People Tech with Mark Feffer and Opal Wagnac on Navigating AI’s Impact on HR

[00:00:00] This episode is sponsored by iSolved. Welcome to People Tech, the podcast of WorkforceAI.News. I'm Mark Feffer.

[00:00:19] Today, I'm joined by Opal Wagnac, iSolve's Senior Vice President of Market and Product Strategy.

[00:00:26] With all the AI solutions being offered to HR, how do you tell what's real and what sales pitch?

[00:00:32] That's one of the things we look at. We'll also talk about how the AI market is maturing, roadblocks customers face when it comes to implementation, and what customers are thinking about.

[00:00:43] All on this edition of People Tech.

[00:00:47] Hey Opal, welcome.

[00:00:50] Let me start by asking you about the state of AI today. How can HR leaders determine what's hype and what's reality?

[00:01:01] Well, that's a very good question. What's interesting is that there is a lot of AI washing going on.

[00:01:09] And I think we don't really have to blame the state of technology right now. We can actually blame the movies.

[00:01:14] All right. The movies started out with, you think of AI, you think of like the Terminator.

[00:01:19] Or you think of that cute robot, Sunny, with Will Smith starring in that movie.

[00:01:25] Or you think about, you know, hey, Vibranium and Black Panther.

[00:01:29] You think about all of the art of the possible.

[00:01:31] But we also know that that's not AI.

[00:01:34] However, the movies definitely created an entire narrative about it too.

[00:01:38] So now here we are actually using or looking into artificial intelligence.

[00:01:43] And I think that the state of AI right now is doing a lot of cleanup.

[00:01:47] It's doing a lot of cleanup to pretty much reverse that narrative that Hollywood created.

[00:01:52] And now actually giving it a little bit more practicality or practical understanding.

[00:01:57] And so it's interesting that we live in this very dichotomous way where at home, we're totally fine with AI.

[00:02:05] You know, as a matter of fact, I say to Alexa, I need to put new paper towels.

[00:02:08] Or if I'm on Netflix, I don't search.

[00:02:11] It's recommended to me.

[00:02:13] Like Netflix knows that I like period pieces with a strong female protagonist.

[00:02:16] Right?

[00:02:17] And it never really disappoints.

[00:02:19] And it's learning over time if my mind changes, you know, about introducing some other things.

[00:02:26] I'm into drama or any type of like thrillers nowadays.

[00:02:30] So I feel that especially at home, we're very much accustomed to understanding that augmentation of making my life easier.

[00:02:37] However, at work, we're a little bit more, a little slower to adopt or we're a little bit more, more risk averse.

[00:02:44] But I definitely see that there is a lot of improvement in a very short amount of time.

[00:02:51] And I say that to say because of a lot of the advancements that have been taking place in our personal lives is really pushing the boundaries as far as what we can expect in our work lives.

[00:03:03] Now, AI has been around in its current form for about two years, which is when ChatGPT appeared.

[00:03:12] Are we approaching or are we any closer to being in a mature market, meaning a time when there's more attention that's being paid to adoption as opposed to development and more attention being paid to training than was before?

[00:03:30] Yeah.

[00:03:31] You know, I can even go even further than that because ChatGPT, that's generative AI, right?

[00:03:35] So that's like another installment, right?

[00:03:38] So if we even go back to, let's say, 2012, you know, when predictors started to come onto the scene, you could predict retention.

[00:03:46] What do you mean by predicting retention?

[00:03:48] And now you can't find an ATM vendor that doesn't have a retention predictor.

[00:03:53] You know, something that's looking to see, you know, or coming up with some type of scoring algorithm to provide some type of analytic, you know, about where you are or where you might be going or if you're of a flight risk or so forth.

[00:04:07] And so I definitely can see that there has been several installments over the years of AI.

[00:04:13] Now, going into generative AI, like you mentioned, ChatGPT, I definitely feel that there is a lot more that's being added into development.

[00:04:21] However, the maturity phases of it are much, much shorter simply because I think the pace of innovation is so much faster, where it's faster than we actually are even humanly capable to be able to receive it and understand it and so forth.

[00:04:38] But by the time you understand it, it's already moved on to the next thing.

[00:04:41] So right now, what we're at, what, ChatGPT, what, 4.0 now?

