Episode Transcript
[00:00:00] Hey, everybody. I'm Podcasting from Paris, France, today. Just finished the Unleash conference which was spectacular. So I want to give you some findings from the tech market very recently here in Europe. Some perspectives on many of the meetings that we've had. We've probably had 20 client meetings here and talk a little bit about two trailblazers technology companies that I want to highlight. So among the many things that happened this week at this conference I want to point out one comment and question that came up almost repeatedly and that was CHROs, heads of talent, heads of recruiting, heads of learning asking us tell me what a skills based organization means. And what I said in the keynote was that the skills market is a bit of a hairball. I'm not sure hairball translates into European languages but the idea is that it's become very complex and confusing to people. So let me take a minute and try to demystify it. The fundamental idea of the skills based organization and I would ignore all of the white papers that have been written by various consulting firms because they're not really very useful is to build a company that manages people based on meritocracy. We want to hire people based on their capabilities and skills. We want to promote people and move and develop people based on their capabilities and skills. We want to allow people to manage their careers and get insights and information so that they can personally develop the skills and capabilities that they want to grow into. And we want to use that information for pay, for rewards, for leadership opportunities, for new business opportunities and also at an organizational level to figure out what we don't know that we need to know to be more competitive or to be more productive, or to be more efficient or to be more customer centric or whatever it may be. And so if you're a pharmaceutical company there's lots and lots of skills you need in science. If you're an energy company there's lots and lots of skills you need in various forms of energy production, exploration, distribution and marketing. If you're a retailer there's lots and lots of skills you need in consumer behavior and management of teams and retail operations, in product management and selection and inventory management, supply chain, et cetera. And by the way, every company has many, many of the same skills and many unique skills because of what you do as a company. So this isn't something where you're going to buy a skills library and you're going to turn it on and everything's going to suddenly be happy. Happy. Here we go. But if you think about this idea of a skills meritocracy, what we're really getting at here is using skills information to make human resources management and human capital decisions in an enterprise way and in a local way and in an individual way. Okay? So given that you have to also reflect on the fact that this is not a new idea. I mean, my skills were assessed by Procter and Gamble and the US. Navy and several other employers in 1978 when I graduated from college, because they did skills based hiring back then. They also did behavioral interviews. They also looked at my grade point average. They also looked at where I went to college and what I studied. I mean, they did a lot of this without the help of AI. So this is not a foreign idea. What's different is the way we're using it. And what we're really doing through the skills, technologies and the organizational approaches that we talk about in the post industrial age is we're moving from a job based company to a person based company in a job or position based company. We basically define jobs as if they are containers in which people live.
[00:03:52] And the job has a title, it has a level, it has a job description, it has a set of job competencies that we may have scientifically or not scientifically created. And we use those competencies to assess people for entry into the job, to help them develop and grow, and to characterize what the job is relative to other jobs. Now, that was great in the 18 hundreds and 19 hundreds when we had industrial organizations with siloed job families and where companies operated on industrial scale. These days, that's not the way companies work. Companies are changing all the time. We're coming up with new products and services. We're going to new geographies, we're going after new markets. We're crossing into adjacent industries. Technologies are changing. And so we can't build a company successfully that is dependent on a rigid job architecture. And if you want to learn more about this, read the research we just introduced called the Dynamic Organization, which I'll talk about more in the next podcast.
