Good morning. Thanks, Maria. Thanks for joining us today. and your continued support in our team.
In our session, I would like to share some answers to 4 key questions. Number one, what have we been doing since the last Investor Day. We've been doing a lot, I'll share it with you. Second, how are we thinking about AI within ADP? And third, what are the key highlights from some of our flagship products and how those products would be differentiated in an increasing commoditized SaaS applications? And the last, as Maria said, how are we going to keep this innovation flywheel keep going with our outside in perspectives as well. So let's dive in.
As I said, we have done a lot since Investor Day. And I want to highlight some of the products and platforms that may not get a lot of limelight compared to our flagship market-facing products. And why is that important? So let me walk you through a few of the products, One Data, One Mobile, One UX, Marketplace, et cetera.
So take One Data as an example. By the way, if you ask engineers to name products, that's what we get, One Data. Basically, all the data in one place, okay? That's what it does. Why is that important? So you may have heard of a national employment report. Nela is there. Hi, Nela. You may have seen her on CNBC every month or every week for that matter. One Data powers our national employment report. So think about millions and millions of data points in one data set and what are the advantages we get and the monetization of data through our data solutions business that does the employment verification, income verification, et cetera. And analytics, we can bring insights across the products into this data set and power insights through analytics assist through AI. So there's more and more applications that come as a result of this One Data platform. And most importantly, as we're taking the journey of AI, AI without data is not that effective. And having all the data in one place powers that AI innovation. So I hope it makes sense why that is a platform that continues to power innovation within ADP. So let's think about that at the database layer. Like if you think about a SaaS application, it has primarily 3 layers: database, processing and the presentation layer.
So let's go to ADP Mobile. We did not build -- for each of our market-facing products, it's One Mobile application. We did not. We build One Mobile application, and that application knows when you log in, which products you are using and what tasks you're trying to get done and it routes the transaction to the underlying product and brings the information back. That's pretty cool. To do that engineering wise, it's very hard, but our team has done that. So that's the advantage of cross-product layers sitting at the top. And same for One UX, all the UX on all of our product looks, feel, behaves the same way. That's very hard to do as well. So my point on this slide is really simple. The whole is bigger than the sum of the products. And all these horizontal things help bring that whole very valuable to our clients.
So let's shift to AI. Before I talk about ADP's strategy, let's zoom out. What's happening for the last couple of decades when it comes to automation as well as AI. You all heard of RPA, heard of big data, you heard of machine learning. We've been at it. This is not new to us. However, what is interesting now is where are we going? And let's focus and zoom in actually on Agentic AI because you all have heard of that. And what is an agent?
Let's take a look at the 3 flavors of Agentic AI. An agent simply is a combination of information and action coming together to do a task. So a simple agent as a logical extension of that is you do a simple task by providing input information and connecting to an action through an API to get a single task done.
So let me give you an example. You all probably hired somebody, right, to work for you. The recruiter initially ask you, okay, okay, you have an opening, and I need to know the details about the job you're trying to hire the person for. So you write a job description. You can go to any market-facing product today in Gen AI and ask the Gen AI to write a job description. But what is important to feed the information of your past company job description. So you have some disclaimers, you have some tone there as well as maybe we should look at the job description of how other companies are doing and see if there are any best practices, we can take it. All of that is information required for this task of writing a job description. So that's a good example to understand a simple agent.
However, an advanced agent is, okay, what happens after you write the job description? You have to post it somewhere in the market, so the candidates can apply. Once the candidates apply, you need to screen them, you have to schedule the interviews and then keep moving forward until you generate the offer letter. Each of those tasks could be by their own definition, agent. An advanced agent, it sequences these simple agents together in such a way that end-to-end process can be done. So that's how to think about an advanced agent. It's a multiturn. But there is always a human in the middle to move it forward one step at a time. The autonomous agent is really the degree of the human in the loop basically. So autonomous by nature is as you move forward in the sophistication of this agentic journey, less and less dependence. So that's how to think about it.
So what are the possibilities as we think about agentic AI. So in that example, I gave a recruiting agent basically. So you can build a recruiting agent that does all these tasks. You can extend that and create an HR business partner agent, you can do a payroll agent, you can do benefits agent. Those are all the possibilities. But as you can see, as you build these advanced agents, it takes time because you need to know all the tasks, how to connect all the APIs. So it takes time. However, we don't need to wait for that long to build all these role-based agents. We can deliver feature sets and which we have done via ADP Assist in all of our products, and I'll share those updates in a bit.
Before we move on on this slide, though, agent interoperability is a must. So you may have seen that APIs, industries, not only HCM industry, all the software industry, they built the APIs. Why did they build the APIs? For interoperability. So think about agents a higher level above that. Agents, our clients would have agents from other systems that they have in their environment. All those providers might be developing the agents you would expect, right? And those agents have to interact with our agents within ADP.
