How Vendors See AI, With Paylocity's Steve Beauchamp

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[00:00:26] Welcome to PeopleTech, the podcast of WorkforceAI.News. I'm Mark Pfeffer.

[00:00:43] Today, I'm joined by Steve Beauchamp, the Executive Chairman of Paylocity.

[00:00:48] He's going to share the vendor's view of AI in HR.

[00:00:52] That includes the employer's role in preparing their workforce, managing the employees who use AI,

[00:00:58] and at the end of the day, what's the value proposition.

[00:01:01] All that and more on this edition of PeopleTech.

[00:01:06] Hey, Steve. Welcome.

[00:01:09] So, Paylocity recently launched an AI assistant.

[00:01:13] I'm wondering if you can tell me about the thought process behind it.

[00:01:18] What was the evolution of the idea and the product?

[00:01:21] Yeah, like most companies really around the world are trying to figure out how AI can be part of that product portfolio

[00:01:28] and add value to our customers.

[00:01:30] And so, we have really made investments in a few different areas, things that might assist our customer at getting the job done,

[00:01:38] things that might be able to provide them insights, and then maybe things that might automate a lot of their work.

[00:01:43] And so, AI assist definitely falls into this assistance category.

[00:01:48] One of the things that we have found is our product has expanded through a large number of modules that we've added over the years.

[00:01:54] There's definitely increased product complexity.

[00:01:56] There's a lot more clients can get done, but at the same time, there's a lot more knowledge they need to be able to kind of navigate that product.

[00:02:02] And so, we get a lot of emails from them.

[00:02:04] We have a lot of conversations with customers.

[00:02:07] Some of it's just how do I do something.

[00:02:09] Some of it's like best practice questions.

[00:02:11] And so, part of our thought process was could we make this available to them in context at their fingertips using an AI chatbot technology

[00:02:18] where we really take the value out of all the data that we have across our platform

[00:02:23] and then be able to surface answers to them easily.

[00:02:25] Not necessarily taking away the other options.

[00:02:27] We still want them to be able to call us and email and interact with them,

[00:02:30] but giving them that really quick answer right at their fingertips.

[00:02:34] And that was really kind of the genesis behind building our AI assist and then just launching that to our customers.

[00:02:41] How long were you guys at work on this?

[00:02:43] I mean, it seems like everybody's jumped into the AI space in the last two years.

[00:02:48] You know, were you sort of part of that wave or have you been thinking about this kind of capability beforehand?

[00:02:54] So, we started a kind of a data science team about four and a half or five years ago.

[00:02:59] Our first product that we launched from that team was a predictive index in terms of who might leave the organization,

[00:03:06] leveraging all the data that you have around why people leave and who leave that organization.

[00:03:11] We've also got another algorithm that we've launched around modern workforce index that is kind of a culture engagement building capability.

[00:03:18] And then many, many other examples over the years since then.

[00:03:22] Specifically on the latest product, the chat bot, we've had that now in beta for about six months.

[00:03:30] Our goal was to be able to answer 85% of the questions that our internal folks were getting from our customers very accurately,

[00:03:39] leveraging all the data that we have about the customer.

[00:03:42] And so, what we would do is we had our internal people working with it, asking it the questions they were getting from customers.

[00:03:47] They would thumb down instant feedback.

[00:03:50] If that wasn't the answer they needed, they would thumbs up.

[00:03:52] And then through all the ones that maybe the tool was getting wrong, we would then train the model

[00:03:58] and sometimes provide it more data, sometimes provide it training,

[00:04:01] sometimes go back to the source material and make sure it's right.

[00:04:04] And through that six-month exercise, we got comfortable that we could get to that hurdle rate

[00:04:08] and felt like this would be really additive to the client experience.

[00:04:13] Now, you'd mentioned that a lot of this is about efficiency.

[00:04:19] And many, many solutions providers, I think pretty much all of them,

[00:04:24] talk about efficiency and AI will free up time so your workers can do more creative and strategic work.

[00:04:32] And that seems to be pretty much universal.

[00:04:36] That's the pitch about AI.

[00:04:39] Will we ever get more than that?

[00:04:43] Are there capabilities that you're thinking about or people are thinking about?

[00:04:48] I do think that this is a platform technology for what all the different uses are.

[00:04:55] We don't know yet.

[00:04:56] And that will definitely evolve over time.

[00:04:59] I think the obvious ones are a level of assistance, chatbot, natural language search, ability to answer questions.

