Episode Transcript
[00:00:06] Good morning everyone. Today I'm going to give you a recap of the irresistible conference we just finished in Los Angeles. It was spectacular. 450 of our best friends came to USC for a whole wide variety of activities and learning and workshops and discussions and debates and celebration of the launch of Galileo. We launched Galileo. We also launched the beginning of our trusted content partner program with four strategic partnerships. Vizier is providing benchmarking data on voluntary turnover and span of control across different industries, job roles, tenures and managerial levels. Itcast is offering its entire skills library, 33,000 skills mapped to more than 1000 job titles and job classifications. So Galileo now knows a lot about jobs. You can use Galileo to design teams and organizations and just get to know how to hire. It'll create behavioral interview questions for you all sorts of amazing things. Oyster is providing more than 100 country specific employment practices. So now Galileo knows how to hire and manage and provide family leave policies and all sorts of other benefits for hundreds of countries around the world. At least 100. And the final is Heidrick and struggles. We have a wonderful relationship with Heidrick, who is the world's executive search firm. They have provided us and you their entire leadership framework which will teach you how to assess and develop great leaders in your company. And of course, that informs every leadership program that you add to Galileo. We have almost 10,000 HR professionals now using Galileo, lots of discussions about use cases and different prompts that people have created, and industry solutions. We're going to be beefing it up significantly over the next year. You can buy Galileo for a workgroup, for a team, for your company, for your enterprise. We do not have a single user edition yet, but we will before too long. So we all spent a lot of time on that. The second thing of course was we had two spectacular companies awarded what we call the HR hero awards, Delta Airlines and Marriott. And I just want to take 1 minute and talk about these two companies. Joanne Smith, who's the senior VP of HR and chief people officer of Delta Airlines, was with us and shared with us Delta's culture and the story of how Delta bonded and thrived through the pandemic. And we really were excited to award Delta a hero award for all of the things they do to take care of their employees and of course their customers and their business and those of you that fly Delta. I actually, unfortunately live in a city that's mostly dominated by United. So we were joking around about that, are lucky enough to know what that airline is really like. And then we awarded Marriott an HR Hero award and Ty Breland, the CHR of Marriott, came and spent a lot of time with us talking about the culture and the growth and the leadership strategy at Marriott. And essentially what Marriott has done is changed this entire leadership model to focus on leadership at all levels and done some very innovative things to develop and deploy leadership solutions to their franchisees, not just their company owned hotels. Marriott has close to a million employees and is by far the largest and probably one of the best hotel chains, if not the best in the world. And I was actually staying at Aritz Carlton, so I got a chance to experience it. So we talked a lot about that. We had a panel on HR in media, entertainment and sports, Disney, the La Lakers, the Boston Celtics. Very, very interesting conversation. We had a lot of time at Disney. We took a group of people to the Disney studios. We walked around and got to know how Disney manages their innovation and creativity and learned about the history of Disney. We talked about the disruption in learning and development. There were a number of sessions about how AI is going to transform L and D. We're going to do more on this. We're actually working on an industry study in this area and we will revitalize our CLO forum, which we haven't done for about a year or so. So that was really fun. Discussions of employee activation, four day workweek, employee engagement, lots of discussions about systemic HR. Nick Benevista from Mastercard basically gave a masterclass on how to think about the role of the HR business partner in the world of systemic HR. And we talked about systemic HR in the context of virtually every industry in every different geography. So I just want to say thank you to everybody who came and next year it'll be better, I promise. It's a big project for us, but it's worth every minute we put into it. And we will be producing all sorts of videos and replays for clients, for members, and you'll be able to get access to all the sessions that you missed. Second thing I want to talk about is the next step in the world of AI and talent intelligence. Now it's kind of funny. SAP did a big analyst briefing last week on their success factors innovations, and they've really come a long way. They've introduced their talent intelligence hub and 40 or 50 AI powered use cases within successfactors. And as I mentioned last week, SuccessFactors is partnering with Microsoft on the integration of successFactors and Microsoft Viva skills. And by the way, Microsoft is also using Eightfold in the middle as its AI engine. And you can look at last week's podcast, or call us if you'd like to hear more about that, because a lot of our clients are trying to figure out how Microsoft technology fits into all the other HR technologies we have. And of course, Workday announced a really pretty good quarter, 18% or so growth in recurring revenue, which is, you know, a big number. A company of their size also made a big point about the number of, of AI use cases they have. So now there's the war in the erps. About how many AI use cases do you have? It's not really the right way to think about this, because each individual use case in the ERP or in your human capital system could possibly be done in Galileo or somewhere else. I think the real value of AI coming forward, now that people are beginning to understand it, is connecting the dots between the different systems. We know this because just yesterday we had about 45 HR leaders in an experiential workshop about systemic HR. And pretty much what everybody concluded was the only way to move their HR function to the next level is to cross train and better integrate the functions of HR and the data and the systems of HR. And that's really what AI is about, is creating a more integrated set of tools and data and systems. So when somebody is unhappy, or a team is trying to grow, or we need to deal with a performance issue, we're looking at all of the different aspects of HR and all of the different opportunities we have for solving that problem and getting the data, how to do that. That's what systems like Galileo and integrated AI systems can do, is give you a complete view of the situation before you rush off and do decide, oh, we have to hire somebody, or we need to fire somebody, or we need to give somebody a raise, or whatever it may be, those are eventually going to be the answers.