[00:04:59] So what we want to do is we want to build a person centric talent model where every individual is able to work in the roles, on the projects, on the teams that are the most interesting to them, but also the most relevant to not only the company's needs, but their skills. So if I'm really a terrible mathematician, I don't like data, I'm not very good at using Excel and so forth. I'm not going to work in an analytics role in the company. I don't even want to do that. So why put me into that job? Why recommend that job or force me to learn that unless I want to learn it? So what we're doing now is we're moving into a much more dynamic model of management where individuals are selected and promoted and retained and developed based on the skills they have and based on the skills they're interested in. Now, that all said, there's a large category of jobs where the skills are fixed. If you are a truck driver, you have to know how to drive the truck safely. If you work in an oil rig or you're a repair person or you work in a retail store, you have to be certified on certain capabilities in order to have that job and you're going to be tested. And if you fail the test, you're either not going to be able to do the job or you're going to create a problem. You're going to break something or hurt somebody or maybe cause a fire or something else. So there's still a lot of compliance based capability testing needed in business and this hasn't really gone away. Still there and a lot of these fancy AI tools are really not that relevant compared to that job class of work. But those things remain. But what's new is these new AI based skills systems. And there are many, many of them now they're used for recruiting and sourcing. They're used for talent, mobility and job change and career management. They're used for learning and development and career planning and career pathing. They're soon to be used for pay and rewards and they're also used to some degree for employee experience and other applications.
[00:07:05] They are much, much more sophisticated. What they do, as a lot of you probably know, is they use AI and they look at all sorts of characteristics about you as a person. I call what skills really are is they're the metadata that describes you as a worker, not you as a human being, but you as a worker. And these systems look at where you worked, who you worked with, what kinds of projects you worked on, what kinds of tests and schooling you've had, comments and feedback from other people. Some of them look at performance management and performance ratings. They look at some of your language that you've used in different software engineering or other tools to try to analyze your level of proficiency and this, that, and the other thing. They're very, very sophisticated systems and they can infer skills that you didn't realize you had or you just weren't really sure that you could vocalize what you had. And they can use that to help you find new roles, responsibilities and opportunities. And then of course, at an organizational level, they help the company at a strategic level look at the skills that might be missing or the skills that are trending in the market or in the company where the company may fall behind. And what our GWI research is all about is doing that exact work industry by industry by industry. So if you want to really see how strategic skills analysis can be, read the GWI report on healthcare, the one on banking, the one on consumer goods, and the one on pharmaceuticals. That's just about to come out and we're going to do more.
[00:08:38] So this is not an exercise in building a taxonomy and sitting around talking about what words we're going to use in skills. Now, one of the problems that a lot of companies have in grappling with this is they believe and they have been told by vendors that if I buy the skills cloud or whatever I want to call this thing, that these software vendors might be selling you. And I turn this on and I fill it with all sorts of libraries of skills. All of a sudden, the company is going to get smarter and smarter and smarter and better and better things have happened. And I've talked to vendors, I've talked to Workday and Oracle and SAP and that's kind of what they're telling you. I mean, there's definitely some truth to that, but that's really not what we're trying to do here. What you're trying to do is use the skills technology to specifically deal with problems and issues that matter in your company. So if you're chevron and you're out there hiring petroleum engineers for exploration, production, manufacturing, distribution and so forth in the oil industry, you're probably able to find those people reasonably well. There's a fair amount of people that have been trained in the oil industry. They're kicking around going from place to place. So you're probably able to run your company pretty well. But when you decide you want to get into batteries or you want to get into solar or you want to get into hydrothermal or some other form of energy and you don't really know who the jobs are or who the roles are, what the skills are, you're going to have a hard time hiring people. You may find that your hiring pipeline dries up completely and you have very few candidates at all. Whereas if you use a highly intelligent skills based system like eightfold, for example, will teach you through the looking at data from many other companies, what are the skills that are available and used in those other domains and how you should look for those people. If you're bare pharmaceuticals and you're trying to grow your agricultural business, you're going to look for science and bioengineering skills that have to do with various agricultural products and additives that will help you with your AG business and that goes on in every