[00:05:08] There's also, I think, automation.

[00:05:10] So if a task was six steps, I may be able to anticipate your next two steps and do that for you.

[00:05:15] And so those certainly fall into that efficiency category.

[00:05:18] I think there's still some burgeoning capabilities around insights and best practices that maybe help you run a better HR organization,

[00:05:28] maybe engage with your people differently.

[00:05:30] So you might think about gathering feedback from employees, identifying hotspots,

[00:05:34] actually then tying that to best practices and action plans that you might be able to then be able to use, monitor, set goals.

[00:05:42] So I think that insight category is something that there are examples currently for that category.

[00:05:49] But I think there's an area where the more experience you get with the models, the more intelligent they get.

[00:05:56] And then probably see different use cases will surface.

[00:06:01] Have you thought about that enough to be able to give me an example,

[00:06:06] you know, sort of paint a picture for me of what a session might look like?

[00:06:10] Yeah. So I think the example I touched on is in our employee voice product today.

[00:06:15] We will provide you a heat map based off the feedback from maybe the several hundred surveys that you've gotten back.

[00:06:21] We will certainly be able to kind of highlight where there's areas you might need to spend time.

[00:06:25] But we could also look through other data patterns.

[00:06:28] Like we might actually see in performance reviews that the scoring of those teams are lower than the company average.

[00:06:35] We might actually see that maybe those folks, if they're hourly, are showing up late more frequently than the rest of the organization.

[00:06:42] So you get to certain behaviors that you would have to run five, six definite reports, go to different parts of the application.

[00:06:49] So you start to be able to combine that.

[00:06:51] And then you can actually suggest goals that you might be able to set for those teams or those areas or best practices.

[00:06:58] Like maybe as an HR organization, you should go to a roundtable with seven or eight of those folks and kind of get direct feedback.

[00:07:04] So it's a combination of data and insights that you can surface.

[00:07:08] And then also this idea of best practices that might go beyond just the software that they're using and might go back into kind of, you know,

[00:07:16] HR normal practices that maybe haven't taken place in a while.

[00:07:21] And I think it's the value of being able to have a platform that goes almost from hire to retire, everything in between,

[00:07:28] that allows you to be able to pull some of those insights that they might take a long time to get to or they might never actually find.

[00:07:36] Now, shifting gears a little bit, it feels like this whole transition toward AI platforms has come about at breathtaking speed.

[00:07:49] And I've seen a lot of research that says, you know, employees tend to be kind of nervous about it.

[00:07:55] They're worried about their jobs.

[00:07:56] They're not sure how it's going to impact their jobs.

[00:08:00] Are you hearing that from users?

[00:08:04] And, you know, what are the concerns that you're hearing?

[00:08:07] Yeah, when we talk to HR practitioners, there's definitely a concern of AI's primary goal and objective is to remove humans.

[00:08:16] And out of the equation and or at least really reduce the number of people that you might need in a certain job function.

[00:08:23] At the same time, when we start to ask them about projects that they don't have time for or they haven't been able to get to,

[00:08:29] there's always a long list of things because part of HR is compliance and task management.

[00:08:34] And there's there's a lot that needs to be done.

[00:08:35] And I've never really run into an HR department that is way overfunded.

[00:08:39] They're typically always trying to find a way to free some time up to be able to do some of these strategic tasks.

[00:08:45] I just use the example of roundtables.

[00:08:47] Lots of times they wish they had more time to spend time with employees and do roundtables and gather face to face feedback.

[00:08:52] And so part of our pitch is I think sometimes the intimidation around AI is maybe the big dream of what AI can do from a longer term perspective versus practically.

[00:09:02] Let me give you an example.

[00:09:03] Do you at times have a hard time finding a report that you need to be able to get the information you want?

[00:09:08] What if we had natural language search that would allow you to surface that report in really efficiently?

[00:09:14] And HR users say, oh, that would be great.

[00:09:16] Now, what if I told you it's AI that's powering that natural language search?

[00:09:20] I think once you get down to the use case level, that's where you start to see the nervousness about AI dissipate because they realize, oh, you've just saved me 30 seconds, five minutes, 20 minutes.

[00:09:31] You've taken six steps down to four.

[00:09:33] But if you talk about AI at the most macro level and all the possibilities, I think it's got to get back down to those very practical use cases.

[00:09:42] And that's something that we really try to focus on our customers is tell us the problem that you have.

