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Hello everyone and welcome back to the SourceForge podcast.
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I'm your host, Bo Hamilton.
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Now, I have a question to lead off today's conversation.
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What is the one piece of software that touches every single person at your company, every pay period, no exceptions?
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It's not email and it's not Slack.
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It's most likely payroll and HR, the software nobody really thinks about until something goes wrong, something breaks, and then you know everybody notices, right?
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That's that's sort of a given.
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Um, today we're talking about a company that's been quietly building in that space since 1997 and is now anything but quiet about it, I would say.
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Paylocity started out as a payroll company outside of Chicago and has grown into a roughly$1.6 billion a year platform spanning HR finance and IT.
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And this summer, they actually made two big moves in three weeks or so.
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They launched Ignite AI, a platform-wide AI with purpose-built agents that automate work across pay time and recruiting, uh, plus a centralized hub to control it all and sort of measure its impact.
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And they also acquired Adora, an AI native company that automates one of HR's most manual compliance-heavy processes, leave management.
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And the timing here, I think, really is important because this is the conversation in HR right now.
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According to a recent report, 39% of organizations have already adopted AI in their HR functions.
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And uh Gardner actually says 82% of HR leaders plan to deploy agentic AI within the next 12 months or so.
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So uh the real question really isn't whether this stuff is coming, it's it's who's actually doing it well.
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So um to dig into all of that, I'm joined by Aaron Nies from Paylocity.
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Um Aaron, I'm really excited to have you here on the SourceForge podcast and get into some of this stuff.
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Yeah, I'm excited to be here and I'm I'm happy to be in front of this audience.
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Yeah, um, so uh before we we get into Paylocity, maybe you can give us just a quick version of of your background.
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What were you doing sort of before this and like what pulled you into the human capital management HCM world?
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Yeah, um, so I'm I'm senior director of product management at Paylocity, and I'm responsible for the AI and data around this uh this platform.
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So the Ignite launch is something that I've been um uh responsible for over the past year.
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And before Paylocity, I was uh in AI and natural language technologies for many, many years since the early 2000s.
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I worked in speech recognition at a company called Nuanced Communications for many, many years prior to it being acquired by Microsoft.
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Um and then I spent about a decade in the um media streaming industry.
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So um uh doing a video uh streaming platform that served large media companies.
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So I uh was using AI and natural language and machine learning technologies throughout my career.
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Um, I got pretty excited about coming to Pelosity, knowing that um, number one, there's a really ambitious appetite for innovation at this company.
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And this is an industry with so much untapped potential in the HCM industry uh to kind of apply the lessons that you see in other industries.
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So super excited.
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Um, and I'm really excited about what we were able to bring to market this year as well.
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Yeah, very, very cool.
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So you were like the quintessential speaker to to talk uh talk about with the AI sort of advancements that that are taking place here.
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And of course, we have to mention that it that's like one of the main sort of talking points with the SourceForge podcast here is just AI and automation.
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It's like everyone is is rolling out um some kind of AI functionality.
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So I'm really uh eager to hear what it is you guys have working on and cooked up here with payloadity and in regards to to AI and agentic AI.
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Um, before we get too you know into the weeds with that topic and that subject, uh maybe you can just help us uh understand a little bit more about what payloads is for folks who don't um, or maybe folks who only know about payloadity as sort of a payroll company.
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How would you describe what the company is here in 2026?
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Yeah, um sort of basic level payloadity is helping organizations support their workforce.
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Um and this means HR payroll, also finance things like expenses, purchase orders, um, IT asset management, managing computers and software access and employee experience.
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So it really covers a lot of different places that a typical company will have.
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Um and bringing these together in one platform uh helps in a few ways.
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It helps with really the productivity about um taking these workflows that happen across the different functions and making minimal handoffs.
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Um, you know, everything is easier, the data is more accurate, you can have more confidence in your process.
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The second we really focus on as a company is ease of use.
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And it's it's really exciting how much um user experience and user experience design we have going on in the company, because a lot of our clients will choose us because the employees really love the software and we have a super high adoption rate that translates into 90%, 90% plus of employees are actually using the app.
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And so that's really important, particularly where it's something like um, you know, an HR software where you don't want to kind of have to pressure people to use it.
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You want them to actually enjoy using it.
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Um and the third really benefit of the platform is the consolidation of not having to go out to all these multiple vendors, the HR tech space and particularly in expenses.
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Um, it can become a fragmented system to manage all these vendors.
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So being able to kind of do it on one platform really simplifies it for our buyers.
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Yeah, the the consolidation and and all-in-one sort of offering is is the the pitch that stands out to me, right?
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You have all the all the employee data, uh sort of the money movement, the business workflows, all that live together instead of being scattered across all a sort of fragmented set of solutions and services.
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Um, obviously this is a a pretty competitive um, you know, market.
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I know payroll and HR software is in a crowded space.
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Um you have ADP, Workday, Gusto, Rippling.
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There's a lot of uh other players out there.
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I'm curious, like, what's the honest answer on like where payloadity sort of wins and like who your ideal sweet spot customer is?
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Yeah, we're serving uh you know mid-size and and some larger customers.
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You know, really we're we're interested in clients.
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We have a really great mobile experience, um, great service and support.
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So when you start to look um very far down market, you can see companies where you can log in and you know have a paywall type service.
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Um, but those things you don't get the customization.
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You can you don't have the service organization behind it when you need to um operate.
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And, you know, something like payroll honestly is something that is uh a business sensitive and business critical process.
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So, you know, just switching your payroll vendor, people don't want payroll to get interrupted.
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Um there's a lot of process involved in just bringing on new customers, making sure that everything's working right, um, making sure that, you know, all the taxes, the compliance is correct, um, making sure that there's a smooth transition there.
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And then once you're uh a client and you're you're running, um making sure that you have the way to go to that support.
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And then obviously throughout, and I wouldn't be here if if this weren't the case, um our ability to deliver innovative solutions with AI throughout the platform that kind of make it easier to use and more productive is one of our differentiators in the market.
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The mid-market like customer base is is kind of an interesting spot to defend, sort of, because these those companies have uh enterprise grade compliance problems, I would say, but but but sort of like the patience that you'd find amongst small companies, at least for for implementations of of um you know the innovations they plan to roll out and stuff.
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So you have to kind of thread that needle if that makes sense of um what you guys are able to accomplish and help um with the mid-size market uh companies out there.
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Um I want to talk about Ignite AI.
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So um I know your July was very busy with the launch of Ignite AI.
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It launched on, I believe, July 21st.
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What is it in like simple terms?
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Like what and what does an agent actually do inside the platform on a day-to-day basis?
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Ignite is really um a set of transformative AI capabilities throughout the platform.
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And this is 24 different, you know, discrete initiatives, discrete agents, but we were really taking the platform and putting it uh to a place where we can do outcome-focused applications of AI in the platform.
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And it really boils down to three things.
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So the simplest way I can explain it is number one is we we have an AI assistant in the platform that's available anywhere you go.
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So you can hit ask AI and ask it questions.
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So Ignite was about really supercharging the intelligence behind the types of questions we can answer.
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The second is workflows.
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So we have workflows in the platform where it's um, you know, kind of doing things like changing, you know, doing a payroll workflow or doing workflows related to time tracking, right?
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The workflows are part of what you're using in the platform day to day.
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So we put AI in those workflows to be able to give you help and accelerate certain things that are like repetitive tasks.
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And the third thing is insights.
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So AI, we all have all this data in the platform, and accessing that sometimes through reports or through dashboards can be difficult.
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So being able to surface insights in a more intelligent way through AI and being able to pull out stuff that help you make business decisions, um, that is essentially the three pillars of Ignite.
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And when we talk about the agents, those are kind of discrete pieces of functionality that are supporting those three big pillars.
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Okay, so I want to I want to continue to unpack the the agenting side of things.
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Um, but there's one thing I that stood out to me with the the announcement um with your Ignite AI um platform and uh is is it was about reports.
