0:00:00.2 Shashank Garg: Hello everyone and welcome to the Intelligent Leader podcast. I’m your host Shashank. And today we have with us, Suvajit Basu. And we are going to have an exciting conversation around the role of AI in the world of business intelligence or AI versus BI. And specifically, look at how AI is disrupting the BI world. Suvajit is an award-winning Digital Transformation and IT Executive who combines strategic vision with hands-on expertise to drive exceptional growth for enterprises. Winner of many prestigious awards, he was recently awarded the Enterprise CIO of the Year at the Inspire CIO Orbie Awards. Congratulations Suvajit, often described as the Chief Inspiration Officer. That’s a new one, Suvajit, I had not heard of that one. Suvajit is known for leading teams with inspiration and translating ambitious ideas into scalable and impactful solutions. His industry expertise spans CPG, supply chain, food and beverage, and media and entertainment. Suvajit, once again, welcome to the show. It’s a pleasure to have you here.

0:01:23.2 Suvajit Basu: Thank you. Chief Inspiration Officer was given to me by Bob Evans, who is a great communications person and it’s not really my official title, but I found it pretty amusing.

0:01:40.3 Shashank Garg: Yeah, of course. Suvajit, just for our audience, would you like to share a little bit more about your professional journey, so our audiences know a little more about you?

0:01:48.0 Suvajit Basu: My professional journey is basically in three parts. And if I go back to my college days, you know, I have a bachelor’s in electrical engineering, then computer science because I fell in love with computers very, very early and then started fortunately went to NCR Teradata. Teradata is one of those huge, huge hardware-based data companies. Right. And that was about 25 plus years ago. So, I was fortunate to work there for four years and after that another four years at SGI Silicon Graphics, the premier company in 3D visualization, et cetera. But they ultimately came up with a product called Data Mine, which was based on ML algorithms and data mining. After that I went on to found a company. We built ERP software for the media business here in New York. That company was acquired by a company out of Silicon Valley in four years and then about 16 years ago went to work for a large CPG company here in New Jersey called Goya Foods and spent the majority of my career there. So ultimately as their CIO. So, it has been a great journey so far and will see what happens next.

0:03:19.5 Shashank Garg: Thank you, thank you for sharing that background and once again great to have you here, Suvajit. Just to get started, I was earlier in the day thinking about how the field of data and business intelligence and AI has evolved over the years. Then I read a stat that said that even today we know that in almost 80, 90% of the organizations, the whole KPI reporting & monitoring is still handled either through spreadsheets or static dashboards. And, you know, executive dashboards sort of continue to dominate board meetings. You know, they give you a pretty good job of giving you a view of what’s happened in the past. However, their focus on the present is very operational. They often don’t tell us what really happened, and very rarely would they tell you what’s going to happen next. That’s what you were talking about earlier, what you were doing 25 years ago. And obviously, by solely focusing on historical data, we are missing the opportunities of the future. Right. And just the context of, you know, is certainly missing. Right. I wanted to sort of open up and let you share your view. How do you think the recent advancements in technology and AI, you know, what’s really happening and how can they solve some of these problems? Just give us a lay of the land in your view.

0:04:47.8 Suvajit Basu: Well, you hit the nail on the head. You are a practitioner, and it shows. So that is what I think is the heart of the issue, right. BI is historical, right? And remember, we had different stages of maturity in BI, right? Where we build to get the data pipeline going, then we build reports, then we build dashboards, and then we build ultimately from that we create knowledge, right? And ultimately prediction and forecasting, etc. I think it’s only now with advancements in AI that we are getting to do all that much more easily. Before that, we had to deal with mathematical models and so on to make the forecasts, etc. But it’s only now that these large LLMs or large predictive models give us the ability to just ask, what is the elasticity of my product? Whereas before we’d have to get a massive set of POS data and make that happen.

0:06:03.1 Suvajit Basu: And just managing that data itself was a huge challenge. Right? So, this is the opportunity that is unfolding right in front of our eyes with AI. And that’s the thing. So, I think AI and BI are complementary technology, like accounting, BI is historical. We are accounting for what happened and then we are presenting that data. One of the challenges with dashboards is, you know, I think most dashboards are like eye candy. You have that funnel chart or a pie chart and that’s it. And there is some tremendous research done by people like Tufte and so on who has spent a lifetime looking at… Steven Few also has done a lot of work in that area, in how to make data actionable. And just by having a funnel chart or a pie chart doesn’t make that data actionable.

0:07:17.7 Shashank Garg: I think you made a lot of good points, and I’ll get to the predictive AI in just a little bit. But just the excitement we had a couple of years ago, 18 months ago, around the large language models and you heard statements like BI is dead. You know, Gen AI is going to replace the dashboards. Everybody’s going just wanted to talk to the data using natural language. And now we finally have the power. You will see things like conversational summaries, contextual insights, role-based personalization, and more pattern detection. And, in a lot of ways, you know, we expected by now, 18 months into it or two years into it, Gen AI would just disrupt BI tools. You would see BI platform companies falling off the charts, and replacements happening, but that hasn’t really happened. Right? And you know, it may be because of various reasons, but I’d like to get your take. Do you know what’s happening there?

0:08:25.1 Suvajit Basu: Well, to me it’s very simple. I think BI and AI are complementary. They’re not one or the other. Right? So people are coming to realize that AI is making certain things easier. But you still need the data foundation, you still need that data pipeline, and you still need the basic reports. And then if you want to create more knowledge or cull more knowledge out of reports, Gen AI is very good at that. Knowledge of data. Right? So, they’re complementary. I see them as complementary technology.

