Tray Chamberlin: I think we’re good. Good morning everyone. Thanks for attending our session. We are here obviously to talk about AI driven go to market strategies. And in particular, what we’re really going to focus on today is not just theoretical. How do we see AI being implemented in commercial models? How are digital health companies, services companies actually employing AI within their commercial models? But I’m also joined by Mike Rola, who is also going to be sharing real world stories from a practitioner individual, such as yourself sitting in there and saying, how do I actually implement these AI strategies as we go through? So first things first, who am I? I’m Trey Chamberlain, I’m a principal with the Alexander Group. We do revenue growth consulting. Essentially what that means is I spend all my time thinking about commercial models. How do I help digital health companies? How do I help Solarity point, click care, whoever it is, how do I help them accelerate their top line growth and essentially make their commercial models. Hum. All right. I’ve been doing this for 15 plus years, and I still remember when AI wasn’t even uttered at him. So we’ve kind of seen it all as we’ve progressed through. I’m so pleased. I’m joined by my my friend and colleague Mike roller here. Mike, do you want to do a quick introduction?
Mike Rolla: Yeah. Hey everyone. Nice to meet you, Mike Rolla. I’ve been in the health tech space for over 20 years. Started out on the provider side. Health systems med device moved into digital just before Covid, which was obviously not planned. And I’ve spent the last six plus years in that space. So great to be here.
Tray Chamberlin: Absolutely. Thank you. So first, what do we have to acknowledge at the end of the day? I made the joke earlier and it’s very true is that, you know, post Covid, you saw a little bit of the AI headlines popping up and hims. It got stronger and stronger year over year. Now, I think if you walk across this floor, you could essentially make a pretty strong bet that 95% of the organizations here are either talking about or touting or thinking about launching an AI product. So before we even start, let’s acknowledge that providers, hospitals, post-acute, etc. there is a massive appetite for AI and I’m going to focus on the products, but then we’re going to kind of focus around how we use AI in the commercial model. We run a semi-annual study, we go out, we talk to hospital executives, we talk to surgeons, providers, etc. what do you think we talk about? We, of course, have to ask them about what do you think is happening in the industry and how do you anticipate ingesting and even kind of inserting AI into your model? Here’s the trends that we’re seeing overall. And you can kind of see, and I’m going to work my way up is that, um, despite what this data is telling you, because this actually shows the prevalence of implementing AI models in the next 24 months. What I would tell you is the administrative and workforce efficiency. That wave has already come.
Tray Chamberlin: The early adopters came. We’re kind of in the mass middle organizations that said, hey, how do I think about reducing the admin burden complexity of my care takers? If they haven’t done that, they’re behind the curve. What we’re seeing is more and more providers are now implementing things that are closer to the patient. It might be a clinical workflow, it might be imaging, it might be things that candidly, again, are touching the EHR, patient records, etc.. And so you’re starting to see this mass kind of push from, hey, how do I kind of take out the admin, uh, responsibilities of my workforce to how do I actually make AI a part of my patient experience and help us get to better outcomes? All right. So products are obviously being pushed into hospitals, into, uh, post-acute sites of care, etc., but we’re not here to talk about that. We’re instead here to talk about great. If hospitals are consuming AI, how do digital health, how do health care services implement or insert AI into their commercial model? A couple of things I would first say is that, well, first of all, the buyer journey is changing. Ai is obviously driving that a good bit. But what I can tell you a couple of stats here from a recent study we did. There are now seven decision makers in any digital health purchasing process, and those kind of fall into five personas. You’ve got your champion, you’ve got the business owner, you’ve got the clinician, you’ve got the enablers, and then the almighty wallet or the financer at the end, right.
Tray Chamberlin: And we’ll kind of talk about that shortly. So you’ve got this much more complex process. In addition to that, four out of five are now also running formal RFP processes. It used to be, hey, listen, I’ve got a great product. Let’s talk through it. You might have one single champion and boom, you’re actually pushing through and you might actually get implementation pretty quick. Much more formal now, right? And they’re meeting once a month on average is kind of the set cadence. You’re also seeing now this best of breed versus stack phenomenon happening. Organizations now will say, listen, it’s great that you might have a single point solution, I need more. How does this integrate in my system? How do I use your product in one, two, three, four, five use cases and kind of solve a myriad of things. And then finally, as even though the buyer journey is getting more complex, more individuals in it, you’re also seeing this idea that no matter what peer reference is still trump everything. So I could go through this whole formal process, jump through all these hoops, and at the end of the day, it’s still whether my, my friend, my colleague, my trusted advisor at hospital, across the street, across the street, across the state, across the country, whatever it is, they’re still the one that makes me feel good about making that decision and then reinforcing it.
