Mike Ohata - The Implications for Tomorrow’s Workforce in Light of AI

[00:00:02] Welcome to the HR Data Labs Podcast, now part of the Work Defined Podcast Network. Join us as we explore the vital role of compensation, strategy, data, and people analytics in navigating today's complex business world. With the resources of Work Defined, we're now bringing you deeper insights and actionable ideas from top experts. Now, here is your host, David Turetsky. Hello and welcome to the HR Data Labs Podcast. I am your host, David Turetsky.

[00:00:29] And like always, we find people inside and outside the world of HR to bringing the latest on what's happening. Today, I have with us our repeat guest, our wonderful repeat guest, Mike Ohata. Mike, how are you? I'm doing well. Thanks so much for having me back. I had to. We had such a great episode the first time, I had to bring you back again. There was popular demand. That's awesome. Thank you. Mike, why don't you tell the audience a little bit about you? Sure. Yeah, I'm actually a leadership coach.

[00:00:59] I spend a lot of time with executives as they work on their own transformation. And, you know, part of what I look for in leaders and executives around their commitment to people and their development, their capabilities, as well as their commitment to make a better place, especially in the workplace, in the workforce. So that's what I do today. Before that, I was a chief learning officer for KPMG. And I'm sure you had lots of fun doing that. Yeah.

[00:01:25] I mean, the professional services organizations are very rich environments for learning. So I wasn't being facetious. I meant that. It must have been a really fun time. Yeah, you know, it's they really are intense environments for learning and development, talent development, leadership development. And I really appreciate that. I had the opportunity to work on those domains in this areas. It really did help me shape around my perspectives and my commitments around the development of people.

[00:01:54] And, you know, part of the really basic thing is we can tend to get really focused on certain skills and still kind of miss the talent question. Right. And as you know, you and I had chatted before, you know, I worked on writing the talent field enterprise. And it was really around sharing what do we need to do as business leaders to really understand, to see the talent in our organizations, to really develop that talent as really sort of the fuel for kind of our business performance going forward. Sure.

[00:02:23] And we're going to get into a little bit of that, but more on a different spin that I can't wait to talk to you about. But first, like we always do, what's one fun thing that no one knows about, Mike? Oh, yeah. One fun thing. One fun thing that's a little bit more recent is I am a big Chuck Todd fan. And he was the host of Meet the Press on NBC for a while. And I thought he did a fantastic job. He has greater than a year now gone to independent journalism for a whole variety of reasons.

[00:02:52] And so he still maintains the Chuck Todd cast. And every once in a while I submit a question and I've had two of my questions answered. Oh, that's cool. Yeah, I don't think I'm as smart or as clever as some of his audiences in terms of their understanding of politics. But I really love learning about politics. And Chuck Todd describes himself as a political anthropologist, and I find it insightful all the time. I think you might be giving yourself a little bit of a short straw there. I think you are a pretty smart guy.

[00:03:22] I think you're pretty brilliant. So as far as the political side, I don't know you that well. But I would imagine Chuck is very happy that he has, I was going to say guests, but has audiences like you who know a lot about a lot of stuff. And especially in this world, I'm sure he might actually be interested in having you on as a guest, just like I do. I don't think I have quite the domain expertise that he brings on. Yeah, but you may never know.

[00:03:49] You might want to talk about, you know, the world of how the world of work is evolving, especially with skills. And, you know, you fit right into that. Fantastic. So today we're going to have a really fun conversation. And we typically take a drink every time we bring up AI. But in this case, the entire topic is going to be the implications on tomorrow's workforce for AI and what skills and competencies are really going to be necessary for the next generation to succeed.

[00:04:18] But I can't wait to get into that after this message. Hi there. I'm Peter Zollman. I'm a co-host of the Inside Job Boards and Recruitment Marketplaces podcast. And I'm Stephen Rothberg. And I guess that makes me the other co-host.

