Name: Meet Rippling AI: Intelligence You Can Act On
Uploaded: 2026-04-29T18:04:07.168Z
Duration: 48 min 8 s
Description: Meet Rippling AI: Intelligence You Can Act On

Transcript for "Meet Rippling AI: Intelligence You Can Act On": Hi, everyone. It's great to have you here today, and welcome to our webinar today. This is Meet Rippling AI Intelligence That You Can Act On. My name is Divya, and I am a platform storyteller here at Rippling. And joining me today are Luke Prokopiak, our product lead for Rippling AI, and Corey Feeley, who is a senior recruiter at ReCom and has been using Rippling AI for a little bit now. In the next forty five minutes, we're going to show you something that I genuinely believe changes how business are going to operate across HR, IT, and finance, and not just incrementally, but fundamentally. And before we get into it, just a little bit of quick housekeeping. We will have polls throughout today's webinar session, so feel free to participate there and share a little bit of your insight along the way. You can also drop your questions in the q and a throughout the course of the webinar, and we're gonna save those last few minutes towards the end to address all of them. So this is what we're gonna be covering today, where teams like yours are losing really valuable time to tedious, manual, and most importantly, avoidable work. An overview of Rippling AI's core pillars of intelligence, action, and trust We'll have a really exciting live demo to show you a sneak peek at just how much is possible within Rippling AI. And we'll also do a deep dive with our customer who's present, Corey, who's been using it in really creative and innovative ways. And hopefully, you leave with a little bit of inspiration. And, of course, I am so sure that you're gonna be really curious to learn more about Rippling AI by the end. So as I mentioned, there is time for q and a and some exciting resources that we're gonna be sharing towards the end as well. So I wanna start us off with thinking about something. I want you to think about your last full work week and how much of it was spent doing work about the work rather than doing the strategic things that you actually want to be focused on. Maybe you're pulling data to answer a one off leadership question or fielding the same benefits questions for the fifteenth time this month alone. Possibly, you're chasing down approval chains or manually reconciling a headcount report at midnight just before a board meeting, just scrambling and handling a bunch of tasks one after the other. And this is what we call admin debt, the invisible tax on every single HR, IT, and finance team. And the irony is that the better that you are at your job, the more of it you'll end up accumulating. You effectively become the connective tissue between systems that were supposed to talk to each other but don't actually. And it's not that this work doesn't matter. It's incredibly important and it does not, but it's not that you should be the one doing it necessarily. So this is the shift that I want you to actually imagine. What if every single question that used to require you to pull three different reports, to clean up a spreadsheet and build a pivot table was actually answered in just a couple seconds. What if every single employee who pinged you asking about their PPO balance or parental leave policy got an instant and accurate answer, but never even had to touch your calendar or go to Slack or anything else. So before we jump into a little bit more about Rippling AI, Corey, I wanna just invite you to share a little bit about what your talent rediscovery process will look like for your team before you started using Rippling AI. Absolutely. And hey, everyone. As Divya said, I'm Corey, senior recruiter at Recom. And, yes, I'd be happy to. I truly think talent rediscovery is an incredibly important aspect of a healthy creative function. There's always those hidden gems sitting in your system that were amazing candidates, but just didn't work out for whatever reason. The unfortunate reality is to find them could be very manual, which that's what ours was essentially. It's very manual and tedious, and also just it's just hard to track. So back in the days, well before some of the even more recent advancements prior to AI, it was very manual of digging through a 100 applications. I think everybody's experienced that if you're in recruiting five to ten years ago, to the more recent capabilities of actually being able to go and pull reports to where you can filter down the data, but still, as Divya said, doing work for the work, at the end of the day. So very manual. Depending on the complexity of the situation, very time consuming too. So that's pretty much exactly what we also kept hearing over and over from other customers that they're spending a lot of time on manual tasks, like reviewing requisitions, just all these different tasks that they're doing again and again. Right? Sorting through notes, pulling reports, all to just find the best candidate, which that's the focus here. So my question to you is, what would you do if you got that time back? And it's not actually a rhetorical question. I want you to actually think about it. The teams that are actually using Rippling AI right now, like Corey, are answering it in real ways and finally getting to workforce planning to comp benchmarking to manager