[00:00]
When we discuss AI, it’s about how you apply intelligence and can you operationalize it and optimize around it. That’s the expectation. Being able to extract what we’ve seen from doctors in the past, how they’re either sensitive to field rep or digital solutions, to different types of content, we can give a better recommendation and better expected results to a client. Clients are expecting the speed to execution and the certainty with the results.

[00:37]
Hi, I’m Lecia Bushak, Pharma Editor at MM+M. Today we’ll be talking about the real power of AI applied not artificial. We’ll cut through the noise and delve into the tangible impactful applications of AI, not just what it could be, but what it is and what it’s truly capable of delivering right now. Here with me today is Michael Cole, Chief Business Officer at Relevate Health, and Tim Pantello, CEO at Relevate Health.

[01:04]
This is the MM+M Agency 100 Strategies for Success podcast with Relevate Health. Michael and Tim, thanks so much for being here. Awesome. Thanks for having us. Excited to be here. We’re now a few years into the AI revolution if you want to call it that and everyone is using AI to some extent in the industry. Moving beyond the hype, what does AI mean to you today? You know, great question.

[01:31]
AI really only matters when it’s contextualized, augmented, and embedded into real world use. And that’s why we call it applied intelligence. And what we really mean by that is the combination of technology and human insights to be able to drive measurable outcomes for clients. So it’s not necessarily about the the raw data or impressive models out of that data.

[01:58]
It’s about how you to apply them to solve everyday problems for our clients and the commercialization space. And the real payoff to that is is activation. How do you engage and activate and change behavior, uh leveraging data and applying it. And we do that really uniquely. And so, you know, as we talk about different approaches, Michael and I’ll share you know practical real-world examples of how we’re doing that right now and how we’re going to be applying it you know more in the future.

[02:26]
So when we think about just kind of how we’re we’re pushing is exactly with that. It’s um the importance of defining use cases that align with the value that we put in the market. There is probably two different approaches. There’s some folks that want to think more about how can they innovate and explore where the technology can go. That’s certainly an approach if that’s sort of where your company’s core value prop where your competitive advantage is or there’s another opportunity with thinking about in our case, we’re on a client business.

[02:55]
How do we understand what the value we deliver to clients are, how we can help their campaigns work better and then how can we define the use of applied intelligence to really advance their businesses. Tim, you mentioned using AI for measurable outcomes um and that you have some of these real world kind of case studies of how you’re using it for those outcomes. Can you talk a little bit about those and how your approach to AI is different from what others may be doing in the market? Yeah, I mean really focus on the application of it.

[03:25]
I think a lot of people talk and a lot of people don’t walk the walk. And so our approach really leans into, as Michael just said, what can we really lean into to about, you know, extract value? And from a competitive advantage standpoint, clients want to know what they can do right now. I think a lot of people use a lot of buzzwords. They talk about what could be possible. We talk about what we can put into action and activate right now and optimize around.

[03:53]
And I think that’s just very different. I also think everyone buys the same data. The question is, what do you do with it? And so people can apply interesting, I would say, small language models to specific use cases. What makes us really different and how we’re applying it for clients right now is we have 10 years of historical data that we blended and appended with all that third-party data that everyone buys.

[04:19]
And we have unique insights into attitudes, behaviors, channel preference, content preference, and that’s really unique. No one has what we have. And more importantly, how we apply it, how we operationalize it, and how we predict around it results. So much so that we’ll guarantee the engagement for the majority of our solutions and off-reads. Yeah, and I think that’s exactly the area that we’re focused on.

[04:45]
So rather than building out our own large language models, we’re taking commercially available technology. We’re taking the agenetic approach, but we’re defining the innovation at top of the stack, defining what those use cases look like. So Tim brought up the perfect example where we’re really strong with sciences, our understanding of audience. What do, specifically HCPs, the doctors, what do they prefer, how do they process information, so helping clients get better insight into audience, better understanding of how campaigns are going to perform. We’ve really focused a lot of our energy there.