[00:04:48] Every other week, we're joined by guests from the world's leading job sites. Together, we analyze news about general niche and aggregator job board and recruitment marketplaces sites. Make sure you sign up and subscribe today.

[00:05:13] So, Mike, getting to that first question, really, when we say the implications for tomorrow's workforce, what are we talking about? And how much of this is actually going to be learning about artificial intelligence skills? Yeah, that's a great question. And, you know, one of the things I was thinking about this topic is around, you know, there's this notion of kind of the doom and gloom of AI.

[00:05:35] And actually, one of the things that I've heard on the Chuck podcast is that it's really only the U.S. that it feels like it really has a sort of negative reaction to AI. And I'm not saying that I need to feel like I need to be Pollyanna about it or like a cheerleader for the technology.

[00:05:53] But, you know, I feel like the doom and gloom piece of it is a bit superficial because it looks at work from a task and process perspective and thinks, you know, kind of goes straight to like, what do we eliminate in the workforce? And, you know, really, if you think about it that way in articles that talk about organizations or firms that are actually, you know, dismissing or letting go people, replacing them with AI, that tells me on some level is that the work that those folks may have done are really like agents.

[00:06:22] They're very much task oriented. They're very much process oriented. And we felt really comfortable getting rid of that skill. And I think one of the things that's really common, right, in the talk around AI is really around AI plus the human leader. And so humans don't go away in this thing. So I think instead of thinking about the work that we're getting rid of is thinking, you know, what really does the future look like?

[00:06:43] And what do we need from a skills level, from a capability level, from the characteristics around the workforce and leaders and managers as they're going to make the work system, you know, operate the way we envision it to operate? But based on your background, you know, the world of training, you know, the world of skills, and you've written books on this stuff. So, you know, we could we could read about it a little bit.

[00:07:08] But the thing that bothers me about it is, is that the rhetoric and the FUD don't match the reality right now. Yeah. Meaning that there's not wholesale changes happening in the work yet. Yeah. That any task that's being automated are probably things that had been slated to be automated anyways. And it really has nothing to do with AI, like things like customer support.

[00:07:35] We've been working on getting customer service and customer support computerized for years. And so I guess my question to you is, how much of this is fluff? How much of this is FUD? And how much of this is really serious at this point? Yeah. Yeah. I think I feel like we're all on a journey. Right. And to your point, I would argue that it feels like we're really at that same point with a lot of technology and a lot of automation. Think like ERP systems.

[00:08:05] Right. And the opportunity has always been, right, is around what we can actually truly change. And, you know, there's like if you're for those consultants who are out there who do technology implementations, there's always discussion around do we kind of lift and shift, you know, the process of yesterday until automated on tomorrow's platform or the new platform? Or do we try to transform and redesign, right, the work? And I'm getting just like I'm getting the sense just like you talk about.

[00:08:35] There's a lot of like, hey, you know, we already know how to that. We think we can automate this low hanging through and we should absolutely do that. And the opportunity I think really is around like, do we really want to redesign work? And I think this is what I think really appears different is because the intelligence around AI is such that we could actually really redesign work. And I actually think it fundamentally asks us to rethink the work system.

[00:09:02] And then so it is going to be this sort of age old question around what is the intersection between technology, in this case, automation, AI and human and understand that. But also just say, like, I think the implications are going to be huge is that it's not just about learning AI skills. It's actually really having another set of capabilities that says that I'm no longer just looking at a process going this way. I'm actually looking at the whole of the work system. And that's a big change, I think, in how we develop people. You're absolutely right.

[00:09:30] The thing that I'm worried about right now is that there's a lot of who move my cheese in terms of work. Like, if you have two people who work together and one relies on the other for getting things done. And the first one that the first one, the let's call the person A, person B relies on person A to get work done. But person A is now changing what they're doing and now including perplexity or or, you know, some other system or clawed into how they work.

[00:09:59] Person B is not there yet. But it's going to affect person B significantly if they don't get on board because A is going to succeed potentially and B won't. That's one thing I'm worried about. The second thing I'm worried about is the acceptance of the AI end product.