development and all the other strategic work that they were actually hired to do and are excited to do. So before we move on, I wanna just quickly shout out here. We have a poll up now to, for you to share a little bit more about what tasks are actually taking up the most time for you right now. So if this is something that resonates for you, I'd love for you to take a moment and share a little bit more about what's on your plate right now. Alright. And we can go ahead and go to the next slide, please. So this is very exciting. I would like to introduce Rippling AI. So most AI tools are built to generate information. They can write things. They can complete searches. And, of course, that's useful, but it's not quite enough. Rippling AI is actually built to understand and then take action based on that information. It knows who you are. It knows what role you're in, what data you're allowed to see, and what your company's actual policies and structure looks like. This means that your org permissions are automatically built in. And this is basically the senior analyst that you've always needed, one that doesn't need sleep, that never misses context, and already knows your org from the inside out. So it doesn't really need that same onboarding. And with that being said, I wanna go ahead and hand it over to Luke, our Rippling AI product lead, who's gonna pull back the curtain on exactly how this works. What's up, everybody? I'm Luke. Excited to be here and introduce y'all to Rippling AI today. It's the culmination of nearly ten years of work we've been building towards this moment. So what started as a system of record for payroll and HR has become a cohesive employee graph spanning not only the payroll and HR, but recruiting, IT, spend, devices, apps, permissions, like, so many other products that Rippling now offers. And that matters because enterprise AI is really only as good as the system it sits on top of. And so if your AI is bolted on to disconnected tools, it can draft messages, summarize docs, but but it doesn't really understand how your company works across all of your different products, and that's where our advantage comes in. We're we're different. We're we've spent years, almost a decade now, building this data model, the permission model, all of it underneath the product. And we've backed that with an r and d investment that our competitors just don't believe in the same way that we do. We're we're heavily invested in tech across the board, and now we're heavily invested in AI. So the shift, is shown on this slide, on the left. And Rippling's your system of record. It's your trusted place for your employee data, your permissions, your business context. In the middle of that foundation allows Rippling AI to give you accurate answers. It's not just the generic answers. It's real answers grounded in live company data, whether that's headcount or compliance, recruiting, travel, any or any custom data you bring into Rippling as well. And then on the right, it starts to allow Rippling AI to take action on your behalf that you can submit leave requests, you can create reports, you can provision IT app access all within that same permission model that customers already trust. So, that's how we move from being just a system of record to a system of intelligence. And, we're not just adding AI on top of that. We're really building this into a cohesive platform that we're really excited about. And one other thing, like, this slide is great and it shows what we're working on today, but I I wanna come back in two months and show you what we're working on in the future because what we launched today is awesome. We love it. We're proud of it. But the rate of change that we're seeing internally on how much we're improving the product is immense. And so there's there's a lot of cool stuff coming. But we'll we'll focus today on what there is today. So we can go to the next slide and just walk you through a couple of the features, in a little bit more detail. So right now, what we've always heard is that Rippling has great data, but it's not easy to access. It's a little intimidating to go create reports. You don't know how or you don't know where the data is, but now Rippling AI allows everyone to have a twenty four seven data analyst, that will create those reports and find those data analysis on your behalf. Next, we've got the the that we're saving time by automating work. And so it doesn't just answer the questions. It completes work, so you can hire people. You can import commissions or bonuses. You can create off cycle pay runs, and do a bunch of different things that typically are a little bit more challenging to find the right UI within Rippling, but now you can just have Rippling AI do it. One thing to note here is that nothing executes without your sign off, or go routing through the same approval policies that you've already set up. And so this is all safe permissioned automation and access to to these actions. Next up, we've got, a big benefit here is that employees who don't spend all day every day in Rippling don't always know where to ask their questions or to find simple things around their paychecks or their benefits or anything there. So we now are, giving employees the ability to ask Rippling AI and get answers to their HR questions. This isn't just a ticketing system like other, you see in a lot of other