0:09:08.9 Shashank Garg: That’s a good way of putting it for our listeners who are contemplating or who are hearing pitches from Cool Gen. You know, Silicon Valley, VC-packed firms who are claiming they will retire all your dashboards. That may or may not happen anytime soon. Gen AI can certainly make it easy. At the end of the day, whether it’s… AI relies on a very well-developed semantic layer. As you would agree, Suvajit, and as you said, the data foundation and the business layer are on top of it. So, the BI tools have done a great job of creating, managing, and maintaining that semantic layer. And that’s really the hard part. And once that is developed, obviously you can use Gen AI to create wonders. Suvajit, do you have a view on, there’s a lot of technology available, and there are companies that just purely AutoML, like an AutoML capability. What’s their role in this whole mix in your mind?

0:10:12.5 Suvajit Basu: Let me give you a real-life example. So at my previous job, what happened is on Monday morning, all these reports, these BI reports came out and it was like this thick, right? And it went to all the C-level executives and key people. Then what would happen is a very smart analyst would basically look at this data, right? And would create an Excel, Word Excel document that, okay, your sellout level for this is this. These are the good points, these are the bad points, these are the things that you should think of in red, yellow and green. It was just a one-page summary of this thick report, but it took two or three days to come out because the person was going through all the data. That is the part that’s going to get disrupted. I think the analyst itself is going to get disrupted with AI, it’s going to augment that analyst, it’s going to be the co-pilot with that analyst which will come from that data. And people don’t have to wait for three days for that analyst report. It’ll be there on that Monday morning automatically available to you. And that’s the opportunity here.

0:11:31.7 Shashank Garg: I like the way you frame this. So there’s a role for everything. There’s certainly disruption, not in the whole cycle, but there is certainly disruption at the, what you’re calling an analyst role. Right? So I remember, we supported a client in the manufacturing space and we’ve been working with them for almost 12 years and we manage the whole data platform and the BI layer and the executive dashboards and all that. Right? And just a few, middle of last year we started working with them and say, can you really talk to the data? And the approach there was we create this analyst assist or, you know, so that where we combine the whole work they had done the foundational work, the semantic layer, the BI reports and then combine AI along with it so that you have an Interactive AI-enabled chatbot for the executives to allow tailored natural language interaction. And it’s done through the text right now, but very soon you can get to voice as they enable the multimodal capabilities in their organization, which they haven’t done yet.

0:12:46.2 Suvajit Basu: I like that. If I can just give you another example. So I’m an executive, on Monday morning, I’m coming into my… I’m driving in, my drive is half an hour 40 minutes. And in a multimodal AI environment, I can have the AI engine look at my reports and I can ask the questions interactively and the engine can tell me interactively and I can listen and be prepared for that meeting at 8:00 in the morning or 9:00 in the morning. Just like I’m listening to a podcast. But here is my own personal podcast for my company. What I’m going to do this week, who am I going to talk to, what decisions am I going to make and what am I going to suggest making my operations run much better? So, it’s a custom podcast of my reports that I’m going to listen to on my way, and I love that.

0:13:44.6 Shashank Garg: The way you put it. I remember a few years ago, I don’t know if you remember this, when Amazon Echo or the Alexa, sorry, Alexa came out, right? One of the first experiments that people did and AWS promoted, they said hey, you could now talk to your dashboards. And that was so bad, that never worked because it wasn’t intelligent. All it was was, was trying to read verbatim and that never worked. But now that you can have an intelligent language model or an AI model-backed chatbot and that’s what we’re experiencing for this company. It’s night and day. You can actually talk, you can actually talk to the data and get to know the state of your business. It can help you prioritize, it can learn quickly even the order in which you are likely to ask questions. So, all of that can be served the way you would like.

0:14:39.3 Suvajit Basu: Shashank, in the last two months I have been spending 8 to 10 hours a day with the different AI engines, right? Whether it’s OpenAI, whether it’s Google’s NotebookLM or Google’s Gemini or what Amazon is doing, etc. All of them have their strengths and weaknesses, but things are moving so rapidly, it’s mind-blowing. You and I have lived through the dot com web era, the social media era. However, this is a whole different ball game. And I encourage companies like yours to kind of build a prototype and show it to your clients. And once they see it, I think the light bulb is going to go and say, Shashank, I want it more. I want it more.

0:15:39.7 Shashank Garg: Yeah. And I have experienced that Suvajit all of last year. We saw a lot of initial hesitation, especially with the compliance and cybersecurity teams clamping down on usage. Then we saw limited internal usage. We saw some models being brought up on-prem to avoid some sort of leakage of data. But it has happened with the cloud. We see that happening with… There are still issues there obviously, so you must be careful as a CIO, but I’m starting to see increased pickup. And you’re so right. The moment you do a pilot; people get excited and then is when they start talking about possibilities because you are… It’s very hard in this space with the pace at which innovation is happening, to predict or to even lay out the business use case. It’s like you do something and then you think about more and then you do something and then you think about more. Right? It just goes on like that. Suvajit, would you like to discuss any more examples?

0:16:37.4 Suvajit Basu: I just want to say one thing from experience, everybody is going crazy about ChatGPT and LLMs and so on. But that’s not the only AI, right? There is predictive AI and then there is Gen AI. So, there is a difference there. Again, those two are complementary things. So, when we look at examples, I’ve seen retail planograms, you know, generation and optimization is a clear area that this kind of technology can play, that can help. Right? Because there is a lot of contextual information that happens with a planogram that is kind of not used except for maybe the dimensions. Right? So, we are infancy when it comes to that. So, I think retail planogram optimization, contextualization, there is a huge, huge potential there. Imagine the number of companies out there, what they’re trying to do in optimization and so on in AI.

