Understanding The Knowledge Graph Of HR: Galileo™ AI Unveiled

December 09, 2023 00:19:38
Understanding The Knowledge Graph Of HR:  Galileo™ AI Unveiled
The Josh Bersin Company
Understanding The Knowledge Graph Of HR: Galileo™ AI Unveiled

Dec 09 2023 | 00:19:38

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Show Notes

This week I want to give you some insights on AI, the use of LLM's, and how to think about a "knowledge graph" for your company, and how it applies to HR. Yes, your AI strategy, your employee experience strategy, and your own career as a professional are all dependent on your "Knowledge Graph."  In HR your knowledge graph includes your understanding of HR practices, vendors and tools, economic and workforce data, company culture, industry trends, HR benchmarks, and the many case studies of successful solutions in the domain. If you think about the knowledge graph in your company, you can develop the right AI strategy for the organization. Additional Information How To Execute The Four-Day Workweek (Harvard Business Review) Introducing Galileo™, The World’s First AI-Powered Expert Assistant For HR Why Are Some Companies More Dynamic Than Others?
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Episode Transcript

[00:00:00] You. [00:00:05] Hey, everyone. Today I've got two topics. One is I want to talk about the job numbers that came out a little bit, but the bigger one is the knowledge graph of HR and the knowledge graph of Galileo. We have now had about ten months of experience with the LLM and HR, and this spectacular things that are happening are just blowing my mind. And the reason I want to talk about this is I think this impacts what you do as an HR professional, what you do for your company, what you do for your employees, how you design systems and what your AI strategy should be. And I'd be willing to bet most of you are working in companies where the CEO is telling everybody, we need an AI strategy because it really is important and it's a very powerful technology that changes a lot of things. So let's go back to our problem in HR. A good example is the four day work week. So you all saw, and you heard what I talked about last week on the four day work week. Let's suppose you read this article that says that Lamborghini has implemented a four day work week in their manufacturing plants, which they have, and your employees read it, and some bunch of people say, hey, we should do that around here. And you're maybe the head of HR or the CHRO, or you're running HR for a plant and you say, okay, let me look into it, and you start to explore what's going on. And I'm just using this as one example. I'll give you another example in a minute. And you say, all right, well, let's see. Was that a benefit they gave to employees to work fewer hours? Was it a redesign of the plant? Was it a job sharing program? Was it a productivity improvement initiative? They did. Did people work 10 hours for four days? Did they work 8 hours for four days and make less money? Did they make 8 hours for four days and make the same amount of money? What days did they work? How do they deal with scheduling? And you go through this mind thing where you start to figure out what are all the questions I need to ask to understand how to do this. And by the way, they're in manufacturing and maybe you're in retail or service, so you're not sure if even the use cases are the same. This is the job we're in. The job we're in in HR is to design and implement various solutions, some strategic, some tactical, to help our company operate more effectively and help our employees engage and deliver and feel good about their work in a new and exciting way. And so in many ways we are dealing with a sort of science of HR that has many, many pools of knowledge. And I call that the knowledge graph of HR. And let me tell you about the knowledge graph of HR, because this is what we're building in Galileo, by the way. Second example, yesterday I had an hour or so with a big company trying to build a skills and internal mobility strategy. Big company, many, many different business units. They happen to be in the media and entertainment industry, and they're asking me questions like, well, if we bring in an AI based dynamic organization platform to sit in the middle of our ERP, what are the things we need to do to redesign our job architecture? So I took a deep breath and I said, well, there's about five things. You need to look at the levels, because if there's too many levels, it'll prevent people from moving around and they won't have the opportunities to do other things in the company because of the levels. You need to look at the job titles and the job descriptions. Are they understandable? Are they consistent? Are they not duplicated? Do they make sense to somebody who's looking for an internal job in a way that they will understand what the role is? Sometimes they do and sometimes they don't. I'll tell you, this is a big problem in healthcare, where a lot of the jobs say things like nurse, and they have a million different definitions of different kinds of nurses. I said, the third thing is the skills and competencies. Some of the jobs in your company, the skills can be what I call professional skills, which means they can be inferred. Others are rigorous operational skills where you're not going to be able to take this job or this project or this opportunity unless you absolutely know how to run this machine or operate this part of the company or use this particular tool. So there's this issue of what skills matter. And then I said, the next issue in the job architecture is, where are you trying to go? Are you redesigning the job architecture to promote mobility? Are you redesigning the job architecture to promote depth? Are you redesigning the job architecture to drive change or integration and save money? There's a bunch of business reasons why we improve mobility or drive skills based balance strategies. And if you don't have some idea what you're trying to accomplish, you're going to have to make a lot of decisions that are going to be hard to make, and you can't boil the ocean. And the other thing I've said to them is there's going to be parts of the company that you maybe don't want to spend too much time on because they're not really that important at this point. But all of them will be affected by the job architecture and this move to this skills based, AI driven technology platform approach to job sharing, mobility, gig work, new forms of careers and so forth. And I told them this is a big deal, it's going to take time, it's going to take years, and it's going to really change the company in a positive way. So having these decisions factored in around these business issues really matters. [00:05:19] And then there's the question of what technology to use, what platform, what vendor, what