companies, but we give them live answers based off of the data in Rippling. And so you can ask things like, why was my paycheck lower this period? And it'll look through and actually check their paychecks, understand where those differences are, and summarize it. You can ask questions like, how does my plan cover for, how does my insurance plan cover for an MRI? And it'll look through those benefits and give them an actual answer. And then, lastly, just to double down on this point of trust, everything is built on this layer of trust and the permissions that we already have baked into Rippling. So you will never have employees being able to ask questions about how much does someone get paid and them to actually see anything there. You can only have access via Rippling AI to data that is already permissioned to you, and so you're able to trust the answers that you're getting and make sure that nothing, is leaking outside of that. So that that's kind of a high level overview of the different areas. I'd love to take over here and start showing you a little bit of this in action. So let me jump in and share my screen. Great. Okay. So I'm gonna go ahead and point you to this new Rippling AI icon in the toolbar. It'll open up a Rippling AI chat window. And so for the first example I'll show today is this, mantra of allowing employees to ask any type of questions. So I can go ahead and say, how do I enroll my newborn in health insurance? And so this type of thing is always a bit confusing, understanding what a when there's open enrollments and are there exceptions to that rather than going through and, see the see, trying to find that in all of your documentation, you can just come directly into Rippling AI and ask the question. And so you'll start seeing these thinking states coming up and you'll see, okay, to enroll your newborn in health insurance, report a qualifying life event in rippling, and it'll walk you through the step by step events. So open the report qualifying life event flow, select birth, and it gives you the button to start this whole flow right here. So you can just go ahead and click on that. It'll open it up. It'll have the qualifying life event already open. So I can come in here and say I had a baby and start going through the flow just as it guides me to here. Oh, this is great. This shows you that you are able to ask simple questions and get answers, that is is really powerful for employees to be able to get these quick answers. But I wanna start showing you some of the things that transform from just a and asking and answering questions based off of simple employee questions and show you a little bit more of the power of where you can start to have an analyst at the at your fingertips. And so how about we go with something like show me everyone outside of at engineering. And so this is our strong typing, functionality. So I can go in here and select the exact department that I mean. So I'll just type at engineering and then select engineering. And if I'm looking for figuring out whether we're provisioning licenses, a software, I can go ahead and say something like, show me everyone outside of engineering that has access to app Jira. And so I can go ahead and do app Jira, and it's an installed app here. And I'll go ahead and just send that off. And so it'll start going through its thinking states. It'll have to understand what is engineering, and so what department, how do I map the employees to this department? How do I then start to look at, where Jira is and then start to go in and, give you the list of, employees there. There's this button up here on the top to go in and, show your chat history window. And so I can go ahead and start to go through what I've asked in the past. And so for this one, I'll just go ahead and click on the same question I asked earlier to show you what it looks like once it has gone through its thinking states and sent. And it'll go ahead and say there are 415 active employees outside of engineering that have access to Jira. That's a great insight right there, but then it starts to break it down in a more intelligent way. So it says, how many people in each department are actually, having access? Your account executives, solutions consultants, customer support, sales, all of that. It'll then give you a full list of all of the employees here that have access and, it'll attach a report as well. So I'll go ahead and just click on this report to show you that this is all grounded in truth. And if I look at this and I say, maybe these people who are in sales shouldn't have access to it. Let me just start looking at the those folks in sales so I can better understand it. So I can go ahead and filter by department name and say sales, and then I can go ahead and apply that. And it'll go through and refresh the, the, the filters, and it'll show all of these people who don't have access to or that do have access to Jira. You can understand in the SQL underneath, and so a lot of this is all backed by SQL, and so you can understand, better behind the scenes what is actually going on for every employee, and, like, where this data is actually being pulled from. Alright. So I'll go ahead and back out of this and not save. And I'll go ahead and just show you a few more, prompts that I think should really show off the power of Rippling AI. And so if you're looking at the, something like you have