[00:10:15] Because we've seen a lot about how there need to be new disclosures put on work that's done in AI so that we know that there were the AI made up a lot of the thought leadership behind the work product that's getting done. So on both of those instances, there's a lot of resistance to AI actually facilitating or redesigning. Right. Yeah.

[00:10:41] So that, you know, tells me, right, that is that we're all on this together, right? We're all kind of trying to muddle through and figure it out. But that tells me there's some really basic things that I wonder how much, you know, firms and organizations really have the kind of the determination, right? And commitment to really get serious around what it takes to work with AI and to create agents. Automation is really great when you get something going. Right. Just the reward you get.

[00:11:09] But like, to your point is like how you really, if you're working with kind of the large language model bit, right? How do you really work on prompting? Right. And I would argue that it's not just doing simple search and query kind of stuff. There's some real work and art and how you prompt that. How do you reframe it? How do you redirect it? And so forth. And if we're just trying to get simple answers out, you know, then we could go down this path where we're just, you know, we may get misinformed.

[00:11:38] But you have to tell the AI in this context around what you're willing to accept and not accept and be super clear about it.

[00:12:16] And if you're going to get an enhanced work product, or am I thinking about that wrong? Like, because I worry about the delayering that's happening, especially the, I'm going to use the terminology ageism that happens, especially these days. Where we're losing a lot of very key intelligence from the people side. And the graying of America is not, it's not going to end anytime quickly.

[00:12:41] And we're losing a lot of those people to layoffs because they're relatively expensive and they're old. And you just can't substitute those people with AI. It just doesn't work. Yeah. I think there's two things that are at stake here. One is sort of the breadth and depth, right, of what I would call the institutional knowledge, I think is what you're referring to, right? Yes. Is around how do you, where does that go?

[00:13:06] And where else does it live otherwise inside the heads and kind of the embodiment of kind of a mature workforce? I think that's a real question. Related to that is this other piece around our basic sensibility around how that judgment and institutional knowledge has been formed. And let me pause for a moment and take you back because I remember when my kids were in high school. And what, you know, the teachers, you know, when you go to those teacher parent night kind of. Oh, yeah. I definitely remember that.

[00:13:35] Yeah. And they point out this really big difference is around the way we were educated as you went from A to B to C to D. You were expected to memorize A to B to C to D. And, you know, this generation of younger professionals, right, sort of the Gen Z, have been in this educational frame that says, we don't expect you to memorize A to B to C to D. We expect you to be able to find and remember or just locate pieces of information.

[00:14:03] So it's not a regurgitation of facts. It's really understanding the sort of the system of information, the world of information. So pause on that, kind of apply that sort of thinking to the workforce. You know, most of our kind of skill and development models is really pretty linear. And there's a whole thing that I'm seeing kind of missing, and this is the transition point we're in, is that how do you get actually a workforce that's full of systems thinkers?

[00:14:31] We collectively have to get comfortable with people not being trained on A to B to C, you know, in this linear process, but really feel comfortable looking at the system of work and going, huh, how does that piece work? Right. What is everything that I need to know about that as it affects this? And, you know, even when you think about task and process automation, we're still thinking it kind of on some level, these things. But, you know, what's built into our understanding of leadership, for example,

[00:14:58] and kind of, you know, maturity of competency is this notion of, you know, it's not just process work, but it's really understanding the system of work. Now, the system of work, I would argue, is not new. But what it's requiring us to do is really shift our focus at just taking people through a linear kind of kind of training and skill development is not going to cut it. We actually have to get them to think about the whole and think about breaking it down. And the judgment formation is going to be different from the way we form judgment.

[00:15:28] It's fascinating you say this because it makes total sense how you're coming up about this, because they live in a world now where all they have to do is open up a browser and get the answer. They don't kind of need to know the A to B to C. They just know that that's the answer. Whereas you and I were both trained on, well, you got it. Before you can get to C, you got to go through A and you got to go through B. And that's fascinating.