[00:07:41] But this integrated AI system is really where we're going. And that's why I'm so high on talent intelligence, because the talent intelligence platforms are integrated AI systems from the beginning. I'm not saying SAP and workday aren't going to get there over time. They are, but they have to do it piece by piece because they have so many functional modules in their applications. And that leads me to another announcement that happened this week. Cornerstone, who's now owned by a private equity company, announced the acquisition of Skyhive. So let me take a few minutes and explain what is going on. So, in the AI platform market of more integrated talent intelligence systems, there are essentially three categories of vendors. And the concept of talent intelligence is that your HR software is a data platform, not just a technology platform. And what the talent intelligence tools like, at least this. By the way, this is the way we, or I define talent intelligence. I have a feeling people define it however they want. And I'll talk about that in a minute. You take a bunch of data about employees all over the world, yours and others. You run AI models against it. You create models for sourcing skills, assessment, career and other patterns. You understand pay patterns, you understand leadership patterns, you understand migration between roles. And then you use that to make better decisions, better recommendations, better offerings for your employees. And the reason that is so powerful is a talent intelligence system is much smarter than the system that only looks at the people in your company, because the people in your company are in some sense a limited view of what is possible. You're really, if you only apply data to the date, to the data of people in your company, you're going to see some great things about what's happening in your company, but you're not going to know what you don't know. And when you're sourcing and recruiting, of course you have to know what's in the outside labor market. And so that's why the talent intelligence system started in recruiting. The primary talent intelligence platforms today are Eightfold gloat, neo brain retrain, to some degree beamery, to some degree phenom and to some degree workday and SAP. And they are certainly working on that. And you have to sort of come to grips with the idea that because these systems are data driven systems, quality of their recommendations and the quality of their advice to individuals is based on the quality of their data. By the way, the other one I left out is Cecap. So these are companies that collect a huge amount of data and build AI models to help you better recruit, source, develop, career mapping, internal mobility, etcetera. In order to get that data, we need to go to somebody who knows how to find it. That leads to the second category of vendors, the data companies. Litecast is the most well known and the most mature. That's why we partner with them. Litecast and others Revilio, a company called Drop D or a U P, which is an incredibly interesting company, founded by the founders of talent Neuron. And to some degree, Skyhive was one of these data companies. And most of these data companies were founded by people who wanted to understand the labor market itself and provide solutions to companies in their sourcing, recruiting, location, placement, skills assessment, skills analysis and strategic planning. Because if you're a big company like Amazon or Disney, or any big company, and you're trying to figure out where to put your next plant or micron technology, for example. We were with Micron this week. You know, they're building chip plants as fast as they can. They need to figure out where to put them, who to hire, where to source people. Are there internal candidates who can staff these things? There aren't enough engineers to know how to run these things. And what are the trends in each of these job roles? What are the new skills that are being created? What are the new technologies we need to be aware of both as an operational issue and as a hiring and staffing issue? We need data. And it turns out these companies drop lightcast, skyhive to some degree. Revillio and others have become very, very good at finding labor market data, economic data, industry data, about what's going on in the world of work. Not general data, but data that can be used for talent, intelligence. So these companies, every time somebody posts a job anywhere in the world, these companies read that job posting and look at what that company is looking for in that job. And what they can infer from that analysis is if there's a new skill or a new technology that's requested in that job, that must be a skill that is affiliated with that job title, that job family, that job classification or a certification, a test or some other characteristic of that job. So what these companies are doing, in a sense, is they're collecting metadata about jobs and work. Now, the most simple view of that is its skills. Data. It's data about a skill. But as you've heard me discuss many, many times, the word skill is a misleading word. If I know how to use the Java programming language, do I have the Java skill? I mean, kind of, but it depends on what I was using it for. If I was using Java to build a front end graphical interface for a laptop computer, that is what drop would call a workload or a capability that I have around Java. But I may not be able to use Java to build a whole enterprise application because I don't know what an enterprise application is. So enterprise application may not be a skill, but Java is a skill. So just because somebody knows a lot about the Java programming language doesn't mean they know how to build the thing you want them to build. So where's that information? Well, these second order data inferences are really important because the reason