0:17:46.6 Shashank Garg: Absolutely. What I’ve realized in our experiences is that the biggest wins are coming where you don’t look at traditional BI in isolation, you don’t look at predictive AI in isolation, you don’t look at Gen AI in isolation, but you look at them all together. So something where we are supporting one of our clients on the digital side and the eComm side and the fact that you can now in real-time identify subject to privacy laws, identify the individual married with their buying history profile, put them in a demographic profile and you know what they are gonna like and not like and then use that too, in real-time using an AI algorithm and then combined with generative AI to create product descriptions in real time. So, when Suvajit goes to the same website, you see a different description because of how your buying pattern is or how they have profiled you versus me, where I’m more likely to buy or more likely to see that product in a better way. So those are the kinds of things that excite me, that you’re not just looking at one of these things in isolation but are combining all these three complementary technologies to really create a huge impact.

0:19:13.0 Suvajit Basu: So, taking your concept of the digital twin and augmenting that knowledge, there is a huge opportunity to make things better, making decision making abilities simpler, more focused is where we need to go and therein lies that opportunity. Right? We can do… We can get out of that store by buying more things that apply to me faster because time is the most valuable resource, and AI is now giving me back a few minutes of my time.

0:19:47.8 Shashank Garg: Absolutely, Suvajit, thank you so much for sharing all those examples. Suvajit, I wanted to shift the conversation a little bit on sort of the state of technology. Technology has a huge role to play in making all the examples and bringing them to life. Would you like to comment on how you’ve seen the traditional technology sort of shifting or evolving? How do you see the current tech landscape? What’s your view there? What’s enabling the shift?

0:20:21.1 Suvajit Basu: I think like I was mentioning to you earlier, they’re basically a few buckets, right? Three buckets. One is the BI tools bucket and we all know that that’s Microsoft Power BI tableau and so on, where they’re coming from and then natural language queries after that. The second is the data science and the ML platform, a machine learning platform where we are looking and going through your data set and automatically inferring some things, right? Whether it’s that decision tree, what questions am I going to ask the customer to sell them this boat or this car, right? Automatically. And then the third is the data platform which is the base data layer. So, in that is Snowflake and Databricks and so on.

0:21:16.6 Shashank Garg: I like the way you segmented the tech landscape and just kind of what I’m seeing is at least with the traditional BI tools, I’ve seen all of them come out with their AI and Gen AI releases. So, one clicks advanced analytics, anomaly detection, top influencers, and conversational interaction with data. The only problem is that they would limit themselves to the data that is modelled on their platform. So, as a user, if your organization uses three different tools, then you’ll have three different talk-to-data agents in that approach. I’m excited to see stuff like semantic layer enrichment in almost all of them. I’m excited to see and talk about data features. I’m excited to see some contextual summarization now starting to happen within those technologies. I think everybody should be looking to use those and roll out those features if they haven’t done that. I’m also seeing a lot of excitement in the Data Science and AI platforms. The data IQs of the world or the Alteryx of the world, all of them have invested in AutoML capabilities.

0:22:29.0 Shashank Garg: So far at least for the simpler use cases where you can predict and data is clean and good. You can sort of reduce the time that used to be required to do the modelling. I think we should all be looking to use that. What’s an increasing trend now, Suvajit? I don’t know if you’ve noticed the Snowflakes and Databricks and even the AWS, the cloud platforms and all their… They started to insert a little bit of BI and AI in their data platforms themselves. So Snowflake came out with packaged offerings like Streamlit integration to build some quick apps like BI apps or reporting apps. They came out with the Cortex AI database has made a play recently, although very recently, they’re starting to add AI plus BI capabilities. That’s what they’re calling it. We’re seeing Amazon AWS making a push with Amazon Q, so AWS Q. So things are certainly starting to get interesting and I believe we are at the point where in all these three buckets you’re starting to see a lot of overlapping features.

0:23:40.9 Suvajit Basu: I actually forgot to tell you one of the biggest use cases that I have been…

0:23:45.5 Shashank Garg: Yeah, please.

0:23:47.0 Suvajit Basu: Working with in AI and BI. For the past, since 2019, 2018. So that’s about six, seven years now. Cybersecurity.

0:24:00.9 Shashank Garg: Oh, okay.

0:24:01.3 Suvajit Basu: Huge, huge. SIEMS, which were SIEM.

0:24:07.1 Shashank Garg: Yeah. Yeah.

0:24:07.6 Suvajit Basu: Is where we send all the log information.

0:24:10.1 Shashank Garg: Absolutely.

0:24:11.2 Suvajit Basu: The BI tools go in and look at it and then try to predict, oh, here is the anomaly.

0:24:15.5 Shashank Garg: Yeah. Patterns.

0:24:17.6 Suvajit Basu: The NextGen stuff that we have been using is all based on AI models. And that’s basically predicting here is a lateral movement or here is a bad actor or here is a breach. Right? And the best tools. And this is a BI talk, so I’m not going to get into cybersecurity tools. The best tools are the ones that use AI technology at its core to understand that a breach potentially can happen or has happened and then give you an alert, an intelligent alert. Right. And that is huge. Imagine the value of that, so.

0:25:01.1 Shashank Garg: No, no, absolutely, you bring up a good point. It’s going to pick up stuff, maybe false positives, but it’s going to pick up stuff for somebody to look at, so.

0:25:08.4 Suvajit Basu: Well, that’s where AI comes in. Right? The rule may give you a false positive. The AI decreases the number of false positives.

0:25:15.7 Shashank Garg: Absolutely. Suvajit, just to be the devil’s advocate, right? We spoke a lot about possibilities, use cases and how disruption can happen. You know, any callouts? What are you worried about? What are you worried about in this whole context?

0:25:32.1 Suvajit Basu: I’m actually very positive about where AI is going and the more and more, we’re using it, I think it’s all positive, it’s net positive because we as entrepreneurs, see the glass half full. So, I don’t think AI in our lifetime is going to take over the world and is going to start shooting at us like a Terminator. I don’t think that’s going to happen at all. So, I take a much more positive view of it. If you look at the brass tacks, one of the reasons AI is kind of hindered right now is because of the high cost of AI.

0:26:17.5 Shashank Garg: Yeah.

