Ep 51: Transforming HR with Skills-Based Approaches to Talent Agility with Jeff Wellstead

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[00:00:28] Hey everyone, welcome to another episode of Elevate Your AIQ. Today I'm joined by Jeff Wellstead, founder of Big Bear Partners and a 35-year veteran in HR workforce strategy and organizational design. Jeff has worked across industries, geographies, and company sizes, from global enterprises to hyper-growth tech startups, and has a passion for transforming HR into a strategic data-driven powerhouse, and we need that more than ever. In this conversation, Jeff and I dive deep into the emerging concept of skills-based organizations and how AI is redeveloped.

[00:01:10] Jeff shares insights on how businesses can use AI to build dynamic skills ontologies, identify talent gaps, and re-imagine the way they hire, develop, and deploy talent. We also explore the role of AI agents in streamlining HR processes, critical importance of reskilling in the age of automation and AI, and how organizations of all sizes can leverage these advancements to gain a competitive edge.

[00:01:31] Importantly, we also hit on some high-level HR personas and how those folks need to be thinking about humans and AI coexisting and what that means for growing the right skills to keep their careers moving forward. Let's dive in. Hello, everyone. Welcome to another episode of Elevate Your AIQ. I'm your host, Bob Pulver. With me today is Mr. Jeff Wellstead. How are you doing today, Jeff? Doing great. Thank you very much, Bob. How are you?

[00:01:54] Doing well. Doing well. A little early for me. I'm only halfway through my first cup of coffee, but I think we'll be just fine. You and I always have a lot to talk about. So let's get into it. I thought you could just take a minute to give my listeners a little bit of background of who you are, what you've been doing, how you wound up across the ponds. No, absolutely. So as you can tell from my accent, I'm an American, an imposter, living over here in England now for the last 20 years, just outside London.

[00:02:23] And I basically, my career spans about 35 years, which is crazy. I can't believe it's landed on that. First half of my career in and around New York City, working for big multinational investment banks, consultancies, tech companies, and pharma. And as much as I work for some extraordinarily prestigious brands and kind of well-known and trusted brands, I really struggled because I didn't get into HR basically to sweat the asset.

[00:02:51] Basically, you try to squeeze as much productivity out of people as possible, whilst at the same time kind of dangling a paycheck over their heads. And that's kind of what it felt like, to be perfectly honest, in terms of HR's relationship, if you will, to the business. And my vision of that was just very, very different. So I had a friend of mine, a former colleague from PeopleSoft, come join me, reach out to me, I should say, because he had taken on a CEO job over in the UK and said,

[00:03:18] Hey, Jeff, how would you like to punch your international ticket and spend a couple of years with me and prep this company for an acquisition? I said, yep, sure, good timing. So I went ahead and did it. And it was fascinating because they basically kind of said to me, you know, in certain terms, we don't like HR. And I said, well, brilliant, because I'm not a big fan either in terms of traditional legacy HR. They said, look, we want to create something really special here.

[00:03:42] We want a place where people come, even though they've never heard of us before, but quit their perfectly good job and come join this incredible mission. Because we have a lot riding on it. We think we're on to a huge winner. It was an antivirus, anti-spam, you know, kind of company that used machine learning back in the day. This was 2004. And so it was great. I got to reinvent HR from scratch. We set it up. We prepped the company for acquisition by Symantec eventually, which was great. And I just kind of like hopped around.

[00:04:12] I kind of fell in love with the notion of working with hyper growth tech startups and ultimately scale ups because you got to basically build the people function and the people strategy around that, you know, very well. So I've now worked with as many as 16 different companies over here, you know, helping them kind of build up and launch or get acquired or whatever it is they're looking to do. That's probably what I do. But I'm also really huge into, you know, human resource technology and specifically the last couple of years.

[00:04:42] Super, super excited about AI. And because it's such a powerful use case, you know, with all the data associated with human resource work and with people generally. That's kind of where I'm focusing my my consultancy these days. In terms of the market, you're not really focused on like big enterprise, right? You're mid midsize companies. Indeed. Yeah. Look, nothing against the enterprise. It's just they're huge. They're big.

[00:05:10] Their decision processes tend to be very slow relative to, you know, startups and scale ups. And so I love working for, you know, super agile folks. I love coming up with a plan on on Monday and by Tuesday afternoon, it's been approved. And we're actually in the process of launching it because this way we can see results quite quickly and kind of A, B test things and all that kind of stuff. So that's that's primarily. Yeah.