Tray Chamberlin: One more critical point as well is that, well, great. The buyer journey is changing, it’s getting more complex, but ROI is paramount. I’m actually going to show you a slide pretty shortly around where we’re seeing the greatest ROI, at least from a commercial model. But when you’re pushing into hospitals, into other sites of care, ROI is paramount. Your CFO almost always the most difficult buyer. Why? It’s very, very hard to quantify the ROI on these very large cash outlays, of which what we can also tell you is that eight out of ten hospitals are actually expecting a net decrease in CapEx in the coming year. Opex is relatively flat, as we’ve done this study year over year over year over year. That doesn’t change as much. The CapEx is going down, right. So you’ve got these financial pressures. We need to see a return pretty quickly. The messages that actually make me choose your digital health solution. Number one, patient outcomes. Gosh, I hope that’s the case because we’re in a world where it’s all about outcomes and patient health. But the second most impactful message is around the financial constraints, considerations. How am I going to see a return on this investment? And then finally, when sales don’t go through about half of the time, it’s because we just couldn’t secure budget. The hospital or the decision makers did not see a viable path to spending this money and then getting a return in any form or fashion on it.
Tray Chamberlin: All right. So what does this mean for us? Well, I’m going to walk you through six use cases. But we’re seeing AI in the commercial model in so many different scenarios. It’s sales, it’s marketing, it’s rev ops, it’s service. Almost everything that typically 5 to 6 years ago, sellers, marketers did our organizations did to position ourselves to influence our buyers can now be supplemented and augmented by AI within that commercial model. All right, so here it is. If you’re looking for the cheat code I’m hopeful this is it. Right. There are six key use cases or what we consider growth plays that we see organizations running the most. I’m not going to walk you through each of these very briefly, but before I do, maybe something also that I think is very important, which is you can actually stop and look and say, well, how many organizations in the Alexander Group survey are actually deploying this strategy? And more importantly, how many are seeing a positive ROI on that strategy in the short term? And you see some pretty great numbers here. The first thing I’ll kind of walk through here is, well, first, what do we want to do? I want to reach customers effectively. What does that mean? Using AI to very specifically say, what is the message? What’s the value prop? What’s the use case that’s going to resonate the most and compel you to a point of action? As you can see, about 1 in 5 organizations are planning to deploy that or have already deployed that within their commercial models today.
Tray Chamberlin: In addition to that, now you have rep intelligence. So it’s not just what’s the message I deliver to you, but what’s my next best step or next best course of action? What should I be doing now? Ai is running through. It’s looking at data points. It’s looking at analog organizations, and it’s also looking at your ISPs or buyer personas and saying, great, Trey, you should go provide this data point. Trey, you should go. You should reference this account. You should do X, Y, Z. And having that as kind of a growth play will accelerate the sales funnel and certainly time as well. Next number three. So what we would always tell you will stand very firm on this is that from a personnel perspective, your first line sales manager is the most critical role in your commercial organization, right? Your sellers are the one in front of the buyers. They’re the ones in the halls of the hospital. They’re the one networking. But the success of your commercial model is almost always dependent upon your first line sales manager using AI and coaching models, active listening, essentially creating almost every single sales activity and call as a feedback point for sellers has seen tremendous return. Just because it’s training these individuals of what should have I done differently? How can I do it differently next time? And how are you going to enable me to do that? Back to to use case number two.
Tray Chamberlin: Next. Number four, churn models digital health, 98% retention rate. About five years ago, that was the data across our database. As we looked at individual organizations, it’s a pretty high sticky rate. At the end of the day, almost 100% net revenue retention, not a bad model. That number is now unfortunately dropped into the high 80s, depending on if I’m looking at EHR, RCN, patient engagement, whatever it is. But churn is your greatest opportunity to keep your revenue story intact. Why? Because keeping a dollar of revenue is almost the easiest thing you can do versus selling more to an existing customer or acquiring new customers. So churn indicators, looking at what are the precursors to churn? What can I look out for? And by the way, what’s the next step of action I should be taking to mitigate that churn? Whether it’s a Tiger team outreach, price concessions, things of that nature. Finally, sales forecasting. This one is where we probably saw the most initial uptick when organizations said, how can I put AI in the commercial model? It’s all about new data sources and it’s all about real time forecasting. And this is all is getting transparency back into leaders hands, right? This is less on the individual level, but more a revops function to make sure that leadership has a clear line of sight into forecasts and revenue predictions.
Tray Chamberlin: And then finally applying opportunity models. We all know that Samsung or Tam Samsung. What’s my addressable model? All that other fun stuff. The reality is that using machine learning, AI agents, etc. you do have a Tam, you do have a Sam, you do have a Sam, and those are all very valid, but some have a higher propensity to buy at the end of the day. And essentially going back and saying, hey, if I have ten accounts that look the the same or we believe they behave the same, which one do I think is, is likely the next one to buy or which one is closest to purchase? And then how do I divert all of my attention and effort to kind of close that deal? All right. So six of the most prevalent use cases we’re seeing from AI within a go to market model here, a lot of kind of professorial academic stuff there, a lot of data points. What I tell you again, is all fine and good, but what about when you start deploying these things in real life? So that’s why I have someone much smarter than me, Mike, here to kind of help us live these use cases in real life. So Mike, why don’t we just start very, very high level? Um, which of these growth plays have you deployed and maybe what are some of the results you’ve seen?
