Operator: Thank you. One moment for our next question. Our next question will come from the line of Allen Lutz from Bank of America. Your line is open.
Allen Lutz: Thanks for taking the questions. I want to follow up on that last one. I guess, Steve, you mentioned that a lot of this is driven by AI, but not all of it. I’m curious, are — is what’s embedded in that 5 percentage point increase in the profitability targets? Is that related to tools — the AI tools that you already have available today, or is it based off of ones that you may deploy in the future? I’m just curious how you think about that in the context of getting to that fiscal ‘29 target and the path with 300 bps of margin expansion per year. Thanks.
Rajeev Singh: Hey, Allen. This is Raj. I’m going to take a cut at that question first, and then we’ll let Steve add any additional color. Think about gross margin expansion or unit economics expansion and overall economic expansion of the business with a couple of drivers. The first is the idea that advocacy customers who are taking advantage of incremental services like primary care, expert medical opinion, and our trusted partners are taking advantage of very high gross margin or strong unit economics offerings as it relates to our P&L and driving extraordinary value for their members. With each cohort that comes on, taking advantage of those services, we have an opportunity to continue to grow the usage of those services and in turn, drive better unit economics and better profitability.
Over the last year to two years, we’ve seen the first cohorts and we’ve seen the results for those first cohorts, which give us great confidence moving forward about our capacity to continue to grow those, to grow the unit economics associated with those offerings and therefore the long-term profit margins of the business. The second wave of that story is the investment in technology that can drive efficiencies in the business. Those investments in technology can be non-AI oriented, meaning things like being extraordinarily efficient, consuming data from the rest of the ecosystem so that our implementation costs are going down on a year-over-year basis, or things like artificial intelligence where we can be more efficient as it relates to wrapping calls and doing call summaries, to consuming benefits information, to responding to queries for our members in a high quality way but at a lower cost.
You would think about those two vectors as significant drivers of value and our capacity to see the whites of the eyes of those drivers of incremental unit economics over the last year to two years being the fundamental drivers of our confidence as it relates to improving our guidance on the fiscal year ‘29. Steve?
Steve Barnes: Yeah, I’ll just add that — to that last point. Allen, I would think of it this way. These are tools and capabilities internally developed or in some cases, tools that we license and bring in-house that we’re using today but have just begun to demonstrate the availability and capability to achieve but not nearly reach the full benefits that we see that will happen over the next several years as we more deeply embed those and get better and better at those and those feed their way back into the platform and the process that we have. So very much based upon tools that were in the earlier stages of deploying and have some proof points that in fact they are driving those kinds of efficiencies and ability to drive incremental usage-based revenues as Raj was starting his remarks with.
Operator: Thank you. One moment for our next question. And our next question is from the line of Stephanie Davis from Barclays. Your line is open.
Stephanie Davis: Hey, guys. Congrats on the quarter. I want to also go down the GenAI route for my question. Just given the differentiation that your care advocates really give the platform, I wanted to hear about what you would view as an automation opportunity versus what you would view as potential opportunity, but not something you would approach because it could potentially dilute your offering.
Rajeev Singh: Yeah, I think it’s a really important question, Stephanie. So first of all, thanks for the question and thanks for joining us. Secondly, the way to think about this in the context of the millions of interactions that we have per year, those interactions can be phone-based, they can be messaging-based, but those millions of interactions involve understanding the member’s need, building a relationship with the member, and then summarizing the transactions and following up on the transaction. The component of understanding the members’ need and building a relationship require a human, require our people, whether those are our care advocates, our nurses, our doctors, or our specialists in any particular field. The capacity to summarize what occurred in those transactions, to follow up on those transactions in many cases with tasks that are repetitive, understanding a benefit, understanding a claim, inquiring from a health system or a health plan about the validity of a claim or the amount of a deductible, et cetera, are areas that could potentially be automated.
Incrementally, another area where the company spends a significant amount of time is actually assessing the quality of our interactions in every one of those transactions — every one of those millions of transactions. And so the capacity to assess the quality of those transactions by understanding our follow-up, by understanding the amount of time we’re taking to follow up, by understanding the strength of the data collected about the interaction are all things where artificial intelligence and just traditional technology can play a significant role. And so absolutely, the idea of understanding a need and the idea of building a relationship, those are human attributes that we would be [loathed] (ph) to change or to in any way try to automate.