single industry. If you're a semiconductor company and you need process engineers, you're going to use skills to find them. So those are very pragmatic, real world use cases. Talent marketplaces are very real world use cases. If I have a skills based matching system inside the company for jobs, I can find gig workers or project people to work on things and I don't have to outsource it to consultants. I can save millions of dollars ditto if I use skills to understand the characteristics of our top leaders, I can go back and look for assessments and possible candidates for leadership that maybe have been lost or completely missed because we have a more political process for that. So there's a lot of simple, easy to understand opportunities and use cases here. So these white papers that talk about it as if it's some global technology project are really getting people kind of astray. Now that said, the technologies really are improving and most of the vendors who have skills related tools are getting pretty good at it. So what? We're now at the state where you're going to have skills based recruiting tools, skills based learning and development and career management tools, skills based talent, mobility and matching tools. Skills based tools for pay that will come soon. Skills based tool for leadership and skills based tools for other things like well being, mental health, turnover, analysis and so forth. And you can decide step by step as you implement those, depending on what the priorities and the problems are in your company. And what we found is when we work with clients is before we boil the ocean of building a global skills taxonomy that everybody somehow thinks they need, which, by the way, may or may not be worth the effort, let's focus on the most urgent problem at hand and start to get comfortable with the governance, the operating model, the process we're going to use, the tools and the behavioral and cultural change of moving to this meritocracy based management and organization strategy. So I'll let me stop there. But this came up over and over and over again and I really want you guys to think about this at a high level before you rush out and buy all these tools and turn them on and suddenly expect them to transform your company. Okay? Second thing I want to mention is a little bit about the trailblazers. One more thing before I do that. So at this conference we introduced this really exciting research called the Dynamic Organization, and this is research we've been working on for a little over a year. And I will talk much more about it in the following podcast. But for those of you that are research members, you should really read it because it pulls together many of the issues we talked about last week in the post industrial age and teaches you and your CEO and your CFO why these new talent models are essential for innovation, growth, and your own market share in the market. Because even though we think talent mobility and internal development and career marketplaces and so forth are part of the HR strategy, they actually are huge business performance tools because companies need to move people around and develop people continuously in order to innovate, in order to grow, in order to solve problems. So read that. You're going to find it very interesting. We're going to be doing much more research at the sea level like this. And you'll find it fascinating because it will really translate all of the language that we use in HR for some of these kind of geeky HR things we're doing into real solutions that matter to the top level leaders in your company, which of course is why we are here. Okay? So let me mention two vendors that I'm kind of excited about from the conference and there's many more to come from the trailblazers. The first is Dochebo. So Docebo is known as an LMS company. They're roughly $150,000,000 company. I think they're publicly traded. They're worth maybe a billion and a half to $2 billion. Very well run organization, growing very quickly, doing a great job of taking a leadership position in the LMS market, which is still fairly fragmented but they're doing quite well. And they just acquired a company called Edugo. And I spent an hour or two with the founder who's an AI guy, really, really good conversation with him. And what they have basically done, and I don't think anybody else has done this quite as well as they have, is they have built a large language model based system that will interpret documentation or existing PowerPoint slides or whatever you have that documents a business process or a domain of expertise in your company. And they will read through it and they will analyze it very much like the way our Copilot works, by the way, which we'll be hearing more about fairly soon. And it allows you, it uses prompt engineering to create instructional assets and instructional experiences to teach people what is in that content and it does it exceptionally well and under the covers they're using prompt engineering and LLMs to do this. And Docebo's team is very sophisticated. They've been working on AI for about seven years. And this particular tool called Docebo Shape, I'm not crazy about the name, I think it's a little bit misleading. But is a tool that allows you to build instructional content directly from an asset library and that is really complicated process to do by hand. You've got to read and consume all the information in there. You have to figure out the relationship between the information and chapters or groups. You then have to do instructional design around that. Then you have to build content, you have to build assessments, interactivities and probably some testing. You might want to do simulations, you might want to do case studies, et cetera, et cetera. And then you have to produce all that. You have to