[00:09:47] And then maybe AI can help us solve it.

[00:09:50] Maybe we have to solve it a different way.

[00:09:51] But getting back at that problem statement, I think, is core to have a good conversation.

[00:09:56] Now, if you think about the workforce beyond HR, just the workforce writ large, there's also been a lot of talk and a lot of research that shows employees are looking to their employers to train them and to explain AI to them.

[00:10:15] I don't want to say hold their hands, but certainly to make them feel more comfortable with the transition and with the technology.

[00:10:24] First of all, do you agree that that is the employer's role?

[00:10:28] And if you do, are employers doing their part?

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[00:11:18] So I think as it relates to how AI might impact their individual job, I think it is absolutely best practice from a transparency, communication, and just general employee retention to be able to have those conversations, right?

[00:11:33] We are very good at having a conversation around cybersecurity and how somebody needs to protect themselves and the organization for cybersecurity trends.

[00:11:43] We need to think about that in the same way from an AI perspective, which is if your job, you will now be interacting with products, solutions, services that have an AI component to it.

[00:11:54] I think explaining that is going to be really part of moving up that acceptance curve.

[00:11:59] And some of that is very high-level training that you can do, very much like cybersecurity.

[00:12:04] Some of it is very job-specific.

[00:12:07] And so it might be, you know, you're creating a spreadsheet with the following data sources.

[00:12:12] You're sorting, you're filtering, you're having to take a whole bunch of activity.

[00:12:15] Maybe with an AI engine, I can reduce the time for you to be able to do that from a 30-minute activity to a 10-minute.

[00:12:21] But then you actually have to tell them what you want them to do with the time you free up.

[00:12:25] Because otherwise, if you leave that story open-ended, they're just going to say there's eight of me.

[00:12:30] Now you're going to need four of me.

[00:12:32] And so I just had this conversation with our team who services our customers about the AI chatbot that we released.

[00:12:38] The first question they said is, are you going to need less people to talk to customers?

[00:12:41] I said, not necessarily.

[00:12:42] I'm going to need you to talk to customers about different topics, much more best practices, much more consultative.

[00:12:49] You will likely be less transactional in your job.

[00:12:52] And I think having that conversation specifically with the employee about their job and how that interaction model might change as you see AI solutions deployed is a really important part for every employer to do.

[00:13:07] And I don't mean to sound cynical, but do you find that most employers have sort of got their arms around that and realized that they have a pretty significant role to play beyond just offering the technology?

[00:13:22] No, I don't think so.

[00:13:23] I think we're early in the curve.

[00:13:25] I think people in leadership are often trying to get their arms around how AI could impact their organization.

[00:13:31] They're also trying experiments.

[00:13:33] They're seeing what works.

[00:13:34] They're not perfectly sure if I roll this out, what's going to happen.

[00:13:37] So I think we're very much in this experimental phase.

[00:13:41] And so, but your point is a good one, which is that is going to end up being part of the role of organizations, HR teams, as well as leadership across the organization.

[00:13:53] Because anytime you've got a broad platform or technology change or any change for that matter, obviously we saw massive uncertainty in a pandemic.

[00:14:00] There's uncertainty around a new topic like AI.

[00:14:03] You've got to be able to handle the questions up front.

[00:14:05] You've got to be able to provide whatever level of clarity you can.

[00:14:08] And then you've got to commit to some level of consistent updates in terms of here's where we are on the AI journey as an organization.

[00:14:14] Here's how it's going to affect your job today.

[00:14:16] And here's the next steps.

[00:14:18] And by the way, once we get to that next step, we're going to come and have this conversation again.

[00:14:22] And that dialogue, I think, is what allows you to be able to evolve as an organization, but yet retain the talented people that you've got.

[00:14:31] It seems to me that it's vendors, solutions providers who are talking a lot about AI.

[00:14:39] And it makes me wonder, are customers leading this charge into this kind of capability or is it more vendor driven at this point?

[00:14:49] Yeah, great question.

[00:14:50] I think you see a lot of vendors leading the charge.

[00:14:53] You see a lot of marketing around AI.

[00:14:55] There's no question about that.

[00:14:57] And it's not necessarily customers coming to us and saying, can you outline your AI strategy?

[00:15:03] Can you tell me about your investments?

[00:15:04] I want to make sure that I understand that.

[00:15:07] Where we see that conversation happening, though, is when my employees are making decisions in benefit enrollment, I want to make sure that they're making the right decision for them and that they're taking full advantage of our benefits.