[00:05:34] What I've chosen is to focus on the smaller, you know, kind of recently funded companies on through to mid mid market, you know, 200 employees, 500 employees. I don't have to say 2000 or so. Yeah, I would say a 36 hour turnaround on from plan to approval is not something that I ever saw in my 25 plus years at large enterprise. That's for sure.

[00:05:58] I imagine just the sort of prepping for acquisition kind of concept just had me thinking about, you know, the cultural, you know, impacts of that exercise. I mean, I spent over two decades at IBM, so I'm quite familiar with what it takes to sort of fold, you know, new companies, younger companies into the mothership as it were. Yeah.

[00:06:24] And so I guess it's interesting that you got to sort of reinvent HR, but then in some ways you have the risk of, you know, the acquiring company just kind of screwing it all up. Did you not stick around for that? No, that was not. That was not of interest to me, to be honest with you. Yeah, no, that's that's fair.

[00:06:47] Sure. So as you talk to people about, you know, what you're saying, you talked about, you know, the onslaught of of AI coming in, certainly a lot of use cases across across the talent lifecycle. Right. So because I just without getting into territorial sort of debates about who's really in HR or not, if you manage any part of the talent lifecycle, I consider you HR in a traditional sense.

[00:07:15] Sure. So as you talk to companies about this, like what what kind of feedback are you getting in terms of their apprehension? Are they, you know, jumping right in? Are they, you know, dipping their toes in the water or are they jumping into the to the deep end and just saying, like, let's let's get after it. But let's see what this can really do. And let's experiment wherever we can, wherever it makes sense.

[00:07:44] Yeah, no, sure. I mean, look, just reading a lot of the studies, because, of course, I don't have time to survey, you know, all the midsize businesses on the planet to kind of say, hey, how are you guys getting along with AI? But there have been a lot of studies, a lot of research recently done by folks like McKinsey and Deloitte's and, you know, Josh Person Group with regard to the relative comfort level with adoption and exploration with AI. And it's low. It's low, as you might imagine, in terms of doing anything corporately.

[00:08:10] I think people individually, for those who tend to kind of be a bit more tech savvy and quite enjoy having a bit of fun, you know, with tools like this and, of course, are willing to pay whatever. It's not a lot of money. We're talking like 20 US dollars a month, you know, for open AIs, ChatGPT or, you know, similar for CloudSign. People are having fun with it. And to some extent, they're kind of using it to kind of augment and or kickstart their thinking about a particular thing.

[00:08:38] Corporately, though, I think there's a lot of reticence. And I think the reasons for the most part tend to be, number one, the persona of the buyer and the comfort level of the buyer. It's just in terms of dealing with technology, but especially AI. There's just a load of noise out there in the system. And unfortunately, not a lot of signal and not a lot of folks out there guiding people to understand the signal within the noise. They say this is what this stuff actually does. And this is really what you need versus what you don't need.

[00:09:06] I think there's also a lot of concern about security. Right. As you can imagine, just in terms of the fact that, you know, there's lots of horror stories out there about people kind of uploading, especially in HR. Right. Uploading personnel data and so forth with all sorts of sensitive information on it. And for whatever reason, you know, didn't set up an API key and the thing wasn't structured properly. So there was no firewall between your information and training modules.

[00:09:32] Right. Of these of these LLMs and the information just disappear. So. But it's actually, you know, it's really easy to remediate. It's just that, again, nobody's there to kind of tell them, don't worry about it. This is how this gets fixed, obviously. And there's a lot of people that making sure it gets fixed. So, yeah. So, I mean, it's we're talking the adoption is probably anywhere between 15 and 24 percent, I think, in that space. Enterprises are definitely kind of going at this.

[00:10:00] They have a very high tolerance for risk in terms of, you know, being able to hive things off and kind of like have engineers, IT engineers or whatever the case, play with the thing and beat it up and, you know, see what the possibilities are and then kind of come back to the general population and explain it to people. Whereas mid-market, you know, companies and certainly smaller companies, unless they're already into AI, yeah, don't have that kind of budget and that kind of bandwidth. So it's a bit of a struggle.

[00:10:25] You know, I hear about use cases related to like skills adoption. Yeah. In the context of AI, right, was a lot of vendors that are building, you know, skills, helping you build, you know, skills ontologies and skills intelligence capabilities. And having some AI, you know, horsepower and intelligence behind how are you mapping skills to goals, people to skills to roles and, you know, triangulating that information.

[00:10:53] But one of the things I was curious about is, you know, for smaller companies, like you said, you know, scale ups and companies in some growth stage into the mid-market. Are they experiencing the same thing or has their agility, their, I don't want to say natural agility, but probably better position to move the chess pieces around a little bit more easily perhaps.