0:26:18.3 Suvajit Basu: So if you do, compared to BI. Right? If you get into AI right now and you want to create a product that will cost, you a lot of money because it takes a lot of computing and so on.

0:26:33.1 Shashank Garg: Absolutely.

0:26:33.8 Suvajit Basu: And that is making innovation for the smaller players harder to get into. It will push the innovation onto the bigger players. You look at what China has done in the last few years and there’s a great book I read, I don’t know where I have it right now, by Dr. Kai-Fu Lee and he was talking about this five, six years ago, superpowers in AI. Right. And you look at in the last few years how AI, whether it’s from Google, was a pioneer in this area. Microsoft ChatGPT and now Meta what they’re doing and Grok. It’s just unbelievable the amount of innovation and speed at which this has progressed and how everybody is catching up. But who is that small-time innovator, the little player working in their garage? Okay. Who’s making this incredible innovation? I think AI is costly, much more costlier than anything else right now. So that’s one of the things I wish and I’m starting to see the cost of AI governance center coming down. They’re coming down quite a bit. You know, Amazon with their announcements show that their costs are much less, 1/10 the cost of other AI. So costs are coming down those are good things. But that’s a good thing.

0:28:14.7 Shashank Garg: Y Yeah, absolutely. I was going to say when I talk to clients, we often advise them, number one, to look at the cost and see whether the incremental value is worth it. And you know now, with the level of innovation is such that the cost parameters change every three, six months, as you said. Right? Nova is, they’re claiming the models are going to be one-tenth of what exists in the market today and that’s disruptive. So I’m excited to see, that we’re starting to do some experiments there and we are actually very bullish on what you know, AWS put out there in terms of their Nova offering and we’ll see a real disruption. That’s what we are projecting. I think we are also seeing that vendor lock-in is, something that you know, clients should continue to, or enterprises should continue to worry about.

0:29:03.2 Shashank Garg: Think about who owns the models, think about who owns the IP because in this whole if you’re going use AI to differentiate then you have to own the IP. Needless to say, the whole privacy and security issues which obviously you know much more about that we have to continuously monitor for. And I think lastly, I think the human element and change management and whether when we have all these models and they’re giving the context and they’re predicting the right stuff, whatever we can do to ensure that people are explainable, and humans can really trust. Because eventually, the change will only happen… The results will only come when the person on the ground, that operational manager, is willing to take action and willing to bet their careers on what AI is telling you. I think that I see it to be an area of effort for all of us as professionals helping enterprises get ahead in their AI journey. Great. Suvajit, anything else you’d like to share or sort of advice for anyone who is considering, contemplating, thinking about moving fast or moving ahead in the use of AI, Gen AI and BI in general? What would you like to say?

0:30:19.0 Suvajit Basu: I think there are three things. Number one, start with a use case. Start with a use case in which you can say there is a definite ROI for the business. If you don’t see a use case like demand forecasting, customer segmentation, or operational optimization, you know, wait, talk to the business groups, they will tell you what it is. You know don’t just get into it. Number two, focus on governance and data quality. Don’t take that lightly. That’s a very, very important thing because data privacy, security, accuracy, those things are very important. Trust is very important. You know there were a lot of stories about Samsung putting in some IP out there and it was available to everybody. Microsoft copilot, kind of taking HR information or whatever, making it available to the whole company. So be careful, very careful with data governance because you’re gonna deal with very sensitive data with BI reports and so on. So be very, very careful with that. That’s very important. And number three is building a cross-functional team. They will help you get the business cases aligned between IT and the business. Go to the business leaders, have your data scientist with you, have your IT data team with you, work together to create that cross-functional team and then what you need also is a program to upscale your IT department and also your population of users.

0:32:08.6 Shashank Garg: Oh yeah, absolutely everyone. Yeah.

0:32:11.3 Suvajit Basu: Yeah. To upskill that and have fun projects around that. So, See, Copilot or Gemini or whatever other tools, you know. So that’s very important. So, three things, use cases, data quality, governance and building that cross-functionality.

0:32:32.6 Shashank Garg: That’s a great way to sort of package your advice. I love all those three callouts from my perspective. Suvajit, I’m genuinely excited about what all of this means for all of us and enterprises in the coming few quarters. I’m certainly expecting a lot more automation or assistance given to the analysts as you were talking about earlier. So the AI-first automated information insight workflows can really transform our decisions and speed up their actions. So I’m really looking for AI-enabled automation. I’m looking forward to the full data stack. So no more sort of isolated data engineering. You talked about cross-functional teams starting with a use case. So the more full stack you can think about and not silo your… You know remember how departments were siloed earlier. Right?

0:33:27.3 Shashank Garg: And even in some organizations, engineering is separate and analytics is separate and AI is separate. All of that is required. But you have to think about all of this as a subsystem for every use case. And the more we can take that approach, I think the more successful all of us are going to be. I’m also a big fan of worrying about the interaction between AI and humans. And I believe that we’re going to see hyper adoption only when… Because AI learns from humans. Right? Their queries, actions, feedback. So, unless we create that hyper-collaboration, hyper, the closed loop. Right. You’re not going to see the benefits that we expect out of hyper-personalization or democratized decision-making. Right. So that AI… Just keep in mind AI learns from humans. So, we have to have the human-in-the-loop to make the AI better. It’s not going to get better by itself.

0:34:17.6 Suvajit Basu: I like that human-in-the-loop, the copilot to make things better.

0:34:22.1 Shashank Garg: Thank you so much for coming on the show today. Your insights on AI, the call out for predictive AI versus generative AI and the whole impact on the data in the field of BI were incredibly valuable. Thank you for taking the time to share your experiences and expertise with us. To our listeners, we hope you enjoyed this episode of the Intelligent Leader, with me Shashank. If you liked what you heard, please consider sharing it in your network. Do not forget to hit the subscribe button and you can share this with the #intelligentleader. See you next time.

