Episode Transcript
[00:00:00] Foreign Good morning, everyone. Today I want to talk about a week of discussions I've been having about AI all over the world, including three days in Europe. And the title of today's discussion is AI is your friend. And the reason I'm using that as a theme is there's two simultaneous activities going on in all of the big companies I talk to. One is a very impassioned focus on learning about AI, adopting AI, finding ways to improve productivity, finding ways to restructure and change jobs, et cetera, which I'll talk about in a minute. And the second is utter fear. People telling HR or other people in the organization that they're afraid of AI. They're not sure if AI is good or bad for their career, how do they get involved in AI projects, and they're just concerned about what it means to them personally. And for those of us that are in the business transformation business, we have to deal with that issue of fear and enabling people and teaching people and bringing people along that yes, things are going to change here, but it's going to be in your best interest and AI is going to be your friend. So let me start with the first topic and then I'll jump to the second topic. So, you know, it's quite confusing for most companies how to go through an AI transformation because nobody's really done it. And the tools are changing so fast that your concept of what to do today on Monday might be different a week later when the tool does something you didn't think it could do on the week before. So essentially the way we're figuring this out is we have two models for thinking about this. The first is the four stages, which is turning out to be a very successful model. Stage one being individual assistance. Stage two being automation assistance. Stage three being multifunctional agents. Stage four being automation, more of a an autonomy system. And we've been applying them to various use cases in recruiting and performance management and L and D and onboarding in pay analysis and all sorts of things that go on in hr. And we just had a meeting this morning with a bunch of people in the big reset. And about 60 to 70% of the people are at stage one, about 15 to 20% of the people are at stage two, and about 5 to 10% of the people are exploring stage three. And in terms of what going on inside of HR, the vendor market is quickly trying to build out agents to sell to you. There's an Eightfold agent and a recruiting that's coming out soon. There's the paradox agent there's the machi people agent and there's agents coming out from workday, there's agents coming out from SAP. And you're going to be at this point fairly soon where you're going to say, well, if we just buy this agent and turn it on, then we can restructure the jobs around the agent and that's going to make life a lot easier if the agent is well developed and mature. But it's not. So unfortunately that may sound like a good idea, but I don't think that's really going to be that easy unless the vendor you're buying from has a lot of experience going through these automation transformations on your behalf. But nevertheless, that's where we're going. And so what we're also finding, getting to my second point on these AI transformations is I think there's essentially two modes of doing this, the top down and the bottoms up. And we have to do them. The top down model is the one that Rejig is working on. Gloat to some degree is working on where you have a business transformation team in HR and in the, in the business unit and you, you might sit at the chro level and you might, you might get a. Let me give you sort of a scenario here. You get an a mandate from the CEO or the CFO that we need to reduce costs and improve productivity. You work with the CFO and the other business leaders to go through some priority setting on where the most important transformation opportunities are in the company. So you don't just do it randomly. You pick sort of a big area of change or a big area of opportunity or high cost. By the way, in a bank that might be the customer experience, not the internal stuff. It's probably the customer experience based on what I've been seeing in financial services. Then you assign a team in HR to work with the business team to analyze the current roles and jobs in the company and how much of those jobs are automatable. This is where Rejig is a great tool to help you do that or drop D R A U P is a tool that can help you do that and you come up with some scenarios based on the tools. And by the way, the way these tools work is they are continuously looking at job descriptions of job postings to show you how these particular roles are changing in real life, not just theoretically. So you can see opportunities to reduce routine work in each of these jobs and then you take that information and you work co design with the business on scenarios to either build or buy AI Solutions that will manifest and implement these new job changes. And you know, this is what's been going on in call centers for years. I mean, I mean, in some ways most of you that have worked in call centers have probably already done this where a new call center system, agent scheduling, whatever it may be, knowledge management system comes along and you buy it. And while you're buying it, you do research either from the vendor or from, you know, references on, you know, how do these jobs change? And you end up with a retraining exercise of retraining the call center agents on not only how to use the new tools, but how to do higher level work around the new tools. And this, and I went through this, by the way, many years ago, I did a really interesting case study of the Wells Fargo calls call bank, they called it call center operations at Wells Fargo and how they step by step reskilled their entire call centers at the time, this was at least a decade ago, to take advantage of the new call center automation that they were implementing at the