[00:15:52] And I think it actually also speaks to the fact that we need to set up a different performance evaluation system to be able to encourage not just the competencies around this, but also getting to that end result. We always cared about how you got to the end result. Now, do we really care? Yeah. You know, I think there is a shift that goes back to, you know, we talk about this in the business setting all the time, is, you know, you've got to care about the outcome. You've got to care about the performance.

[00:16:22] And I do think outcome becomes really super important. I think there's a lot of skill and competency development. It's really about coming up with the answer. The answer is not necessarily the outcome. Right. Right. So think about your typical, and this is not beating on any function within an organization, but think about your finance person or analyst who's constantly being asked to come up with the answer to question X, the question Y. And, you know, the really basic kind of business strategy outcome question is,

[00:16:52] what are we trying to achieve with all these questions? But what we've done to the workforce is get them to be really good at answering questions on demand. And now the shift really is like, collectively, what's the outcome that we need to get to? But Mike, and I hear you, and I totally agree. But don't you think that there's a difference between regulated types of work like finance and kind of more, I guess you could say, open-ended work like sales?

[00:17:19] Sales, you know, the salesperson just cares about making, you know, the sale of that widget. Like a car salesman. You know, you can get a car salesman who's high pressure, and you can get a car salesman who, you know, goes, hey, listen, I'm on salary. I can sell you this car if you want. This is the price. Boom. Or you can get a really high-pressure salesman who's like, you know, you'll never find a car like this again. Great deal. It's got to close now. Don't call your wife. You've got to sign this right now. And those are two different ways of getting the outcome,

[00:17:46] which really go back to the culture of that organization and how they want to be. And in that case, it definitely does matter about how you get there. But with finance, which is more highly regulated based on GAAP and other standards, you can't. You can't dream up a number and go, well, it's a number. It has to actually pass through those rules. Or there's consequences. Yeah.

[00:18:13] What I would argue, having worked in finance and accounting, and having worked in a public accounting firm, and even part of my career was doing finance transformation with clients, is there are things that are really process kind of specific, like getting the GAAP, right? Understanding kind of management reporting and so forth, and the connection between the two, for example. Those are pretty straightforward. It's the other sort of 80% of the time that's spun up trying to figure out,

[00:18:39] oh, what's the lever over here that's going to make this thing move? And that's the kind of stuff where what I think we're really talking around is, and this is a great point, is around going back to your thing around where is their judgment, right? And then kind of broader when you talk about sort of the culture of our organization, where do I need empathy? Where do I need people? Is customer service only around transacting my complaint, or is it really around saying like, you know, listening to like, wow, this person needs help,

[00:19:07] because if I can help them, if we can help them, our brand is better. If I can understand the pain that they're in, not just trying to transact the pain away, then I actually may get to some insight around things that might be breaking for other people. That's a place where humans really are going to be important in this sort of this new world. I mean, it's always been important in these things. If we think about the world of work and the fact that like 60, 70% of the world of work,

[00:19:35] especially in the U.S., is non-exempt work, you know, more hourly roles for frontline workers, for example, do you think that there's the same kind of level of interest in AI? Like you're saying, customer support, definitely a place where we could have AI enablement or potentially even AI replacement, depending upon whether you're using an avatar or something like that. But does it change your thought process at all

[00:20:03] on the efficacy of AI involved in the world of work if we're talking about basically two-thirds to three-quarters of our population? Yeah. So, you know, I think, and let me kind of poke a little bit around this, is I think the way we framed the question really kind of goes back to it, is that we're still looking at work the way work works today, right? Right. It operates today. And, you know, and you know this, right? In HR, we always have this conversation around like the exam to non-exempt, right?

[00:20:32] Where things are so much transactional or kind of and so forth versus where we expect someone to apply judgment. Right. Because what, you know, because really one of the big drivers behind this is that we are going to pay less. We are going to be in a compensation model that's much more controllable because we think the skill and the kind of the task level stuff is something that's not as high of value. Right. And then we get really frustrated when they're not a good team member.

