← The Digital Executive
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Foreign.
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Welcome to Coruscant Technologies, home of the Digital Executive Podcast. Do you work in emerging tech? Working on something innovative? Maybe an entrepreneur? Apply to be a guest at www.corazon.com brand welcome to the Digital Executive. Today's guest is Paul Naiman. Paul Naimond is a Silicon Valley sales leader and AI entrepreneur with over 18 years of experience scaling enterprise technology platforms and driving digital transformation across multiple industries. He is the co founder and Chief Revenue Officer of Areti Health, a venture backed company using generative AI to revolutionize patient engagement and clinical trial recruitment for Fortune 100 pharmaceutical companies. Paul's unique background combines deep technical expertise with proven sales leadership. He began his career as a software engineer building enterprise platforms at companies including Ariba Good Technology, which was acquired by Motorola, then BlackBerry and Coral 8. His engineering expertise developing real time data processing systems, AI powered analytics platforms and enterprise SDKs provides authentic technical credibility when engaging with CTOs and engineering teams. Well, good afternoon Paul. Welcome to the show.
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Hi Brian, excited to be here. Thanks for having me.
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You bet my friend. I appreciate it. You're hailing out of that Bay Area, Silicon Valley area in California. I'm in Kansas City freezing my you know what off.
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But I just cold.
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It's very cold. But this is typical for Kansas City in January, February. So without further ado, Paul, let's jump right into your first question. You started your career as a software engineer before moving into sales leadership. How has that technical foundation shaped the way you sell, build credibility and differentiate in competitive enterprise deals?
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Yeah, great question. And it is a bit of an unusual path. Usually once you start an engineer you kind of continue down that path. I did start it. I really wanted to talk to people and solve the problems that they have rather than just work on code all day long. And I think that transition gave me ability to articulate how the product is built, what it does, what it delivers with much greater authority. When you are in complex enterprise deals, when you have multiple stakeholders involving IT and compliance and the business, you can understand their pain point and pivot a lot quicker than a standalone sales guy with a BD or an exec. And normally you would have a sales engineer on the call. And now you are your own sales engineer. You understand the customer's environment, you can dive as deep as they need to into the tech stack. You speak with much better credibility and you're able to create effective pilots on the fly. You can discuss everything from integrations that they run to the stack they're on, answer their questions. I think that leads to Much higher rate of deal closure than just a standard sales approach.
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That's awesome. Really appreciate that. Love the story. Started out as an engineer, but you'd rather talk to humans and solve problems, which I think is amazing. That's awesome. The advantage was, and you mentioned this, is being having that engineering background, you're able to speak the technical, talk to other engineers and clients, but also interact and speak the business language. So you really bring the best of both worlds and I really appreciate that. So thank you. And Paul, at Already Health, you're using generative AI to transform patient engagement and clinical trial recruitment. What's broken in the traditional clinical trial model and how does AI fundamentally change the equation?