time. And so we as HR want to be part of that because the issue of job titles, job roles, what is the pay for these new jobs, what are the levels, how do we put them into the HCM system? You know, we want to be involved in that because this, because we have to maintain those backend systems and those support systems around it. I think, you know, interestingly enough, inside of HR right now, these transformation projects are oftentimes done by a business transformation team or a work transformation team. And so I think we need to build work transformation teams of people that have done org design before and job work before. And they can be sort of specialists on this inside of hr. And then they can teach the HR business partners over time how to be more facile in these projects and do some of these projects more locally with the business leaders. The other thing that's interesting about this whole transformation process is what happens to the job architecture. And one of the things that Bill and I have been talking to companies about is I think just like everything else is becoming dynamic and we're not, we're moving from episodic change to continuous change in virtually every part of the company. That is what's happening to the job architecture. I think a lot of the job architecture work that, you know, we've done for the last decade or two has been, oh, we have a new system, we have workday, we have success factors, we have, whatever it may be, let's sit down and look at our job titles, our job levels, our Job descriptions and let's redo them as a project to put them into the new system and make all the decisions we need to make about flattening the organization, creating specialist roles, creating cross functional COEs and so forth once. And then take this one time re architecture, stick it in the new system and we're off and running. And I know that's still going on all over the place inside of companies. I think where this is going unfortunately though is this is going to be a continuous change. And so instead of thinking about the job changes as a one time thing, think about them as a continuous thing. And so what you're going to end up with is job titles that are a little bit more vague because they're going to be more morphable. And the example that I would give you is one that I remember vividly when I was talking to, I think it was a software company here in San Francisco. And I forget the name of the company, but there were all sorts of specialized job titles and software. There's Test Engineer, UI Engineer, Full Stack Engineer, Database Engineer, Security Data Security Engineer, Data Scientist. I mean there's hundreds of job titles in software engineering that are based on very specific functional roles. Well, unless you're a gigantic company, people don't just do one role in software, they have expertise in these different domains which would be more of a skills profile. But the work they do varies. And so what this particular company did is they said, you know what, we're going to get rid of those descriptive job titles and we're going to give you a job called Engineer Engineer, Level 1, Level 2, Level 3 or 4 or 5, whatever it was. The senior levels are engineers that have deep levels of experience in multiple functional areas. The more junior engineers are engineers that have more junior experience in fewer functional areas. And then we're going to keep track of the capabilities of these more full stack engineers over time. So we know from our work profile, our job profiles, from our rather employee profiles, not from our job profiles of the things that these more advanced engineers can do. And in the job descriptions we explicitly, we don't talk about every skill that is needed. We talk about the progression from a single functional engineering specialist to, to a multifunctional systems or Full Stack engineer as the way you go from level one to level two, level three, level four, level five. And it worked really well for this company. He told me that when they finally accepted the fact that they were going to do this, all sorts of things opened up. People that had expertise in different areas were suddenly available. To work on different things. The pay levels got normalized more quickly, they could more easily hire people. And so to me, that's an example of not just a one time job architecture change, but rather a dynamic job architecture change where we've changed the job architectures to create a more dynamic company so that we can operate in a more dynamic way to use AI or whatever. The next thing that comes along is better and better and better. So I know a lot of you are probably utterly confused by this whole conversation, but believe me, you're going to get involved in this because the AI stuff is coming so fast and we don't know everything that it's capable of doing and not every company is going to implement it in the same way. Now that begs the question of whether workday, Oracle, SAP need to do anything in their core systems to make this easier. I don't think so. It's not really their job to do this for you, although I think their systems probably need to become more dynamic also. But to me, this is the future and I'll put together a picture of this over the next couple of weeks and put together a nice article to explain it. But the second issue is the issue of fear. So let's talk about that for the next couple of minutes and then we'll wrap up. So I think I mentioned last week that we had a lot of chros expressing this to us in the last Chro Council we had. It came up when I was in Europe talking to a lot of people about some of the SAP work we're doing there. A lot of the Galileo customers have talked to me about it because AI is sort of an intimidating topic for non technical people. There's a general census consensus of many people that this stuff is going to bite us. And I don't think Elon Musk and Doge is doing us any favors in the way they're treating people.