So we are building interoperability as a foundational principle when we do our agents. Okay? And we're using market-leading framework such as MCP and ATA and few others, I'm sure the market would be introducing. So that's an important point, agent interoperability.
But to do all of these things, as Maria said, you need data. And we have the biggest data set in this industry. Just having the biggest data set is not enough. When you think about data, there are 2 ways to think about it: Structured data and unstructured data. Also, there are multiple flavors of data. Public information, let's say, IRS are a country-specific regulations or minimum wage rules. So this is all public information you need to know. And then you have client level information, how many locations, how many employees, where do they operate from. And then you have groups within the company, what departments, sales, service, operations, R&D and what are the policies in each country that you need to apply to.
So you see some of that is restructured, some of that is unstructured. How do you organize that data in such a way you can build meaningful relationship between different entities? It takes some knowledge and good engineering work. So I go back to the One Data platform that I introduced you on the first slide. We have that. We've been at it and we've been organizing the data with the right way so that we can power AI moving forward, okay.
Let's look at strategy. So now we understood all of that. What is our strategy? Our strategy simply is based on 2 pillars: One, we will build role-based agents in our client-facing products. I already talked about recruiting agent. So just the extension of that. I don't know if you guys know this, I used to be CHRO of this company before I took this job 2 years ago. So HR departments are not homogeneous as you might think. There are multiple groups. There is a recruiting department. There is benefits department. There is a HR business partner, there is compensation person, et cetera, et cetera. And each of those roles have set of responsibilities. So our strategy is to build those role-based agents, okay? That's great. What about internal ADP operations? And we are applying the same principle to our internal ADP operations. So what are the roles within ADP? We have service people. We have implementation people. We have R&D people. We have sales. So we are building agents for each of those roles. So that's the way to think about the overall strategy of ADP AI, okay.
Now what we have done so far in our products? As you see, 4 million interactions on ADP Assist. So one of the key persona that our clients have is an employee, right? It's a very generic employee. And employees have questions and they want to do some transactions. So in the past, SaaS applications have self-service capabilities, do you come to an HR system payroll system, you do those self-service transactions. And now you can apply AI agents on top of that, and that's what we have done. And we have plans to roll out more capabilities throughout. So those 4 million transactions you're talking about are through the ADP Assist, they are employee self-service, manager self-service, and practitioner self-service, okay?
So let's think about -- let's go to service. Similar to the HR role-based agents for our service agents, what we have done was so far call summarization. Because if you think about the services say, what happens, there's a reactive and a proactive approach, somebody calls you for information on an action or you're reaching out to a client for information sharing or having them take an action. So what we are doing is we are providing answers in such a way they are accurate as well as very effective in connecting all the dots from One Data platform.
When I say One Data, by the way, it's not just the product transactional information. We're ingesting our One Data platform with our call transcripts, our chat transcript, transactional logs. So all of that will be there so that we can take meaning out of it, information, insight and power the answers that the service assists are required to do. And we are seeing very good productivity from there.
So let's keep going to sales. So what are we doing in the sales agent realm? In the sales agent realm, we are answering 3 key questions. We delivered that and 40% of our sales assists are already using it. Who to call, when to call and what product to pitch. Simple, you would think that, well, it's not that hard to do. It's not that hard to do. Adoption, changing the behaviors and effectiveness of all these things powered through the AI channel, we're seeing good results. And we would be delivering more capabilities through our sales agent realm very soon.
Last but not the least, let's talk about R&D. As you see, 100% of our developers have access to our AI tools. And what they are focusing on is documentation, test automation as well as the code generation. And we are seeing very good results so far. And we will continue to power and build a developer agent along the way, okay.
So in summary, think about role-based agents inside our product and inside our operations, all the tools that we are using with our associates, okay? That's our strategy for AI, and we're making good progress.
So let's shift to our product portfolio. The breadth and the depth is unparalleled in this space. We cover 1 employee company, as Maria said, to 1 million employee companies. We have products that do software as service and all the way up to outsourcing. Domestic to global. Great portfolio and very proud to represent that. However, differentiation, how do we think about in an increasing commoditized role, differentiation. This is where we came up with a philosophy, what we call it, make it easy, make it smart, make it human. What does it mean? Let me walk you through an example so that you understand what that means. It's a philosophy. And we're trying to operationalize that within our products.
Most of the software vendors try to get the easy part done, meaning introduce automation, introduce self-service so that a particular transaction is easy to do with less friction. That is "easy" to understand. However, smart. With AI coming in, you can build insights on top of the transaction so that the decision-making power gets better and better, okay? Let me -- and the human part is hard to understand, but let me walk you through an example.
