[00:00:01] JASON: Few people have a view of the AI landscape the way Mark Pfeffer does. And on today’s episode of The New Talent Code, we’re looking forward to hearing his take on where the industry stands and where it’s going. As editor of Workforce AI News, Mark reports on HR technology and workforce issues. In our conversation, he’ll share thoughts on changing attitudes of, uh, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah. Few people have a view of the AI landscape the way Mark Pfeffer does, and on today’s episode of the New Talent Code, we’re looking forward to hearing his take on where the industry stands and where it’s going. As editor of Workforce AI News, Mark reports on HR technology and workforce issues. In our conversation, he’ll share thoughts on the changing attitudes of leaders and employees towards AI, the evolution of regulations around the technology, and why self education is key to getting started.

[00:00:55] This week’s episode of the New Talent Code starts right now.

[00:01:02] LIGIA: Few people have a view of the AI landscape the way Mark Pfeffer does. And on today’s episode of the New Talent Code, we’re looking forward to hearing his take on where the industry stands and where it’s going. As editor of WorkforceAI. news, Mark reports on HR technology and workforce issues. In our conversation, he shares thoughts on the changing attitudes of leaders and employees towards AI, the evolution of regulations around this technology, and why self education is key to getting started.

[00:01:33] This week’s episode of the New Talent Code starts right now. But the first, the trailers

[00:01:47] Welcome back, everyone, and welcome, Mark. Thanks so much for joining us on this podcast.

[00:01:55] MARK: Glad to be here. Thank you.

[00:01:57] LIGIA: So as you probably know, um, as a way of introduction, we are fascinated by non linear career paths. We do believe people should be hired and promoted for their potential, not necessarily for their credentials. That’s just a limited view of what people can do.

[00:02:11] So with that, tell us a little bit more about your career path. Did you always think that five year old Mark think he’d be sitting here today writing about human capital management technology and

[00:02:23] MARK: I, I can pretty much guarantee that at five years old, I was not thinking about writing about HCM technology, but, um, I, I came, I suppose, a semi traditional way for a writer these days. I started at Dow Jones in the, excuse me, in their online services group. in 1984, then left to start my own company, which I ran for a number of years, and then, uh, went to work for, for Dice as their managing editor, decided to leave so I could go back to freelancing, and looking around, saw that there wasn’t much media that really stuck to the news about HR technology.

[00:03:05] Lots of opinions, lots of blogs. Nothing that was just news. And I thought that that was a good opportunity to explore. So I jumped in and did it.

[00:03:15] JASON: Now, Mark, there’s been a lot of news this week. It’s been very active. A lot of topics, a lot of things happening. And for the folks that listen to this podcast, you know, we talk about what’s happening in the world of work and trying to crack the new talent code. Um, you know, you cover workforce issues, technology and careers, and all the things that people are doing to try to address a variety of issues in their organizations and what’s happening in the world of work.

[00:03:42] Uh, what are some of the things that, that you’re seeing around kind of AI driven practices or, uh, you know, Uh, some of the kind of hot topics in the world of work, uh, top of mind for you these days.

[00:03:54] MARK: Well, it’s, AI is definitely top of mind for me. Um, and I focused my, uh, my website into a, into a site that focuses on AI in the workforce. And you, I think you can see if you look at anything to do with HR and the workforce, AI has somehow inserted itself into the discussion. I’m not yet. I have to word this carefully.

[00:04:19] I’m not yet convinced that AI is at a point where it’s going to be earth shattering. Um, it’s definitely an amazing technology, and it’s definitely got to get more advanced. Um, but I think there’s so much that’s unknown about its impact. For example, we keep talking about how jobs won’t be replaced, they’ll just change.

[00:04:40] I’m, I’m skeptical about that. And a lot of, a lot of people, a lot of employees are too. And I think, uh, employers are just kind of, you know, ignoring it, hoping that the jet settles itself somehow. But I think the most interesting thing about AI is the way it’s knitted itself into every corner of HR. Even before Open AI came along, there were time clock Manufacturers out there saying that they were processing their data with AI, and I’m sure they weren’t.

[00:05:14] Um, AI was sort of a label slapped over anything that had to do with statistics or analysis or anything like that. I think now it’s moving more towards the real world, and people know that, and I think people are nervous.

[00:05:28] LIGIA: Do you think, does your skepticism come more from a lack of general awareness of sort of the power and potential use cases of AI and like you’re saying, slapping it sort of like peanut butter across everything?