[00:11:20] And so I was just curious if skills and the skills gaps or whatever are as much of a concern. Really interesting question, Bob. And it's funny, right, because I've been completely steeped and dove straight into, you know, this notion of building skills based organizations. Initially, I was skeptical about it. I was sort of like, oh, this sounds like some kind of a silly organizational design fad that may not last. And I was like, I really wanted to kind of understand what sort of the use case, what is the thesis behind, you know, doing this?

[00:11:48] Because it can be arguably disruptive. I've learned since actually it can operate in harmony, you know, with your sort of existing system. It's just the way you choose to kind of blend it. But it's this notion that we care less about sort of the traditional roles and job titles and the sort of boxed in, you know, kind of job descriptions that typically accompany those.

[00:12:11] And the hierarchical, you know, kind of attachment and thinking and reporting relationships that we've always ever kind of been used to, you know, all the way through the 1990s and even the early 2000s. And it's this notion of, you know, we need to kind of rethink this thing because here's the problem. Employees represent from a cost perspective about 75 to 80 percent of the balance sheet, right? That's a huge, huge chunk of money. We bring them on board with best of intentions.

[00:12:39] We hope that we properly better them. We hope that they're going to be productive and engage quickly and help, you know, kind of exponentially drive results forward. All we have to kind of know anything about these people are potentially the goals they set, performance assessment against those goals and other bits, and broadly what other people say about them, as subjective as that is. And that's basically what we get to know about people, which is a bit ridiculous considering, you know, the cost and expense associated with it.

[00:13:09] And importantly, we also kind of tout that, look, without people, we can't deliver, you know, the stuff that we say we're going to deliver. People are our greatest asset. It's like, great. Then why don't you know more than, I don't know what, 15 percent that you currently know about them and dive deep. Now, along comes this notion of a skills-based organization whereby instead of this whole silly process of I'm going to present to you a doctored up CD,

[00:13:39] I'm going to somehow kind of trim a cover letter that looks and feels a lot like the job description you've kind of put out there. And hopefully I get an interview and hopefully I can con everybody all the way through the process that I'm the right guy for the job. And that, you know, the whole offer and the salary thing goes smoothly. And then there's a whole onboarding process and all this other kind of stuff. Instead of kind of doing things in that horribly inefficient and very data-poor way,

[00:14:01] wouldn't it be amazing if we were able to kind of, you know, create an intelligence skills taxonomy and ontology for every kind of role type in the business to kind of understand that? And don't panic, folks. There are some big, massive enterprises that do this, and they have been doing it really from scratch, working with, you know, consultancies like Accenture and McKinsey and the usual suspects.

[00:14:26] But now there are AI tools that basically can, you know, scrape the internet and scrape all the job sites and do all sorts of like wonderful kind of like information gathering. And very, very quickly, you know, kind of help you develop a skills, you know, kind of taxonomy. And then eventually something called an ontology, which is sort of the relational, you know, components to skills that are weighted in some way, shape or form.

[00:14:49] So it's super exciting because when you know what critical skills, at what level of proficiency, based on any role in the organization look like, you can then actually, you know, kind of independently assess all of your employees and potentially candidates who are in a process and say, you know, tell us what you know about these particular subjects, where it'd be like, you know, programming languages,

[00:15:12] or whether it be about your soft skills in negotiations and influencing, collaboration, things of that nature. And basically that assessment now becomes a data point. You can then measure and overlay, you know, with the existing model and say, does this person fit? This doesn't look like a great fit. We'll probably pass on that. Others come much closer and you think that's, those are the folks I need to talk to as a first instance.

[00:15:39] It's not a panacea, it's not a cure-all, but it sure as heck gives you a lot more insight into, into like who these people are. Fast forward. And then suddenly somebody comes into your organization having been matched, you know, primarily by, based on skills and proficiency as assessed. And then basically you can, you can basically set them up quite quickly with all the goals that they need to accomplish and everything else.

[00:16:03] Get them plugged into a performance assessment process, have them actually take a look at what skills gaps they currently have. And then automatically through the use of AI, link that directly to, you know, pre-designed and pre-tagged learning instances online. You can also have them tagged to mentors and all sorts of folks. So that there's sort of like suddenly this process of highly personalized and targeted, you know, kind of development and learning for somebody, which is great at the employee level. Right.