[00:04:44] You know, and then so you're like, wait, what happened to 1.0?

[00:04:47] I was like, oh, you missed it, right?

[00:04:49] And so just even a scene that how much of that type of development, I feel like development and training are definitely merging.

[00:04:57] That's the beauty, I would say, also of AI because AI understands that it's constantly having to learn.

[00:05:04] So as long as learning is always a constant thing, that's where machine learning comes in.

[00:05:09] That's actually the proof point that AI even exists.

[00:05:12] I think similarly, as humans, we also can understand that this is also going to be a constant thing.

[00:05:18] So there's always going to be some type of training or some type of development.

[00:05:21] No one can really be in a place where they can say, all right, I know enough and I never have to learn again.

[00:05:27] And then guess what? The technology is going to change yet again.

[00:05:30] And then here you are entering into some new technology and it's already on the 8.0 version.

[00:05:35] Right.

[00:05:37] So do you think that users are keeping up with all of this change?

[00:05:42] Some are keeping up, but in a very stealth way.

[00:05:46] Like it's, when I say stealth, I mean like if I didn't tell you you were using AI, you wouldn't have noticed.

[00:05:54] All right. So like, or even for like my own children, my children will never say, oh, what type of AI solutions do you have?

[00:06:01] They will never say that, you know, like they don't know that AI is there, but they know the absence of AI.

[00:06:07] Like they know like, wait, what do you mean?

[00:06:08] Like this is not recommending or the search is like flawed or, you know, things like that.

[00:06:13] They understand like what's not optimal, you know, because they are native AI.

[00:06:20] They're not just native digital, the native AI digitals.

[00:06:24] And so even understanding that I would say that the AI is going to become more of a baseline, really no different than I think it was Steve and Stephen Ng who said like AI is a new electricity.

[00:06:37] Right. And so electricity, I wasn't around when it was discovered and played and people did wiring for buildings and stuff like that.

[00:06:43] I wasn't around for that. However, it did have its own maturity curve where people walk into buildings and was no longer lit by candles or people like, wait a minute, out of the ceiling, there's lights.

[00:06:53] Like, what is this? You know, or people thought it was like, oh, it's the apocalypse.

[00:06:57] You know, it was that level. And now you walk into a building, you don't ask to see, do they have electricity?

[00:07:03] It's pretty much assumed. Right.

[00:07:04] Right. However, in my lifetime, I did see how the Internet definitely took took off where I work for a SaaS company.

[00:07:12] So you think about it, the company I work for probably wouldn't even exist or be selling a very, very different product.

[00:07:17] It's pretty much assumed that the Internet is there.

[00:07:20] Like you don't really even walk into a place and ask, do you have Internet access?

[00:07:23] You pretty much ask, what's the Wi-Fi passcode?

[00:07:26] You know, like that or what's the Wi-Fi that I need to connect to?

[00:07:29] It's already assumed that it's there. And I believe that, of course, that was a much shorter maturity window than electricity took.

[00:07:37] Electricity took a lot longer. Internet was much shorter than that.

[00:07:40] And I can believe that AI is going to be very baseline very, very soon, too.

[00:07:45] So I think the maturity curves, especially as innovation continues to mature, too,

[00:07:49] they get much shorter and shorter to the point that adoption is not necessarily one of those things that we're really paying attention to.

[00:08:00] It's just one of those things that's like it's being introduced.

[00:08:03] So everyone who's using Alexa, they didn't ask them like, hey, what about AI?

[00:08:08] Or you're not you weren't asked any questions about AI.

[00:08:11] You just say here's a new technology and then you just started using it.

[00:08:15] If I told my my mother that she was using AI, like I am, you know, so there's so we might we might be looking at it as like how we would adopt a new technology.

[00:08:26] But I feel that the more adoption is more and played in a stealth manner.

[00:08:32] We're not even going to be able to truly understand the signage on adoption, so to speak.

[00:08:40] Now, what about customers?

[00:08:43] And in this case, in the case of iSolve, it would mean employers, the people who are buying the applications that include AI.

[00:08:52] Are they becoming more comfortable with the whole idea of AI and comfortable with its potential and understanding of its limitations?

[00:09:02] You know what? Absolutely.

[00:09:03] I definitely can see here at iSolve, we run a lot of our own surveys, especially with our customer base.