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Um so basically you pay lossity, you guys said something to the effect of like a report shows what happened, not why, and clients need to explore their data on their own terms.
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How does how does Ignite sort of change how an HR leader actually interacts with their own data?
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Can you help kind of unpack that portion of things?
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Oh, yeah, of course.
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So, you know, our traditional software, we'd collect a lot of data, and then you'd go in and pull a rapport and it would have, you know, fields and filters, and then you'd pull down a big table and it would you download it to Excel, do some analysis.
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Or if you were a little more visually oriented, you would have a dashboard where you would have, you know, some filters and you could get graphs and pie charts and other stuff like that.
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Um, but today's technology, you can actually just type a question and hand it to an AI assistant, and AI assistant will pull all that data, it will figure out what charts to make, um, what analysis to do.
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And what's really cool about it is because you're using the AI technology, you're using a large language model, it can do um analysis, it can do some root cause, it sort of does some thinking about it.
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So you wouldn't ask your rapport to go find me the trends of you know overtime in the business locations where we're at and find out which ones are having the biggest problems.
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You'd almost have to have a preconceived idea of the data that you'd be pulling.
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But if you do that with AI, you can send AI that problem and it actually reasons out the data it's going to look.
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Maybe it finds some data and it doesn't, you know, it doesn't get to the area that's interesting, and it'll try and keep going and iterate a little bit.
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And that's what's really powerful is you can kind of give it this idea of show me the place where the overtime is trending, and it'll go figure out that and it gives you back the report.
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So it really saves you a lot of time.
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You can go straight from like business question to some kind of insightful answer with some reasoning and analysis behind it, which is driven by the AI.
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Yeah, you know, it's it's funny because it seems like every every company has has like, you know, floats AI, like they have all these AI tools, AI this, AI that.
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Um, but a lot of times it's sort of bolted onto old software, doesn't really um, I don't know, it's not as maybe impressive or effective as one would imagine.
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But this one sounds like it's it's integrated across the whole platform and it's it's already able to go out and accomplish things sort of autonomously, like based off of um just like uh a simple sort of question or or prompt that you described with it or talked to.
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Um how how is like, have you already, I mean, I know it just launched like a month ago, but have you already started to get a lot of feedback about how it's helping clients, like with efficiency and with, you know, um freeing up, just freeing up space to accomplish uh uh tackle more sort of maybe uh meaningful uh tasks that employees can can now work on?
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Yeah, we're we're certainly getting that.
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And and one of the cool things we do is um we do these co-creation groups essentially.
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So we work with customers to make sure that we're dialed in on solving the correct types of problems.
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So a lot of times when we get feedback, it'll be because we're you know physically on site with some customers and kind of looking at what they're struggling with and stuff like that.
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You know, we get things like um one customer um corrects craft as a boat manufacturer and had had kind of grown up by acquisition.
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So they have 15 brands across seven states, and they keep adding all these disconnected tools and stuff like that.
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And when we talked to them, they said, oh, normally we'd have to run these Frankenstein reports because we'd pull a report from one system, we'd pull a report from another system and a third system, and then try to reconcile these three reports, and that's where the errors creep in.
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And so, you know, what really excites them is like, you know, now when you have a tool like this where you can ask a question, it can actually go out and pull all the pieces of data and just put it into like a compiled, you know, summary.
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So it kind of saves with two things.
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One is just tracking down all that information, but also it's cross-checking against it.
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And that's something like AI can do pretty well.
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Um, you know, these are the types of things.
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Um, you know, we also see a lot of great feedback on the um on the recruiting side because that's an area where you can see an impact almost right away when we have like recruiting technologies that we put out there.
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Um, you know, people are able to like parse through more resumes more quickly, um, evaluate a candidate pipeline at the top of funnel, um, get through to qualified applicants much more quickly.
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And, you know, right away they can see how many hours of saving per week.
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Well, I imagine, yeah, the feedback is just going to continue to keep rolling in and you guys are gonna continue to make changes and roll out some some new features and capabilities.
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And um, I I you know obviously there's like a lot of AI, you know, tools, consumer-facing tools that can can tackle some of these like report building and stuff, but having it built into your platform, I imagine just allows you to work with a lot more internal data and whatnot.
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So that that you know, a consumer public-facing AI uh tool wouldn't have access to.
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And so you have a lot more of the context and just a lot more capabilities there, right?
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Yeah, that that's absolutely right.
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And if you look at those scenarios, like you say, hey, I have this external AI consumer-facing tool here, a lot of times what people are doing is they're extracting the data into a spreadsheet, and then they're pasting the spreadsheet into the AI tool, asking a question, getting it back, and then going back to their platform and typing in information.
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Like all of those extra steps, when you're integrated, like you can just say, hey, um, you know, what is our labor cost trend at our Syracuse, New York site?
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And it already has access to that data.
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You don't have to like do all this copy and paste business.
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You can give it the same type of analysis question to say, hey, you know, give me the trend over the last three years.
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It'll come back with a graph.
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It's right there.
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And then, you know, you're writing your tool ready to go.
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This kind of leads me to the uncomfortable um part of the discussion.
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And um, that's has to do with, you know, AI sort of um interacting with pay hiring and employee data, um, which is all, of course, very, very sensitive, um, almost as sensitive as it gets, right?
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So, how are you sort of thinking about trust, governance, and and just keeping humans in the loop?
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Yeah, absolutely.
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So we have a um pretty mature process for governance.
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Um, we go through a governance committee looking at all of these things.
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PII data gets protected in multiple places in the system.
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So there's multiple guardrails where we're either suppressing it or just saying, like, look, the AI doesn't need to know about this particular piece of data.
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So actually, when you when you type in the question and we're pulling data, we're actually not sending every piece of data to AI.
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In some cases, we're not, you know, we're we don't want to send bank account numbers or anything that's not needed for the particular question.
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So there's a lot of of guardrails in the system where we have protections in place.
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Number two is uh we have in particular, we have external bias audits on the particular technology.
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So there's a lot of sensitivity in the case of recruiting and hiring, where you don't want to have technology that's subtly bias in unfair ways in hiring practices.
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So we actually have you know third-party bias audits to make sure that everything is is clean from a technology perspective.
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And then number three is putting human in the loop.
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So we actually have a part of our system um called the uh the AI hub uh under Ignite, and that's actually to give transparency and ability to, at a pretty granular level, turn things on and off for a company.
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So if a company sees that they don't want AI used in a particular use case, um, they'll have the ability to go in and turn it off for their particular company.
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And so that is part of trust, and it's also part of like honestly, um, explaining things in a simple way is as important as disclosing it.
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Um part of trust is like explaining it, but also explaining it in a way that people can understand it and make educated choices about their usage of it.
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Totally.
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Yeah, I think that should be really reassuring for organizations listening and um prospective clients and customers of yours, because a lot of the surveys I've kind of um skimmed through, it sounds like organizations are are really less worried about whether to adopt AI at HR.
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They're just more worried about how to use it responsibly, right?
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So so going through those, listing those pillars like you did, I think will be really um will resonate really well with with um organizations thinking about this this neck next pivot.
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Um I feel like it's kind of inevitable, but like again, like having sort of an audit trail, keeping a human in the loop, those sort of things really do um, you know, go far with with giving people that trust.
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That's that's right.
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And also just like honestly, just selecting, um, making sure that, you know, when it's decisions about, you know, people's livelihood or their pay or their salary, um, there's certain things where you really do need humans to be responsible for the decision.
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And that's part of like, you know, our guidance in applying some of these things is like look at the the decision and the impact of the decision you're making, particularly in the HR space, and make sure that your AI is focused on some of the areas that are routine tasks that don't carry like this kind of human impact, because ultimately humans own these decisions and you know, in their in their totality.
00:20:56.480 --> 00:21:15.920
So we've been focusing on things like um, you know, routine approvals and you know, stuff that's kind of high volume and repeat work where it doesn't carry a lot of that like decision weight as opposed to you know making decisions about you know whether someone gets promoted or not.
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Really, that should be a human decision.