[00:16:33] It's also great for their line managers because then they don't have to think about everything there is to know about this person's career path and try to kind of nurture and curate that. Often doing it badly because there's just not enough time in a day, leaving a bad taste in the employee's mouth. It's great for HR. So, you know what I mean? Because at that level, they've got all sorts of really powerful insight and so forth in terms of the density and the talent inventory, if you will, across the business.

[00:17:02] And they immediately can then start making recommendations, right, to the C-suite, to the leadership. Say, here's where we've got critical gaps. We're going to be sunsetting this product. We're going to be launching this product. We need to kind of like upskill, reskill a whole bunch of people because, again, we don't have the same critical elements that we need. I mean, you can start making commercial decisions in that regard. And it just changes, I guess, the landscape in terms of what it is we know about people and what their capabilities are.

[00:17:29] And people also then don't have to feel like they're stuck or typecast in their role and they can never, ever break out of it because the skills may, in fact, point them in very different career directions. A couple of things that I just wanted to call out from what you were describing. One is, you're right, there's a lot more sort of talent insight these days because you have all those different data sources.

[00:17:55] You've got sort of qualitative, like rich text information in terms of people's contributions on different platforms. And you can infer a lot from that if you're so inclined and you've gone out and aggregated data from those public sources. You certainly want to look way past the resume and cover letter as you described.

[00:18:20] I mean, certainly, I'm a pretty big critic, vocal critic about that whole process. Part of it is the manipulation, essentially, like the contorting, sort of automated contortion that's happening with these customized AI generated, you know, customized resumes.

[00:18:39] Basically, just finding a way to subtly or not so subtly, you know, regurgitate, you know, what you saw in the job description, which in itself is a flawed, you know, representation of what you're going to experience when, you know, if you're lucky enough to get the role, you know, that role, I'm sure is in flux.

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[00:20:36] Listen today. Industrial, you know, work, whatever. And, but you have no idea. They don't put a percentage next to that. So that, as the job changes, as the job changes, as it needs change, and as they understand your skills a little bit better, you know, that could explode. But anyway, the point is, like, you've got the direct, readily apparent skills, and then you've got some skills that you may not have thought about, which might be hard to document.

[00:21:06] And I don't know if I would want to rely on the intelligence of one of these matching engines to do, you know, skills, you know, inference and skills adjacencies to really, you know, fill out that complete, you know, that more complete picture of someone's profile. You know, and, but the other thing you mentioned about, you know, once you have that skills information, being able to recommend, you know, a coach or a mentor or learning course and things like that.

[00:21:35] You know, if you've got, you know, if you've got, you know, an internal talent marketplace, maybe that's your sort of orchestrator of all of that. But you don't necessarily need that if you've got some of these other pieces and you've got the integration where those things can talk to each other, right? You could, you know, like you said, you could figure out where people need to upskill and reskill and move them forward.

[00:21:59] And then they stay engaged and they become more sort of, you know, marketable for future roles. I think one of the challenges, and this is top of mind just because I was just talking to someone from Rejig. And they talk about, to your point, you know, work ontologies and some of the blueprints and things like that across different industries.

[00:22:22] But my conversation with Nuno and just some of what I've heard the CEO, Siobhan Savage, talk about is like getting skills, mapping not just people to skills and skills to roles, but the actual, you know, tasks, the actual like work product that needs to be mapped as well.

[00:22:47] And so sometimes I think that's, it's logical, but that seems like a whole nother layer and exercise that I'm not sure everyone has invested in doing.

[00:23:01] But it's a really important part, especially in the age of AI, where we have to really think about, well, what's getting sort of automated or what are the right ways to use advanced technology to take things off our plate? And what does that mean for the skills gaps that we have?

[00:23:24] Not that AI, I guess part of the point is that AI is not automating or taking skills away from you. It's taking tasks away from you. But at the same time, as AI advances, it is capable of more things. And we often think of skills and capabilities in the same context. So it gets a little complex for the average organization. Yeah.

[00:23:50] For the smaller organizations, you also may not have some of the staff to interrogate that data. You may not have the workforce data science team. You may not have a people analytics team or whatever. But these are critically important. I mean, you've been working on this for a long time. It's like getting the right people in the right seats. Indeed.

[00:24:18] You know, your business strategy, the technical strategy, these aren't self-executing, right? You need the people to do this even in 2025. Well, you need a couple of things. Like you touched on one of the really super powerful use cases, right? And it's funny because it dates all the way back to when I first began working in corporate world, when I was working with, I'm going to date myself, Arthur Anderson, before they went down with Enron. And I was working in the tax department in New York City, right?