a lot of employees across different countries and you wanna understand your payroll costs by country last quarter compared to the previous quarter, You're able to come in here and it'll, compare q four twenty twenty five to q one twenty twenty six. And Australia, had a 163,000, but there was a 42% increase this quarter. The UK had a 38% decrease. Ireland had a 41% decrease. US had a 10% increase. And so it's not only servicing the data, but it's also giving you key observations around how to interpret the data. So this is really what we mean as we go from just a, something that can answer questions to more of a data analyst. Now this is a great, example. So far we've been looking at payroll and IT. Now Now if you wanna start looking at performance reviews and saying things like analyze our latest performance ratings compared to Carta equity data, not only can it compare these, your latest review cycles data, but it can also pull in CARTA data and look at the equity that's being granted and compare, are we actually rewarding our best employees with proper equity grants? And so it shows that, yes, we've got 131 employees that are exceeding and we've got 30 employees that are below. But if you start to come in here, you'll see that the average equity value for exceeding is 25,000 and below is 23,000. I mean, there is a 10% more equity there, but that's not necessarily enough. So this is the type of insight that you can go from and say, hey. Now we need to spend a bit more time rewarding our best employees and maybe not giving the same level of grants to employees who aren't performing as well. Something that's super powerful for those who have hourly employees is starting to look at how your work breakdown can be. So if I do look at this prompt of an analysis of overtime last quarter, and I have a sense that it was higher than it should have been. So I'd like to see which teams and managers generated the most broken down by type, time and a half, double time, etcetera. So here, it'll give you an executive summary. We had 18,000 total overtime employees over 50 over, overtime hours across 52 employees. And it'll start to break that down by manager. And let me go ahead and just make this a little bigger so you can see the data come across. And so you can break it down by managers and break it down by those who are getting a 1.5 x and the two x, the total overtime hours, the team size, break it down by departments, across customer support, sales, human resources, and then look at it by individual employees as well. This is the type of thing that would have taken hours, if not days, to compile all of these different reports for and all of the calculations that go into create them. And now you can get all of it generated at the click of, one prompt and then an analysis based off of all of that as well. And then as always, the reports underneath that you can click into if you wanna dig in more. So I think that, I wanted to show just a few things that have pre, built so you can just see the power of it. But I I wanna jump back in here, and I'll just show you an what it looks like to take an action. And so if I wanted to go in here and, will you add a $1,000 spot bonus to the upcoming pay run for at Jonathan Goldstein? I'll go ahead and send that off. It'll start jumping in and, finding how where what is the upcoming pay run, what is the current status of John Goldstein's pay run, and it'll, start to, get this prompt ready to send off the action. And so it'll go ahead and, in a moment here, come back with the, one of these things that will never, as I mentioned earlier, actually take the action without you first, confirming that you actually want it to take place. And so for this, we'll see it come back with a bit of a, hey. Okay. This is what the this action would look like if you do wanna move forward with it. If you don't wanna move forward with it, totally fine. It'll never do it automatically without that confirmation. And so it's finding what this pay run is, and it'll take just a moment to to come back here. With these types of things, it's, this is a simple example of me just adding a thousand dollars to as a spot bonus, but you can also upload an entire CSV of a number of different spot bonuses across different countries, to do all of the types of things that would take you a lot of time to make manual adjustments to maybe a a sales quarter with, anything like that automatically. And so this is what the UI looks like as it comes back. So it'll be applying this to John Goldstein. It'll be on this pay run here that you can, get more information about in The US. This is a old spot bonus. This is the new spot bonus, and I can go ahead and click proceed and move it forward. I think my my demo is already running a little bit long here, so I will go ahead and make sure that we we have some more time for for to hear from Corey in a bit. So I will go ahead and leave the demo at this point and then, just give you a little bit more information before we we move forward. And we have a quick poll that we just launched now. If you'd like to share which of these use cases is resonating most for you, what you think someone on your team would benefit from, And, yeah, it's a great way to just kind of explore how you would actually be able to use Rippling AI in your own work. Back to you, Luke. Sweet. So we, you you saw me demo a few things, but