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That's a good point.
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That's a good point.
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Yeah, it's it's yeah, nobody nobody really gets fired for the the AI feature that um that worked.
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They sort of get fired for the one that nobody could audit and sort of the the ultimate, like the human that caused the decision in the first place, perhaps.
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Or like, you know, didn't catch it, maybe.
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I I want to uh pivot slightly to talk about the other big July headline, uh, which was you know, where when you went out and bought a company.
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Um so I want to talk about Adora.
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Um Palocity just acquired Adora, it's an AI native leave management compliance company.
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Um so my question for you is just is is why is leave, of all things, such a nightmare that that it needed an AI native solution?
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Yeah.
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So, you know, leave management is is one of those things that has a lot of a lot of rules and a lot of implications across the system.
00:22:11.039 --> 00:22:29.200
So when you think about it, like when someone goes out on a leave of absence, there may be, you know, company-specific policies and rules that need to be weighed against regulations and other stuff like that about eligibility for leave and the amount of time you're eligible for leave and all that kind of stuff.
00:22:29.359 --> 00:22:43.440
So helping people through those type of questions is something where it's one of those things where having some guided conversational system to kind of help you through those things can really be appealing for people.
00:22:43.839 --> 00:23:04.720
And then the second is once you do actually make a decision to, you know, have someone out or have someone take a leave of absence, um, there's a lot of parts of the system from you know time cards to payroll to benefits, you know, across the board that have to be coordinated in a way where it all makes sense, you know.
00:23:04.880 --> 00:23:12.720
And I actually have, you know, personally, um, I actually have two people out on paternity leave in my organization right now.
00:23:12.960 --> 00:23:15.759
And um, you know, there's things you wouldn't even think of.
00:23:15.839 --> 00:23:22.319
Like we have a performance review cycle, and performance reviews are uh are due on a certain date.
00:23:22.640 --> 00:23:31.839
And, you know, they need to be kind of like pulled out of the performance review um cycle so that that can happen off-cycle because they're just not there to get them.
00:23:32.079 --> 00:23:43.920
So, you know, there's just lots of little details about what happens in a leave management scenario that having a platform that can kind of coordinate that is super valuable to a lot of companies.
00:23:44.319 --> 00:23:57.440
Yeah, I mean, you can't uh you got to think about too, like the um uh a single sort of parental leave can involve um, you know, federal, uh, federal policy like FMLA, uh, state paid leave programs.
00:23:57.599 --> 00:24:01.279
It has to go through sort of local ordinances and and company policy.
00:24:01.359 --> 00:24:03.839
And it's it's all sort of stacked on top of each other.
00:24:04.000 --> 00:24:07.759
And the rules just sort of vary by by state to state.
00:24:07.839 --> 00:24:16.960
And so I can just imagine the the complexity of trying to keep track of it all without some sort of dedicated um, I don't know, platform solution.
00:24:17.119 --> 00:24:22.559
Um, and and in this day and age, without some sort of dedicated AI to handle it all, you know?
00:24:22.720 --> 00:24:35.920
Um and it seems like it's it's just a great example where I think uh AI is really going to earn its keep here with with what it's able to do and able to keep track of all the sort of regul uh compliance um regulations and whatnot.
00:24:36.319 --> 00:24:37.759
Yeah, we're we're optimistic.
00:24:37.920 --> 00:24:46.079
And and one of the things AI is really good at is reading a lot of documents and making sense of the ones that are relevant, the ones that are not.
00:24:46.319 --> 00:25:00.640
So these type of things where there's just like a ton of rules, regulations, and try to navigate through, um, that's something AI is pretty good at and can kind of get to the ones that are relevant and summarize them in a succinct way.
00:25:01.119 --> 00:25:04.160
So we've we've covered, you know, what the agents do.
00:25:04.240 --> 00:25:11.759
We've covered uh, you know, the acquisitions, the uh the recent um the new launches that you've you've um you've had.
00:25:12.000 --> 00:25:16.079
I want to uh ask you about where everything is going.
00:25:16.240 --> 00:25:26.640
So what does HR, what does the HR department of, I don't know, 2029 or 2030 look like if agents are handling most of all the the busy work?
00:25:27.279 --> 00:25:58.160
Yeah, I mean, honestly, like when I look at the types of businesses that are out there, because we do spend a lot of time with um with customers, you know, even though we have HR processes, you know, and and automations around that, like, you know, HR at a large restaurant chain or HR at a large healthcare organization, there's a lot of human factors that go into these things, you know, from performance reviews to recruiting good people to quality of care and things like that.
00:25:58.400 --> 00:26:02.480
So I don't see the HR function going away in any sense.
00:26:02.720 --> 00:26:09.279
What I do see is that for HR professionals, they'll be getting more done on a daily basis.
00:26:09.440 --> 00:26:26.880
So I think for those routine um like policy-related um onboarding checklists, um, you know, time card um management and that kind of stuff, like a lot of that stuff I expect will be be automated in a more serious way.
00:26:27.119 --> 00:26:35.039
And so I think, you know, also like the handling of routine questions that people get for is this in policy or not?
00:26:35.440 --> 00:26:43.039
You know, I have questions about benefits, a lot of that stuff um could be answered, you know, through AI essentially.
00:26:43.279 --> 00:26:51.599
I think that'll leave AI, I think that'll leave HR professionals um honestly making more decisions and more impactful decisions.
00:26:51.680 --> 00:26:57.279
Um, it'll, it'll be helping them like look at uh how can we better manage our workforce?
00:26:57.519 --> 00:27:17.359
How can we, you know, how can we have the right culture and the right recruitment and get people in this organization who can deliver the type of service or the type of care or the type of product that we want to deliver and start to make more strategic decisions with the help of some of that AI analysis.
00:27:17.519 --> 00:27:43.200
And then I also think there'll be um probably, you know, the risk management portion of A uh of HR, which would be like um, how do we correct or you know, or anticipate employee churn or turnover such that, you know, we have people who are, you know, at a great point in their productivity or at a great point in their organization, they'll be able to like look at that.
00:27:43.359 --> 00:28:01.920
So I do think it kind of shifts HR from a operational, like, you know, keep the onboarding checklist going to a decision type of organization, where they're like really trying to build strategically the workforce for an organization and retain that workforce.
00:28:02.400 --> 00:28:10.640
So, so less busy work, more decision making, ultimately more kind of fulfilling, soul-enriching work, I would say.
00:28:10.880 --> 00:28:11.519
Right?
00:28:12.000 --> 00:28:13.440
Yeah, I I assume so.
00:28:13.759 --> 00:28:15.920
Some people may love the the busy work.
00:28:16.000 --> 00:28:18.960
And, you know, I I I don't discredit for them for that.
00:28:19.039 --> 00:28:21.359
They will, you know, I'm sure there will be some of that.
00:28:21.519 --> 00:28:29.279
But um, yeah, like really the the thinking part of the work, which is like how can we make decisions that are impactful to the organization?
00:28:29.920 --> 00:28:38.559
So uh last last couple of questions I have for you is it's just like what's the what's the roadmap look like for maybe the the remainder, you know, remaining half of the year?
00:28:38.720 --> 00:28:41.039
I mean, you've had such a busy July.
00:28:41.200 --> 00:28:46.559
Um what can we get excited about um, you know, as as prospective customers?
00:28:46.640 --> 00:28:49.680
And maybe what's what's what are you getting excited about?
00:28:50.160 --> 00:28:59.039
Yeah, so so we're super excited about um what we're doing in payroll, um, what we're doing in time and time tracking.
00:28:59.200 --> 00:29:02.079
So we have a ton of stuff going on there.
00:29:02.400 --> 00:29:03.599
Interesting stuff.
00:29:03.759 --> 00:29:08.400
Um, you know, we have a series of agents that are going to be rolling out for the remainder of the year.
00:29:08.720 --> 00:29:13.440
So um the first few sets we've had are aimed at recruiting.
00:29:13.599 --> 00:29:18.319
Um, they're aimed at like this data analysis, decision support, stuff like that.