0:00:07.5 Shashank Garg: Welcome to the Intelligent Leader Podcast. I’m your host, Shashank Garg, and today we have a very exciting leader with us. We have Fady Boctor, Fady is the President and Chief Commercial Officer of Petros Pharmaceuticals. Just to introduce Fady, he’s a bit of a data nerd, loves data and AI and infusing technology to do business. In fact, he’s, it wouldn’t be unfair to say, Fady, that you’re almost betting the firm, on this. I’ll let you talk about the recent announcements around what Petros is trying to do.

0:00:47.4 Fady Boctor: Thank you, Shashank. It’s good to be with you today and great topics, worthy of discussion. Yeah. In many respects, we have high bets, high bets on technology, the infusion and the conversions of technology led by way of artificial intelligence, but so much more. And so, yeah. Just about 25 years in the industry, been with the big players, the traditional innovative products. Everything from respiratory to cardiovascular to hospital grade, carbapenems, antibiotics. So phenomenal side and journey on the innovation part. The pharmacological innovation part. Recently, found myself gravitating towards leveraging tools of old, but in new ways for new purposes delivered in expanded access and leveraging various technologies. And I’m pointing directly around technology that helps consumers appropriately, self-select as over the counter options of prescription grade medications. So, yes, to answer your question, a bit of a data nerd, high bets, high risk, high reward, and loving the journey that I’m on.

0:01:51.3 Shashank Garg: Fady, do you wanna describe for the audiences a little bit about what exactly that means, the self-service, self-selection, and how does that change the pharma industry, why are you so excited about this and what’s the opportunity here?

0:02:06.9 Fady Boctor: So the opportunity really revolves around leveraging fantastic therapeutics that have been on market for a decade plus, but maybe have only had a certain population that have accessed it because they’re limited to prescription access alone. In our particular case, for our flagship asset that will sort of become the debut for this new technology. It’s in the erectile dysfunction marketplace. So, to quickly just quantify the answer, 25% of the 30 million men in the US have been estimated to seek treatment. That means three quarters of 30 million men in the country have not, simply because, among many reasons, cost, physician access, prescription medications, taboo stigma, embarrassment, any number of those reasons, they bind prescription medications to a limited population. So the technology we’re going after, and, I think the marketplace that’s currently before us, the FDA has understood this. It’s nuanced prescription medications that are not easily converted to over the counter, such as ibuprofen or antihistamines, but product that have significant contraindications and have significant concerns, still safe, largely safe, but with nuanced contraindications. How do you put those in the hands of the consumer without the burden of a prescription?

0:03:28.6 Fady Boctor: So the FDA has mentioned before, middle of 2022, indicated that they’re opening the door for technology assistive devices and for manufacturers to be innovative in the development of those technology platforms. We’ve been working on that since, and we currently have a proprietary web application that is in pursuit of a software as a medical device approval that essentially helps the consumer to appropriately self-select or deselect based on their clinical profile to purchase a prescription grade product over the counter. And that is the exciting frontier. And it’s critical because we think those medications, their innovation still has not been fully tapped into by the subject population, the patient population it was intended for.

0:04:10.6 Shashank Garg: And that’s exciting to hear. And Fady, what just you’re describing in my mind, I’m going through, this business is not even possible without the right use of data, web technology, and building that self-service platform. You just couldn’t do it, the way you’re doing it is the only way of doing it. And the fact that FDA has come around and said that we will support it, is, you couldn’t be in better times. This is the best time to be. And in things like these. Right?

0:04:43.6 Fady Boctor: Agreed. The landscape is ideal. In fact, 10 years ago, this idea came up with FDA and they called it back then, ensure non-prescription utilization, and the acronym goes on, but nothing happened, nothing formed from it. So, to your point today, the market landscape, the average American has spoken loud and clear. The American public has spoken loud and clear. The FDA has started to acknowledge technology’s emerging, and there is this new vacancy, this new emerging potential to meet tremendous need of self-care with prescription grade medication. So to answer your point, yes, this is the right time to do so.

0:05:20.9 Shashank Garg: Awesome. And Fady, just to introduce a little bit about myself and the firm, InfoCepts, the sponsor for this podcast. So we’ve been in the data analytics and AI industry for two decades, and what you just described is the exact set of transformations that excite us. Data can serve many purposes. It can be operational reporting, it can be keeping everybody informed. It can, ensuring that we support decisions, executive decisions at the right time. In the data business we always talk about giving the right data at the right time in the hands of the right stakeholders, and that achieves a certain outcomes. But now to be able to take the same data, marry it with the technologies, the regulation, and create an entirely different ecosystem where you can serve many more men in this case, the American population, is a huge transformation. And there are several such examples that we are supporting with many clients, we work across industry clients, retail life sciences, media, and we are starting to see many such transformations happen across the industry. But it’s really a pleasure to have you, personally, who’s driving this transformation at Petros, and to talk about that. Just moving a step forward. Fady, could you lay out for our audiences when you talk about this platform, what does it really mean? So what’s happening behind the scenes and what role data and AI is playing in implementation of such a platform?

0:07:04.3 Fady Boctor: Yeah. I appreciate. I think one key component for any technology is, what is the foundation that it’s built upon? And in our case, it’s built upon the drug facts label. So, the drug facts label represents millions of dollars of investment, thousands of patients tested in consumer, sort of, examined. And we understand what is safe use and what is not safe use. So, we take the drug facts label, we build algorithms based on that drug facts label, those algorithms then fuel certain questions, consumer layman friendly questions. And these questions, basically ask the consumer about their medical history, their medication history. The algorithm of the DFL or the DFL is a source driving the algorithm behind each of the questions, helps the consumer understand, is this appropriate for me or isn’t it? So as they’re answering questions, they’re establishing a profile of appropriate user or inappropriate user. And as in its most basic form, that’s what this proprietary web application is intended to. There are additional artificial intelligence components, machine learning components, that help seal the deal, help seal the picture. But that is in its essence what we’re doing. It’s supposed to be transient, just a few minutes of engagement right there in our retail pharmacy shelf and aisle. You’re engaging with this web app, within minutes you’ve been deemed appropriate or inappropriate based on a clinically defended, algorithmically driven medical history questionnaire.