[00:21:00] That they didn't show up, right, in a certain way. And it's like, well, what are you paying me for? Exactly. Are you paying for, are you paying, you know, like, I mean, just I remember once, you know, talking to somebody who was blocking my driveway because they were in the catering truck for the venue that was behind our house. And the person looked at me and just said, I just work here. And I thought, that's absolutely right. I mean, that was the sentiment that was bestowed upon the person. And that was the affect

[00:21:30] that they were going to have around, you know, I mean, it's a trade work, so it's not going to quite go away with automation. But that, you know, that sentiment that the person kind of conveyed to me is like, yeah, you know, we put them in a spot that said, like, they didn't care about the fact that the driveway was blocked for me. They just had to get through their. Exactly. Right. Right. And so the question for us, this is the real leadership question, I think, in organization is, how brave do we want to be if we want to be kind of like this notion of a frontier firm

[00:21:58] to really go back and kind of redesign the work system and say, okay, tasks are tasks, right? You know, discrete processes are those things that we're going to go after. But let's really kind of redesign this, right? And if we only think we're about replacing widgets with other automated widgets, then, you know, we'll get the outcome that we look for. And a lot of times, that's the myopia of how management has made decisions, which is, I still want the work to get done, but I want it to be done in the cheapest possible way.

[00:22:28] I don't care about safety. Well, I do, but I don't. You do. I don't care about, I don't care. I don't like my insurance premiums going up, but let's just do whatever we can on the margins to be able to get this work done without having to spend much more money. So, yeah, I hear you. Why don't we transition, though, to talk about how all of this affects the world of leadership and management and talk about the leadership competencies that we're dealing with and how do we make the leadership understand

[00:22:57] how the world is changing and how do we actually get them there? How do we educate and train for tomorrow's workforce to be able to make different decisions and not just replace a current process with just a widget maker who now is robotic instead of having the person doing it? Sure. Yeah, and, you know, I think you and I and our audience here would agree that in a phase one sort of replacement of tasks and processes of automation, that's okay. Like, we should absolutely go and do some of that stuff

[00:23:26] because we're all going to learn in the process. And when we step back and think about, okay, what is tomorrow's work design? Some of those characteristics, I think, are going to be, you know, the things we already know, like innovation, right? Creativity, judgment, empathy, right? Those are all things that I think are super important. And there's some really tactical things, too, that I think are going to be really important is that, you know, managers and leaders do need to have a kind of a, I would just say,

[00:23:55] a competence around understanding what AI can do. Like, and because it's new, they do have to have this view around just not knowing the names of tools, but getting involved in the tools on some level, right? So that they can actually begin to navigate what that intersection of human and AI is going to be. And then it's getting your whole workforce, right? So part of this is like change management, like one of those really basic sort of areas. Like, you know, leaders and managers have to be really good at change management around understanding,

[00:24:25] like, how to bring people together, get them to collaborate and redesign work. And I don't think anything that I just mentioned in the past, like, 30 seconds here is anything that's really new in terms of leadership development. It's just that the context is a little bit different, right? And some of those technologies are a little bit more specific. And the capability here is really, it's not just like, you know, big automated systems. It's the intelligence, right? That comes with this.

[00:24:53] It's the facility. It's the access. It's the speed, right? That kind of stuff is really, I think, what is grabbing our attention is that there is sort of this access to intelligence that's quite new. Do you worry a little bit, though, that the headlines are driving a lot of the decisions about AI? And even using the word AI is really a ridiculous thing because the LLMs, the chatbots, the agents, they're all different. They might be utilizing

[00:25:22] similar backends, but they're certainly not. To the extent at which we keep broad brushing this with just using the terminology artificial intelligence, really kind of hides the fact that there's a lot of richness in the differences between those models and between those technologies, right? Because I think one of the problems I see is that leaders say, we're going to chop 4,000 people because of AI.