[00:06:58] LIGIA: Okay. So Mark, does this skepticism come because you feel the market at large, um, lacks awareness in terms of the use cases and potential applicability of AI and therefore are, um, applying it, like you said, like you mentioned, like peanut butter, maybe in some places where it doesn’t apply, or are you referring to the nascent aspect of the market and that we haven’t yet seen the true power of AI sort of come through.

[00:07:27] MARK: That’s exactly it. We haven’t seen what AI can really do. And we haven’t seen what AI’s impact on the workforce is going to be. So what workers are hearing instead of real predictions is a lot of speculation. Um, a lot of inflation of what AI might be able to do. And employers are sort of rushing toward.

[00:07:52] AI as a target. Um, it, it reminds me of, well, you know, when personal computers first came out, wasn’t running around selling MS DOS, they were, they were selling computers and they were selling what the computers could do. It seems to me AI is part of an operating system and you have to ask yourself, can an operating system have the impact that people say AI is going to have?

[00:08:20] And again, we don’t, we don’t know.

[00:08:23] JASON: I’ve heard, I’ve heard kind of the adoption of AI and the impact of AI described as having a J curve. That is part of the transformation and reluctance of it. In some cases, it may even cause a setback before it causes an enhancement because people aren’t sure how to apply it or people fight it, or they don’t understand how it transforms, how they need to use it.

[00:08:45] So in some cases there may be a regression before there’s an improvement, but once there’s an improvement, there’s a significant change. Do you think that’s part of what’s happening? Because I think a lot of people think, you know, um, to put it in kind of analyst terms, it’s the trough of disillusionment before the plateau of productivity.

[00:09:05] But part of this is this may not reach the plateau of productivity. It may not be a plateau. It may be even a steeper increase. Are we, are we experiencing something that just operates very differently?

[00:09:16] MARK: I don’t know yet, honestly, um, I can only look at other instances of technology, specifically the launch of the browser in the early nineties, the launch of the, um, of the PC and the, what was that? The late seventies, early eighties, and then especially the, um, the appearance of MS DOS, which kind of changed everything.

[00:09:39] Um, it’s interesting. It’s bear in mind that when the personal computer first came out, Windows, not Windows, Microsoft ruled. You know, they were the ones to beat. Apple came out and everybody thought they were crazy. Great products, too expensive. And I think there were many, many people that thought Apple was not going to survive.

[00:10:03] Now I think they’re bigger than Microsoft if my memory serves right. So you don’t know how these things are going to play out once they’re in the market and actually being used. Microsoft

[00:10:14] LIGIA: For those use cases and applications where AI makes sense in HR, where are you seeing, um, HR organizations facing challenges in integrating AI into workflows?

[00:10:33] MARK: I think it’s, it’s easier to answer where are they not having challenges, um, because it’s being used increasingly in talent acquisition with a lot of, um, a lot of success.

[00:10:47] LIGIA: Correct.

[00:10:48] MARK: Um, it’s being used in self service. Again, with a lot of success, it’s being used in performance management. I think the, uh, I think the jury’s out there.

[00:11:01] Um, it’s being used a lot for employee experience and I think it’s kind of sort of having a positive impact there, but still too early to tell. I don’t think it’s going to be useful. For evaluating a person beyond a certain, beyond a certain level, um, I think the problem is employers can try to do that anyway.

[00:11:26] JASON: Do you think part of the challenge is people use the phrase AI interchangeably and not all AI and not all the tools are the same?

[00:11:34] MARK: Yes. Yeah, absolutely. Um, again, it goes back to that operating model thing, operating system thing. Um, do users really care? That, you know, uh, an application has AI built into it. Uh, I would say probably not. They’re probably much more interested in whether or not it’s adding things up correctly or identifying slices of information or perception that they couldn’t get before.

[00:12:05] LIGIA: What is the, so if they, if they don’t really care that AI is being used, but there is a lot of hype and I would say there’s a lot of, um, tops down sort of direction in terms of you should, you should leverage AI in doing your A to day to day job. What do you think is the general perception of, um, employees and workers in the use of AI and HR, if they know at all that it’s being used?

[00:12:31] MARK: Um, I think if they know it’s being used there, they’re getting into that fear factor or just in general questioning what it’s going to mean and especially what it’s going to mean for them. Um, if they do. That’s if they do know that it’s being used. I do think there’s a significant number of people out there who don’t know that it’s being used, but are assuming it’s being used, and that’s where some of the affair comes from.