0:08:36.4 Shashank Garg: That’s great. And for anyone who’s listening this, we talk about democratizing data and AI to a larger population within the organization. What we’re talking about from hearing from Fady is an example where they are democratizing this information and determining eligibility for a particular drug which is approved, clinically approved by the FDA. So ensuring that the regulator is fully on board and doing that in a safe way. And the fact that we can do that at scale, is just fantastic and the right time to be in the data and AI industry. So thanks for sharing that, Fady. Moving on and just sort of looking at the broad pharmaceuticals industry, and pharma industry talks about building such, and if I may use the generic term, patient education platforms and supporting self-care, not just to do what you are doing, which is taking prescription drugs over the counter now, but just supporting themselves. Hey, could you share how to do this right? And what can go wrong in this because it’s very sensitive. So how do we do this, right?

0:09:51.8 Fady Boctor: I think we do this right by educating the patient within the engagement. So sometimes you go to your doctor’s office and you can tell, a good doctor will not only ask you questions and write down your answers and include your answers and then make a decision, but a good doctor will counsel you and he’ll educate you or she’ll educate you. So as they go through, they ask a question, they’ll say, “Here’s why I’m asking the question, or do you understand why I’ve asked this question?” So the right way this is done is to help a deeper engagement with the healthcare environment, meaning we know that the patient is interested in receiving therapy, we’re gonna ask them questions about their health, but then do we educate them as to why we’re asking those questions? What else could be happening? What is the source of ailment? Is this a comorbidity to our larger deal?

0:10:35.8 Fady Boctor: Is there something larger underlying happening? Or, basically said, if we’re asking a question about how often you have so-and-so symptom, we’re explaining why that’s of concern to us. We do that. And not only are we correctly and appropriately educating the patient, but we’re also getting their accurate disposition. And I think that is the right way to do it. And that’s what our technology intends to do. We educate them as we ask the questions. We offer them tabs for more information. We don’t take it for granted that they’ve understood everything that we’ve said or everything that we’re asking them, which either way, the FDA holds us accountable to such a value. We couldn’t just say, we wanna take this prescription drug and convert it to OTC without a public health value. So the public health value is intrinsic in our altruistic mission. FDA holds us accountable to that as well.

0:11:21.2 Shashank Garg: Thank you, Fady. Let’s, moving on. I’m just, maybe spend some time broadly talk about where you are seeing similar use of data and AI in pharmaceutical industry in general. Where do you think this is becoming very real, and very useful? If you wanna share from your experiences and examples there.

0:11:43.9 Fady Boctor: It’s interesting because there are a diverse… There’s a plethora of diverse uses of various technologies. Everything from clinical study or research study, implementation of artificial intelligence technology to help streamline the process, to help simplify and streamline and expedite results, expedite protocol. So there’s that facet, I see that happening quite a bit. Some for good, some not quite yet ready for showtime or primetime. And there’s also utilization, to your point, in terms of physician history and physician profiling and anticipating consumer behavior. There’s a lot of technology around that, whether it’s prescription data or generative AI data that looks at, if a patient comes from X geography with X profile, here’s what they’re likely going to prefer. We see those types of models occurring. I think many of them are still largely nascent. Then there’s, obviously technology and AI that’s leveraged, in our case, which I have not to be honest with you seen in this particular field.

0:12:47.2 Fady Boctor: And that is how do we validate the consumer? How do we make sure they’re not spoofing? How do we make sure that they’re not abusing the system, they’re not counterfeiting their profile? So there’s some security elements to make sure the person who’s answering the questions is in fact who they say they are by way of age, gender, and government ID, and so forth. And so from all the facets, I would say a lot of activity occur across the board, but there’s much work to be done to validate it and to get FDA buy-in across all fronts.

0:13:17.8 Shashank Garg: I remember, so one of our clients at a large pharma firm, our teams, and you talked about generative AI as well. So just for our audiences benefit. We don’t always have to look for transformational examples. They are the ones that require a lot of buy-in right from the top. But in my mind, in the whole field of data analytics innovative AI is starting to play a very important role, something as simple as, what we talk about, conversational BI or being able to ask the right questions. We have a solution called Decision360 and just organizational information. Sometimes it’s just very hard to find data, and especially if it’s a combination of structured and unstructured data. There’s technology available where on the top of your structured data, data warehouse, data lakes, you can marry it with unstructured data and make it really easy for your operational users to just ask questions.

0:14:16.0 Shashank Garg: And they can come in a narrative, where you check for hallucinations and you’ve done it in a secure way. So at least within the walls of your organization you’ve made it very easy for people to get to answers, which even after three decades of work, that’s how long I have been in this field, is unfortunately not that easy. One of the projects we did is for the CFO’s office, around the whole topic of clinical trials, cost management, it’s huge cost involved. And the CFO’s office will, for somebody in commercial, will come down very hard and heavy and say, “Why are we burning so much money?” And just make it easy for their analysts to talk to the financial data. Just made a huge difference there. Fady, I also see a lot of excitement and possibilities around enabling the commercial organization, the ones that the sales representatives who, actually call on the physicians. Are you starting to see any interesting examples of use of AI there?