[00:25:51] Okay, so what exactly are you doing? And how are you doing it? Because those 4,000 people, they're critical to your business. And how are you actually changing the world of business to be able to use bots to do that stuff? You know, what are you doing? I worry about that, that FUD, again, influencing the wrong behaviors. Yeah, you know, I think one thing to consider is like, you know, you talk about the 4,000

[00:26:20] and I know sort of the reference you're thinking about is, but you know, to your point, where are those 4,000 distributed, right? How much is of this announcement is virtual signaling to the marketplace around the kinds of things that we should be doing to adopt the future, right? Or to embrace the future. And then, yeah, really, you know, it also tells me that there is a low-hanging fruit that says, you know, we were probably 75, 80% there on the automation part of it. And the level of analysis, right, that was being done,

[00:26:49] say it's all analyst work or what have you, is stuff that's like commodity low-level work. I mean, so if you inverse or flip around kind of the analogy, you know, and you're saying agents are doing this now, it's kind of like, so you're basically telling me your analysts were agents. Right. You know, you were prompting them to do basic things that now you can get a model to do, right? And I go like, yeah, that makes sense. You should absolutely swap that out. Now, the question is, what else are you going to do and is there a place for some,

[00:27:18] like in that workforce, like if you have the time, and you have the wherewithal, who in that part of the workforce could you repurpose? That to me is like the home run kind of question. It's not just cutting the people, but realize like, I still have a workforce tomorrow and if I can reduce the cost over here, right, the people that expect me to reduce the cost, well, what can I do with the talent and the skills that they have? Like, who has, who has a disposition? Like, I was talking to a president of a liberal arts college and who was the champion for really doing

[00:27:47] the entrepreneurship program and I, and I just like, I was like, I think everybody should go through entrepreneurship. Yeah, absolutely. It's not that you're going to be an entrepreneur, like quote unquote, whatever that means, but like the skills, well, how do you come up with a concept? How do you market it? Like, how do you understand your supply chain? How do you fail? Yeah. How do you learn from your failures? Yeah. Like, you know, and then all the tactical things around project management, right, and communication and it's like, wow, like how is that not needed

[00:28:16] in today and tomorrow's kind of work environment? Right. Cause you're going to need people are going to be going to sound like, right. Some of work is this thing that we're trying to do. Like, how do we, how do we kind of design around that? Where do I get help? Where do I get automation? Where do I get agents? Right. And so forth. And so I, I think there's all kinds of things like that, like in the, in the development of, you know, right. Not only folks coming out of say, out of, you know, higher education, but like, well, what do you with the workforce that, you know, we need some of that

[00:28:46] because the approach is different. And when you think about entrepreneurship, it's kind of a holistic outcome approach then versus kind of a, you know, a list of skills to kind of check off. And I think that if I hadn't learned lessons in the entrepreneurial world early in my life and early in my career, failure would have been more devastating than had I not had it. So I agree with you. I think it's, I think they should be

[00:29:15] teaching that kind of stuff in high school. Because some of the kids nowadays, some of these kids nowadays, I should have said it like that, are going to be much more open to things like electrical school or being an electrical technician or a plumber or somebody who works with their hands because there aren't enough of those jobs. I mean, there aren't enough of those people. There are plenty of jobs open and even more necessary

[00:29:43] given the infrastructure needs of AI and the construction industry. And if you've ever had to, you know, if your toilet leaks and you try and find a plumber and they say, I can come over today, oh my gosh, you live in a great place because there's a lot of not, you know, not today, you know, next week or the week after. And where I'm going with this is that I think that there's going to be a need for plumbers and electricians,

[00:30:13] but there's certainly, even with plumbing and electrician, there are going to be need for them to utilizing the tools that you're talking about to be more effective. So teaching entrepreneurs entrepreneurial skills to those people will help them from failing a lot after they graduate with that degree. And, you know, just having worked with a lot of trade folk in the past, there is this point in their own career and how they think about it where they go,

