business solutions.
Amar Drawid [
00:25:45]:
And that's what we actually, even internally as well, I mean we are, you know, there are different, there are specific technical solutions that we have. But then for different business questions that come in, we're like, okay, well for this one, you know what? This particular tech solution is very applicable to that. So let's use that. So as a result of. That's why we were able to build so many business solutions so quickly is because of that. Because. But you have to under that, not only do you have to understand the technology very well, but you also have to understand the business very well to be able to connect all of that.
Steve Swan [
00:26:17]:
And I know I'm probably beating a dead horse. I'm going to say it again. You also got to know the data. You got to know what you're dealing with there, too. Right? Because if Omar comes in and he's dealing with a research group, maybe their data is great, maybe their data is in good shape. But the commercial team maybe not so good. You or your team or somebody in your organization has to be able to assess that because you could have that technical solution. That's awesome.
Steve Swan [
00:26:41]:
But again, garbage in, garbage out. So if the data is no good.
Amar Drawid [
00:26:44]:
Yes, yes. And that. That's why it's. Each business question should be treated as its own thing. I mean, that's. That's the important thing, right? Yes, you have.
Steve Swan [
00:26:56]:
That's good. I like it.
Amar Drawid [
00:26:58]:
Yeah. So, yeah, the tech solution is there, but here's the business question that you have. How do you solve it? And what is the data that you have? And then you do actually, you. You actually see how the solution is giving you answers. I mean, one of the things that we're doing, one of the solutions, we build data, detective, which is basically, you give it your structured data, and it's answering questions like, why? Why did that happen? Why not? So it's interesting, we developed that for clinical aspect. So it's amazingly like it was. You know, we asked, okay, well, give me some correlation between a couple of these parameters in the clinical trials. And was by itself was figuring out, well, there should be a chi square test that should be done for this and giving, like, statistical answers.
Amar Drawid [
00:27:43]:
Well, you know what, the basis of this is similar when we go to the commercial aspect. So commercial data, detective, but commercial people don't want the statistics. They want more about. Okay, well, what was the engagement? What are the sales? How are the sales? Up and down. So it's. The underlying tech solution is very similar, but then the way it should be expressed, the way we are doing a lot of basically how we develop the shell of that is completely different. So you have the commercial data detective, which is very different from the clinical data detective. Even though the inner workings are the same, the outer aspects are very different, and they're very much focused in answering what the business stakeholders in that function want.
Steve Swan [
00:28:29]:
Sure. Yeah. So you just answer, it's a technology the same, you just answered a different variable. Right. Huh. Interesting. That's pretty cool. I like that.
Steve Swan [
00:28:36]:
I'm glad we're putting this together. Right. Because there's so many different pieces that I read about and see and talk to people about, and this is, in my opinion, I don't know, it seems like we're putting the whole thing together as we're talking about this and helping me understand it. I don't know about my, my viewers, maybe they already understand all this. I don't know, but it's getting in line for me. So are, are there any other trends that you see that you think about? Because I don't want to keep you all day. Right. So I just want to see if there's anything that there that you've been thinking about or that your company's heading towards that you want to share with us or anything along those lines.
Amar Drawid [
00:29:10]:
Yeah, I think there is going to be. I think the trend had just started. I haven't really seen it very big time into being played out in pharma yet, but I think a lot of the data operations are going to be automated. A lot of the, like the data quality, the data governance work for which some of that is. Yeah, there's some, some people are still doing it manually. There's some automation around that, but I think it will go to a higher level. A lot of the computer programming that's needed or data engineering that's needed around that, that will be automated with Genei. So I do believe that as time goes on, a lot of the data operations aspect will be more smoothened out and then also will be much more automated, will be much more, I would say, yes, streamlined.
Amar Drawid [
00:30:05]:
That's one trend I think it's starting. We're going to be able to see, because, see, one of the big advantages of generative AI is it can write code as well, just the way it can get insights or it can generate content. Right. Those are like review content. So some of the, those are some of the things they can do, but it can also write programming code. So I believe that that's something that will be coming over the next couple of years or so and that will change how the data organizations will be in the future.
Steve Swan [
00:30:39]:
I agree. Yeah. Because right now you've got the really essentially, and I think you and I talked about this when we sat down, you know, you got some of those junior level data scientists who really doing data wrangling, if you will. Right. And they're doing what you're talking about. My daughter's a computer science major, data science in college, and she spent the last two years essentially creating, there's a hundred million person database. And she made it structured enough for them to run AI, you know, to use it with AI. So before that it was unusable.
Steve Swan [
00:31:10]:
But to your point, she had, she didn't do that in two years by herself. She had to automate, you know, her code, you know, different stuff to do different things and. But there's no reason why, to your point, next higher level up, almost eliminate her and let the, let the thing do it all on its own, you know, and it would just, it would just automate all that process. So.
Amar Drawid [
00:31:30]:
Yeah, and I would say that instead of needing, let's say, ten data engineers, you may need just five data engineers. Right. Because, I mean, I'm assuming for a lot of these aspects, each person will have a copilot who they can just type questions and then the copilot is able to answer them those questions very quickly. So as a result, you don't need that many people. And also one person is able to do a much deeper, is able to get much deeper insights than what they're able to do right now because right now they have to do the coding themselves and figure this out. So I believe that that will happen over the next couple of years or so.
Steve Swan [
00:32:08]:
Yeah. Yeah. And to your point, I think it'll recognize it. It'll take care of it and it'll handle it all. I think that's what AI is going to do for everybody. It's just going to make them smarter, faster, better. I mean, I've even read articles about leaders in their management styles and being able to manage employees. AI is going to help them become a better leader and a better manager.
Steve Swan [
00:32:28]:
You know, it's just, it's going to hit everything, you know, eventually.