0:15:25.6 Fady Boctor: So it’s interesting you had indicated, for training, certainly, so to develop multiple and diverse perspectives in training. So reps in training with physician profiles, those physicians that like to put up smoke screens. How do you leverage artificial intelligence, generative AI to ensure that the patient, the consumer, the representative rather, is diverse enough in their ability and capacity to communicate essential elements through the various profiles. So to be able to establish a variety of personalities for the representative to train on is fantastic. And I’ve seen that starting to emerge more and more. And I think that’s really, a largely, where there is a great deal of momentum. Targeting is another area. How to target certain geographies, certain prescribers, prescriber specialties, and then the story goes on. I think targeting payers is another area where you want to understand based on history and based on behaviors and contracts, where are the best, most productive contracts. So those types of things are currently in play from a commercial perspective.

0:16:31.0 Shashank Garg: Yeah. I’ve heard of a few things that you talked about, but what’s interesting is you earlier spoke about training, which is an angle that, I personally, haven’t seen as much, but that’s a very good one. And that’s where the generative powers of AI can be leveraged. You’re essentially saying that to train a sales rep to be able to respond to or deal with all the different smoke screens that a physician is likely to put up, can be very useful. And instead of just enabling them with data, “Okay, here’s the physician order history and here’s what you’ve done with them and here’s what they like you to do.” You also use some generative powers to, make them talk to an agent, for example, before they actually show up to a physician.

0:17:18.8 Fady Boctor: That’s right. It’s, you’re giving life to a profile and then giving the ability to also be, in many respects, unpredictable.

0:17:26.4 Shashank Garg: Yeah. Talking about algorithms, Fady, I know one of the last times we were talking about you had shared that sometimes it is not as easy to get the work right. And, can you throw some light there with your personal experiences, any areas that went bad didn’t pan out the way you expected?

0:17:51.0 Fady Boctor: Yeah. I think, and correct me if this is not the scenario you had indicated, but I think artificial intelligence can go wrong if any company decides to implement it without fully understanding it’s source model. What are the assumptions? What’s the model? What’s the model that fuels it? Artificial intelligence in and of itself is not capable of identifying its bias. It’s not able to correcting its bias or correcting its model. It’s subject to whatever model is fundamental to its thinking. And so in our case we, this was a vendor that leveraged AI in their, in one of our research study results. And unfortunately they leveraged AI in a way that was not overseen well, and therefore it was, the AI was left to interpret the results. Although it did relatively well, relatively in the 90% or better, probably in capturing certain transcriptions, it missed certain key elements. It was ill-equipped to understand the difference between one word and its implications versus another. So although it didn’t necessarily do it wrong, it acknowledged the right word in the right way, but did not catch the implication of that word in this context. All that to say, yeah, FDA took notice.

0:19:08:0 Fady Boctor: And even if you’re 1%, 3%, 4% off, they’re going to take notice and that can’t happen. So I say that to say, there always has to be human governance, there always has to be oversight. And the person who established the model, the person, the vendor that that designed it should quickly and upfront identify, be thoughtful, be careful, look for these elements, pressure test and make sure that you know you’re not taking for granted. This is 100% accurate. So yeah, for all those reasons, I would say be mindful. Do not give artificial intelligence its own legs too soon. Without or without ongoing human governance.

0:19:43:0 Shashank Garg: You bring up a good point. And I’m starting to see across our clients and I’m realizing is that at the leadership level, because there’s so many AI initiatives and experimentation going on, because you want to promote experimentation, that’s how we move forward. I see some of some of the organization getting overwhelmed by just sheer number of initiatives that are going on. And to be able to pressure test everything is a challenge. So, what we’re starting to do is at least internally at Infocepts, we’ve developed our own sort of experimentation platform. We sort of call it, you know, sort of governed experimentation, right? So, you can load up all your use cases and the anticipated business value and the checks and balances that you want to put in place. Show result, data issues, the data quality, all that and sort of one seamless platform. So, at least for somebody like you or a person who’s overseeing many such experiments, they can decide, you know, which experiment is ready to take it forward to production and which one requires more pressure testing.

00:20:44:15 Shashank Garg: So Fady, what we realized also as the data scientists are sometimes not very good at being able to explain what the model does, and even when they do, because they’ve built the model, they’re so close, there is inherent bias in the way they’re gonna explain the model to a business user. So we sort of invested, create a framework around explainability and compliance. So the platform comes in built with those two things. And you can talk to the model, you can create different scenarios with the model, independent of the data scientist who’s actually trying to make the case, whether we should use this model. So just put an extra check on what you were saying that not every AI model is ready for prime time, especially coming in from the people who built it, and you have to have that oversight extremely, extremely important. Just around the topic of experimentation, Fady, is there anything else in your organization that you’re experimenting with that you’d like to share around this whole topic of data AI tech?

0:21:42.6 Fady Boctor: It’s interesting, I think we are in a place today, in addition to what we’ve talked about, there’s a significant amount of interoperability and data exchange. So, we think of TEFCA and QHIN and what’s happening with electronic health records, what’s happening with interoperable health information exchanges, the story continues on, there’s so much potential for experimentation. The beauty behind this experimentation is you’re building integration, models of integration. You’re not forming your own equation. You’re not forming your own, sort of, unproven model, you’re leveraging interoperable data availability to help your consumers, the patient, make decisions for themselves. So, we are excited to be a part of that experimentation process, ’cause like never before, the consumer’s data is so handy and available to them, well, why not leverage it and let it speak to their needs and let it customize solutions to wherever they wanna go, whatever they wanna treat. So we’re excited to be a part of that, sort of, development and that momentum, but that is definitely where we hope to experiment in the near future.

0:22:49.2 Shashank Garg: Fady, one more topic that I like to talk to executives about is this whole topic around change management and whenever you are disrupting, whether it’s internal or external change management and maybe we talk internal. So within your organization or your past experiences, whenever introducing, being more data driven or models, things that humans are not very used to, what has worked for you or not worked for you around the topic of change management adoption?