00:29:18.480 --> 00:29:30.799
And then as we get toward the later part of the year, we'll get more into kind of like the payroll processes and the and the time and labor tracking processes um to try to streamline those and um and fire those up.
00:29:31.039 --> 00:29:32.640
So that's what we're pretty excited about.
00:29:32.720 --> 00:29:35.599
And then going into next year, I mean, you know, who knows?
00:29:35.759 --> 00:29:39.599
Like we have um, it's amazing how quickly the market moves.
00:29:39.920 --> 00:29:49.359
So, you know, if six months from now we we see some piece of technology and we say, hey, this is really um really a great use case.
00:29:49.599 --> 00:29:55.200
And in these customer co-creation events, like this is the unlock for something that they've been really struggling with.
00:29:55.359 --> 00:29:58.240
Um, you know, we will absolutely pivot toward those things.
00:29:58.480 --> 00:30:07.200
But for right now, we we do have um, you know, we are watching, you know, just the developments and the you know the rapidly evolving ecosystem.
00:30:07.680 --> 00:30:08.240
Oh, totally.
00:30:08.480 --> 00:30:13.200
It's so hard to it's so hard to tell what's what's come around the corner in even just a few months from now, you know.
00:30:13.359 --> 00:30:32.160
And and so yeah, that's you gotta have all these things uh, you know, in the works what you have um like rolling out, but at the same time, you gotta be kind of agile and fluid to to kind of go with what's currently uh being sort of talked about and prioritized and and what is is making the biggest sort of impact in a positive way for for HR.
00:30:32.240 --> 00:30:34.640
And and just I mean it's it's wild.
00:30:34.799 --> 00:30:55.279
It's wild to think about um how fast things are going and you know, this this supposed bubble of of AI and um it that just keeps on getting bigger and bigger and um you know it makes you question even if it is a bubble because there's so much actual real tangible use cases here now that are that are being um you know worked on.
00:30:55.359 --> 00:30:58.640
And so thank you for everything you've shared with us.
00:30:58.720 --> 00:31:03.599
It's really exciting to hear about what you have working on um and currently have rolled out.
00:31:03.759 --> 00:31:09.279
Um for those interested in learning more um about Palocity, where where's the best place to go?
00:31:09.680 --> 00:31:14.960
Yeah, it directs you just to um payloadity.com and um you know you can get to the contact us.
00:31:15.599 --> 00:31:16.000
Perfect.
00:31:16.079 --> 00:31:19.200
And then we can connect with you over on LinkedIn, I assume, right?
00:31:19.759 --> 00:31:20.880
Uh yeah, that's correct.
00:31:20.960 --> 00:31:22.640
Uh LinkedIn Palocity.
00:31:23.119 --> 00:31:23.440
Perfect.
00:31:23.519 --> 00:31:24.720
LinkedIn Palocity.
00:31:24.799 --> 00:31:31.279
And then um, if anyone would like to get in contact with you, go follow um Aaron Nies over on LinkedIn.
00:31:31.359 --> 00:31:33.839
We'll have links down below in the show notes.
00:31:33.920 --> 00:31:36.799
Um, but Aaron, thank you so much for everything you shared with us.
00:31:36.880 --> 00:31:37.519
This has been great.
00:31:37.599 --> 00:31:40.720
And um, yeah, hopefully we can have you back in the future and talk updates.
00:31:40.960 --> 00:31:41.839
Yeah, I love that, Bo.
00:31:42.000 --> 00:31:42.480
Thanks so much.
00:31:42.720 --> 00:31:43.440
Appreciate it.
00:31:43.680 --> 00:31:45.920
Thank you all for watching the SourceForge podcast.
00:31:46.000 --> 00:31:47.440
I am your host, Bo Hamilton.
00:31:47.599 --> 00:31:53.039
Make sure to subscribe and follow along for more B2B software related podcasts like this one.
00:31:53.200 --> 00:31:54.880
As always, I'm Bo Hamilton.
00:31:55.119 --> 00:31:56.640
Talk to you in the next one.
00:00:05.040 --> 00:00:08.080
Hello everyone and welcome back to the SourceForge podcast.
00:00:08.160 --> 00:00:09.599
I'm your host, Bo Hamilton.
00:00:09.759 --> 00:00:12.160
Now, I have a question to lead off today's conversation.
00:00:12.320 --> 00:00:18.480
What is the one piece of software that touches every single person at your company, every pay period, no exceptions?
00:00:18.719 --> 00:00:21.039
It's not email and it's not Slack.
00:00:21.280 --> 00:00:30.000
It's most likely payroll and HR, the software nobody really thinks about until something goes wrong, something breaks, and then you know everybody notices, right?
00:00:30.079 --> 00:00:31.280
That's that's sort of a given.
00:00:31.440 --> 00:00:38.159
Um, today we're talking about a company that's been quietly building in that space since 1997 and is now anything but quiet about it, I would say.
00:00:38.320 --> 00:00:48.640
Paylocity started out as a payroll company outside of Chicago and has grown into a roughly$1.6 billion a year platform spanning HR finance and IT.
00:00:48.799 --> 00:00:52.640
And this summer, they actually made two big moves in three weeks or so.
00:00:52.719 --> 00:01:04.560
They launched Ignite AI, a platform-wide AI with purpose-built agents that automate work across pay time and recruiting, uh, plus a centralized hub to control it all and sort of measure its impact.
00:01:04.719 --> 00:01:13.200
And they also acquired Adora, an AI native company that automates one of HR's most manual compliance-heavy processes, leave management.
00:01:13.439 --> 00:01:19.439
And the timing here, I think, really is important because this is the conversation in HR right now.
00:01:19.599 --> 00:01:25.760
According to a recent report, 39% of organizations have already adopted AI in their HR functions.
00:01:25.840 --> 00:01:32.879
And uh Gardner actually says 82% of HR leaders plan to deploy agentic AI within the next 12 months or so.
00:01:33.040 --> 00:01:38.719
So uh the real question really isn't whether this stuff is coming, it's it's who's actually doing it well.
00:01:39.040 --> 00:01:43.840
So um to dig into all of that, I'm joined by Aaron Nies from Paylocity.
00:01:43.920 --> 00:01:48.959
Um Aaron, I'm really excited to have you here on the SourceForge podcast and get into some of this stuff.
00:01:49.280 --> 00:01:53.120
Yeah, I'm excited to be here and I'm I'm happy to be in front of this audience.
00:01:53.439 --> 00:01:59.280
Yeah, um, so uh before we we get into Paylocity, maybe you can give us just a quick version of of your background.
00:01:59.439 --> 00:02:05.920
What were you doing sort of before this and like what pulled you into the human capital management HCM world?
00:02:06.319 --> 00:02:15.199
Yeah, um, so I'm I'm senior director of product management at Paylocity, and I'm responsible for the AI and data around this uh this platform.
00:02:15.280 --> 00:02:21.280
So the Ignite launch is something that I've been um uh responsible for over the past year.
00:02:21.599 --> 00:02:30.639
And before Paylocity, I was uh in AI and natural language technologies for many, many years since the early 2000s.
00:02:30.879 --> 00:02:38.719
I worked in speech recognition at a company called Nuanced Communications for many, many years prior to it being acquired by Microsoft.
00:02:38.879 --> 00:02:43.680
Um and then I spent about a decade in the um media streaming industry.
00:02:44.000 --> 00:02:50.240
So um uh doing a video uh streaming platform that served large media companies.
00:02:50.479 --> 00:02:58.000
So I uh was using AI and natural language and machine learning technologies throughout my career.
00:02:58.159 --> 00:03:07.840
Um, I got pretty excited about coming to Pelosity, knowing that um, number one, there's a really ambitious appetite for innovation at this company.
00:03:08.159 --> 00:03:16.400
And this is an industry with so much untapped potential in the HCM industry uh to kind of apply the lessons that you see in other industries.
00:03:16.560 --> 00:03:18.080
So super excited.