[00:12:58] JASON: You know, Mark, you mentioned how you’ve been covering this space for a while, and you’re really focused on really following the news and and how this technology is being used from a, from a news based perspective. One of the things that we’ve been talking about on the new talent code is kind of shifting the conversation and the frame of HR initiatives and HR projects for HR sake.

[00:13:24] Yeah. To things that are really impacting the business. Are you seeing that shift? Are you seeing increasingly more of a focus on truly driving business impact, or are you seeing still a challenge of making that connection?

[00:13:39] MARK: Well, I’m definitely seeing an evolution. of, um, of HR toward being more involved with, with the business. Um, five, six years ago, that just didn’t happen. The, the key phrase was always HR wanted to have a seat at the table. I think now they do have a seat at the table. Um, and the question is, how effective are they going to be used?

[00:14:04] With talking, when talking with the others that at that table, and that just needs time, there’ll be some companies that do it very well. There’ll be others who won’t, but I think there’s definitely a change in attitude. Um, About HR and what they can do with technology to help the greater business.

[00:14:23] JASON: As a result of that, we’ve seen a lot of the tools in the HR space start to blend. Right. And even in the talent space, talent acquisition and talent management and contingent tools start to blend. Are you seeing this happen kind of across the technology space in general, especially when we think around things like copilot?

[00:14:47] MARK: Yes and no. Um, yes, I think there’s more inclination to integrate products. Um, so that, or, or, you know, excuse me. I think there’s more of an inclination to integrate products where it makes sense. I think, um, that comes from both the vendor side and the customer side. Um, But I’m not sure that I see that being the long term outlook.

[00:15:13] Oh, I’m sorry, I’m misspeaking today. I’m not sure everybody loves that model, but on the other hand, I think, um, there’s a lot of reasons people don’t want to get saddled by a single system where everything they do goes from UKG or ADP or SAP or, or whoever. Um, you know, every company’s different and it makes perfect sense that every company would HR technology and different priorities, different things to do.

[00:15:48] LIGIA: I want to circle back to, um, your comment about fear, fear on the worker side, and maybe having and not having knowledge around where AI is being used, maybe having some desires about where AI should be used, you know, and maybe, um, some of the application of. Organizate of HR of A. I. In places maybe where I, you know, doesn’t belong potentially.

[00:16:10] What are your thoughts? You also cover a regulation. Obviously, what are your thoughts on how, um, you know, regulation around A. I. Is evolving both in the U. S. Um, and abroad,

[00:16:22] MARK: I think, um, privacy. And letting the individual have a say in how the data is being used is going to be the focus for probably quite some time. Um, the technology may evolve to a point where people know that different flavors of AI do different things or behave in different ways. Um, and, Start, some regulation may start to be applied to that, um, just in the way the EPA governs emissions.

[00:16:52] Um, but I think right now, especially in Europe, the focus really is on, are you, are you using the, the capabilities of AI, uh, properly?

[00:17:06] LIGIA: and maybe establishing some standards on some level.

[00:17:09] MARK: I think there’s going to have to be at some point. I, I, I don’t quite know what those are going to look like. I can’t get my arms around that either. Um, because if you’re. If you’re regulating something like AI, which is essentially a tool that’s going to do things. Well, what, what do you regulate? Do you regulate outcomes?

[00:17:29] Do you regulate proportions of outcomes? Do you regulate code? Um, that’s being kicked around by a lot of people, but I don’t think there’s a clear path yet.

[00:17:42] JASON: A month or so ago, we had our cultivate conference. And, um, as I was traveling the floor of the conference, I saw you there, Mark. And so I know you had a chance to visit and attend some of the sessions. One of my takeaways was that people were here. We’re so focused on AI for AI sake and increasingly talking about outcomes and use cases and how they were transforming different parts of how they’ve done HR in the past and how they’re trying to do HR in the future.

[00:18:12] So to me, it seemed like we were in a different phase or a different environment. Part of the journey. What were some of your takeaways from the event?

[00:18:23] MARK: one of them was very similar that there’s a disconnect going on between the users and the vendors and the employers. They’re not, they’re not all in the same place. Um, I, it wasn’t at, at Cultivate, but I attended another session not too long ago where they gave a big presentation as their keynote.

[00:18:43] About their AI capabilities and what they were doing. The audience was engaged, it was fine. They had a separate road back section where they were talking about new things that were going to be introduced immediately. And one of them was some kind of tool that made it easier to upload spreadsheet data to their existing platform.

[00:19:04] That got a round of applause. Nothing in the AI session got a pause. And I think there’s a lesson there that if, if you’re not talking to the users, the end users, um, you’re not going to have a real clear idea of what value your tool brings to the table. And I don’t see that changing. I think we’re going to have that disconnect for a while.