[05:15]
The second kind of key value that we bring to client is how we can quickly develop and deploy content. So building out use cases that expedite that that do that at a faster pace that get them through MLR quicker. And the last base that where our biggest value is deploying and optimizing our campaigns.

[05:32]
So those are the three use cases that we really want to focus on and there’s a lot of existing tools that are already doing a really good job rather than spending a time investing and building out we’re defining the use cases building out the agents that apply that and it allows us to the new tool comes along, we’re able to swap it out to kind of stay ahead, but it’s really focused on that extraction of value for our clients.

[05:53]
And I think you both brought up an important point that there’s a differentiation between sort of these grand promises and visionary road maps of the future of AI and what it might lead to in the future and kind of what clients need practically today and you provided those three use cases. Are you hearing from clients that there’s a need for practical uses of AI in any other ways that they need today rather than in the future. Sure. So I’ll I’ll give one very clear example.

[06:20]
I’ll blind it, but it’s a client the GLP-1 space, highly competitive market they’re looking for where they should be best investing. So the example of what applied intelligence does is relatively quickly our data model can help them identify the most profitable markets to focus into. So it’s looking at all that to reference sort of the commercially available data. So it’s looking at growth in claims in GLP-1, high levels of epidemiology and obesity, but also like favorable access and reimbursement strategy.

[06:47]
So what that gives you is a map with 40-50 dots on it that tells you these are the best markets. But what we sort of then layer on to that is how best to attack those markets. So real practical example, Chicago and Dallas roughly the same size. But based on what we’ve seen, Chicago has high level of HCPs in no-call or no-see doctors. You got to focus a digital campaign there. Dallas is the other space. Or another example where we look get sort of content differences.

[07:14]
Atlanta and Nashville roughly the same prospect of where a client in the GLP one could space could win. But Atlanta has got a high focus of doctors looking at local data, whereas in the Tennessee space you see a lot of high level inflence of local KOLs. So you have a different content strategy. So being able to extract what we’ve seen from doctors in the past, how they’re either sensitive to field rep or digital solutions, different types of content, we can give a better recommendation. and better expected results to a client.

[07:44]
Basically kind of more of like a tailored and personalized approach, based on geographic location and HCP population. Exactly. It could be on again most of these models allow we can do it at a geographic location. We could do it actually down to a hospital level even to an individual practice level. Being able to say this is where their preference lies and apply that into the types of campaigns we run. That’s that’s where we’re getting past the hey this is a fun tool to build and to a inherently a client’s looking for for every dollar they spend how do they maximize ROI?

[08:14]
This gives them those use cases that pushes toward that. Yeah, just to build on that, clients have an expectation of precision today. You know, a national message in one strategy doesn’t actually work today. Unless you’re just focused on awareness, majority of brands and categories that our clients operate in are highly competitive and highly fractured at the hyper-local level.

[08:40]
If you can apply the data and have the historical data or something unique to really drive in power recommendation and be able attribute the precision to actual results in a real return and an ability to optimize on that. That’s where we talk about artificial intelligence. It’s quite ironic, isn’t it? Artificial means it’s not real or it’s fake. That’s a good point, yeah.

[09:05]
And that’s why when we discuss AI, it’s about how you apply intelligence and can you operationalize it and optimize around it. That’s the expectation. And we’re uniquely different than any other partner or company because we have actual unique data, the historical application of that data, and then applying that to intelligence to recommendations, and then the ability to fight, operationalize, and attribute it at a precise level.

[09:35]
Looking ahead to the future, sort of a two-part question. You know, I’m curious if you have any thoughts about where AI in general in the industry will be going next if there’s any kind of emerging trends in 2025 and coming years that you kind of expect to see emerge or and sort of the second part of that is at Relevate Health specifically, what should clients expect from your AI road map over the next year?