[00:15:19] Because I know if they do that, then I'm going to be able to have higher retention and better satisfaction.

[00:15:23] And so then the question is, well, how do I solve for that?

[00:15:26] Because that's a complicated step-by-step process that you've got to be able to go through.

[00:15:29] Okay, if we have data from all of the history and we can surface that in AI that allows the employees to make better decisions, is that something that you would find valuable?

[00:15:38] That's how the conversation typically occurs with our customers is we start with a problem that they have typically surfaced to us.

[00:15:45] And then if we can apply our software solution to that and it has an AI component, we try to draw the conversations that way.

[00:15:54] And that's where I think it becomes value added.

[00:15:56] If we try to do it in reverse, which is we've got AI all over our platform, it's going to assist you.

[00:16:01] It's going to automate.

[00:16:02] It's going to drive efficiency.

[00:16:04] Customers don't know exactly what that means until you get down to the examples.

[00:16:08] Yeah, and you have to wonder, I think, or I wonder, from a marketing point of view, everybody's talking about AI so much.

[00:16:18] Is it sort of meaningless?

[00:16:20] You know, when you say, you know, we have a new product coming out, which has AI, is that saying enough to the customer to really make them understand the value?

[00:16:32] I don't think so.

[00:16:33] I don't think most customers know what that means.

[00:16:38] And it's very much like if I said to you, you know, we've got a really strong mobile platform.

[00:16:44] And if I don't tell you that these are the things that are in the mobile platform, these are things your employees can do that they don't have to come to you anymore.

[00:16:50] And obviously, we've been through a lot of that mobile transition.

[00:16:53] We're at the early stage of this AI transition that they don't know exactly what that means.

[00:16:58] And so from our perspective, we just have to insert this into the storytelling of the problems that the customer is having to create differentiation.

[00:17:09] Us saying we've got AI in 100 different places in our platform has very little value because customers don't know what that means.

[00:17:17] But still, it must make it really tough to differentiate yourself, you know, in the market right now because everybody's talking about AI.

[00:17:26] So you're kind of expected to have it.

[00:17:29] So it's a great point.

[00:17:31] So my general point of view is you want to be able to say enough that you don't get knocked out early in the process and that they know you understand AI as a trend and that you have a point of view and a position and have made investments.

[00:17:41] If you do that and then you get to the software demo stage and then you can actually demo the use cases, to me, that's where the differentiation can really happen versus some marketing or positioning statement around AI that is going to really create differentiation.

[00:17:58] Because for the most part, people are talking about the same types of high-level statements when it comes to AI.

[00:18:04] The proof really is, does this make your product better?

[00:18:08] Does it make it easier?

[00:18:09] Does it make it faster?

[00:18:10] Is it drive efficiency?

[00:18:11] Am I going to learn something I couldn't otherwise learn?

[00:18:13] Are employees going to be more self-sufficient?

[00:18:15] It's all of those types of questions that need to come out in a demonstration.

[00:18:21] So how is all of this impacting your product roadmap?

[00:18:26] I mean, actually, let me ask the question for Paylocity, but also throughout the business.

[00:18:31] Are vendors, you know, thinking differently than they did before about the kind of features they can build and they can offer?

[00:18:39] It's a good question on the kind of features they can build and offer.

[00:18:43] I don't know if it substantially at this point changes your roadmap in terms of very different features.

[00:18:50] I think it does change, though, how you address building new features for your product.

[00:18:57] And for us, at least the way we've approached it, we've kind of got a platform team and data science and machine learning and AI.

[00:19:04] And that team becomes a resource for all of our product teams.

[00:19:07] And so when our product team is designing a new wizard, they're designing a new onboarding process.

[00:19:12] They're designing some, you know, new data visualizations.

[00:19:16] They go to our AI team and say, here's what I'm doing.

[00:19:19] How how leveraging the platform capabilities that you've got?

[00:19:22] Could we deliver a better experience?

[00:19:24] So you think about like natural language interactions, you think about chat interactions, you think about automation and reducing steps.

[00:19:33] And so that's what we're trying to drive across the organization is every feature we build, our product manager should have an opinion of whether there's an AI component or not.

[00:19:42] Sometimes there might not need to be and it might not fit other.

[00:19:45] But I want to make sure that they've gone through that evaluation to be able to say, would leveraging some AI platform technology make this a better experience for the client?