it's one thing hearing it from the person who's been helping build this product. But, the awesome thing that we've been just over the moon about is the number of customers that have come out and loved Rippling AI as we've been giving early access to several customers. We've had over 400 people post on LinkedIn, Twitter, other social medias about how Rippling AI is transforming the way that they work and how much time it is saving them, on their day to day. To get into a little bit more specifics here, you don't need to necessarily read the exact quotes, but if you'd like to there, these are a few use cases that our customers have highlighted. So for HR users have found that Ribbon dot ai is acting like a twenty four seven compliance officer, instantly surfacing policy answers from the handbook, policy, and or other company policies. In finance, Rippling AI can instantly flag unusual spend patterns or payroll variances that usually require, manual export to Excel. IT teams are using AI to conduct security health checks, identifying which terminated users still have active sessions and third party apps. And, it also handles the low hanging fruit of employee support, like guiding employees through self-service password resets, checking their expense reimbursement status, and and reviewing transactions. So that is Rippling AI from our perspective, which, but I'd love to now pass it over to Corey, to hear directly from one of our rock star customers on how Rippling AI has has helped him with his day to day work. Over to you, Corey. Awesome. Thanks for that, Luke. Hi again, everyone. Really excited to talk to you about this amazing tool that we got access to. And we've been using Rippling for over two even prior to that and then to be completely honest, I have even, demoed a few tools. And I'm sorry. Some right in the middle of, coming out of the woods. So now I'm talking to you guys here. But so basically, we, you know, been using it for two years. We used other tools prior to that, and then maybe even demoed a few tools, over the course of the past two years just to make sure that we're using the best technology for what we need. And Rifling has still been that technology. Ultimately, we we just kept hearing kind of a promise that the the tools that are outside of here were going to save us time. But at the end of the day, it really just sounded like it was changing the work. So we stuck with, we stuck with Rippling. I'm very happy, to say that we did because and now I'm up here. Sorry, guys. Because life before Rippling was very much utilizing a lot of manual tasks, and this is really Rippling AI. So even inside of Rippling, oh, and we can jump to the the next slide. So life before Rippling AI was pretty manual. Even Luke was mentioning about how you have to really dig into the reporting aspects of the tool, which, you know, over time, got better and better. It could have been the tool getting better and the reporting capabilities getting better. Maybe I was getting better at it because it could have been user errors in the beginning. I'm not sure. But, in the recruiting world, finding the best talent at record pace is always our top priority. So to have a lot of time, digging into the hundreds of applicants in a specific requisition that being sped up to going into reporting felt great, but there's still a lot of, a lot of time consuming aspects to the recruitment process that we were hoping ultimately could be solved at some point. So when we recently opened a new VP of sales role, one of the issues I faced was this rediscovery of prior, prior applicants because we had two other recs that we had opened in the past, and those two recs were opened up in 2024 and 2025. It's a sales role that gets hundreds of applications to it each time. And even when we used this evergreen rack, which means we just kind of left it open as we were hoping to hire people throughout the year, means there's even more people in there. And some of them were great, but some of them, you know, maybe didn't work out at that time. So, a lot of it was likely to do the fact that we are strictly looking for people in the South Florida area where our headquarters is. And this new position was actually more open on location. So thinking about all of the people we could go ahead and and reach out to, within our system was exciting, but also daunting because I knew we had to go through and sift through you know, the applicants are again, actually go and do the reporting, which this process on the reporting side saves a lot more time. And if I'm doing it right, thirty minutes usually to pull the report, go through the system, make sure I have the right parameters. But again, you know, that can get better over time the more you use it. And then ultimately, you pull out the report, filter it down, and try to find those exact people or that exact data point that you're looking for points for that matter. So realistically, to get to that short list where I can start reaching out to people, it's usually an hour to two hours, which in my mind is a lot of time. But, hey. You know what? I don't like to miss anybody and leave people behind that we can reach out to. So that's the lingering stress I think that any recruiter has felt in the past is leaving people behind. So I'm trying not to do that. Right? We have to be thorough. So now fast forward into life