[00:19:30] LIGIA: What other potential uses of A. I. Are you seeing or predict to address overall business problems?

[00:19:37] MARK: Um, I think it’s going to take on more of an advisory role. Um, you know, I’d said before that AI probably shouldn’t get involved in making decisions about performance reviews beyond maybe the true analog ones. Um, but I think it, it can look at different episodes, different personalities, different behaviors, and make a prediction of what might happen.

[00:20:04] And so it can, Present managers with different scenarios of how a person. is going to move on in their career, where are they, where will they be valuable and where will they not be valuable? Um, you know, one of the things we still see a lot is corporations kind of assume everybody wants to get on a vertical path.

[00:20:27] You know, they want to, they want to start as some type of contributor, and then at some point, they want to jump over and become an executive, which is completely untrue. Um, at Dow Jones, I worked on the news desk. Um, three years later, I can tell you sometimes I wish I just stayed on the news desk. It was fun.

[00:20:50] I was good at it. I felt like I was giving back to society because of, um, I buy into all the journalism stuff. Um, that’s a really hard thing for managers to do, is to sort out who’s really going to be a leader and who’s going to be an organizer versus who’s just really good at getting the job done.

[00:21:13] JASON: One of the things to think about, especially kind of along the lines of that story is there’s a lot of organizations that are currently trying to figure out how to remove levels, right? And increasingly become horizontal rather than vertical and try to figure out how to make quicker decisions and drive agility.

[00:21:33] And some of that Sounds like concepts, but increasingly they’re trying to put it into action, right? Remove layers. So it’s not, you know, the operating for promotability, right? It’s, it’s developing people with consistent skill development for employability. But it’s in more of a horizontal type of a career, uh, aligning teams or aligning projects, aligning groups versus that traditional career ladder with that vertical mobility, because the organization is increasingly not vertical, it’s horizontal.

[00:22:10] MARK: That’s true. But I think this is, this is an example of where AI, excuse me. This is, this is an example of, of where AI can play an advisory role and, and also an identification role, say, where it can pick out the good candidates for certain positions. Um, it can predict the skills that are going to be necessary five years down the road.
[00:22:35] Um, and be able to provide in a readable form or not understandable form, the data that its decisions are being based on. I think that’s going to be part of the mix too.

[00:22:47] LIGIA: You know, it’s interesting. Um, we talked a little bit about fear and educating yourself and potentially applicability of AI in places where it doesn’t make sense. What advice would you have for listeners in terms of how to become familiar in terms of the potential of AI today? We’ve had some customers talk about setting up and you.

[00:23:06] AI, uh, governing body inside their organization, not just for AI, potentially in HR, but this uses across the rest of the organization. What are the, some of the things you’re seeing and some advice you might have?

[00:23:18] MARK: Well, everybody’s coming out with white papers or some kind of eBooks to help educate their, their customers. And some of them are very good. Um, but you sort of have to read them skeptically. Um, there are a lot of people out there talking about this and I think comparing notes, um, is, is, could be very valuable.

[00:23:41] And I think if you, if you’ve got the time and can do it, auditing a college Course on the introduction to AI or basics of ai, I think that would be really valuable because that’s really gonna immerse you in it.

[00:23:54] LIGIA: How do you use AI? Are you using AI in your everyday work?

[00:23:58] MARK: No, um, what I basically use it for is if I wanna know, um, when did Neil Armstrong land on the moon? I’ll ask that, but I don’t use it for, I don’t put my notes in and ask it to give me a, a rough draft. I know I’m in the minority there. Most people I already talked to are doing that.

[00:24:19] LIGIA: Yeah,

[00:24:19] MARK: Um, but, uh, I can’t look, I have to say it, Chet GPT and Gemini, the ones I’m most familiar with, they’re really lousy writers.

[00:24:30] You know, I, I wouldn’t want, I wouldn’t want to hand something in without completely going through it myself, um, ever. Um, but, you know, I think it, it’s a lot like an encyclopedia for me anyway, where I can get the very basics. information I need, then once I’ve confirmed it, go off and do what I need to do and ask the questions I need to ask.

[00:24:56] LIGIA: it’s, it’s a brilliant example. Um, because I do think one thing people don’t understand is about AI. It’s self learning. So it needs over time. Uh, it needs time to improve and it needs feedback to improve. And like you said, specifically to your examples, um, it, Points back to what you said earlier. It’s meant to recommend it’s meant to be an advisor in your case.