0:23:20.9 Fady Boctor: Well, the thing that jumps out at me most to that question is in our industry, because we’re so heavily governed by FDA and so many other subcommittees to sort of just the healthcare system, it’s important that we do not experiment on vital consumer engagement or vital healthcare and delivery. So with that said, one of the key areas is leverage existing technologies that have been refined, sharpened, and work phenomenally well, but maybe outside of your industry, maybe outside of your sphere, and bring them into your sphere. I think that’s where the exciting frontier comes in. And that also helps with change management because when you introduce something that’s new to your sphere, new to your landscape, but it’s been proven in its birthplace, you stand to have a much better, a platform of credibility, ’cause then now you’re interpreting how that proven technology can now be applied to your current sphere and how it could help drive momentum and progress in your current service area. So I think in terms of change management, go there first. Always look to see what phenomenal technologies already exist in other industries and see how they could apply and be customized and be transformed to your particular area. I think you’ll find tremendous mileage, tremendous amounts of options available to you with little disruptive, people are afraid of bringing on new things in new ways, change the new thing and just bring it into a new way.

0:24:50.9 Shashank Garg: Yeah. I like the thought process there. And you’re right, in a heavily regulated industry, people are gonna be that much more shy, so the easier or the smaller the change, at least the perception of the change, the smaller it is, the higher the chances of adoption. Makes sense. Fady, just a couple of more questions that I wanna get your thoughts on, one, if you look at broad pharma healthcare industry, what sort of technology trends that you’re most excited about going forward, anything that you’d like to share there?

0:25:28.2 Fady Boctor: Yeah. I’ll say that what I’m about to say speaks to an evolving healthcare marketplace, meaning we have about 120,000 physician shortage at hand, primary care specifically. And so what that said is a lot of the primary care and common reasons for a visit are starting to become difficult and more and more difficult to seek treatment for you, you’re on waiting lists, often you’re going to the CVS MinuteClinics, which don’t necessarily have a deep relationship with you, but they’re good in case of urgency, emergent care. But primary care is becoming more and more difficult. So what I see as an emerging trend that I’m excited about, are two things, one, first, the availability of our health records. So today I’m one of millions that has access to MyChart. MyChart gives me access to all of my healthcare records from, even from disparate institutions, I can see everything where I’ve been, what I’ve done, what’s been done, test results, appointments, everything right there on MyChart.

0:26:25.5 Fady Boctor: The consolidation of disparate health information has, is becoming more and more consolidated and more and more available. So that’s one key exciting factor. How does that feed into the next change? Empowering the consumer to be able to access that, to make critical decisions for their primary care. And with that, we’re a part of that expanded access by bringing more prescription medications to over the counter. So how do we integrate to that healthcare data, better, and equip the consumer to leveraging their healthcare data to make those critical decisions without a learned intermediary. And the story goes on. TEFCA and QHIN being developed over the last few years, and just the interoperability of health records in general, EHRs, Health Information Exchanges, they are maturing and they’re maturing quicker than we know maybe, than many of us realize.

0:27:14.9 Fady Boctor: That is an exciting new feature available in the healthcare marketplace, because now we’re starting to bring the consumer into the picture of healthcare delivery, where we’re starting to rely less and less potentially on primary care practitioners for the more transient, easy to diagnose, relatively easy to diagnose, relatively easy to treat conditions. And now we’re starting to empower consumers to make those decisions and seek those treatments for themself, leveraging their own custom healthcare data. I think we’re, it’s maturing quickly, but we’re still infants in this model. The future’s coming quick. I would say everybody should be on the lookout by the close of ’24, certainly by 2025, we should see some fairly significant leaps forward on those fronts. And it’s, that’s an exciting place to be.

0:28:03.8 Shashank Garg: And, well first of all, thank you for sharing these upcoming trends. And you’re right. And I was just thinking in, what’s going on in my mind is, so I know I track the data analytics and AI tech trends very closely, and what I’m starting to realize is the tech coming up so fast and combined with what you just said, the next 18 months, 24 months, can be very different for everybody in the life sciences healthcare industry. What sparked off as a, sort of, revolution or disruption with the whole generative AI launched less than 18 months ago, about 18 months ago, and we all gasped at the costs of training some of these large language models, and every quarter I’ve been tracking that very closely, it just keeps coming down.

0:29:8.7 Shashank Garg: It keeps coming down. And what we thought would be the cost to feed in all of your organizational data to a large language model, and to be able to talk to it in a meaningful narrative, in a narration, has come down a thousand times, has come a thousand times. So while the industry is progressing as you are talking, I think the tech is progressing very fast as well. And as soon as the cost starts coming down and we can get the right balance of experimentation and oversight, if I may call it, I think we are up for really, really cool things and transformation things in the coming future. Fady it has been fascinating talking to you. Any sort of last advice that you would want to leave our audience with?

0:30:57.2 Fady Boctor: Yeah. I’d love to, and I appreciate the time with Shashank, thank you, great topics of discussion. I would urge anyone who’s listened to our story to monitor PTPI, which is our taker on Nasdaq Petros Pharmaceuticals, and visit petrospharma.com, learn about our process, learn about our technology, the emerging technology and our current process in bringing the first prescription ED medication to potentially over the counter status by way of our technology, which could open the door for future portfolio assets. So, love the discussion. I think it’s the field that you and I are both in love with and passionate about and immersed in. And they should, people should tune in to see where we take it next.

0:30:34.2 Shashank Garg: Awesome. Fady, it’s been a pleasure having you on the show today. Your insights on AI, data change management, self-service, patient education and sort of the future of pharma and healthcare, incredibly valuable. Thank you so much for taking the time to share your experiences and expertise. And for our listeners, we hope you enjoyed this episode of the Intelligent Leader with me, Shashank Garg. If you liked what you heard, please consider sharing it with your network using the hashtag Intelligent Leader. And don’t forget to hit subscribe on our podcast by hitting the subscribe button. You wouldn’t wanna miss the next episode. Thanks for tuning in and see you next time.