[00:09:10] You know, having 177000 customers, you learn a lot.

[00:09:13] Right. And so we definitely can see that AI has moved beyond its experimental stages.

[00:09:19] So in one of our reports, we saw that a one percent of HR professionals at companies use AI tools on a regular daily tasks.

[00:09:26] And so it's not until we actually start to uncover what are the tasks that you're using that we are able to share with them.

[00:09:33] Hey, guess what? That's AI.

[00:09:35] And a good example of that is the retention predictor.

[00:09:38] You know, retention predictors have been around since, what, 2012?

[00:09:41] And now it's pretty much a mainstay in HCM solutions to the point that people are actually recognizing and seen as a score that they want to pay attention to.

[00:09:49] So right then and there, that is actually them using AI.

[00:09:54] There's also others where our customers are using things, especially in terms of recruiting.

[00:09:59] You know, if you imagine you put a job posting out there and you get hundreds of hits.

[00:10:03] It's so much, especially depending on the job market, it can literally be thousands in minutes.

[00:10:09] And, you know, that's always good that you have that kind of draw, but who's going to manually go through all of those resumes and all of those applications?

[00:10:19] Very quickly, probably you get to a resume number 10.

[00:10:22] You start leaning into your bias.

[00:10:24] Like, OK, yeah, I can't I don't I don't I don't know what this is or is this even a qualified candidate?

[00:10:33] And so when we start to even understand, like what humans are good at and what machines are good at, it's really a matter of making sure that the machines are augmenting that, you know, what what what humans can do.

[00:10:45] So machines, they're very good at being able to process large volumes.

[00:10:49] They're very good at doing that very consistently, you know, by having like defined criteria.

[00:10:55] And they also can do it very quickly and they don't get as tired as quickly as I do.

[00:10:59] Right. And then you have those things like what humans are good at, too.

[00:11:02] So I feel that the area of recruiting, it takes advantage of those things that humans that machines are good at being able to process and understand and do that type of candidate matching.

[00:11:12] Here's my job requisition. Here's my criteria. OK, here's all the things.

[00:11:17] And being able to like really remove what I would say, identify within that resume.

[00:11:25] What are the skills? What are the experiences? And then score appropriately where they fall as far as being able to being able to consider them a good job.

[00:11:32] A good match, a good candidate based on that criteria. And then here's where the human part comes in the area where we're good at. We're good at connection.

[00:11:42] You still want to be able to meet that person. You want to understand what's their sense of humor.

[00:11:46] There's certain things that don't really come across in a resume that you're going to need to be able to establish that.

[00:11:50] So humans are very good at connection. They're very good at having values, also expressing empathy.

[00:11:57] You know, so we can really lean into those things that we're really good at and combined with the things that machines are really good at and be able to have a very, very good product coming out.

[00:12:07] Not just the product itself, but the human as well as the machine really being working together to come up with that product.

[00:12:13] And so the area of recruiting is one of those areas where we see a huge amount of benefit.

[00:12:19] So using our customers, using those solutions, such as like candidate match, or even being able to, how do you author the right job ad?

[00:12:27] And so just seeing things like chat GPT type of functions where you want to be able to say, hey, type up an email for me that sounds kind of quirky and fun, but letting someone know that, you know, I want to make sure that we keep our open lines of communication go.

[00:12:45] You know, and so chat GPT is able to do that.

[00:12:48] So by us leveraging that same type of functionality, we realize that oftentimes, especially in recruiting, it's not even about how well you can write a job description.

[00:12:59] It's really a marketing exercise to be able to attract the right candidates.

[00:13:05] So by leaning into something that HR may understand how to write a job description, like they know how, they know the qualifications that are needed.

[00:13:14] They know what benefits are going to be available for this job.

[00:13:17] They know a lot of, I would say like the nuts and bolts of a particular job.

[00:13:22] Now imagine you being able to put that into a chat GPT like function and generate a job ad.

[00:13:27] And that job ad is like, I'm looking for someone to pilot this team.

[00:13:32] And, you know, it's saying all the right words to attract.

[00:13:35] So it's really a marketing type of message.

[00:13:37] And then being able to do that.

[00:13:39] And if someone who doesn't understand, like, how do I punch this up so that people will want to, you know, apply, right?