[00:24:47] So huge, you know, kind of multi-floor skyscraper, a bunch of tax accountants at all different levels from partner all the way down to junior staff member.

[00:24:57] It was my job as the assistant director of HR, believe it or not, to be one of the people who would determine over the course of somebody else's career whether or not they had the right skills, the right experience, the right knowledge to be staffed internally on various different tax engagements. And these were not small engagements, right? These were engagements sometimes had 15 people working on them, like for Halliburton, you know, basically the FTSE and Fortune, you know, kind of 500.

[00:25:28] And it was down to me, a kid who just got out of school. I didn't know anything about tax, managed corporate taxes or anything, not even personal taxes for that matter, to basically kind of get smart about what everybody did there, what previous engagements they'd been on. And then to extrapolate, basically off the top of my head, who might be actually a good fit to go work for this partner or this manager on this particular account based on, I don't know what, industry expertise or whatever the case. Nobody ever gave us instructions we didn't even know.

[00:25:57] And so here I'm in charge of all these people's careers and I'm running around like a banshee trying to say, you know, hey, you know, convince people to drop whatever they're doing and come work for a different thing. It's nuts. And that's basically not changed for a long, old time. It really has, you know, come down occasionally, you know, maybe down to performance, you know, performance assessments and things of that nature. You start to kind of build some kind of a digital footprint over the course of time, say, if you're working at a place like a consultancy.

[00:26:25] But it can really be any business where you have to resource projects, hopefully with people who know what they're doing, who've had experience doing things. Because if you can put those people together and then throw a couple of folks who've never done it before so they can learn from the others and work on teams to go deliver projects and things, that would be, you know, the best way to do it.

[00:26:45] But when you have sort of like the skills database and you have the ontologies in place and everything else, you can really support a lot of pretty deep and pretty far reaching commercial, you know, kind of decisions. For instance, sunsetting certain products and developing other products, going into new markets, going into new geographies, going through a restructuring, you know, in preparation for, I don't know, wanting to look attractive as an acquisition target potentially.

[00:27:14] Instead of the traditional, we don't really know, frankly, HR doesn't even know, you know, what people are great people versus not other than this sort of like highly subjective performance data that we have recorded in our central HR system. That's all we have to go on. But bottom line is CFO says we have to save, you know, half a half a million or a million or 15 million or whatever it is, because people are the most expensive thing. Let's get rid of a whole block of those folks right up front.

[00:27:43] HR, you go sort out, you know, who are the weak performers, you know, that kind of thing. And then and then hopefully we end up exiting the right people. And in the meantime, we're going to shift priorities and kind of focus on some new kinds of works and new products and new directions over here. That is that has been my nightmare, basically, in HR, primarily in the first half of my career. Wouldn't it be amazing?

[00:28:06] The CEO and the CFO and whoever else is involved with the top trying to make these commercially powerful decisions about the direction of the company would turn to HR in the first instance and say, here's what we need to do. Got to exit this. Got to start this. We got to launch this. We got to terminate this. We're going to shift the organization in this way. HR, can we do that based on the talent, you know, kind of inventory and density that we've got?

[00:28:32] And to be able to go away and in less than a day come back with a report saying, look, here's basically what the sort of like school, the talent inventory looks like. This is kind of the distribution of capabilities from an ontology perspective. Here is the relative adjacency of skills to other skills, which means the closer they are in terms of being adjacent, the faster we can reskill or upskill those people to kind of go take that stuff on. So that process could possibly take a month, maybe a month and a half and get that all done.

[00:29:02] And, you know, probably save yourself 17 million in, you know, kind of unrecoverable redundancy costs and legal costs. And, you know, not to mention, you know, kind of like horrible morale issues and things of that nature for those left behind. And just deep disruption to the business. There are a lot of powerful use cases for taking a much more kind of data driven approach, you know, with this kind of process. There are challenges to it, though, right?

[00:29:31] Because who determines, you know, what actual skills and what level of proficiency are appropriate for particular jobs or like you were mentioning for a particular project, right? Do you want to launch people? But that's so cool, because if you get used to, your organization gets used to the language of skills and gets comfortable with, you know, probably some intelligent tooling. There's a lot of interesting tooling kind of emerging out there, whereby you're tracking all these people.

[00:29:57] You're tracking, you know, the skills ontology and taxonomies, and you're continuously updating. And the AI within these tools is going out to the world to say, how are these roles changing? You know, it watches GitHub, as an example, in terms of the different kind of, you know, kind of development engineers and so forth that are on there. And they kind of get a sense and a feel for, oh, these are twisting. So, you know, Python and React and, you know, all these different kind of languages actually are starting to merge. I don't know.