[00:19:53] Because that's really what we're trying to do.

[00:19:55] We're not trying to jam AI into all sorts of places.

[00:19:58] And I think it's taken us some time to be able to build the muscle around that.

[00:20:02] And in some ways, we're still building muscle around that.

[00:20:04] But if that we believe that will give us the most prevalent use cases that customers can benefit from.

[00:20:12] When it comes to talent, the employees with the skills to build these solutions or on the customer side to take advantage of these solutions and implement them.

[00:20:27] Is there enough talent out there or is it really hard to find people who really know what they're about?

[00:20:34] Yeah, I would break the talent up into different buckets.

[00:20:37] You know, certainly it's always difficult in technology to find great talent.

[00:20:41] So start with that overarching statement.

[00:20:43] I think when you get into some of the newer technology and the pure engineering technologists, yes, those are definitely hard to find.

[00:20:51] And it is rapidly changing.

[00:20:52] And those folks need to be able to continue to stay involved.

[00:20:56] The good part is it's an area a lot of people are interested in.

[00:20:59] So you can certainly find people internally that you want to train up to that.

[00:21:03] You can bring some external resources.

[00:21:05] I think there's talent there.

[00:21:07] I think on the product manager side, they're still trying to figure out because they're really like I'm a payroll product manager.

[00:21:13] So how do I take a payroll audit process and streamline it from 10 steps to five?

[00:21:17] I don't naturally think about AI being part of that solution.

[00:21:21] And so there's a lot of training for those folks to figure out, you know, where AI can be helpful and where maybe it can't be.

[00:21:28] And I think that has, to me, been the bigger challenge than actually finding the technology resources is kind of ingraining that across everybody who's involved in building different parts of the product so that they actually are getting the value out of some of these platform capabilities, which are very different than what you would have imagined three, four or five years ago.

[00:21:48] And just one more question.

[00:21:51] You touched on this before, but I'm curious if within Paylocity, are you having the experience that a lot of your professionals are nervous about what AI might do to their jobs, the impact it might have?

[00:22:06] Well, what I would say is we've actively been having that conversation with our team.

[00:22:13] So certainly our product and technology teams more comfortable, you know, naturally more interested in that space and interested in learning more.

[00:22:21] And so that has been, you know, a big topic of conversation that we've had.

[00:22:24] We've had hackathons where we've had AI being component of all of the different solutions that they might be building.

[00:22:29] So we're trying to continue to build that comfort.

[00:22:31] That's probably been the easier part of the equation.

[00:22:33] If you look at our folks that are implementing and servicing our customers and answering their questions, they're probably a little bit more behind the curve, understanding AI, knowing where it can have an impact.

[00:22:46] And so what we've really tried to focus on is as we roll out new capabilities, we really do a lot of training with them to say it's actually our investment in AI that is allowing us to improve the search results for some common query that they have.

[00:23:00] Or we're able to actually reduce the number of steps or improve the audit over here.

[00:23:06] And so we really try to make sure we train them so that they know there's an AI component to this that they might not actually see or even know exists.

[00:23:13] But our investment in that space is producing a better result for the customer.

[00:23:17] And ultimately, once they see that, they kind of get excited about the investments in AI because what they usually come back to is, oh, my gosh, we could do that here.

[00:23:26] Here's three or four other places where maybe I get customer feedback that we need to improve.

[00:23:31] Could that help in this case?

[00:23:33] And so it's about kind of generating excitement in terms of what it could do for our customers.

[00:23:38] And I think we've been building momentum towards that.

[00:23:41] And I'm really happy with how engaged the organization has gotten around that concept.

[00:23:47] Well, Steve, thanks very much for taking the time.

[00:23:50] It was great to talk with you.

[00:23:50] And I hope we'll do it again.

[00:23:52] Yeah, I really appreciate it, Mark.

[00:23:53] Thank you very much.

[00:24:05] My guest today has been Steve Beauchamp, the executive chairman of PayLocity.

[00:24:10] And this has been PeopleTech, the podcast of WorkforceAI.news.

[00:24:15] We're a part of the Work Defined Podcast Network.

[00:24:18] Find them at www.wrkdefined.com.

[00:24:24] And to keep up with AI technology and HR, subscribe to Workforce AI today.

[00:24:30] We're the most trusted source of news in the HR tech industry.

[00:24:34] Find us at www.workforceai.news.

[00:24:39] I'm Mark Pfeffer.