[00:30:43] well, I've been working for this shop for a long time, you know, great, great, you know, owners, great team, and I want to go do my own thing. Right, exactly. You know, and they become entrepreneurs kind of in that kind of classic set of entrepreneurs, but you know, they got to manage supplies, they got to manage inventory, there's all these things and it's, you know, I'm listening and talking with them because I'm just curious about, you know, how they go about it and it's not a simple thing, like how much do you carry in your van? Like that takes a lot of money to have all these parts

[00:31:12] and things and, you know, and so getting organized, you know, scheduling work, getting subcontracted, and, you know, and it's just, it's really, really fascinating, but yeah, the skills are the skills and we all need them. Yep, and I think artificial intelligence helps them get there, it can certainly help them organize, it could certainly help them with customer support, like a scheduling system, which, you know, all of us need on a daily basis these days. So yeah, I totally agree. Why don't we go

[00:31:42] to the last question because I think this will bring us all home, which is, if you think about the enterprise and the roadmap for adoption of AI technologies, where does training fit into that as well? How does this relate to the analytics and the data that people use at work? Is it something that, you know, is tied together and how does that story end? Yeah, you know, I think some of the kind of the people data and analytics that, you know, that we have and been talking about, and some of it we haven't quite fully realized yet,

[00:32:12] like for example, like skills inventories kind of idea. I think there's, it's not a super, I would say accessible, like I think sometimes it's hard to find databases that have skills and things like that and certifications and so forth, especially among knowledge workers. But, you know, we've been talking about skills kind of focus or skills kind of, you know, based organizations. I think that's still really true because then you can kind of put your shift on to like go back to the talent question around, okay, we're going

[00:32:41] to cut 4,000 people. What do we need tomorrow, right? So I think having that, continuing down that path of really trying to understand the skills and competencies and having your own firm's point of view around what are the capabilities I need. I'm a really big believer that, you know, you could try to cookie cutter this thing, but based on your industry, based on your position in the market, based on where you want to be in the market, you know, your decisions around the skills you want to have and the competencies and characteristics may vary.

[00:33:11] But knowing that and then understanding how your workforce kind of measures up to that is super, super important. And if we don't know that today, running into AI doesn't change the problem. It's just a different problem. Do you think that means we have to have our own ontologies around skills? And for those of you who don't know what an ontology is, it's basically the skills that make up the work that you do. So it describes the work you do by getting into skills, behaviors, and levels that enable us to describe how do we want to have our work done?

[00:33:41] Do you think we need to customize that for us? Not just from an industry perspective, but really from a cultural perspective? Yeah, you know, I think on the ontology front, my sense of it is that that's something that I think automation, right, and AI could really help us around the connectivity between things that are discrete. Like your classic sort of ontology is around, so the specific kind of parameter or application of a skill in a context, right? And if you kind of connect all those things, how are they

[00:34:11] interested, there's a sort of higher level of those things that if I have this cluster of skills, would I not only, would I also have this cluster of skills? And I think, again, automation can actually bear that out by looking at, you know, what we know about people and the skills at least that they declare that they have. would I try to guide organizations just be really simple around the things that you're looking for when it comes to leadership and management. Like, are you looking for empathy, right? Are you looking for innovation? Are you looking for creativity, right?

[00:34:41] Or is it an agility question? These are kind of like those characteristic things. Take a little bit longer to develop, right? It's not just a skill training kind of equation. It's really more around the developmental kind of disposition of your organization, like what you're willing to do over a period of time, develop this sort of quality of workforce and quality of leader. It's typically the way we think about it. But have a view on that because the skills are going to be tactical. Things are going to come and go and so forth. If you're in engineering,

[00:35:11] if you're in medicine, you kind of know what those things are. What I always find interesting in my wife's family medicine physician was when they get down to being who's going to be a great doctor, it's around some of these more characteristic things because they understand the education, they understand the training, they know what your clinical skills should look like and so forth. But the ones that are going to come down to really basic things like who has learnability and things like that. But I don't think an organization needs