we're doing all this skills stuff, which, by the way, I still think we're wasting a lot of time on it, but I know you guys are doing it anyway is to figure out who's going to be the right person to do some job or role or get promoted or get paid more money or whatever. So the skill data is a piece of what we need. We also need to know certifications. Are they compliant? Do they have the requisite government qualifications or other qualifications to do this job? What languages do they speak? What technologies are they comfortable with? I mean, there's a lot of characteristics or metadata that are important other than just the skills. Now, the skills are useful, because if you look at the Lightcast skills library of 33,000 plus skills, there's a lot of information in there about what people do. And you can call it a skill or a capability, whatever you want, but if you don't have access to that data, you're going to have to invent it yourself. So talent intelligence as a category is dependent on great data, which is one of the reasons that products like the workday skills cloud haven't been that useful, because until workday could get good data into it, your data about your company wasn't really that useful. Workday was one of the investors in Skyhive. I've met the founders of skyhive multiple times. These are labor market economist type people and altruistic people trying to improve the world of work. And by the way, a lot of these data companies come out of public sector companies. Mz was, too, because they really do have sort of a bigger, broader public good mission, even though data collecting is actually really quite a difficult engineering problem. So Skyhive was a data company in a sense, and decided because of the excitement in talent intelligence, that they would get into talent intelligence software. Well, talent intelligence software is like HR software. You've got to build an application that people can use from the data, and that's not that easy. It's actually very hard. It took gloat a long time to build a really integrated, compelling talent intelligence based talent marketplace and mobility system. It took Eightfold a long time to build out their sourcing, recruiting, job architecture, and other functions in their system. So Skyhive had not been able to do that very well. A lot of companies were prototyping it and looking at it. And of course, because they were funded by Workday Ventures, everybody assumed that workday was, in a sense, endorsing them. By the way, companies that get money from workday ventures don't necessarily get support from workday. Workday, of course, as an investor, wants to stay close to them. But just because they got money from workday Ventures doesn't mean, they're a workday product per se. So anyway, Skyhive was having a little bit of a struggle competing in this market, because it's a very competitive market. And along comes Cornerstone.
[00:16:59] Cornerstone is a very fascinating company that's been around a long time. They were one of the early pioneers in learning management. They got into talent traditional end to end talent management. They built a content company and acquired a series of content companies to get involved in learning. They were really big in the learning industry. Acquired Saba, acquired sum Total, acquired Lumess, acquired Halogen, recently acquired a VR company. So they have a lot of technology. This is a billion dollar plus company that many of you have a lot of their products in HR. And, you know, I talked to them many times. I said, you guys, this is all great, but if you want to be part of this skills wave, you also have to really grapple with the talent intelligence space and build a data system like, you know, the big data systems from the other vendors. And they heard me and went out and decided to acquire skyhive. So what cornerstone is planning on doing, and this is, you know, was announced, is taking the skyhive data and technology and integrating it into their skills. They call it a skills fabric, and I think the reason they call it that is because it didn't really have a clear definition, but they have skills engine in the LMS skills engine in their talent management system, and their recruiting tools. And now that they get access to skyhive, they can be more benchmarking oriented and data centric about this. And they actually did a really interesting project last year, and I was kind of encouraging them to do this, where they built a data system in learning, where if you're a pharmaceutical company or a manufacturing company and you use the Cornerstone lms, you can actually see what courses and content other companies are consuming, at what rate. To benchmark your learning investments against other companies learning, that's not kind of mission critical, but it's really valuable and interesting and can be very informative. So let's hope that this goes well and that the skyhive technology forms and allows Cornerstone to build a more integrated talent intelligence system. Cornerstone, by the way, did build a talent marketplace based on the data that they had. This should make it work better. Cornerstone has a lot of customers using their recruiting, and obviously a lot of customers using these different learning platforms. So we'll stay close to this. But that was kind of an indication that the bar is getting raised on this stuff. And just because you slap the name talent intelligence on your software, you're not necessarily going to compete. Now, unfortunately, the word talent intelligence is such a snazzy phrase that everybody uses it. If you don't define it the way I define it, that's fine, but I just think we're going to have to all learn how to dig in a little bit more and write rfps that are really representative of what we're trying to do. Now, getting back to this whole issue of skills and AI, I don't think it's very