Rohan Kulkarni — Healthcare and Life Sciences Research Leader, HFS Research[00:21]
Greetings and welcome to HFS Unfiltered. This afternoon, I have with me Nikhil Mendhi, who is the Chief Operating Officer and President at Exponential AI. And I’m Rohan Kulkarni, who leads industry research in healthcare and life sciences research at HFS Research. So what we really want to talk about, Nikhil and I, is to get some clarity between what health plan buyers are saying in terms of what they want, the approach they want to take in terms of how they go about buying, and aligning with suppliers. And Nikhil will talk about what he’s experiencing on the ground as they work with a number of their clients and their prospects. So with that in mind, our interactions with healthcare buyers, both on the health plan side as well as with health systems, we find that there are essentially two kinds of buyers, right? You’ve got the procurement as well as the IT buyers. IT buyers are oftentimes fronting for business buyers, specifically in the context of AI. So we’re essentially picking up three key themes. Theme number one is health plans and health system buyers want to maximize the potential of AI. They want to be able to create products and services that truly differentiate for their audience, whether they are members or whether they are patients. There’s also a little bit of a sense of wanting to create a new kind of value, aside from differentiation, from a marketing narrative. They also want to create a fundamental new value in terms of how they address the needs of their members and how it is that they’re able to deliver care in a very different manner as they go forward. The last aspect we’re also picking up is that there’s a sense that the buyers want to create a new position for themselves within their enterprise, so that they have more influence over the buy dollars as they go forward. So those are key things that we’re seeing and hearing in terms of them wanting to maximize AI. The second theme that we’re also picking up is around risk mitigation. AI in so many ways is still fairly new for health plans and health systems, though they’ve been experimenting with it for a few years now in various different forms and shapes, but it’s still fairly newish, right? Especially for enterprises that have an enormous amount of tech that is based on a legacy tech stack. AI introduces a new level of risk, and that is a level of risk that they don’t fully appreciate. And so they are still very much in the area of trying to mitigate risk, and so a lot of their buying behavior is informed by this level of risk awareness, the aversion that they have fundamentally. And then lastly, there’s unpredictability. Buyers generally have a fixed budget that is set for the following year, at least three to six months before the year starts. They’re working with a fixed amount of budget, and therefore predictability is extremely important. Though there is the narrative around outcome-based buying — whether it is gain share, whether it’s value based, whether it is other forms of outcome based — we get the sense that they are still very, very interested in predictability, and predictability typically drives a sort of flat-level pricing, if you would. So those are the key three, and obviously there’s also the sense of wanting to simplify their buying process, which really aligns with how they’ve been buying in the past, right? So those are the three key themes that we’ve picked up. Number one, maximizing AI. Number two, managing risk. And number three, ensuring that there’s a level of predictability. So that is kind of the essence, the sense that buyers have as they begin to explore new ways that they can leverage AI in their enterprise. So that said, Nikhil, you work with health plans and health systems on an everyday basis. You’re working with their buyers. What is your sense? What are you experiencing on a regular basis?
Nikhil Mendhi — Chief Operating Officer and President, Exponential AI[04:42]
Rohan, first of all, thanks for having me on this podcast. So you’re right. With the advent of generative AI across industries, specifically in healthcare, there is an aspiration, and a very strong aspiration, for organizations to become an AI-first company. Now, how do we become that, or how do they get there? The approaches vary significantly based on the size, demographic, and the appetite of these organizations. So what we are seeing, or more experiencing, is that AI awareness is significantly increasing, but everybody is touching and feeling the different parts and portions of this elephant in the room. Everybody puts their experienced hat, or the past hat, on, and is trying to solve for the micro problems rather than taking a look at the macro issue. And that’s what is reflecting in the buying behavior. So if you look at the industry leaders or the largest players in the industry, they have made some bold moves to build their own center of excellence where they want to incubate the AI concepts, so to say. But there are challenges around how to take those incubated ideas and bring them to life in production. On the contrary, there are the others, the mid-sized payers, who typically would lack the muscle or the financial strength to make those big investments up front, and they’re always the fast follow after the large ones. They are more in that experimentation phase, where they need help figuring out where to start, and that where-to-start itself is creating a lot more activity as well as confusion in their buying behaviors. And I want to reiterate the fact that AI is not just a technology challenge, it’s a mindset challenge for all the healthcare organizations. And healthcare being how highly regulated it is, it adds to the complexity in that buying behavior. Why it’s a mindset challenge is that most of the organizations today are wearing their traditional technology buying hat and looking at the AI technology solutions, products, and services that are available out there in the market. And that’s what creates this tug of war between the AI vendors and the healthcare organizations. There are infrastructure limitations, there are data limitations, there are governance limitations that the payers have to tackle, which in turn reflect in how they are buying AI products and services in the market. And the expectation today is that the vendors would have solutions for most, if not all, of these questions that the healthcare organizations have. So there is an upfront change management that all the AI service and product companies have to invest into for them to accelerate or streamline their sales process, or their interactions with the healthcare organizations.
Rohan Kulkarni — Healthcare and Life Sciences Research Leader, HFS Research[08:20]
Got it. Excellent. So clearly there seems to be a little bit of a gap between the buyers’ aspirations in terms of maximizing the value that they can extract out of AI, and then the ground realities that you’re experiencing. I guess our overall sense — and I’m sure you’ll have a point of view on this — is that even though the ground reality doesn’t quite match the aspiration, that’s just a function of time. As they become more comfortable with AI, as there’s a level of integration in terms of the way they’re being informed about the capabilities of AI in reality within enterprises, their buying behaviors will also sort of change. So the fact that there’s a gap between aspiration and reality is just a function of time. Would you agree with that?
Nikhil Mendhi — Chief Operating Officer and President, Exponential AI[09:31]
I would partially, definitely agree with that. It’s a function of time, but what I believe strongly, having worked in a payer environment for pretty much 75% of my career, is that there needs to be bold moves, or top-down leadership decisions, that will enhance the awareness of the healthcare workforce and help the organizations build a point of view on what AI really means for their business. Right now, a lot of it is at the theoretical level, and for that theory to become reality, it is on the workforce to take it on and define what AI means for their organization. For example, it’s easy for any organization to depend on the cloud vendors for AI tools to train and upskill their workforce, but the reality is that the workforce needs to understand the operational and strategic challenges that every organization has, and then translate them into the problem that they really want AI to solve for them. Which means they need to have the best of both worlds. And apart from the fact that the healthcare industry goes back 100 years — so the historical data, the regulation — it plays a pivotal role in the success of any new technology that healthcare adopts, and factoring that in, in fact, is a bigger challenge that the organizations have to solve for. If they start with AI awareness first, and then overlay that with their existing reality of infrastructure, data, governance, and regulation, the workforce will be in a much better position to approach any potential AI solution and then recommend a potential solution that’s ideal for their business.
Rohan Kulkarni — Healthcare and Life Sciences Research Leader, HFS Research[11:40]
Got it, got it. Perfect. Well, on that note, Nikhil, I appreciate your comments. I think the takeaway for healthcare enterprises is driving awareness within their workforce and, two, leadership being bold in terms of their decision making. Excellent. Thank you very much for your time. Really appreciate your insights.
Nikhil Mendhi — Chief Operating Officer and President, Exponential AI[11:57]
Thanks, Rohan.