[10:01]
Our focus is really on making AI or applied intelligence really invisible to the client. What they see is better outcomes, optimization, and speed. Client don’t really care about anything other than the results and the outcomes and your ability to be extremely precise and be able to report a return on that an individual MPI level.

[10:27]
And I believe if you can’t do that, you’re not meeting the client at the speed of the need and that’s where we really excel. Yep. I think Tim hit the nail it. So they’re certainly going to be in the optimization space. I also think those speed and content creation will be one of the most important areas. The current timelines, every client wants to get faster, they want to get more module, they want to get more personalized, the rate limiting factor is how fast they can make content, deploy it, and understand its performance. So we’re already starting to see that as a the big area that we’re focused on.

[10:56]
So, so many cases how we produce video. We uh we start playing around with the technology called Hey Gen. So, it creates digital twins of us. We actually if you see any of our own individual videos, we have digital egg called AI Timmy is out there. But we use that ourselves and so the ability to make a video we’re doing it 85% faster than we used to. We’re seeing the same area in terms of our video game division. If they were building video games by coding every single line, the time to market would be unmanageable.

[11:25]
Would would never meet a client’s need. We’re able to get a game into NLR review in a series of weeks, most of the the work and it’s really through the use of that. So, I think you’re going to see a lot more focus in terms of not necessarily original creation, but repurposing and speed of content creation and content publishing. And I think the other portion is how quickly tools at the last mile will be used to to drive optimization.

[11:49]
So, whether that’s out in HCP, whether out of patient level or even in the reps hands, then being able to make more custom and modular content on the fly is two areas where we’re really kind of seeing the biggest focus. I think most of our clients are going to see that as the biggest opportunities for them. They can kind of approve that core messaging and core claims, but those minor variations is going to be where we’re going to see the biggest opportunities.

[12:12]
Yeah, and I think the entire industry is going to be put on tilt over the next five years, and I think even just over the next 24 to 36 months, the historical model of a time materials.

[12:26]
Our clients are very intelligent how they buy services and what they expect from partners is pushing outside of the historical model of time and materials and clients are expecting not only the specificity and the precision we mentioned earlier but the speed to execution and the certainty with the results.

[12:52]
And I think the expectations are rapidly changing for what represents value to our clients and can you prove it? Yeah, and on that point, how do you go about measuring those results and making sure that your investment in AI is leading to the outcome that the clients are looking for? Like, do you have certain metrics in place that can measure that? Sure.

[13:17]
And I think Michael, you can expand upon it, but so in our data spine, 10 years of historical engagement data We’ve scored all of that at an atomic level, what content, what channel preferences, and blended and appended that to an individual NPI.

[13:34]
So when we talk about the promise of precision, we also have to pair that with the ability to really tailor and personalize and then optimize to deliver the right content in the right channel at the right time. And I think companies like ourselves that can really weaponize that and commercialize that in a hyper practical way, you know, having operationalized it.

[13:58]
And then so much so that you can predict and guarantee the engagement is really unmatched. And I think that our entire industry is is facing a challenge and I I don’t believe most are prepared for it because they’re not native to it. And that’s where we’re a really unique organization that can unlock new and different value and and continue to stay ahead of the client needs. and meet the client at that speed of need.

[14:27]
Yeah, one of our core values as a company is transparency. So, we’ve been selling with engagement guarantees for last four years. We started embedding ROI reporting proactively into it. So, we want clients to be holding us accountable for where their money goes. Also, we feel that what we’re giving is high value and it puts us on a means to kind of create an apples to apples. But where the biggest focus is going to get is the value of the optimization, how much we can continue to make proactive recommendations.

[14:53]
And so, AI helps us in two ways one it helps us make better, smarter recommendations. It starts us off with guarantees and knowing what performance looks like. It also frees up smart people’s time to do that, right? We embedded AI in our data and analytics team to do all the reporting. So like I can pick the off-the-shelf partner roll stock, we think they’re great. So they’re creating the PowerPoint reports. I don’t need data experts making PowerPoint charts. I need data experts looking at opportunity making recommendations to clients or working with our other AI engines to make those optimization.