[00:25:15] It’s still nascent. So probably a lousy one in terms of how to write. Um, you know, whereas you’d be considered an expert given your years of experience in your role. So it, yeah, it’s valid. I think part of that is expectation setting in terms of what you’re going to walk into as well. And it goes back to your comments about understanding, educating yourself, playing with it, experiment, experimenting to understand its applicability, but also understand it’s the nature of its nascency today.

[00:25:42] MARK: Well, and, and also, um, well, as well as doing this education of itself, self educating, it can be educating itself in the wrong way. Um, so that’s just something that has to be paid attention to. Um, are the conclusions it’s coming to the right, the right conclusions.

[00:26:02] LIGIA: And that’s part of the awareness, I think, as organizations are looking to implement AI from the myriad of vendors, like you said, who are building it into their operating system isn’t just what the AI can do, but also how it’s been trained and what it’s been trained to do. And to some degree, like you said, compare notes with others because of the veracity or accuracy of its recommendations compared to what you’re wanting it to recommend in your organization.

[00:26:29] MARK: Exactly.

[00:26:30] JASON: Mark, as you’ve been, you know, covering the space and writing a lot around what’s been happening, what type of articles or what type of topics draw the largest interest from your readers?

[00:26:44] MARK: Um, my readers are pretty specialized. They’re mostly executives, um, or senior contributing contributors to the company. So they, they get turned on by anything that’s got a brand name in it. Um, so if I do a story about Cornerstone on demand, launching a new AI service, it’ll get It’ll get decent, decent traffic.

[00:27:09] But if I do the same thing with ADP launching a new service, everyone’s going to read that, that story. And I think what that indicates is people are aware and believe that AI is going to have an impact and whatever the big players are doing with it is also going to have an impact. So they want to follow.

[00:27:32] Follow what’s going on on the business front of AI.

[00:27:36] LIGIA: So a less salesy approach to the news, for sure.

[00:27:39] MARK: Yeah, I, I don’t, yeah.

[00:27:44] LIGIA: Who are the movers and shakers in the market for sure.

[00:27:49] JASON: And then what are you thinking about next? Is there, are there any specific, without giving too much away, are there any specific reports or news items you’re working on next or you’re thinking about for the work ahead?

[00:28:01] MARK: Um, yeah, the one thing I’ve been, two things, I’m sorry, that I’m really trying to get my arms around is first this notion of Where are the jobs going to go? Are they, are they really going to just, is AI really just going to lead to new jobs, but the same number of jobs or not? Um, the other thing is, how can it be used to predict skills, um, and skills needs further, further out?

[00:28:29] Um, it’s one thing if you’ve got a survey that says, well, the next three years we’re going to need X number of this kind of professional, but it’s all a different thing if you come out with a credible report that says in 10 years, we’re going to need. Y numbers of this kind of professional that gives executives a whole lot more running room when it comes to their strategic planning.

[00:28:50] Um, and so, uh, they’re both, um, they’re both, they’re both kind of dull. I mean, when you, you know, I mean, when you talk about the, the impact of it, it’s not, but talking about how that’s going to get figured out. It’s for data scientists, but business people. HR people have to be aware of that too.

[00:29:14] LIGIA: Any practical advice, uh, you’d like to give, uh, our listeners for organizations out there or HR who’s leaning into AI.

[00:29:24] MARK: I think the best thing to do is talk to other people in your space, um, who are anywhere in the process, but you want to get the complete process. So talk to others who may be just considering it to see how they’re approaching it. Talk to others who’ve had a successful implementation. Um, Talk to people, if you can find them, who did not have successful implementation.

[00:29:48] Um, one of the, the, the things I hear whispered about a lot is deploying these products is incredibly hard. Um, and that’s where often they, they run into problems. Everything’s going along smoothly. The, the, the product works, but then knitting it into the overall infrastructure of the organization becomes a real bear.

[00:30:08] Um, But I think the more, the more stories you can get from the real world, the better you’re going to be off.

[00:30:18] JASON: That’s a big part of why we have this channel of the new talent code so people can come together and learn from each other as people are on this journey to build the future of work. So, uh, we appreciate you joining us, Mark, and sharing some of the world of HR tech as you see it. And we hope that everyone, uh, listening, join, uh, enjoyed the conversation today.

[00:30:37] So thanks again for joining us, Mark.

[00:30:39] MARK: Thanks again for having me.

[00:30:41] LIGIA: All right, guys, that’s a wrap.