industries do these vendors focus on and so forth. So what I'm getting at is in both of these situations and there's millions more we've got to figure out. What is the design criteria for the thing we're trying to do? What is the implementation process or set of decisions we need to make around the company to implement this? Well, what are the tools, vendors, technologies that are going to play? Who can I use as examples in my industry or peripheral industries that I can learn from? What benchmarks do I need to know about how much to spend, how much this should cost and what are the industry specific issues that I'm going to run into that are very, very important in my industry that maybe my vendors or my consultants or my case studies and examples don't tell me. And virtually everything you do as a designer or a consultant or an advisor in HR or a business partner or even as a recruiter involves this process of figuring out through these what I call knowledge graph or pools of information, what we're going to do next. Well, that's what we're doing in Galileo. [00:06:32] I've done this now for 25 years as an analyst and a researcher and we've done extremely well with it. Now that we put it in Galileo, it's amazingly accessible to everyone. And the deep pools of knowledge that we have to reflect on in HR include all of the best practices. And you guys know we've done thousands of best practices and we've scored them against maturity models and analyzed them by industry and company size. There's the technologies and tools. I think there's got to be 500 to 1000 vendors that companies tend to use, but there's probably more like 5000 vendors. Which one applies and what are the strengths and weaknesses of it? And by the way, what's going on in that company and will they apply to our company size and our industry and so forth? There's the examples, the case studies. We have more than 500 case studies in our library. And they're very valuable because you see around corners what other companies ran into and how they innovated in a particular solution area. There's the benchmarks. Well, how much money should we be spending on this? We know it's going to take time. Should we pour a whole bunch of money into something big and take the risk that it might not work out, or should we start small and iterate over time? And what should the budget be and what do other companies spend in this area? There's all these benchmarks. There's also labor and economic data. If we're going to do a big initiative to hire engineers out of universities, how much are we going to have to pay? What universities should we go to? What cities, countries, locations are going to be the best for this job, this fit? There's all sorts of economic data and jobs data there. So my point is that an expert, a professional HR person who's really good at their job and has done this for a long time, kind of knows a lot of these things. In their mind. That's what an expert is. But when you start a new domain and you go into something you haven't done before, you don't really have all that and you're starting from scratch. And so the way we've got Galileo all tuned is really designed for three or four kinds of use cases. One is I'm getting started and I don't really know what to do and what's going on. Give me an overview. Give me some case studies. Give me some examples. Tell me where I am in the maturity model. The second is I'm a designer. I'm designing a new onboarding program and designing a leadership development program. I want some very specific examples, and I want to know best practices, and I want to know tools, and I want to know vendors, and I want to know what's worked and what's not worked for other companies. The third is I'm a business partner. I'm a consultant, I'm a talent advisor, and I'm dealing with a problem, a business problem. It's retention, it's turnover, it's errors. It's customer service. Maybe it's low productivity. It's employees showing up for work late, et cetera. What do I need to know to address that issue in my more consultative solution? And then there's the audience of analysts and researchers who are saying, I'm responsible for putting together the plan for this stuff we're going to do. I need to find a ton of ideas. And I've been using Google and going to conferences, and I get little bits and pieces here and there. How can I get to see the big picture? This is what Galileo is doing, and we've been really excited about it because it's allowing us to deliver granular levels of information at a speed we never could before. And the reason I'm even giving you this in the podcast is because every single one of you is scratching your head and saying, how am I going to use AI? What's the role of AI? To improve my career, improve my company, improve my employee experience. And this generative AI stuff, implemented in an enterprise level, which is the way we've done it, is incredibly good. Now, it's not perfect, but it's really, really good. And a lot of the success is based on the data. Know, I had a good call yesterday with Donna Morris from Walmart and the AI system they build for their employees. She told me that the average call center agent who gets a question from an employee about benefits gives the employee the correct answer about 60% to 70% of the time. The AI agent gives them the correct answer 90% to 95% of the time, because the AI agent has the information and can see across all the benefit plans almost architecturally, in a way that a human can't, and is more likely to give a synthesized view of the answer that's optimized for the employee's request than an individual who might not know a lot about this, but know a lot about that. And that's what's going on in all of HR. We're all good at certain things, but when we're asked to solve a problem, we have blind spots because of what we don't know. And Galileo, amazingly enough, addresses that. We have had so much fun with this, and it's gone so well so far that we're going to roll it out to all of our corporate members around sometime in February now. So we're really cranking along here, and then obviously we'll get lots more feedback. And the purpose of me telling you this is a lot of you are building AI strategies for your own company and you're trying to get agreement on what to do. I think we're at a point that we're going to be able to advise you on that very, very well. We're already working with four or five companies on their AI strategy, and of course, there's all the AI tools within the vendor market, which we're pretty up on. Most of them, not all of them. And one more thing about this knowledge management. [00:12:06] Knowledge management was really a bad phrase for a long, long time. I worked in lots of jobs and tech where we tried to build knowledge management things, search engines and stuff. We're actually going to solve it