[00:30:27] A lot of different things. Yeah. And they get smart about it, right? So, anyway, so the bottom line is, is that it's just adding a powerful dimension and being aided by AI is actually much easier than it's ever been to kind of go and pull something like this together. Yeah. And I think that some of the AI piece, you know, if you can pull that in, I know it's hard to predict what AI is going to be capable of doing and, you know, things done in a... You know what you should know?

[00:30:56] You should know the You Should Know podcast. That's what you should know. Because then you'd be in the know on all things that are timely and topical. Subscribe to the You Should Know podcast. Thanks. You know, closed experimental, you know, sandbox kind of setting aren't necessarily ready for prime time to scale across an organization.

[00:31:20] But you still have to sort of think about the trajectory of what it can do as a variable that goes into that sort of workforce planning, right? Because what you're describing there is sort of, it's almost like, do you have the tools and the folks that can actually do that scenario planning to say, here's what's going to happen? What if we move these people over here and things like that?

[00:31:46] So there was a tool that I was investigating when I worked at an RPO firm called Chart Hop. Oh, yeah. Sure. And they had the ability to sort of take a work chart and make it dynamic and say, well, what would happen if we, you know, move these folks around or whatever? The whole concept of a skills-based organization came sort of after I was looking at tools like that.

[00:32:09] But if you pulled all that stuff together, it seems like you could say, okay, well, on a conservative side, we're going to have this gap that's going to be this big. Do we have other people inside the organization that could be sort of re-skilled and put into those roles? Or do we need to go and start fishing again? And so it's in some ways a build versus buy decision about people. Well, it's funny you said that, right? There's a company I've been doing some work with because I quite like their price point.

[00:32:38] They're actually targeted to the mid-market. That was my initial attraction. But then I got deeper into what it is they do. And it is this notion of creating both an internal marketplace, but also actually more importantly, building the skills taxonomy and ontology so that you can kind of use it for a variety of use cases, whether it be hiring or onboarding or training and development or whatever it is.

[00:32:59] But the other thing is using it for resourcing, projects and things of that nature, assigning people around the world to particular projects based on their skills base and experience, but also using it for things like pay and promotion decisions for succession planning because all of this stuff kind of meshes together. But one of the things you talked about that was super important was scenario planning.

[00:33:22] And that seems like such a simple, oh, yeah, sure, we can kind of do that in our heads and kind of say, well, if Jake left tomorrow, what would Martha and John do and all this other kind of stuff? But it's not horribly scientific, to say the least. And it's based entirely on subjective data. This is super powerful because when DevSkiller kind of like they sent a message out, I think, yesterday on LinkedIn. And sorry, I don't mean to kind of necessarily shamelessly plug these guys versus others.

[00:33:47] But what I thought was cool was they talked about this new module they created called I think it was called predictive or something like that. And it was this notion of taking the skills based model, taking all the history of all the interactions and transactions that have occurred on it, basically, and have that then feed into a model scenario planning model. So that you basically can start to move chess pieces around and kind of see how, you know, the fault lines change and see where gaps either increase or decrease or whatever it is.

[00:34:16] And look, that is nirvana, right? I kind of think about, you know, that movie Minority Report, right, with Tom Cruise. They had that computer thing. They put the, I don't know what it is on their hands, but they suddenly have these screens all over the place. So they can just like throw that over there and put this over here and say, well, what if we shift this? That to me would be super, super useful. Especially if it's backed by decent data. Because then you can start making some very intelligent, very quick and well-informed decisions about the direction of the company. For sure.

[00:34:45] Yeah, I was, you just had me thinking about when I was at IBM, I was working in market intelligence and specifically in social media analytics space. And we set up this whole, I don't know if you remember, like this concept of like a command, like a social media command center. Yeah, right. Where you're monitoring all mentions of the brand and is there a reputational risk over here and what's going on with this product launch or this division or whatever.

[00:35:12] And so I've never heard of anyone doing that for talent, having like a talent intelligence command center. That'd be pretty cool. And yeah, the haptic gloves might be taking it a little bit to the extreme. But you look at professional sports teams. I mean, that's, this is, I mean, these people, there isn't a player or a coach that doesn't have a freaking book written about them in terms of all the statistics throughout the entirety of their early career, all the way to current day. Right. Right.