[00:13:47] We added that into our own recruiting solutions as well.

[00:13:51] So being able to give that, not only to be able to attract the right candidates, when the candidates come in, being able to score appropriately and process in large volumes.

[00:14:00] Many of our customers have seen about a 41% increase.

[00:14:06] I mean, it was a decrease in that time to hire.

[00:14:09] And so those jobs are being filled a lot quicker simply because they're leveraging AI.

[00:14:15] And it's not one of those things where they make a mention, oh, today I'm going to use AI.

[00:14:21] Like, it's not like that.

[00:14:23] But it's the more that it becomes more of a baseline part of your process.

[00:14:28] Now, if we were to rip it out, they would be upset.

[00:14:33] So this is give you an example of especially those areas like recruiting, where our customers have definitely been able to embrace the AI solutions we've put to market.

[00:14:44] Well, that actually leads to my next question.

[00:14:46] There's a saying I've heard, nobody goes to the hardware store because they need a quarter inch drill bit.

[00:14:53] They go to the hardware store because they need a quarter inch hole.

[00:14:57] And I'm wondering if all the talk of AI right now is kind of missing the point.

[00:15:03] Are people getting to a point where they're thinking about what needs to be done as opposed to what technology is going to do it?

[00:15:13] Yeah, I think there's a lot of opportunity.

[00:15:18] But first, it is usually approached with the practical pieces, right?

[00:15:23] So looking at HR's job function today.

[00:15:26] So many of those HR professionals are looking at it.

[00:15:29] Wow.

[00:15:29] I spend about, I think one of our reports said that about four hours a day just answering repetitive questions, you know, from the employees.

[00:15:40] However, you still want the employees to be able to have a good employee experience.

[00:15:44] And a part of that good employee experience, it all relies on connection.

[00:15:48] It all relies on empathy.

[00:15:51] It all relies on values.

[00:15:53] Remember the things that humans are really, really good at.

[00:15:55] Now, imagine being able to augment that where you're able to train an AI bot to be able to respond to some of those questions.

[00:16:04] Because those questions come up and they don't come up just only between 9 a.m. and 5 p.m.

[00:16:09] They come up also over the weekend too.

[00:16:11] So imagine that you have something that can be there to not only just process at large volumes at scale, not only just based off of the employee size, but even the amount of questions that may also be coming in, maternity leave and so forth.

[00:16:25] It can definitely elevate that employee experience because it's now like HR is no longer this phantom, right?

[00:16:33] They can be able to get their questions, answers, as well as if you work in a very, I would say, decentralized type of organization where that store manager literally has to act also as that HR deputy.

[00:16:48] Where the store manager, I'm here to manage the store, but they get hit with a lot of HR questions too.

[00:16:52] Should they also be the one that should be that deputized HR agent?

[00:16:58] And oftentimes the answer is no, they're not trained for that, but they end up being the one to answer a lot of those questions by using some type of bot or being able to do that.

[00:17:10] Everyone in that store can be able to get access to the same type of knowledge base, the same type of HR related question answers to questions, and so forth.

[00:17:19] And so that is a lot of the practical pieces that HR tends to look at how they can lean into the advantages of the AI.

[00:17:27] However, I do feel that there's also opportunities.

[00:17:30] So now that I feel that AI is covering a lot of the practical bases, then comes the art of the possible, right?

[00:17:38] HR didn't get into, everyone I speak to that's in HR, even one of my best friends is in HR too, they didn't get into this business to push paper.

[00:17:47] They didn't get into this business to do all of the tactical things that they're impressed upon.

[00:17:54] So we always see, talk about this and we hear about, you know, especially from analysts and influencers and the like talking about focusing on more strategic initiatives.

[00:18:03] But no one has really painted a picture on what those things can be.

[00:18:06] So I think we're really going to be at a point where AI will give HR the ability to imagine the art of the possible.

[00:18:14] What are some of those things that you wish you could be doing, you know, and make AI basically be a part of that particular journey?

[00:18:23] So what are you hearing from customers about all this?

[00:18:28] That's the first question.

[00:18:29] The second part of the question is how do you work it into product development?

[00:18:32] But you're out there talking to people.

[00:18:35] So what are the interactions?

[00:18:37] What's the message you're getting?