[00:12:11] And another piece of evidence on this that came out in the job numbers today in fact, and some article, a really interesting article that came out in the Atlantic is that job mobility has really frozen for some reason, which I'll explain in a minute. The number of people changing jobs and changing companies has suddenly gone shatteringly low. Do you remember during the pandemic the entire workforce was going through a 30 to 40 to 50% job change each year. It was at a spectacularly high level because the pandemic was forcing people to reallocate their, you know, their entire career strategies. But now it's the opposite. There's two reasons for this one is companies are in fact trying to eliminate white collar jobs. So there are a lot of white collar jobs really are decreasing. And even though the unemployment rate is not going up, the unemployment rate among white collar work is going up, but unemployment rate among frontline and blue collar worker is going down. So we have this massive shortage of workers in frontline and hands oriented physical jobs and a sort of surplus of workers in the white collar work. And so the average looks low. And I hear this from Heads of Talent Acquisition. I was in a big meeting with heads of Talent Acquisition last week. This week I hear this from them too. So we have this situation where, you know, we're going to be hiring people with higher level roles, of course, fewer maybe, but it's harder and harder to hire them because they don't want to leave the companies they're in because they're nervous about what might happen to them. And this is discussed extensively in the Super Worker report that if people are afraid of getting laid off or afraid of automation, they will behave differently. They will be more loyal to their employer, they will be more fearful about new opportunities, they might be more fearful about opportunities inside of the company. And they will seek opportunities where they can work for a company that will teach them and train them and support them in their new life. In the world of AI now, you know, for somebody my age, this isn't something new I've been through. I actually got laid off twice. So I've been through this myself. And I know what it's like to work for a company that isn't taking care of your career. Because what happens is you wake up one day and you realize, wow, this company has been holding me back and everybody else in my role is doing stuff that I don't know how to do. So why am I working here? It's not in my best interest.
[00:14:40] But I think the more positive way to think about this is we have to build education, training, growth, mindset, inspiration, super worker programs that remove this sense of fear. Now, I had this conversation long, long ago with Diane Gerson when she was at IBM. I remember I was sitting at a cocktail dinner with her and she was talking at the time, many years ago about how many mainframe COBOL programmers they were going to have to hire lay off at IBM because they didn't need them anymore. A lot of those, you know, old MVS systems just weren't really growing. And I said, well, why, you know, why can't you reskill them? You know, they all know object oriented programming because COBOL is an object oriented program. So they should be able to learn C and those things she said, she said they don't want to, they're afraid to learn this new stuff. We've tried. They don't want to do it. And this is a very big cultural thing, is creating a cultural environment in the company from the CEO down that says we are a place where you can reinvent yourself. We are a place for you to grow. We are a place where you will advance your career here. That's a really important to me, value of a high performing company today. I had a little bit of a debate on LinkedIn with some people about Nvidia. I think that's one of the reasons Nvidia is successful. There are people that have been in Nvidia for 10, 20 years when it was a company making, you know, game cards for your PC XT when I worked at IBM. And they are still working there because Nvidia has given them an opportunity to reinvent themselves in their careers. So in terms of making AI your friend, it isn't just about job design and tools and vendors and architectures. It's also about creating an environment and a set of developmental programs so people can come along for the ride. And I, you know, I'll share something that I got from the Chro of Seagate. They put together a framework for AI education for the manufacturing workforce at Seagate, who basically are people who do manufacturing things using AI systems and manufacturing and a separate education program for the white collar office workforce in AI. And when you look at the framework, it's pretty cool. I mean it's, it's not rocket science, but it's a well developed education program. And most of you would look at it and say, wow, I wish my company would give me one of those. So this is part of our job for you guys. We're going to be giving you all sorts of training and education in the JBA and in the new versions of the JBA coming out this summer. But let me just sort of leave you with that, that the world of AI transformation, which is happening faster and faster than is a lot of culture and inspiration and storytelling and positive thinking and you know, even if you're a little bit frustrated about some of the stuff coming out of Washington about DOGE and layoffs and you know, the stories that keep getting written about this, we don't have to put up with that. We can create a positive experience for our companies and a positive experience for our workforce and that will make the transformation happen quickly and in a powerful, value oriented, customer centric way, as opposed to people hanging back and saying, well, I don't really want to be a part of this because if I jump into this new project, I might lose my job, which doesn't do anybody any good. Okay, that's my 20 minutes for this week. I'm happy to talk to any of you about this. I didn't get a chance to talk about large reasoning models, but next week we're going to be reintroducing a whole bunch of new features into Galileo that have to do with the large reasoning models. So I'll save that for next week's podcast. Hope you guys have a great weekend and talk to you next week.