put it into an LMS and test it and then get people to take it. That's a lot of work. I mean it's fun work and there are people that do it, but this tool essentially does that for you. And I really think it's maybe slightly under positioned for the potential, but it'll give you a great example of how AI can revolutionize the process of creating training for your organization. By the way, we also use a really amazing tool in our academy called Arist, A-R-I-S-T which does this for mobile micro learning. In fact, our micro learning course on AI, which a lot of you have probably signed up for, I promoted it at the conferences I've been going to was developed mostly through machine learning, where we gave the AI engine a research report we wrote on AI. And it interpreted it and built a set of small interactivities and discussion and reading materials, including a couple of audios and a couple of videos on what is AI. Just to teach you the basics. Now we had to do a little bit of tweaking around it, but it was mostly developed by the machine. So take a look at Docebo Shape and take a look at Arist and how it generates content and it will blow your mind on new things you can do in L. D. Now, I had a meeting with a bunch of L. D people yesterday, and they were asking me what happens to our jobs when all this starts working. And the answer is you're just going to get to do higher level stuff. The second vendor I want to talk briefly about is Cornerstone. Cornerstone is a privately held PE backed company. Now it's run by a lot of very savvy technology people, many of whom did not come from HR. But there's a lot of really senior L d. Folks and LMS technologists in Cornerstone. They own Saba, they own some Total, they own Halogen, they own a lot of technology companies. So that's kind of a messy technology infrastructure under the covers. And then of course the Cornerstone LMS and Talent Management System and they've been doing a lot of work on AI in different parts of the system. They have a skills based database now and kind of a skills inference engine and talent marketplace. But the part of it that I think is more interesting is the work they're doing on content. And because Cornerstone is so big in the learning market, they have data about content, content libraries, courses, programs across all of their customers. And I encouraged them two or three years ago to pull that data together and work on it. And they've been doing that and they're now at a point that they can look at all of the content consumed by industry and they can benchmark your content consumption against other content consumption clients in that industry. And that is an actually pretty interesting opportunity. I don't think anybody else has ever really done that. Because if you find out that your employees are taking courses on stuff that's kind of elementary compared to the rest of your industry, you might scratch your head and say, we either have the wrong content strategy here, or we got some problems in the whole company and how we're set up and what we're trying to do. Of course you can use it to rationalize content that nobody's using. The vendors can use it to figure out whether their libraries are any good and whether people really like them or not. I'm not sure they're going to license that data, but they might. You can use it to determine the effectiveness of the courses and programs that you create versus others. I mean, there's just all sorts of interesting applications of that. And I would anticipate, based on talking to them, that they're going to use AI in this and they're going to be able to recommend content to individuals, maybe better than ever before. And by the way, content recommendations and learning is a really important feature. SAP announced a bunch of this last week or the week before last, and most of the LMS and LXP companies have been doing this. The better and better that works, the better for your workers, because most people don't want to browse around courses looking for the one that matters to them. I have spent too many hours looking around LinkedIn learning for a course on SEO or something else. I needed to understand going through a bunch of the courses that weren't the right topic or were too elementary or were too advanced, till I finally found the one that I wanted. And then I was kind of so frustrated, I kind of didn't have time to actually go through it. We really need to make content recommendations much, much smarter. We need to give people access to smaller branches of content, not the whole thing, so they can learn what they need quickly, which is a lot of what we're working on in our Copilot. And I think Cornerstone's efforts in that area are very, very interesting. So I suggest you take a look at them. Most of you already have Cornerstone, or you've used Cornerstone for a variety of other reasons, and they have lots of other products too. But I think that's an example where AI will probably end up having a revolutionary effect on the user experience of Cornerstone Systems if they manage that project well. Okay, so that's a little bit of an update from Paris, France. We've had a really fun time here. This is a great conference. I think Unleash is just a very unique collection of people, and there will be another Unleash coming up next spring. So if you like them, you can obviously go to that conference too. And we will continue to talk to you about all these topics in the next podcast, which I'll try to do either this weekend or next week. I will talk about the Dynamic organization, and I suggest you read Kathy Enderis article on LinkedIn about it. She did a really good job of summarizing the research in a really spectacular piece on LinkedIn that's connected to the Josh Burson company user ID in LinkedIn. Thanks, everybody. Have a great weekend.