00:03:18.159 --> 00:03:22.479
Um, and I'm really excited about what we were able to bring to market this year as well.
00:03:22.879 --> 00:03:24.000
Yeah, very, very cool.
00:03:24.080 --> 00:03:31.759
So you were like the quintessential speaker to to talk uh talk about with the AI sort of advancements that that are taking place here.
00:03:31.840 --> 00:03:38.879
And of course, we have to mention that it that's like one of the main sort of talking points with the SourceForge podcast here is just AI and automation.
00:03:38.960 --> 00:03:43.360
It's like everyone is is rolling out um some kind of AI functionality.
00:03:43.520 --> 00:03:52.400
So I'm really uh eager to hear what it is you guys have working on and cooked up here with payloadity and in regards to to AI and agentic AI.
00:03:52.560 --> 00:04:07.599
Um, before we get too you know into the weeds with that topic and that subject, uh maybe you can just help us uh understand a little bit more about what payloads is for folks who don't um, or maybe folks who only know about payloadity as sort of a payroll company.
00:04:07.759 --> 00:04:11.520
How would you describe what the company is here in 2026?
00:04:12.240 --> 00:04:18.879
Yeah, um sort of basic level payloadity is helping organizations support their workforce.
00:04:18.959 --> 00:04:33.680
Um and this means HR payroll, also finance things like expenses, purchase orders, um, IT asset management, managing computers and software access and employee experience.
00:04:33.839 --> 00:04:38.720
So it really covers a lot of different places that a typical company will have.
00:04:38.800 --> 00:04:43.439
Um and bringing these together in one platform uh helps in a few ways.
00:04:43.600 --> 00:04:54.240
It helps with really the productivity about um taking these workflows that happen across the different functions and making minimal handoffs.
00:04:54.319 --> 00:05:00.000
Um, you know, everything is easier, the data is more accurate, you can have more confidence in your process.
00:05:00.240 --> 00:05:04.639
The second we really focus on as a company is ease of use.
00:05:04.800 --> 00:05:26.399
And it's it's really exciting how much um user experience and user experience design we have going on in the company, because a lot of our clients will choose us because the employees really love the software and we have a super high adoption rate that translates into 90%, 90% plus of employees are actually using the app.
00:05:26.560 --> 00:05:34.879
And so that's really important, particularly where it's something like um, you know, an HR software where you don't want to kind of have to pressure people to use it.
00:05:35.040 --> 00:05:37.199
You want them to actually enjoy using it.
00:05:37.360 --> 00:05:48.240
Um and the third really benefit of the platform is the consolidation of not having to go out to all these multiple vendors, the HR tech space and particularly in expenses.
00:05:48.399 --> 00:05:52.079
Um, it can become a fragmented system to manage all these vendors.
00:05:52.160 --> 00:05:56.480
So being able to kind of do it on one platform really simplifies it for our buyers.
00:05:56.879 --> 00:06:03.439
Yeah, the the consolidation and and all-in-one sort of offering is is the the pitch that stands out to me, right?
00:06:03.519 --> 00:06:17.920
You have all the all the employee data, uh sort of the money movement, the business workflows, all that live together instead of being scattered across all a sort of fragmented set of solutions and services.
00:06:18.160 --> 00:06:24.399
Um, obviously this is a a pretty competitive um, you know, market.
00:06:24.720 --> 00:06:28.079
I know payroll and HR software is in a crowded space.
00:06:28.240 --> 00:06:31.120
Um you have ADP, Workday, Gusto, Rippling.
00:06:31.360 --> 00:06:33.199
There's a lot of uh other players out there.
00:06:33.360 --> 00:06:41.279
I'm curious, like, what's the honest answer on like where payloadity sort of wins and like who your ideal sweet spot customer is?
00:06:41.759 --> 00:06:46.240
Yeah, we're serving uh you know mid-size and and some larger customers.
00:06:46.399 --> 00:06:49.279
You know, really we're we're interested in clients.
00:06:49.360 --> 00:06:54.399
We have a really great mobile experience, um, great service and support.
00:06:54.639 --> 00:07:04.000
So when you start to look um very far down market, you can see companies where you can log in and you know have a paywall type service.
00:07:04.079 --> 00:07:06.560
Um, but those things you don't get the customization.
00:07:06.720 --> 00:07:11.439
You can you don't have the service organization behind it when you need to um operate.
00:07:11.680 --> 00:07:19.600
And, you know, something like payroll honestly is something that is uh a business sensitive and business critical process.
00:07:19.839 --> 00:07:24.959
So, you know, just switching your payroll vendor, people don't want payroll to get interrupted.
00:07:25.040 --> 00:07:37.519
Um there's a lot of process involved in just bringing on new customers, making sure that everything's working right, um, making sure that, you know, all the taxes, the compliance is correct, um, making sure that there's a smooth transition there.
00:07:37.759 --> 00:07:44.560
And then once you're uh a client and you're you're running, um making sure that you have the way to go to that support.
00:07:44.720 --> 00:07:58.399
And then obviously throughout, and I wouldn't be here if if this weren't the case, um our ability to deliver innovative solutions with AI throughout the platform that kind of make it easier to use and more productive is one of our differentiators in the market.
00:07:58.879 --> 00:08:17.920
The mid-market like customer base is is kind of an interesting spot to defend, sort of, because these those companies have uh enterprise grade compliance problems, I would say, but but but sort of like the patience that you'd find amongst small companies, at least for for implementations of of um you know the innovations they plan to roll out and stuff.
00:08:18.000 --> 00:08:28.319
So you have to kind of thread that needle if that makes sense of um what you guys are able to accomplish and help um with the mid-size market uh companies out there.
00:08:28.560 --> 00:08:31.519
Um I want to talk about Ignite AI.
00:08:31.680 --> 00:08:35.919
So um I know your July was very busy with the launch of Ignite AI.
00:08:36.000 --> 00:08:38.320
It launched on, I believe, July 21st.
00:08:38.639 --> 00:08:41.120
What is it in like simple terms?
00:08:41.200 --> 00:08:45.600
Like what and what does an agent actually do inside the platform on a day-to-day basis?
00:08:46.080 --> 00:08:52.720
Ignite is really um a set of transformative AI capabilities throughout the platform.
00:08:53.039 --> 00:09:09.440
And this is 24 different, you know, discrete initiatives, discrete agents, but we were really taking the platform and putting it uh to a place where we can do outcome-focused applications of AI in the platform.
00:09:09.600 --> 00:09:11.440
And it really boils down to three things.
00:09:11.519 --> 00:09:19.200
So the simplest way I can explain it is number one is we we have an AI assistant in the platform that's available anywhere you go.
00:09:19.360 --> 00:09:22.080
So you can hit ask AI and ask it questions.
00:09:22.240 --> 00:09:29.519
So Ignite was about really supercharging the intelligence behind the types of questions we can answer.
00:09:29.759 --> 00:09:31.360
The second is workflows.
00:09:31.519 --> 00:09:41.120
So we have workflows in the platform where it's um, you know, kind of doing things like changing, you know, doing a payroll workflow or doing workflows related to time tracking, right?
00:09:41.360 --> 00:09:45.679
The workflows are part of what you're using in the platform day to day.
00:09:45.840 --> 00:09:53.679
So we put AI in those workflows to be able to give you help and accelerate certain things that are like repetitive tasks.
00:09:53.919 --> 00:09:56.000
And the third thing is insights.
00:09:56.240 --> 00:10:05.759
So AI, we all have all this data in the platform, and accessing that sometimes through reports or through dashboards can be difficult.
00:10:06.000 --> 00:10:18.320
So being able to surface insights in a more intelligent way through AI and being able to pull out stuff that help you make business decisions, um, that is essentially the three pillars of Ignite.
00:10:18.480 --> 00:10:25.759
And when we talk about the agents, those are kind of discrete pieces of functionality that are supporting those three big pillars.
00:10:26.159 --> 00:10:30.240
Okay, so I want to I want to continue to unpack the the agenting side of things.