[00:35:41] The level of prediction that they try to kind of then apply to, okay, this is what they've done so far. How are they going to do next year? You know what I mean? Yeah. Where should therefore we place them and play them and what should we pay them and all this other kind of stuff. I mean, in a way it might sound like horribly big brother and very intrusive and everything else, but it would be kind of cool though to kind of actually have people know I'm pretty kick-ass on a whole bunch of stuff you didn't even know I was good at. You know what I mean? Yeah.

[00:36:08] And that's going to, that's going to be my visa to try out some different, you know, career avenues and things of that nature. Maybe get an uptick in pay. I don't know. You're right. I mean, the, the level of granularity that you can get to with, because they have everything on film and it's all part of, you know, how you're evaluating people. How did they do in this context, you know, against this other team or against these other, you know, obstacles in a more HR sense.

[00:36:35] But yeah, there's a lot of data to use and scary that a lot of it isn't used that people haven't matured where they can go and compliment, you know, maybe their, their gut and their experience with some data to either refute or support some of their assumptions. Because that's basically what a lot of it is.

[00:36:56] It's, it's assumptions and it's, there's, you know, human bias, you know, involved what you think someone's capable of because they have similarities to this other person that you worked with and they had success or they did not have success or what have you. So, uh, one of the things I wanted to, to hit on Jeff is we were talking about like the personas in HR and, you know, why people got into HR in the first place.

[00:37:22] And some of the reasons that some folks just don't have a great perception of HR is because it's a traditional HR setup. They're doing core HR functionality. A lot of it is sort of to play defense for the organization. Yeah. And then like, there's another one that says, look, I came into HR because I want to help people advance.

[00:37:48] I want to make sure people are getting a fair chance at opportunity. I want to make sure that they're developing themselves and, you know, into areas and skills that they really are passionate about and going to help them along.

[00:38:04] And so I think in, in both cases, not that there's only two personas, but in both of those particular cases, you know, AI, they're coming at the AI piece with, with a particular, um, for the folks that are more sort of traditional HR, their compliance, um, legal, you know, working with structured, you know, data and things like that.

[00:38:29] I think you've got, I think you've got a lot of disruption coming in terms of what AI can almost completely take off your, your, your, your plate. And so you've got to start to think about what are my transferable skills? Because, you know, if AI can do half the tasks that are on my plate now or more than, you know, the writing's on the wall, right? Like it's only going to get more, more capable.

[00:38:56] It's only going to, these agents that my organization may implement are going to start to connect and talk to each other. So I've got to really think about where I want to, how I want to position myself either at this organization or elsewhere. And so what are my, you know, upskilling and, you know, reskilling opportunities. And then you've got the, the other persona that says, look, I really want to help people.

[00:39:21] And, you know, I am providing this necessary human interaction. I'm building relationships, you know, things like that. I hope you're not thinking that AI can do what I do. And these folks need to be careful as well, in my opinion. And I'd like to get yours because you've got to think about things, not from your own perspective, but also from the candidate's perspective.

[00:39:49] Would a candidate rather be ghosted or would they rather talk to an AI? Yeah. To me, like, you know, if we were talking about this a year from now, it would be like so obvious. Like, yeah, of course I want to know. I want to be in the know about my own sort of progress. And especially in early stages, whether it's high volume hiring or not in the early stages, like when recruiters do what they often call a recruiter phone screen, right?

[00:40:15] Like just that initial conversation to feel somebody out, describe the role, see if it's a fit, see if we even need to, you know, continue the conversation. You may not, as a candidate, you may not need to form a relationship with that first touch point, right? Like you may never talk to that person ever again.

[00:40:36] Yeah, so I don't want people to get too sort of complacent in the sense that, like what I do is this, you know, irreplaceable value that I provide to the candidate, to the hiring process. And I just, I think you're putting yourself at a disadvantage and you're going to be caught flat footed as this technology advances. Yeah.

[00:41:00] I'm curious to get your perspective on, you know, any thoughts from some of your clients or just in general, like how that's going to be absorbed. Well, you spoke about a couple of things. One is sort of like the kind of typical persona in the sort of HR profession. And I say typical. It is an extraordinarily deeply regulated aspect of the business, right? By all sorts of legislation.

[00:41:24] And it gets worse, you know, when you go from the U.S. and then start to work your way back to Europe and everything else, things start to get really, really onerous. And as a result of that, you know, these roles basically have kind of taken on very much a heavy administration, you know, compliance and governance, you know, kind of angle to them and have attracted a lot of people, right, who are particularly good at that, who really enjoy doing that kind of work, which is great. They tend, though, not to be extraordinary risk takers by any stretch.