valuable for you to evaluate any of these new AI oriented systems by the number of use cases or the number of features. The reason we used to do that in traditional software, because traditional software was kind of dumb. All it did was transactional work. So if it didn't do the thing you wanted to do, you weren't really going to get a lot out of it. Well, these are data driven systems, so really the evaluation criteria is how good is the platform? What kind of data do they have, how much AI experience do they have building AI models? Is there an AI native engine under it or not? Or are these small, incremental machine learning models trying to work together in some way? By the way, you know, a machine learning model is great, but it won't necessarily work with another machine learning model unless it's truly AI architected. So, you know, I think we're going to have to just get smarter about how to evaluate these systems. And the most simple way to do it is to do what we call falling in love with the problem, which is when you're looking at products, don't just look at the product, apply it to the problem. Is it a recruiting problem you're trying to solve? Is it internal mobility problem? If so, what kind of career paths and jobs do you have in your company? What are the career migrations that you'd like to facilitate and improve? Are you trying to build leaders? Are you trying to deal with technical professionals? Are you trying to deal with certified compliance oriented roles? Are you trying to deal with a high volume situation where you have lots of turnover in your company? Those are the things you need to take into the market and then look at the different products and see how they work Eightfold. For example, built a whole skills based resource management system, which nobody else has because they work with eyes. So they have something in talent intelligence that's unique to them. It's not just a feature on a checklist on a bunch of checkboxes. It's something they built because they have customers that really, really needed it. And so as the AI market gets more and more frothy and the names and the technologies get thrown around. I think we've got to go back to the basics, which is what do we want this system to do? What are the applications that we would like to implement in our company that will really drive value? How are we going to implement systemic HR, and therefore, what is this system capable of doing? I don't have any judgment call on the cornerstone acquisition yet, because they're just getting started, and I'll stay close to it, but I think it's a pretty good signal about what's going on in the market. The final thing I want to talk about is GPT four omni. We, of course, work with a very highly esteemed AI partner, Sana. And Galileo already has access to Omni. And so we're in the process of working with the Sanaa folks to figure out in our particular case, what are the implications of having a real time conversation with Galileo, of being able to show Galileo pictures of things and take photos. I can't tell you what that's going to be yet, but as we were at the conference, I had a lot of time to talk to a lot of people. We're brainstorming lots and lots of use cases now about AI that are going to go deeper. One of them, by the way, is AI driven coaching. A coach or psychologist is someone who knows you and knows how to ask you questions and support you in your job and your role in your career or your family life. And it's hard to do that with a chatbot that types. Honestly, it's not the most compelling experience. But if the chatbot can talk to you in a voice that you feel comfortable interacting with, then all of a sudden the AI can come to life. So sure enough, there are a bunch of vendors building coaching centric AI chatbots. As soon as they get their hands on GPT 4.0, I'm sure they will start to talk to you. We had a company by the name of Valence at our conference that's been doing this. There are a whole bunch of other ones coming. I can't tell you how good any of them are yet. We haven't looked at all of them, but we will. We are going to turn Galileo into a coach as well. By the way, Galileo already is a coach, because the way Galileo works is if you ask it a simple question, it will come back and ask you a question to try to better understand what you're trying to do and make you a better HR professional. Simply through its narrative we don't have any kind of voice interaction, although you can press a listen button and Galileo will read all of the document and answers that it gave you in a voice. By the way, it happens to be trained on Myvoice. You get a chance to talk a little bit to me. So that's going to be really exciting. And one of the things, by the way, that AI is also doing is it's starting to get smarter about human interactions. The AI, for example, that looks at teams or Zoom conversations, actually knows who's talking and actually remembers what people say and how they say it. And actually, I think it's not going to be very long before these AI systems are picking up skills and tenor and moods and various human interactions through our daily life, because you know that most of our computers are listening to us all the time, certainly our phones are. So whether we like it or not, this is going to happen. And relative to HR, kind of keep vigilance on it and we'll use it for good things like coaching and development and performance improvement and training and leadership development and all those great things. So that's coming. We'll stay as close to it as we possibly can. For those of you who came to irresistible, I cannot thank you enough for your generosity, your time, your goodwill. It was just so positive and so spectacular. We'll do it again next year, same week in the third week of May. So put it on your calendars. See you guys again soon. Talk to you next week.