00:10:30.320 --> 00:10:39.440
Um, but there's one thing I that stood out to me with the the announcement um with your Ignite AI um platform and uh is is it was about reports.
00:10:39.600 --> 00:10:48.720
Um so basically you pay lossity, you guys said something to the effect of like a report shows what happened, not why, and clients need to explore their data on their own terms.
00:10:48.960 --> 00:10:53.360
How does how does Ignite sort of change how an HR leader actually interacts with their own data?
00:10:53.519 --> 00:10:56.240
Can you help kind of unpack that portion of things?
00:10:56.639 --> 00:10:57.840
Oh, yeah, of course.
00:10:58.080 --> 00:11:12.960
So, you know, our traditional software, we'd collect a lot of data, and then you'd go in and pull a rapport and it would have, you know, fields and filters, and then you'd pull down a big table and it would you download it to Excel, do some analysis.
00:11:13.279 --> 00:11:22.559
Or if you were a little more visually oriented, you would have a dashboard where you would have, you know, some filters and you could get graphs and pie charts and other stuff like that.
00:11:22.799 --> 00:11:38.000
Um, but today's technology, you can actually just type a question and hand it to an AI assistant, and AI assistant will pull all that data, it will figure out what charts to make, um, what analysis to do.
00:11:38.159 --> 00:11:50.799
And what's really cool about it is because you're using the AI technology, you're using a large language model, it can do um analysis, it can do some root cause, it sort of does some thinking about it.
00:11:50.960 --> 00:12:04.480
So you wouldn't ask your rapport to go find me the trends of you know overtime in the business locations where we're at and find out which ones are having the biggest problems.
00:12:04.720 --> 00:12:09.519
You'd almost have to have a preconceived idea of the data that you'd be pulling.
00:12:09.759 --> 00:12:16.080
But if you do that with AI, you can send AI that problem and it actually reasons out the data it's going to look.
00:12:16.399 --> 00:12:24.639
Maybe it finds some data and it doesn't, you know, it doesn't get to the area that's interesting, and it'll try and keep going and iterate a little bit.
00:12:24.960 --> 00:12:35.120
And that's what's really powerful is you can kind of give it this idea of show me the place where the overtime is trending, and it'll go figure out that and it gives you back the report.
00:12:35.279 --> 00:12:36.799
So it really saves you a lot of time.
00:12:36.960 --> 00:12:47.759
You can go straight from like business question to some kind of insightful answer with some reasoning and analysis behind it, which is driven by the AI.
00:12:48.639 --> 00:12:57.120
Yeah, you know, it's it's funny because it seems like every every company has has like, you know, floats AI, like they have all these AI tools, AI this, AI that.
00:12:57.279 --> 00:13:06.159
Um, but a lot of times it's sort of bolted onto old software, doesn't really um, I don't know, it's not as maybe impressive or effective as one would imagine.
00:13:06.240 --> 00:13:23.360
But this one sounds like it's it's integrated across the whole platform and it's it's already able to go out and accomplish things sort of autonomously, like based off of um just like uh a simple sort of question or or prompt that you described with it or talked to.
00:13:23.519 --> 00:13:42.960
Um how how is like, have you already, I mean, I know it just launched like a month ago, but have you already started to get a lot of feedback about how it's helping clients, like with efficiency and with, you know, um freeing up, just freeing up space to accomplish uh uh tackle more sort of maybe uh meaningful uh tasks that employees can can now work on?
00:13:43.440 --> 00:13:45.440
Yeah, we're we're certainly getting that.
00:13:45.600 --> 00:13:52.159
And and one of the cool things we do is um we do these co-creation groups essentially.
00:13:52.320 --> 00:13:58.480
So we work with customers to make sure that we're dialed in on solving the correct types of problems.
00:13:58.720 --> 00:14:08.960
So a lot of times when we get feedback, it'll be because we're you know physically on site with some customers and kind of looking at what they're struggling with and stuff like that.
00:14:09.200 --> 00:14:18.399
You know, we get things like um one customer um corrects craft as a boat manufacturer and had had kind of grown up by acquisition.
00:14:18.720 --> 00:14:27.840
So they have 15 brands across seven states, and they keep adding all these disconnected tools and stuff like that.
00:14:28.080 --> 00:14:44.639
And when we talked to them, they said, oh, normally we'd have to run these Frankenstein reports because we'd pull a report from one system, we'd pull a report from another system and a third system, and then try to reconcile these three reports, and that's where the errors creep in.
00:14:44.879 --> 00:14:58.480
And so, you know, what really excites them is like, you know, now when you have a tool like this where you can ask a question, it can actually go out and pull all the pieces of data and just put it into like a compiled, you know, summary.
00:14:58.720 --> 00:15:01.440
So it kind of saves with two things.
00:15:01.600 --> 00:15:06.399
One is just tracking down all that information, but also it's cross-checking against it.
00:15:06.480 --> 00:15:09.360
And that's something like AI can do pretty well.
00:15:09.600 --> 00:15:12.320
Um, you know, these are the types of things.
00:15:12.399 --> 00:15:26.000
Um, you know, we also see a lot of great feedback on the um on the recruiting side because that's an area where you can see an impact almost right away when we have like recruiting technologies that we put out there.
00:15:26.159 --> 00:15:39.519
Um, you know, people are able to like parse through more resumes more quickly, um, evaluate a candidate pipeline at the top of funnel, um, get through to qualified applicants much more quickly.
00:15:39.679 --> 00:15:43.759
And, you know, right away they can see how many hours of saving per week.
00:15:44.159 --> 00:15:53.200
Well, I imagine, yeah, the feedback is just going to continue to keep rolling in and you guys are gonna continue to make changes and roll out some some new features and capabilities.
00:15:53.360 --> 00:16:10.240
And um, I I you know obviously there's like a lot of AI, you know, tools, consumer-facing tools that can can tackle some of these like report building and stuff, but having it built into your platform, I imagine just allows you to work with a lot more internal data and whatnot.
00:16:10.320 --> 00:16:16.399
So that that you know, a consumer public-facing AI uh tool wouldn't have access to.
00:16:16.559 --> 00:16:20.559
And so you have a lot more of the context and just a lot more capabilities there, right?
00:16:21.200 --> 00:16:22.639
Yeah, that that's absolutely right.
00:16:22.799 --> 00:16:42.639
And if you look at those scenarios, like you say, hey, I have this external AI consumer-facing tool here, a lot of times what people are doing is they're extracting the data into a spreadsheet, and then they're pasting the spreadsheet into the AI tool, asking a question, getting it back, and then going back to their platform and typing in information.
00:16:42.879 --> 00:16:53.759
Like all of those extra steps, when you're integrated, like you can just say, hey, um, you know, what is our labor cost trend at our Syracuse, New York site?
00:16:54.000 --> 00:16:56.240
And it already has access to that data.
00:16:56.399 --> 00:16:59.519
You don't have to like do all this copy and paste business.
00:16:59.679 --> 00:17:06.880
You can give it the same type of analysis question to say, hey, you know, give me the trend over the last three years.
00:17:07.039 --> 00:17:08.480
It'll come back with a graph.
00:17:08.559 --> 00:17:09.359
It's right there.
00:17:09.519 --> 00:17:12.720
And then, you know, you're writing your tool ready to go.
00:17:13.119 --> 00:17:16.720
This kind of leads me to the uncomfortable um part of the discussion.
00:17:16.799 --> 00:17:29.119
And um, that's has to do with, you know, AI sort of um interacting with pay hiring and employee data, um, which is all, of course, very, very sensitive, um, almost as sensitive as it gets, right?
00:17:29.279 --> 00:17:34.079
So, how are you sort of thinking about trust, governance, and and just keeping humans in the loop?
00:17:34.480 --> 00:17:35.680
Yeah, absolutely.
00:17:35.920 --> 00:17:41.039
So we have a um pretty mature process for governance.
00:17:41.200 --> 00:17:44.559
Um, we go through a governance committee looking at all of these things.
00:17:44.880 --> 00:17:49.440
PII data gets protected in multiple places in the system.