[00:41:53] In fact, they're kind of always ever kind of looking to remediate risk and kind of bring it down and document it and say, right, this is how we've got that locked in. That being the case, when they look at something like AI with all the hype, you know, and all the kind of positive and negative talk about it. And the negative stuff is just as loud as the positive stuff. It's going to take over the world. You know, the military is going to use it. It's all going to go haywire. And, you know, before you know it, we're going to get chased by robots down the street and all sorts of dystopian notions.

[00:42:22] Or it could be a lot more subtle than that. Having said that, oh, my God, you know, the promise of it in terms of, you know, kind of creating some level of super intelligence and actually can develop vaccine cures rather that kills all viruses, as an example. I mean, that's like a thing. I mean, I've been just inundated with all the particular use cases out there for this.

[00:42:41] It just it falls on deaf ears, though, you know, for folks who have been trying to protect the organization, to your point, playing defense and kind of making sure no baddies, you know, kind of get through their hopefully impenetrable wall. And AI is one of those things, right? If you don't use it right, information can go missing. If you don't apply it properly, you know, the bad information suddenly gets amplified and communicated. So it just feels like another layer of difficulty.

[00:43:11] But in truth, I mean, like very quickly this year, we are going way beyond large language models, you know, where you kind of just like put a bunch of information, upload documents and videos and all sorts of things. And then ask AI a bunch of questions about it and get some good answers. There is going to be this huge influx of things called agents, basically, which are actually capable of performing tasks that you aim it to.

[00:43:35] So you can actually create like HR agents, as an example, through sort of a bot kind of modality and then have that thing just exist on every form of communication that your company uses it. So it's like HR on tap 24-7, 365. Click here. Ask me any question, whether it's about taking holiday or whether it's about going on maternity leave or about getting a promotion or getting a pay raise or you name it. You ask it.

[00:44:03] This thing knows everything because you've uploaded all that information structured and unstructured into it. And it suddenly takes out the bottom two layers of chaff, of annoyance, of HR help desk work that you basically have been a slave to all this time. And you can build in all sorts of like clever things like, you know, red flag when it appears there's a compliance issue and you can kind of put in the parameters for that. So you can always have a human in the loop. But these chatbots are remarkable, right?

[00:44:33] AI agents. You can also then program them to integrate with all your HR tools, right? And potentially with finance and whatever it is you want to connect them to and give it the capacity to actually perform transactions. Again, without anybody having to do it. And, you know, it's just you at two o'clock in the morning for some reason can't sleep. I've just got to, I don't know what, I got to take a look at my, you know, kind of like balance sheet for my 401k, my retirement plan and everything else. I'm just going to redo it while I'm thinking about it.

[00:45:01] And you could do potentially all of that if the AI agents are programmed into it. So there's a lot that can be done. And it gets you out of the business of the base stuff. So you can then start to focus on the truly strategic stuff like we were talking about in terms of creating a skills-based organization and all of the incumbent insights that that might give you. So that's the direction things are going. Yeah, there's a lot to think about.

[00:45:25] And I think just for the folks in HR, to your point, whether it's, you know, an assistant that takes, you know, that level one support kind of stuff off your plate. And these are, even that is not like the conversational AI and the chatbots we had, you know, seven, eight years ago even. And then, you know, moving up this sort of AI maturity scale a little bit, these agents that are actually executing tasks on your behalf or whatever.

[00:45:54] So there's, I guess the message here is don't be complacent in what you're doing. Don't assume that your sort of, you know, quote unquote human touch has the longevity that you might think. And pay attention to what's going on around you and don't be caught flat-footed because we're talking about your livelihood and your career. One more thing I'll just add at the end here.

[00:46:20] Like you talked earlier, right, about the fact that a lot of people got into HR because they wanted to help people, right? They wanted to help people realize their potential and everything else. Right. Well, this potentially, this technology, if you kind of like, you know, set it all up correctly, can in fact give you the time and headspace to kind of focus on things like coaching and giving people direction, on healing wounds, on dealing with, you know, helping people navigate the trajectory of their careers and things of that nature.

[00:46:46] Whereas, you know, then all the bulk work is actually being taken care of by machinery, but don't run away from machinery. It's important for you to know what it does and what its capabilities are and, you know, what its strengths and weaknesses are as well. Excellent. Jeff, as usual, excellent conversation. Really, really appreciate your time. Thanks, Bob. It was fun. And I'm sure there's a lot of good insight for my listeners. So thank you again. My pleasure, mate. Thank you very much. I appreciate it. Absolutely. And thank you everyone for listening. We will see you next time. Bye-bye.