Mike Rolla: Yeah. Great question. I don’t know about the smarter part, but let me let me. Temper expectations and say, I think what we’ve deployed, at least that I’ve seen thus far, is when you look at machine learning for assessing, you know, where your solution is appropriate across a given population. That’s something we’re doing as well as like subtle agentic to make sure you’re engaging with the member in our case. So we run a virtual cardiometabolic practice. Again, I come from the digital health world. So this is what we know. We’re using a genetic, you know, genetic AI governance communication to make sure we have like the non provider visits, we have async visits and you know, all those things. When I look at this, I actually think that the highest adoption or maybe the most interest that I’ve seen is on the churn prediction and or prevention. If I want my CFO to call me back, that would probably be the one I would target off of this the most because in this case, he’s going to say, look, that goes right to the bottom line of the company. It’s a lower lift. I’m not talking about more incremental spend. He’s like, I can see that right away. So one of the things that Trey talked about is the need for ROI. I would say I would add in year ROI has now become something because we’re just so saturated with these AI solutions, nobody can differentiate unless you’re bringing forward to true in year ROI.
Tray Chamberlin: So very nice hearing a lot about AI. And what I would tell you is there are many instances where AI is going to start playing a bigger and bigger role within these use cases. So it’s good to certainly hear, um, I guess as you think about then implementing AI solutions within your team, what are the pitfalls? Right? It all sounds good on paper. It all sounds like, hey, this is going to work and it’s fantastic. But what are the booby traps? What are the watch outs for?
Mike Rolla: Yeah, there’s, there’s two that come to mind. One, this is fairly obvious, but you need to say it out loud. How clean and usable is your data? Right? So no matter the size of your organization, I’ve worked at both large and small, public and private. But really it’s it’s how clean is the data? How structured is it? What’s the accessibility? And then also, you know, the vendors, for better or for worse, they like to get their, their claws into things. So is that truly transferable? Did you retain ownership of the decisions you’re going to be making as a result of this data? Um, for engaging with any company on the process side, I’m super excited about some of the things I’ve seen in RFP response, um, peer to peer peer referral. So in other words, if you’re at a decision standpoint and you want to know who’s somebody that would influence that decision maker, there’s some great agentic tools there, but I want that data to stay with me. So I’m not continually relying on the vendor the next time I have that question so I can scale it across my, um, my sales org. The, the last pitfall I would talk about is actually on the commercial side for the reps. So reps are going to gravitate towards things that make their life easier. That’s fairly obvious, right? How does my hospital make money? How can I find out this about said person? And they’re going to shy away from things that look like surveillance. So it is a little bit about how the design of the AI product feels to the person using it day to day, because they’ll shy away from that surveillance.
Tray Chamberlin: I guess, to double click, because we have experienced that where there’s always some level of, of kind of hesitation or skepticism around new AI tools, particularly within your sales force, who they know everything. I don’t know if you guys knew that or not, but sellers, they know everything. They’ve got all the answers. And so anything that that deters from that narrative is sometimes tough. Um, being transparent, making it feel like it’s not a big brother. Any other maybe best practices or is there anything you do different in the communication and the rollout to the team?
Mike Rolla: If, if you can involve them early so that the design, like we talked about multiple decision makers here, you know how this goes. Um, the nuance that I’ve found is it’s really hard to predict when a customer is going to make a decision, right? I can give you all the information on the buyer. I can give you all the information on their organization. I can try and do the web crawler. Creepy. You know, what do they, what is their cat do on weekends thing and give you that information, right? But ultimately, how do you figure out when they’re at a decision point? Um, that’s the most critical thing. And you only get that by working with the team. Demand gen marketing, commercial, uh, starting with the team from the get go. So not a perfect answer, but that’s one of the things I think is really important.
Tray Chamberlin: Excellent. Okay, uh, any last thoughts or maybe words of wisdom for the audience as they’re thinking about implementing AI in the commercial model?
Mike Rolla: Yeah. I think that whether you’re on the vendor side or whether you’re on the organization, procuring the technology, um, simple is best. You want to start with a single use case where you can really understand your data, how it moves through that workflow. So for example, how are we preventing cancelled surgeries? How are we making sure that, you know, follow up to radiology visits take place like it is about that most simple atom or building block that I’ve seen the most success with, right? When you, when you’re presenting an AI solution to a company and it’s like, hey, we’re going to manage your, you know, risk adjustment, your, your stars, like all of a sudden you’re going through these things. It’s like, I know what that is, but I don’t know how you’re going to do any of it. And it’s far better to say like, we will reduce the number of cancelled surgeries you have, or we will get this information to you quicker for the purposes of X. So, um, single use case works way more than we can do at all because it’s overwhelming.
Tray Chamberlin: Excellent. Some great advice. Wonderful, Mike. Really, really appreciate your time. If anyone wants to have any additional conversations around go to market strategy, AI in the commercial model, we’re right over there at the Alexander Group booth two to the side, but appreciate your time and enjoy the rest of the show. Thanks all.
Mike Rolla: Thank you all.