00:17:49.519 --> 00:17:56.559
So there's multiple guardrails where we're either suppressing it or just saying, like, look, the AI doesn't need to know about this particular piece of data.
00:17:56.640 --> 00:18:03.920
So actually, when you when you type in the question and we're pulling data, we're actually not sending every piece of data to AI.
00:18:04.079 --> 00:18:11.039
In some cases, we're not, you know, we're we don't want to send bank account numbers or anything that's not needed for the particular question.
00:18:11.200 --> 00:18:17.200
So there's a lot of of guardrails in the system where we have protections in place.
00:18:17.519 --> 00:18:24.640
Number two is uh we have in particular, we have external bias audits on the particular technology.
00:18:24.720 --> 00:18:36.079
So there's a lot of sensitivity in the case of recruiting and hiring, where you don't want to have technology that's subtly bias in unfair ways in hiring practices.
00:18:36.240 --> 00:18:43.599
So we actually have you know third-party bias audits to make sure that everything is is clean from a technology perspective.
00:18:43.839 --> 00:18:47.119
And then number three is putting human in the loop.
00:18:47.279 --> 00:19:02.640
So we actually have a part of our system um called the uh the AI hub uh under Ignite, and that's actually to give transparency and ability to, at a pretty granular level, turn things on and off for a company.
00:19:02.799 --> 00:19:11.279
So if a company sees that they don't want AI used in a particular use case, um, they'll have the ability to go in and turn it off for their particular company.
00:19:11.440 --> 00:19:23.279
And so that is part of trust, and it's also part of like honestly, um, explaining things in a simple way is as important as disclosing it.
00:19:23.440 --> 00:19:32.240
Um part of trust is like explaining it, but also explaining it in a way that people can understand it and make educated choices about their usage of it.
00:19:32.559 --> 00:19:33.039
Totally.
00:19:33.200 --> 00:19:49.920
Yeah, I think that should be really reassuring for organizations listening and um prospective clients and customers of yours, because a lot of the surveys I've kind of um skimmed through, it sounds like organizations are are really less worried about whether to adopt AI at HR.
00:19:50.000 --> 00:19:52.319
They're just more worried about how to use it responsibly, right?
00:19:52.480 --> 00:20:02.480
So so going through those, listing those pillars like you did, I think will be really um will resonate really well with with um organizations thinking about this this neck next pivot.
00:20:02.559 --> 00:20:12.880
Um I feel like it's kind of inevitable, but like again, like having sort of an audit trail, keeping a human in the loop, those sort of things really do um, you know, go far with with giving people that trust.
00:20:13.440 --> 00:20:14.240
That's that's right.
00:20:14.400 --> 00:20:29.839
And also just like honestly, just selecting, um, making sure that, you know, when it's decisions about, you know, people's livelihood or their pay or their salary, um, there's certain things where you really do need humans to be responsible for the decision.
00:20:30.079 --> 00:20:56.240
And that's part of like, you know, our guidance in applying some of these things is like look at the the decision and the impact of the decision you're making, particularly in the HR space, and make sure that your AI is focused on some of the areas that are routine tasks that don't carry like this kind of human impact, because ultimately humans own these decisions and you know, in their in their totality.
00:20:56.480 --> 00:21:15.920
So we've been focusing on things like um, you know, routine approvals and you know, stuff that's kind of high volume and repeat work where it doesn't carry a lot of that like decision weight as opposed to you know making decisions about you know whether someone gets promoted or not.
00:21:16.000 --> 00:21:18.000
Really, that should be a human decision.
00:21:18.400 --> 00:21:19.039
That's a good point.
00:21:19.119 --> 00:21:19.599
That's a good point.
00:21:19.680 --> 00:21:25.839
Yeah, it's it's yeah, nobody nobody really gets fired for the the AI feature that um that worked.
00:21:26.240 --> 00:21:34.240
They sort of get fired for the one that nobody could audit and sort of the the ultimate, like the human that caused the decision in the first place, perhaps.
00:21:34.400 --> 00:21:36.720
Or like, you know, didn't catch it, maybe.
00:21:36.880 --> 00:21:45.359
I I want to uh pivot slightly to talk about the other big July headline, uh, which was you know, where when you went out and bought a company.
00:21:45.440 --> 00:21:47.039
Um so I want to talk about Adora.
00:21:47.359 --> 00:21:52.079
Um Palocity just acquired Adora, it's an AI native leave management compliance company.
00:21:52.160 --> 00:21:59.599
Um so my question for you is just is is why is leave, of all things, such a nightmare that that it needed an AI native solution?
00:22:00.400 --> 00:22:00.799
Yeah.
00:22:00.960 --> 00:22:10.880
So, you know, leave management is is one of those things that has a lot of a lot of rules and a lot of implications across the system.
00:22:11.039 --> 00:22:29.200
So when you think about it, like when someone goes out on a leave of absence, there may be, you know, company-specific policies and rules that need to be weighed against regulations and other stuff like that about eligibility for leave and the amount of time you're eligible for leave and all that kind of stuff.
00:22:29.359 --> 00:22:43.440
So helping people through those type of questions is something where it's one of those things where having some guided conversational system to kind of help you through those things can really be appealing for people.
00:22:43.839 --> 00:23:04.720
And then the second is once you do actually make a decision to, you know, have someone out or have someone take a leave of absence, um, there's a lot of parts of the system from you know time cards to payroll to benefits, you know, across the board that have to be coordinated in a way where it all makes sense, you know.
00:23:04.880 --> 00:23:12.720
And I actually have, you know, personally, um, I actually have two people out on paternity leave in my organization right now.
00:23:12.960 --> 00:23:15.759
And um, you know, there's things you wouldn't even think of.
00:23:15.839 --> 00:23:22.319
Like we have a performance review cycle, and performance reviews are uh are due on a certain date.
00:23:22.640 --> 00:23:31.839
And, you know, they need to be kind of like pulled out of the performance review um cycle so that that can happen off-cycle because they're just not there to get them.
00:23:32.079 --> 00:23:43.920
So, you know, there's just lots of little details about what happens in a leave management scenario that having a platform that can kind of coordinate that is super valuable to a lot of companies.
00:23:44.319 --> 00:23:57.440
Yeah, I mean, you can't uh you got to think about too, like the um uh a single sort of parental leave can involve um, you know, federal, uh, federal policy like FMLA, uh, state paid leave programs.
00:23:57.599 --> 00:24:01.279
It has to go through sort of local ordinances and and company policy.
00:24:01.359 --> 00:24:03.839
And it's it's all sort of stacked on top of each other.
00:24:04.000 --> 00:24:07.759
And the rules just sort of vary by by state to state.
00:24:07.839 --> 00:24:16.960
And so I can just imagine the the complexity of trying to keep track of it all without some sort of dedicated um, I don't know, platform solution.
00:24:17.119 --> 00:24:22.559
Um, and and in this day and age, without some sort of dedicated AI to handle it all, you know?
00:24:22.720 --> 00:24:35.920
Um and it seems like it's it's just a great example where I think uh AI is really going to earn its keep here with with what it's able to do and able to keep track of all the sort of regul uh compliance um regulations and whatnot.
00:24:36.319 --> 00:24:37.759
Yeah, we're we're optimistic.
00:24:37.920 --> 00:24:46.079
And and one of the things AI is really good at is reading a lot of documents and making sense of the ones that are relevant, the ones that are not.
00:24:46.319 --> 00:25:00.640
So these type of things where there's just like a ton of rules, regulations, and try to navigate through, um, that's something AI is pretty good at and can kind of get to the ones that are relevant and summarize them in a succinct way.
00:25:01.119 --> 00:25:04.160
So we've we've covered, you know, what the agents do.
00:25:04.240 --> 00:25:11.759
We've covered uh, you know, the acquisitions, the uh the recent um the new launches that you've you've um you've had.
00:25:12.000 --> 00:25:16.079
I want to uh ask you about where everything is going.