after, Rippling AI or maybe more so life with Rippling AI, I should say. It's it's it's been it's been quite amazing, which I'll go through in a second here. So, here's the part that's harder to put truly in a spreadsheet, which is when you're not spending the hours of time like I was normally doing doing the grunt work of candidate archaeology, and you remove that, you actually have the mental space to be more strategic, more creative about how you find people. More importantly, speed up that direct candidate interaction to the process. So have you ever gotten a role, a brand new role, or been a hiring manager with a brand new role and you wait a week or however long to get some candidates in the process of recruiting? Well, guess what? With this, this shift, we're able to go ahead and reach out to people a lot faster. So then again, we can be more strategic in other ways and think more creatively about the search. So what I typically do now with Rippling AI is I one, I love using it similar to how I use outside of Rippling AI's. Like, I'm not gonna name any of them because I'm sure and I'm not sure if that's kosher, but what I'm gonna say is utilizing the personal AI, I've utilized outside of rippling it as a little bit more of like an assistant or an analyst for a thought process for helping me rethink through ideas or different things that are going on in life. And that's how I wanted to look at rippling if I could, but I wasn't sure yet. So thinking about it is like my recruiting analyst, hopefully, being able to do a lot of the legwork and pump out answers to me a lot quicker than I can do myself. So, I wish I could have had it two years ago, but we're here now. So very happy about that. And, honestly, I've used a lot of different tools. I know what a good tool feels like, and this is definitely it, because it just is very responsive. And Luke and his team has done an incredible job, so thank you guys because, I know there's a lot of work that had to have gone in to do this. So now I'm gonna show you a quick use case of, basically what I did and what I've been talking about here. So when I when I heard that we had access to Rippling AI, I got really excited because I just love new technology and count me in a 100%. And ironically enough, the reason why I keep talking about this VP of sales role is because it just so happened that same week we had opened it up, and I had not done the deep dive into the system yet. So I thought, hey. I wonder if Rippling could help me Rippling AI could help me go through this. So I went into Rippling and I typed literally what you see here, which is I'm gonna shorten and just say, help me rediscover candidates for a VP of strategic partnerships job. That's it. Plain English. I didn't have to go and do filters and build a big report, no boolean strings, and I let it go. It said thinking as Luca described before, and I was like, okay. Cool. I'll let it think. It won't let it, you know, bother it why it's thinking. Boom. Two minutes later, not even. Sorry. Thirty seconds later, it pumped out what you see on the screen here, and it blew my mind. So within a few minutes or thirty seconds, it analyzed two separate job reqs, which first it asked me, hold on. This is a clarifying question. There's two here that I see. Which one do you want? I said both. Of course. I wanna see as much as I possibly can. Let's see what you can do. And it actually responded very well. It went through both of them that had hundreds of candidates now sitting in the rejected stage, which if you know in recruiting life, you have to go in and now you have a bunch of candidates all staged exactly the same. You have to try and figure out which ones made it through a different milestone or a different stage in the system, where this did it automatically without me having to do too much. And then it's it surfaced an exact match, which I couldn't believe I didn't think of because I know this gentleman very well. I had talked to him for multiple weeks, almost every single day at times throughout the recruitment process, gotten to the offer. And, ultimately, it just didn't work out because he was living in New York City, and he couldn't relocate. So when I saw this, it's it's it wasn't just a name to me. It was it was pretty incredible and went even further than that to tell me why it shows that, which I didn't necessarily need because I knew I knew the name, but I thought that was really incredible to be able to give me that feedback of, hey. This is what I did, and this is why I did it. And this is the, you know, great person that I found for you. So then from there, I got even more excited and intrigued. Okay. So I found this person, which then I went ahead and texted immediately because he still had his phone number. Then I said, okay. Well, this role is now more open. So there must be other people, that we haven't that's maybe didn't make it to the offer stage, but maybe got rejected because I do like to set those kind of knockout questions at times. We're being very specific about a location to try and make sure we're keeping tight the applicant pool and people who can specifically be located where we need them to. Well, now we're opening that applicant pool up. So maybe there are great people in there that applied but got rejected because they weren't, able to be located where where we needed them to, and maybe they were in