[15:23]
So I think that’s the biggest focus that we’re trying to push in. Yeah, and I believe we’re seeing this application not only in the back office of how we run operations and can do things at a at a faster pace. But it’s It’s the mix of talent, the ability to focus on what’s really important. Activity and time is not where clients expect and want value and paying for time.

[15:53]
They’re focused on outcomes. They’re focused on performance. They’re focused on how do you help me commercialize my product. It’s not about just being a great partner and a service provider. It’s about performance. Absolutely. And I’m I’m curious if uh either of you have like personal favorites of AI tools that you’ve come across. Are there any tools that you’ve used in your work in the last year that you’re really excited about?

[16:22]
I mean like I’m sure everyone’s heard of ChatGPT obviously, but are there any other AI tools that you personally are very excited about using? I think the larger language models are going to continue to win the race, you know, whether it’s Gemini, whether it’s Perplexity obviously everything that Sam Altman’s doing Open AI.

[16:43]
Those larger language models are accelerating at such a rapid rate that it’s it’s not only how you apply it but your ability to augment that with human intelligence. And over the next five years, you’re going to see this this ability to create to go beyond scraping the web to get to true, human inference.

[17:10]
And I think that’s where we can apply it most in in the marketing and communication space, which is not only providing prompts to engines today, but how do those prompts become actual recommendations.

[17:25]
So right now we’re in the world where being able to prompt those engines and create the the parameters to extract better recommendations on a content perspective, a code perspective, a creative production perspective. Um those all help us accelerate the ability to produce uh I would say a volume of options.

[17:52]
Once we get to a more inference space, you’re going to start to see a more dramatic collapse of certain functions within creative organizations. And I think there’s a variety of tools as it relates to the large language model. I think what’s going to be very unique in a highly regulated space like ours is the ability to apply smaller language models to more specific use cases and be able to operationalize those.

[18:18]
And there’s a variety of there’s hundreds of AI tools and utilities and you have to be very specific in which ones you want to take and use to apply and create smaller language models around that so we can accelerate that inference. Spot on. Yeah, to me again, I settled out our approach is to be top of the stack and more flexible, but of the commercially available, I referenced a few earlier, we are we like RollStock for data visualization.

[18:45]
We love Heygen for what it does in terms of video creation. Our creative teams really like mid-journey in terms of exploration. I was talking to our I had a product yesterday and one of our largest areas is how we focus and innovate within the point-of-care space. So all those health information technologies are right when a doctor is working and he was experimenting with cloud specifically in terms of developing an optimization recommendations in the point of care. So we understand which business rules and which areas to to push into.

[19:13]
But we’re going to continue to focus on using commercially available tools and essentially horse racing against our various different use cases. And then with us, which makes us really unique is that 10 plus years of historical data, that first-party data that we get to uniquely apply at a precision NPI level, and that’s what allows us to do things very differently with these tools. You know, we take all that commercially available data.

[19:42]
We’re taking all the AI utilities that are out there in the large language space. And then the the more specific tools and utilizations that we can apply to specific use cases is where we’re attacking different opportunities, whether they be more in the performance orientation or in the or analytical models where we can take people’s time dramatically reduce it so that They can focus on the higher order value of their skill.

[20:10]
Yeah, I tend to try to say that a simple mantra for how we want to apply intelligence is I want to turn our data advantage into a performance advantage for our clients. We have a large stack of data to out organize it well carriable. But us just having that to be smart or interesting isn’t necessarily the intention. It’s exists to help clients make better decisions, faster decisions or to see higher returns. I think that’s a great way of summing it all up and I think the main takeaway is that AI should be called applied intelligence not not artificial intelligence.

[20:39]
Michael and Tim, thanks so much for joining us. Thank you so much. If you’d like to learn more, please feel free to explore more at relevatehealth.com. I