[00:35:40] to exhaust that. I think they just need a really clear vision around what is the basic capability of the organization I need and I want to have. And start there. You were talking before about the 4,000 and the ability to utilize some of those people throughout the organization and I love the word reskilling. And the reason why I'm tying it to this conversation is because for a lot of those senior leaders or for a lot of leaders that get put into that 4,000,

[00:36:11] if we thought that they were good enough to be a leader and we thought that they would be good enough to get promoted into that kind of level role, wouldn't we want to utilize their skills, and I guess this really can apply to everybody, wouldn't we want to use their skills and their knowledge and their know-how and all that other stuff but look at the new need like you were talking about before and then retrain them or reskill them more as a first reaction than a second reaction which

[00:36:39] is first is to let them go and the second would be to hire someone who's just like them, a little cheaper, just like them but has the skills that we need. Yeah, so what you're talking around is really the classic thing is around what is the objective that I have or what is the outcome that I'm trying to get to. If my objective is to cut costs and I don't really care about the second, third, fourth objective then I'm going to cut costs and you know from a business standpoint totally makes sense

[00:37:09] totally understandable if you're sort of answering to shareholders as it were totally makes sense in that context. If my objective is actually to get the best optimization of talent and also cut costs, I have a different outcome with a different motivation and what I find fascinating is that the short-term desire for the efficiency of business performance is great and then we kind of scratch your head and say, oh crap, I don't have the right people

[00:37:39] or I need more people or I lost this bit of my workforce and all I try to suggest is that instead of say either or, why can't we do a little bit of both? And it's totally okay if you're only making a cost decision, totally understandable, but just understand the trade-off decision we've made. Can I challenge that just to a certain extent? You mentioned the shareholders in that breadth and I just want to challenge it to one degree which I want to challenge on a lot of degrees

[00:38:08] but the one I want to challenge it on is you're assuming and leadership assumes that shareholders only care about money today. And yeah, I mean a lot of shareholders do because they could sell the stock tomorrow or they can diversify tomorrow. But it often seems very myopic to make short-term termination decisions that your intellectual property walks out the door without second consideration to your other point about

[00:38:37] A, B, and C, and D and I would suggest that the shareholders if they really cared about the company and they were shareholders we wanted to hang on to that they would care about the people that we have invested in long-term unless there's performance issues of course and not worry as much about

[00:39:08] getting rid of that person is permanent it also causes downstream ramifications as we know. Yeah, I appreciate that and I think it's a narrative that the leadership of any firm needs to walk through and cultivate and glean from the big institutional shareholders or the big shareholders In my days at Cape and G's I had this really great opportunity working with some private equity folks

[00:39:37] who were in a very specific part of the market they were in nuclear waste management and the nuclear waste that comes out of hospitals etc. like that and these were classic finance guys sitting around looking at sports super smart super energized and they cared about the integration of the thing they were acquiring to make sure it worked well it wasn't just about what's in the

[00:40:07] portfolio what goes out of the portfolio they really cared about what the entity that they were buying and making sure that it actually worked that it operated well and it wasn't just a numbers game and I really appreciated that about them so to your point is not everybody is wired the same way hopefully that's the argument or the discussion we're going to have as we go through this adoption broadly speaking of AI is around really having an

[00:40:37] appreciation for where humans are in the picture and where people are and what does it mean I'm optimistic that we'll find a way through it now will it be painful likely I'm sure there's pain we've already seen the pain so yeah so but there's some really big questions around ethical questions and so forth and just take the public debate that was going on with Anthropic and the Pentagon and so forth so

[00:41:06] there's big discussions that I collectively to your point I do think people broadly speaking have a much kind of value space sort of outcome that we're trying to get to Mike I think I could talk about this with you forever but we have to go but I want to tell you that it's always a pleasure having you on we're going to have you back and look at this another way through a different lens and I can't wait

[00:41:37] wonderful thank you so much for having me back thank you for being here thank you all for listening take care and stay safe thank you for listening to