New York City, which is one of the core areas we're thinking about. So as you can see here, I asked that question and brought in the search. They asked for a wider pool. It handled that effortlessly. I produced a report, it produced a report within a few minutes. I think this one take maybe take two to three minutes, with over 20 profiles. Each one, it gave me actual reasons and clarifications that were vetted, ranked, and explained, and all within under five minutes, which again, you know, as anyone knows, we're going through reports and all this stuff that I've been describing, it takes quite a while. So I had top tier leads now, including one absolute knockout, and a full bench of 20 plus stronger candidates to consider. That process, again, used to take me minimum thirty minutes just to pull the report, maybe even up to two to three hours to get down to this level of detail, which is reviewing and making sure I have all the interview scores lined up and making sure that I'm pulling those pieces out. So if I'm really in the weeds, which I tend to be, I wanna make sure I'm not missing anybody. It's gonna take me that three hour mark. Well, fortunately, I can confirm Rippling AI didn't miss anybody, which was incredible. So now going forward from there, obviously, I was very blown away. I wanna put some real numbers on this because I know that's what leadership is going to ask. And, when you bring this back, there's always that what's the use case, but then what's the real value behind it, which for this use case alone, talent rediscovery, what used to take me hours, now in less than five minutes, that's a six x improvement on a task I do regularly, which, you know, I think that speak numbers. And it's not just speed, it's confidence. And I didn't have to wonder. I didn't sit there thinking, okay. Should I go back and check its work? I could tell just by the level of detail that it gave to me with the justifications that it had done that work for me, and I felt fully confident. So that's a different feeling even from some of the times when I go through things or I ask somebody else to do something for me. I know that that got done well based on the justification. So that's the kind of thing that changes how you show up in a hiring meeting and, even the leadership at times when you're giving a review of, of the data, and of the information that you're doing. So that is my use case. So, Corey, I'm really curious. Like, this is such an incredible use case that clearly had a very tangible impact in the context of your Workday. So when you got those results back, the 20 plus, the 24 candidates resurfaced from just one follow-up question. What was your reaction beyond, like, the wow of time saved? Like, how how did that feel in that moment for you? Again, you know, I think I I think I I'd try to be as descriptive and realistic in my LinkedIn post as I possibly could. It blew my mind because, again, I was just thinking about how much time that would have taken me to to physically do and also just I didn't want to do it. It's it's so arduous and it's just, painstaking again. You have that little bit of anxiety, like, did I tweak the report somehow to miss somebody or whatever? And, I knew that, the AI had gone gone through it very effectively. So I was blown away. Absolutely impressed. Amazing. Well, thank you so much, Corey, for sharing your experience with us and giving us a little peek into your world. So now that you've all learned a little bit more about Rippling AI and how it works and you've seen it in action, I'm sure you have plenty of questions. I've seen all the questions coming through in our q and a queue, and there's some great ones in there. So I wanna make sure we get to a couple of them, that really speak to what matters the most. Let's take a look here. So a question coming in from Allison Sadner. Will I get different answers? Oh, sorry. It just jumped. Give me one second. So strange when it does that. Okay. I'm not sure where the question jumped to too. So I'm gonna take a different one, but we will try to come back to that one. From David Bard, for outputs that include a report that the AI generates, is it is that report just a draft that the user can then choose to save or not thinking in relation to those on plans with a limited number of advanced reports? Luke, do you wanna take this one? Yes. Absolutely. The, the reports are not saved until you explicitly save them because every most of the data answers type questions will return a report. We don't wanna litter your all of your reports that you may curate with every single report that AI creates. But if you like any of them, feel free to adjust them, save them, everything from there. Awesome. Okay. Another question coming in from Melody. In the example of compa ratio, do you need to have the compensation module in Rippling already for Rippling AI to work, or will it be able to analyze the data based on annual salary? Yeah. For for compa ratios, it needs to have a comparison. And so if you you would need to have comp band set up per your job families and levels and everything there to get accurate information. If you don't have those set up, it can still do salary analysis across people at the same level and whatnot, but it just won't calculate an actual comp ratio because comp ratio needs to have what the midpoint of the comp band is for that combination