Amar Drawid [
00:32:31]:
Yeah, it will. It will. The question is how? And I mean, some of the things that I'm seeing interesting. Another trend is I think a lot of people are asking us about automating the clinical writing, the medical writing. So that's, we have a big focus on that. You know, that's where people spend a lot of time. And that, and a lot of times, you know, the, one of the reasons it takes so long to submit to the FDA a lot of the documentation is the amount of time it takes to actually write the documentation. So if that can be made faster, that actually has a direct connection to revenue, because if you can write the documents faster, you're able to submit faster, you're going to get approval faster, and then the drug gets on the market much faster.
Amar Drawid [
00:33:20]:
Of course, it's much better for the patients. And then the company also starts realizing the revenue much, much earlier. So that's one of the big use cases where I can see the value play very much clearly rather than some of the others. But as a result of that. So, I mean, as I said, we're getting a lot of demand on that. We're working on a lot of projects about writing clinical documents, at least the first draft of the document. But that. That will save people time.
Amar Drawid [
00:33:50]:
I mean, days or weeks, and hopefully we can bring the submission time down for that.
Steve Swan [
00:33:57]:
Well, there you go. I mean, if you're able to measure that time, then you're able to measure the direct RoI on it. So that's. Yeah, that's a solid, solid use case right there. Right? So.
Amar Drawid [
00:34:06]:
Yes. Yes, very much. Well, good.
Steve Swan [
00:34:08]:
No, I appreciate it. Thank you. Well, so if there's anything else you want to cover, I always have one last question I ask of my guests. I didn't tell you about this question. I never do. Nobody ever makes to this part of my podcast or something because. So they don't. They don't know I asked this question.
Steve Swan [
00:34:22]:
But if you have nothing else more to add, I have one final question, if that's okay with you.
Amar Drawid [
00:34:28]:
Sure.
Steve Swan [
00:34:29]:
So I like music. I'm a live music kind of guy. As a matter of fact, tonight I'm going to Madison Square Garden to see Pearl Jam. Going tomorrow night, too. I'm going two nights in a row. But my question to you is live music. A, do you like live music? And if so, b, what would. Would you say in your entire life has ever been your favorite that you've seen live, any concert, and if you haven't seen any of that's fine, too.
Steve Swan [
00:34:56]:
You know, some folks haven't seen any, but I like throwing that out. Since I'm such a music guy, I figured, you know what? I'm going to put a little Steve Swan twist on some of these and just ask folks about live music.
Amar Drawid [
00:35:06]:
The best experience I've had of live music was we were in Morristown. Morristown music, I think there's the art center there, and we were at a concert, and there was a Mozart music, that Mozart piece that was being played. And I was there with my wife, and she was pregnant, and my daughter, who was in. In her tummy, she started kicking, and that was to be hilarious that it was a Mozart piece. And then she started kicking. So we're like, yes, she loves music and of course she is. I mean, she is a really good piano player. Both my kids are.
Amar Drawid [
00:35:51]:
But so to me that was really funny that how like once the music started, I, the baby started kicking around. So that's. That's an experience I remember quite a bit.
Steve Swan [
00:36:03]:
There's a lot to be said for that, you know, I remember my kids are a little older, 24 and 21, but when they were little ones, you know, and they may still be around. I don't even know, there was this company called Baby Einstein and this woman, I forget her name, she sold it to Disney right when my kids were young. But anyway, it was a lot of the classical music with just kind of videos going and stuff. But back then we had the VCR tapes maybe when even went into dvd's at some point. But it was a lot of that, you know, baby the Mozart and all sorts of different things. It was really cool.
Amar Drawid [
00:36:36]:
Although I think they did the study with Baby Einstein and they didn't really find that like, you have the kids in like, who are pretty young do that. It doesn't necessarily increase their ability. I think I read somewhere around that. So, like, for my kids, I never, never had like baby Einstein or so, but it was just more about just getting. Having them listen to that and then, and then working with them on that, right. And then like having them like, play, you know, a finger, some of these instruments. I think that was essential for them. I mean, my, as I told you, my, my kids just came back and my daughter just playing the piano for like 3 hours a day.
Amar Drawid [
00:37:22]:
And like, my son is getting mad because he's not getting time, so it's a good problem to have, but I guess it was so, yeah, I get to listen to live music every day.
Steve Swan [
00:37:33]:
That's awesome. I love that. You know, my nephew sits down at the piano and could just bang away a friend of mine's son. It's just like, I'll walk in the house and I'll just hear them, you know, just going and I'll just sit there and listen. It's like, oh, hey, mister Swan. I'm just messing around in the piano. I'm like, really keep going. Forget I'm here, you know, it's amazing to me, you know?
Amar Drawid [
00:37:53]:
Yeah, absolutely.
Steve Swan [
00:37:55]:
Yeah.
Amar Drawid [
00:37:55]:
And we were, I think they were just telling me, they were like, when we're there visiting a relative and lives in an apartment and someone just. And they had like a play on the keyboard, and a neighbor came in, like, who's playing the radio? And they were like, no, it's not the radio. It's like live music. Actually.
Steve Swan [
00:38:13]:
It's pretty cool. That's good stuff. Well, good. Well, I appreciate your time. Thank you very much for joining us here today.
Amar Drawid [
00:38:21]:
Well, thanks for inviting me. It's been a pleasure to talk to you, Steve, and be able to talk about AI and Gen AI in life sciences. It's my favorite subject. So really appreciate you giving the opportunity to talk about it.
Steve Swan [
00:38:34]:
Well, and I think we got a long way to go here, but this is great that, you know, we got folks like you that are helping everybody out. That's awesome. Thank you.
Amar Drawid [
00:38:42]:
Thank you.