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Yeah, so we, if you think about it, patient recruitment, patients is the number one pain point that is cited by sponsors, by CROs, by sites that the clinics, they actually do the research and statistics, just something that you can go ahead and look up. But it's everywhere. Statistics are pretty dire. Something like 80% of clinical trials do not meet their targets on enrollment. And unless you have that enrollment, the studies can go forward. If the studies don't go forward, you don't deliver your drug or medical device to the market. And the reason is that it's currently a very manual process with several bottlenecks. So with AI, we're bringing automation where none existed for many years. It is still a process that is heavily dependent on call centers, on professionals reviewing medical charts, doing it manually. They have to allocate the time. It is incredibly time and labor consuming. And with AI, we resolve several bottlenecks. And we'll start with the first one is ability to find high fidelity patients. The trials today are getting increasingly complex. There are a lot of a ton of inclusion exclusion criteria. The drugs are getting more precise, the medical devices are getting more complex. And so you need to understand where are my patients coming from? Do they fit into a trial or not? And one of the existing ways to do it is to review their medical record. Now there's a couple of problems connected to it. Some of these medical records could be extremely long and complex, 300 pages long, a qualified medical professional. It can take anywhere from 20, 30 minutes to an hour and a half to two hours to analyze just one medical record to understand whether the patient is a fit. Now understand you have to sit through thousands and thousands of patients to get to the those needles in the haystack to complete the enrollment and make sure these people stay in the trial throughout, which is another problem by itself. And so you start with a very large funnel that takes an incredible amount of time and labor to just throw. And now that we're bringing AI, we're able to one, analyze those records in minutes, pull them in minutes, analyze everything that's in the record. We're talking not just old style, old fashioned index keyword search for a particular indication. No, with AI we're able to look at unstructured data and analyze everything from doctor notes to lab results to prescriptions to your image scans holistically within long record until definitively we're 99% confident this patient is a match for this medical trial. This patient is maybe 75%. This one is 50. This is don't bother with the rest, right? Don't waste your time on it. Don't try to cast a net super wide and burn a lot of resources trying to bring in people that will screen fail altogether in the end because they're not a fit to begin with. Okay, that's great, that's step one. Now step two, this sponsor says fantastic, you've identified who is a potential fit. How are you going to bring the name through the door? Because unless these people show up, it doesn't really matter that they exist. You got to interest them, you got to engage them, you got to super screen them and schedule them with a site, with a clinic runs the clinical trial. Okay, great. This is the game. This is where AI comes in and removes the obsoletes, rather obviates the need for large call centers because it can operate 24 7, it can speak your language, it doesn't take breaks, there are no holidays. We immediately engage anyone we identify, we pull the medical record, we found that you're a match. You're getting a call, you're getting a text. Everything is customized to you based on what we already know about you to increase that engagement to get your interest high. In the study, you came in through social media, you saw an ad on Facebook, you're potentially interested while losing a beat. We are immediately texting, calling you depending on the time of the day. Again, based on what we know about you, you came in, you were interested in a high blood pressure study. Let's see if you can prescreen. And while we do that, we're able to anonymously talk to you, educate to you about the study, talk to you compassionately, empathetically, spend as much time with you as needed to make sure you get your question answered so you're an educated, willing, interested participant. And then step three, we build automations to deliver these patient profiles to the people that will actually be interacting with them to the sites directly into their clinical trial management systems. Schedule that appointment, allow the patients to pick a good time and stay in touch with them. Nurture them all the way through the visit to make sure that their interest in the study stays high, that they don't just forget about the visit. This is yet another place for AI to come in that consistent cadence of touch points to make sure you are aware your visitors coming up, they're still interested. Maybe there is a concern that we could address. You don't like needles? We'll talk to you about why the blood draws are important for the study and so on and so forth, but really keep you engaged all the way through the, through the visit to the clinic. Wow.
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A lot to unpack there. But that's amazing the fact that you're leveraging AI to do everything from doing the research. You know, you talked about patient and clinical trials, one of the hardest industries to recruit for. Very bottleneck, very manual time and labor intensive. But AI is bringing that faster, smarter level of reviewing medical charts to do the matching of the for the enrollment trial. But that level of accuracy, the matching, it goes as far from basically from beginning to end. Right. Cradle of the grave. You talked about successfully engaging enrolling with the patient, handling the marketing, the ads, speaking with the patient, their language. There's just so much that AI can do. But you've really honest harness the power of AI and bringing this all together to streamline this and make the process more engaging for the patient, but more accurate in the end for the trial. So I appreciate that, Paul.
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Yeah, absolutely.
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Many AI startups struggle to translate technical capability into clear business value. What messaging or proof points resonate Most with Fortune 100 executives when evaluating AI driven platforms?