now. Genai actually does knowledge management. If you organize the tools and information you have in the company, well, the tools like Galileo will make them very available to experts, novices, people that are new, people that have rotated in and out in an amazingly effective way. One of the companies we're actually working with on Galileo is a large defense contractor who builds aeronautical and defense systems. And they're going to be using the same platform that we're using in Galileo to build a knowledge management system for their science and engineering. Because in many ways, if I think about HR, we're in a science. We have certain things that are proven, certain things that are not proven, certain things that are best practiced, certain things that don't apply in different conditions. That's the way engineering is. By the way, if you're building a new battery for a car, you can't just kind of copy it out of a book. It depends on the voltage level, the geography of the car, the temperature and conditions, the size of the motor, a whole bunch of factors. I don't even know. But every engineering problem, I mean, real engineering problem, has just as complex a corpus as the one we have in HR. So those of you that are working on AI strategies, you're going to have some interesting awakenings that maybe the tactical stuff you're working on to build a little training program or whatever it may be you're working on at a particular point in time. And I don't mean it's little, but it might seem small relative to everything else going. The company is actually an example of something else that's going on in the company where they're trying to redesign a product or understand a customer scenario or a fixed customer service or whatever. So this stuff is really interesting, and I just wanted to give you a little bit of my personal journey where we're going now. The second thing I want to talk about is the job market. So, Friday we had this pretty good set of numbers from the Bureau of Labor Statistics in the United States that around 200,000 jobs were created. A lot of jobs were created in healthcare. A lot of people in the auto industry went back to work when the strikes were over. That was 30 or 40,000 jobs. But the interesting thing about the data, and then the unemployment rate went down again to 3.7%. So it's still really low. But the interesting thing about the data is it really does reinforce the post industrial age narrative we've been talking to you guys now about for several months. And that is the long term labor shortage we're living through. And we're going to live through. The number of workers in the United States hasn't gone up hardly at all. And you would think the way it used to work is when there's really low unemployment, more people go to work because there's more jobs and the wages are going up, but that's not happening. And some of the pundits think it's because people are depressed. They're in a bad mood about the post pandemic environment. There's a bunch of issues with the politics and war and so forth, and so they're kind of cranky and they don't want to work quiet, quitting and all that. That's true. And there is a narrative you're going to read about in their predictions about the workforce that's very, very different than it used to be. And that's why things like the four day work week are going to happen little by little, because people are thinking about their jobs and their lives differently. But still, within the data and the BLS is the story that some parts of the economy are under tremendous demand for workers. And the one that struck me was healthcare, where there were 77,000 jobs created in one month, and that's expected to be half of the new jobs created in the United States for the next ten years. Where are all those people going to come from? And the fact that the number of workers hasn't gone up hardly at all. So I'm not going to tell everybody to go out there and have children, but I will tell you that as an HR person, get comfortable with planning for next year. In a world where even if there is an economic slowdown, you're not going to be able to find a huge number of new people in the roles you're in. You have to be more selective about hiring, more selective about internal development, more selective about internal mobility, and more selective about training and upskilling the people in the company now, and also building a strong culture so that people stay. I don't need to beat that one to death because you guys have heard this from me many, many times. One more thing I will mention relative to the job market and this issue of intelligence and AI, there's also a pretty big brewing issue of DEI and anti Semitic practices and anti Muslim behavior going on because of the war in Israel and a lot of this falls in the lapse of the head of DEI, and I think there is a political movement in the United States and the right to eliminate DEI as an initiative. But I would argue the opposite. I would argue that DEI is even more important now. Whatever you call it, you can call it the chief inclusion officer or chief belonging officer, whatever you want to call it. It's really becoming even more important because there is so much argumentation and discussion going on in the political world and the cultural world. People are going to bring it into work and they're going to want to know, where do we stand here as an organization? And while CEOs certainly don't like to play a role in this, I would say the two CEOs of Harvard and Penn didn't behave very well this week because maybe they hadn't thought about it enough. And one of the people in the company who is really good at thinking about these issues is the head of DEI. So I want to shout out a thank you and a great appreciation for the people that take those roles. They're very difficult roles. They're not always well supported by the rest of the organization, sometimes not by the CEOs either. But we need people to monitor and stay on top of these issues. AI will be one of the things that will come up, because if the AI is introducing bias, of course the head of DEI is going to want to know why and what are we going to do about it. But anyway, we're entering a world where we'll probably have a lot of discussions about DEI. We're going to have a lot of discussions about labor unions. We have a lot of discussions about the four day work week next year, and nicely enough, we're going to have generative AI to help us. Okay, so that's kind of a lot of musings today. We're going to be around over the holidays. I'm working on my predictions. I'll give you guys an update on the job market and some other interesting technology stuff next week. Have a great week, and I hope you're starting to chill out a little bit for the holidays coming up. Thanks, everybody.

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