of job, family, and level. Okay. Let's see. Okay. Someone Kelly is asking, can it help build review cycles? Yeah. That's a there's a lot of different ways to look at that. So from it can help you analyze the reviews that come in. If you want to have it help you draft a review cycle, it is able to look at the information that the employee has on rippling in order to give you some some data, around maybe you have a one on one with this employee, and so that it tracks all of the things that have been said in the one on ones over the time. Or if you start to bring in external data, it's able to reference reference some of that as well. We always encourage for sensitive things like performance reviews to keep them human, drafted. Just we that's our our encouragement to everyone, but it can help around the edges of analysis, synthesis, all of that if if you ask. Eric Haft is asking, for bulk updates, do you prefer that a template is used, or can it intake multiple formats for something like the spot bonus example? Yeah. Absolutely. So this is the power of AI. So before, you had to have everything formatted in the specific CSV that you download and add to and upload. Whereas now AI can read your super scrappy CSV that has different currencies and countries and start to interpret what you actually intend. Or if you just type it all out and you say, hey. I wanna add this to employee a and employee b and do it for this pay run. That's the power of what we're building here is that it can interpret it, add it, and then before it does anything, it will still always surface that confirmation to you. And this is what I'm going to do. Is this what you intended? And confirm that to to make it Okay. Cool. Let's see. happen? Okay. Oh, here's a good one. Har Simran is asking, very interesting. I'm curious to understand how the AI links to payroll and enables change within. What would that workflow look like? So it's built on top of the employee graph that has access to everything that you are tracking with payroll. And so it's able to go in and look at payroll and and create reports based off of that. If it is something where you wanna make an update to a pay run, it'll recognize what pay runs you have, who your employees are, and add the whatever you want to that employee in the future. And so it just has since it's built on top of our employee graph, it has access to everything that would be accessible to a report or even beyond. Okay. Cool. Alright. Let's take one more, and then we're gonna move forward. So Jessica is asking, how is Rippling AI going to evolve into all of its tools? We're thinking on core versus pro platforms overall and for tools, specifically, trying to understand how AI will function in these tools overall and with what functionality. Yeah. This is a a fantastic question, and there will be a lot more information that's shared about this in the future. I won't I won't go into too many details right now, but what we're talking about today is Rippling AI as a platform component. So you have this interactive chat interface to all of the data that is going to be separated from let's say, you're on Recruiting Pro and you have access to application review or interview assistant, and those are features that are tied to the Rippling Recruiting Pro SKU. And so if you don't purchase Rippling AI and you just wanna use the AI features that are already in Pro, you will not lose access to them. But we are aiming to have a more cohesive frippling AI, kinda have it all brought together in the most cohesive way possible in the future. And so we'll we'll continue to make updates here, but just to assuage any fears, you will not lose access to to AI features elsewhere. Amazing. Well, thank you so much, Luke. And for the rest of you, here's the thing about what you saw today. You can actually have this too. All those cool use cases, all that time saved, all those mind blowing benefits, that could be yours too. And I don't mean someday in the future. I mean, you could have it right now. So you are welcome to click the book a demo button at the top right of your screen or follow one of these two paths. If you're already a Rippling customer, your account team can walk you through getting Rippling AI activated for your business. If you're not yet a Rippling customer and you came today to understand more about what's possible, you can fill out the poll that's launching now to indicate if you'd like a demo. You can also do that if you're an existing customer. Or, again, if you're someone who's not, yet using Rippling, you can also sign up for a trial of Rippling during your self guided onboarding. So, again, we have this the survey up on the screen right now. I'm sorry. The poll, where you can indicate if you're interested and someone will be reaching out. And just like that, we've walked you through one prompt, a couple of prompt, a couple of use cases, but there are actually hundreds more that you can discover. Your follow-up email for this webinar is gonna include a Rippling AI prompt library built from real customer use cases across HR, recruiting, IT, finance, and so many others. So all you need to do is start with one prompt and see everything that's possible. Thank you so much for joining us today. It was a pleasure to have you, and thank you to all my cohosts for joining today as well. Thanks, y'all. Thank you. Thank you, everybody. Yeah.