[00:18:39] Well, we get a lot of accolades from our customers, especially in the areas that's hitting them the hardest, such as like the high, I would say more the more transactional areas, like recruiting.

[00:18:52] You know, places where people are really feeling the brunt of not having enough of the, I would say the labor force and all of the market forces that are coming down on them there.

[00:19:02] Also in the area of like payroll is also a great one, too, where having that anomaly detection further upstream, having that understanding and be able to say, you know, pump your brakes.

[00:19:15] Here's you need to take care of this now or else is going to be a problem for you later on.

[00:19:19] Later on.

[00:19:21] Also in the area of just really understanding if there's some type of fraud to detect.

[00:19:27] Right.

[00:19:27] So the area of payroll, if you imagine, of course, that's any type of money movement is going to be very, very sensitive, as well as a lot of the criminals will also try and intercept.

[00:19:39] And so a big part of that is not only as a vendor, we're doing our part, but it's also important that our customers also do their part as well.

[00:19:47] So helping our customers with being able to do some type of fraud detection, or we've identified about 25 different types of markers or levers to be able to push or pull things that they need to do to check and balance some of those things.

[00:20:01] Those have been well received, especially from our customers who may have had some type of issue, you know, somewhere in their past.

[00:20:09] Another area of interest is definitely in terms of anything around talent, especially around like learning, learning experiences.

[00:20:20] One thing about, especially for the multi-generational workforce, everyone has their own path.

[00:20:26] And we're no longer in that place where oftentimes many managers would assume that, oh, Mark, you're trying to take my, you're going to come and take my place.

[00:20:36] You know, you report to me, so I'm going to train you up to be a manager one day.

[00:20:40] And what if you're like, no, I want to be the chief revenue officer.

[00:20:45] How do I prepare you for that?

[00:20:49] Right.

[00:20:50] And so many people coming into the workforce, you can no longer assume that the people that are working in your team want to take your spot or even want anything remotely close to that.

[00:21:01] They may have other dreams.

[00:21:03] And so the entire like opportunities for learning, some may be outside of the domain and something, some may be, some things may be within, but it's really about empowering that employee to be able to understand what are their interests?

[00:21:17] What are their motivators?

[00:21:18] What are their detractors?

[00:21:20] You know, and of course, being able to align the appropriate learning experiences with that is kind of like the same Netflix paradigm.

[00:21:27] They better not offer me up some anime.

[00:21:30] I will be highly upset.

[00:21:33] No disrespect to anyone out there who likes anime, but it better not be in my feed.

[00:21:37] But so so similarly, you don't want to offer up a learning path or learning experience that's going to really detract people from wanting to even be there because it's no longer.

[00:21:47] It's not even a part of their purview as far as their interests go.

[00:21:50] So just even understanding those things.

[00:21:52] So like going from talent acquisition, payroll, even into talent management, I think we pretty much have gotten a very good grasp on different areas to be able to give our customers that leg up.

[00:22:04] And so those customers have definitely come back and said, wow, this is great.

[00:22:08] At the same time, they're also being pushed to do more with AI.

[00:22:14] And it's a pretty nebulous type of ask when it comes to that, simply because AI is already proven to be to help you with productivity in our, again, in our personal lives.

[00:22:26] So they're also being asked, which ends up the question that they also bring to us is what AI solutions do we have available today?

[00:22:35] What type of AI solutions will we plan on bringing to market to be able to make their lives easier in what capacity?

[00:22:42] And how do they show up more strategically in those rooms?

[00:22:48] Opal, it was great to meet you.

[00:22:50] Thanks so much for joining me.

[00:22:51] And it was fascinating talking to you.

[00:22:53] Awesome.

[00:22:54] Thank you so much, Mark.

[00:22:55] I appreciate this.

[00:23:07] My guest today has been Opal Wanya, the Senior Vice President of Market and Product Strategy at iSolve.

[00:23:14] And this has been People Tech, the podcast of WorkforceAI.news.

[00:23:18] We're a part of the Work Defined Podcast Network.

[00:23:21] Find them at www.wrkdefined.com.

[00:23:28] And to keep up with AI technology and HR, subscribe to WorkforceAI today.

[00:23:33] We're the most trusted source of news in the HR tech industry.

[00:23:38] Find us at www.workforceai.news.

[00:23:43] I'm Mark Pfeffer.