So let's say I am a shift worker, and my manager is setting me up for a shift change because something else happened. So through ADP Assist, he just says, change Sreeni's shift tomorrow from X time to Y time or change tomorrow to the day after. I'm not supposed to work day after. But imagine the ADP Assist is smart enough powered by our One Data insight to say, that is the day Sreeni's son is having a birthday. But the manager has no idea, right, because that has nothing to do with the shift change. So imagine the ADP Assist comes back and tells that manager that, hey, here is an issue. Are you still okay to do it? And at that point, manager has a choice to make. Let's say he has no other choice, he still needs Sreeni to do the shift. He says, "Yes, I still need to do it." But then ADP assist comes back and says, "Do you want me to send a birthday cake to Sreeni's home?" That is human. Because what happens when Sreeni goes home after the shift, there is a birthday cake sent by the manager. That's what I'm talking about.
We build people systems but we stuck at the easy layer how to change the shift easily. But what are all the things that you should be thinking about. That's what we believe in. Because ADP is always designing for people. And what do people have? Emotions. Nobody is addressing those things through our people systems. So why can't we do that? Why can't we do that? Not every single transaction is going to be fit for that. But those few that you raise the plain from easy, smart to human, that's when our users would allow our products even more. That's worth the journey to go after, and that's what we are going after. So that's the philosophy we believe in, and we hope we can continue to deliver on that promise.
Let's go through quickly our products. These are flagship products. You guys are very familiar, 900,000 clients, amazing. Even the skeptics would agree with that. And one of the things that key for RUN platform is the integration of retirement solutions and insurance solutions. This is absolutely value added within our RUN platform and our clients tell us every day, and they are not well understood. But I want to highlight the seamless experience that our clients have with RS and IS integration. And then how do we continue to move forward within that RUN platform? Embedded Payroll, Maria talked about Clover integration, which is live and few other things that we're thinking about that.
We're not satisfied just putting it in the other wholesale providers, but the operations-wise, digital onboarding, we continue to see huge progress and especially with AI coming in. So -- and then on top of that, we have a few other things. I hope you get an opportunity to see the demos today, our RUN platform right there and WFN analytic.
Let's keep going. This is our mid-market leader, WFN. You all know that. And the scale is, again, amazing 900,000-plus. In mid-market, having HR, payroll, benefits, compensation all in one place is very, very key because they don't have the sophistication, the resources to go put best-of-breed products together and integrate it all into one. So that is a key factor in the buying decision and our product delivers on that promise.
And what else is happening in the platform is we released our next gen in a couple of market segments, and we're seeing fantastic traction and in very near term, that is going to be the leading offering in the mid-market, WFN NextGen. It's connected to our single Global Payroll platform. To continue to the scaling of this product, we are integrating it with all the major ERPs and we are also doing industry-specific solutions such as construction, restaurants, et cetera, coming through. And obviously, AI infusion, as I described earlier.
Let's go through our newest entrant, Lyric. First time ever in ADP's history, we have a Global HR platform. And why is that important? So think about -- we are known for Global Payroll, now we're going upstream, connecting that to Global HR with the integration of WFS that we bought recently. We have global time, and we're also known for global service. So with these 4 components, we have created a unique offering in the market, Global HR, Global Payroll, Global Time, Global Service. Nobody has this in the enterprise segment. Our competitors have a few of those, but nobody have put together these assets in such a way that it is market compelling. And we are very enthusiastic about this and the traction from sales and implementation backlog is amazing.
And last, I want to cover ventures, as Maria talked about. The hypothesis behind this is really simple. As good as we are, and we have been good for 75 years, we also believe, they're truly differentiated solutions some start-up founders have been developing. And their dream is to put their product in front of as many clients as possible, and we have the biggest distribution network. So why can't we combine the forces, bring those products, give it to our distribution and let's test them out. And of course, we will invest in those companies where it makes sense so that we can influence the road map, better integration with our ADP product and see how the market reacts to those innovative solutions. If the market loves those things, then potentially, they become our M&A pipeline. So the venture fills that innovation flywheel very well, and we are very excited about that.
So in conclusion, we'll continue to build great products with a philosophy that is easy smart human, continue to drive AI infusion with role-based agents inside and outside and then continue that innovation journey with an outside in perspective through our venture capital.
So thank you for your time. Appreciate it. Also, it's a privilege to be standing in front of you representing the work of all the associates at ADP, all the good work they've been doing. All this innovation, though, is meaningless if we can't distribute it. So fortunately, we have the best distribution in this industry. So to talk all about that, let's welcome David.