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Yeah, I don't think I have any super truths here. I think everyone who's in the business of sales should intuitively understand this. It is of course, what is the value, right? What is the value that I'm bringing? Why would a Fortune 100 onboard this technology when I'm speaking to a C level exec? What is it that I'm delivering for them? What kind of pain point am I solving for them? So you offer we can do features XYZ to we can effectively solve a problem XYZ for you. And of course they're interested in being a Fortune 100. How much are they betting on it? Are they bidding a farm or is this a de risked decision? Are there case studies by now? Everyone understands it's not a magic bullet, right? So we're over the hype of AI. Everyone understands there's 80, 20 conditions of it's working. Yes, there is 80% of cases we can resolve. 20% what is going to be escalated to a human or we need a human in the loop or this is simply not a good fit. We're not going to move the needle there. They want to understand if it's going to be safe for the business. For healthcare specific, this industry is notoriously conservative. Clinical trials are regulated by review boards and so they got to have their say. Is this something that can be used when interacting with patients? Is I'm not going to say something that's not ethically approved. Out of bounds. Are there guardrails around this? We did a lot of work on compliance and all these guardrails to make sure that we choke study only that we detect what's called an adverse event, that we can resolve this or quickly escalate to someone responsible for working with the patient in all these cases. And of course there's still humans present, but we remove that. You can show that you can remove the labor and repetitive tasks, the minutia, the administrative portion of it. Nobody likes. And this is where you kind of get stuck. I think that helps a ton. And of course it's really the bottom line for the business. What are we resolving for healthcare in particular here? We are compressing the recruitment timelines. We are improving operational efficiency. We're saving them a ton of money because a day of delay for large sponsor is an incredible, incredible numbers just in terms of resources and the money that they spent.
- [12:53]
B
Thank you, really appreciate that. And there's a lot there you talked about what are those executives or board members looking for as far as what's the value you're bringing for them? Are what problem you're trying to solve? What is at risk for them? Is it safe for their business, for their customers? What about compliance? You talked about guardrails ensuring there's no adverse events. At the end of the day, you need to provide them a successful use case so that they can actually digest some of those that have been proven in the market. And that's one way to obviously again, getting that message across to Fortune 100 executives and board members are sometimes pretty tough. So I appreciate the insight. And then Paul, the last question of the day as we look ahead. How do you see generative AI reshaping enterprise sales and healthcare innovation over the Next, let's say three to five years? And what skills will future CROs need to succeed in that environment?
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Yeah, well, there's already A ton of AI helpers ranging all the way from the sales process, from SDR replacement to sales enablement. As a CRO, you just kind of have to decide where is the value that your BD rep is bringing. Are you going for overall numbers with the shotgun approach? And you can do mass email generated by AI and you don't care. There's a little bit of AI slop that slips through. Or are you working operating in a very tight industry, a conservative industry, where the existing sponsors already get absolutely demolished by pitches and so you gotta stand out above the noise? So this is the approach you decide on, then use the right tools. Do you need AI to better polish your pitch? Do you need AI to monitor your SDRs performing? Do you need AI to take over your call center or initial outbound calling? This is a decision that is tightly dependent on the business you're in and where you go from there. Again, what the future stars need to succeed, I think it's ability to stay current in the market. Every single tool, from the CRM to your sequencers to your data enrichment tools, they try to squeeze AI in there. So you got to decide on the overlap. What are you paying for? Does this really make your workforce effective or is it slowing them down? It's just ability to filter out the noise from the true, truly enabling features within the stack, the sales enablement stack that you use.
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Thank you. And I think that's important. The big thing I took out of that was obviously staying current, but really it all depends on the business and what you're doing. But you talked about what is your strategy and what value are you bringing. There's a whole process in that. You talked about sales process, sales enablement. But again, going back to what CROs need to be successful and is being able to stay current in the market using things from data enrichment tools to CRMs. I think that's important. So I appreciate your insights. And Paul, it was such a pleasure having you on today and I look forward to speaking with you real soon.
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A
Wonderful. Thanks for having me, Brian. Appreciate it.
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Bye for now.