00:25:16.240 --> 00:25:26.640
So what does HR, what does the HR department of, I don't know, 2029 or 2030 look like if agents are handling most of all the the busy work?
00:25:27.279 --> 00:25:58.160
Yeah, I mean, honestly, like when I look at the types of businesses that are out there, because we do spend a lot of time with um with customers, you know, even though we have HR processes, you know, and and automations around that, like, you know, HR at a large restaurant chain or HR at a large healthcare organization, there's a lot of human factors that go into these things, you know, from performance reviews to recruiting good people to quality of care and things like that.
00:25:58.400 --> 00:26:02.480
So I don't see the HR function going away in any sense.
00:26:02.720 --> 00:26:09.279
What I do see is that for HR professionals, they'll be getting more done on a daily basis.
00:26:09.440 --> 00:26:26.880
So I think for those routine um like policy-related um onboarding checklists, um, you know, time card um management and that kind of stuff, like a lot of that stuff I expect will be be automated in a more serious way.
00:26:27.119 --> 00:26:35.039
And so I think, you know, also like the handling of routine questions that people get for is this in policy or not?
00:26:35.440 --> 00:26:43.039
You know, I have questions about benefits, a lot of that stuff um could be answered, you know, through AI essentially.
00:26:43.279 --> 00:26:51.599
I think that'll leave AI, I think that'll leave HR professionals um honestly making more decisions and more impactful decisions.
00:26:51.680 --> 00:26:57.279
Um, it'll, it'll be helping them like look at uh how can we better manage our workforce?
00:26:57.519 --> 00:27:17.359
How can we, you know, how can we have the right culture and the right recruitment and get people in this organization who can deliver the type of service or the type of care or the type of product that we want to deliver and start to make more strategic decisions with the help of some of that AI analysis.
00:27:17.519 --> 00:27:43.200
And then I also think there'll be um probably, you know, the risk management portion of A uh of HR, which would be like um, how do we correct or you know, or anticipate employee churn or turnover such that, you know, we have people who are, you know, at a great point in their productivity or at a great point in their organization, they'll be able to like look at that.
00:27:43.359 --> 00:28:01.920
So I do think it kind of shifts HR from a operational, like, you know, keep the onboarding checklist going to a decision type of organization, where they're like really trying to build strategically the workforce for an organization and retain that workforce.
00:28:02.400 --> 00:28:10.640
So, so less busy work, more decision making, ultimately more kind of fulfilling, soul-enriching work, I would say.
00:28:10.880 --> 00:28:11.519
Right?
00:28:12.000 --> 00:28:13.440
Yeah, I I assume so.
00:28:13.759 --> 00:28:15.920
Some people may love the the busy work.
00:28:16.000 --> 00:28:18.960
And, you know, I I I don't discredit for them for that.
00:28:19.039 --> 00:28:21.359
They will, you know, I'm sure there will be some of that.
00:28:21.519 --> 00:28:29.279
But um, yeah, like really the the thinking part of the work, which is like how can we make decisions that are impactful to the organization?
00:28:29.920 --> 00:28:38.559
So uh last last couple of questions I have for you is it's just like what's the what's the roadmap look like for maybe the the remainder, you know, remaining half of the year?
00:28:38.720 --> 00:28:41.039
I mean, you've had such a busy July.
00:28:41.200 --> 00:28:46.559
Um what can we get excited about um, you know, as as prospective customers?
00:28:46.640 --> 00:28:49.680
And maybe what's what's what are you getting excited about?
00:28:50.160 --> 00:28:59.039
Yeah, so so we're super excited about um what we're doing in payroll, um, what we're doing in time and time tracking.
00:28:59.200 --> 00:29:02.079
So we have a ton of stuff going on there.
00:29:02.400 --> 00:29:03.599
Interesting stuff.
00:29:03.759 --> 00:29:08.400
Um, you know, we have a series of agents that are going to be rolling out for the remainder of the year.
00:29:08.720 --> 00:29:13.440
So um the first few sets we've had are aimed at recruiting.
00:29:13.599 --> 00:29:18.319
Um, they're aimed at like this data analysis, decision support, stuff like that.
00:29:18.480 --> 00:29:30.799
And then as we get toward the later part of the year, we'll get more into kind of like the payroll processes and the and the time and labor tracking processes um to try to streamline those and um and fire those up.
00:29:31.039 --> 00:29:32.640
So that's what we're pretty excited about.
00:29:32.720 --> 00:29:35.599
And then going into next year, I mean, you know, who knows?
00:29:35.759 --> 00:29:39.599
Like we have um, it's amazing how quickly the market moves.
00:29:39.920 --> 00:29:49.359
So, you know, if six months from now we we see some piece of technology and we say, hey, this is really um really a great use case.
00:29:49.599 --> 00:29:55.200
And in these customer co-creation events, like this is the unlock for something that they've been really struggling with.
00:29:55.359 --> 00:29:58.240
Um, you know, we will absolutely pivot toward those things.
00:29:58.480 --> 00:30:07.200
But for right now, we we do have um, you know, we are watching, you know, just the developments and the you know the rapidly evolving ecosystem.
00:30:07.680 --> 00:30:08.240
Oh, totally.
00:30:08.480 --> 00:30:13.200
It's so hard to it's so hard to tell what's what's come around the corner in even just a few months from now, you know.
00:30:13.359 --> 00:30:32.160
And and so yeah, that's you gotta have all these things uh, you know, in the works what you have um like rolling out, but at the same time, you gotta be kind of agile and fluid to to kind of go with what's currently uh being sort of talked about and prioritized and and what is is making the biggest sort of impact in a positive way for for HR.
00:30:32.240 --> 00:30:34.640
And and just I mean it's it's wild.
00:30:34.799 --> 00:30:55.279
It's wild to think about um how fast things are going and you know, this this supposed bubble of of AI and um it that just keeps on getting bigger and bigger and um you know it makes you question even if it is a bubble because there's so much actual real tangible use cases here now that are that are being um you know worked on.
00:30:55.359 --> 00:30:58.640
And so thank you for everything you've shared with us.
00:30:58.720 --> 00:31:03.599
It's really exciting to hear about what you have working on um and currently have rolled out.
00:31:03.759 --> 00:31:09.279
Um for those interested in learning more um about Palocity, where where's the best place to go?
00:31:09.680 --> 00:31:14.960
Yeah, it directs you just to um payloadity.com and um you know you can get to the contact us.
00:31:15.599 --> 00:31:16.000
Perfect.
00:31:16.079 --> 00:31:19.200
And then we can connect with you over on LinkedIn, I assume, right?
00:31:19.759 --> 00:31:20.880
Uh yeah, that's correct.
00:31:20.960 --> 00:31:22.640
Uh LinkedIn Palocity.
00:31:23.119 --> 00:31:23.440
Perfect.
00:31:23.519 --> 00:31:24.720
LinkedIn Palocity.
00:31:24.799 --> 00:31:31.279
And then um, if anyone would like to get in contact with you, go follow um Aaron Nies over on LinkedIn.
00:31:31.359 --> 00:31:33.839
We'll have links down below in the show notes.
00:31:33.920 --> 00:31:36.799
Um, but Aaron, thank you so much for everything you shared with us.
00:31:36.880 --> 00:31:37.519
This has been great.
00:31:37.599 --> 00:31:40.720
And um, yeah, hopefully we can have you back in the future and talk updates.
00:31:40.960 --> 00:31:41.839
Yeah, I love that, Bo.
00:31:42.000 --> 00:31:42.480
Thanks so much.
00:31:42.720 --> 00:31:43.440
Appreciate it.
00:31:43.680 --> 00:31:45.920
Thank you all for watching the SourceForge podcast.
00:31:46.000 --> 00:31:47.440
I am your host, Bo Hamilton.
00:31:47.599 --> 00:31:53.039
Make sure to subscribe and follow along for more B2B software related podcasts like this one.
00:31:53.200 --> 00:31:54.880
As always, I'm Bo Hamilton.
00:31:55.119 --> 00:31:56.640
Talk to you in the next one.
