Episode Transcript
[00:00:00] Hello, everyone. Today I want to introduce our HR 2030 vision of where human resources is going. And this is a major collective program we're doing now to identify and plan and educate everybody in this wonderful profession. What's going to happen to human resources over the next four to five years as AI gets smarter and smarter? And I would say that for me, having done this for a while, I've evolved my thinking quite a bit. In the earlier years of the AI revolution, I was a little skeptical about what a role AI would play in hr. But I've changed my mind. I think it's going to really change everything in business and HR in a very significant way for one big reason, and that is that HR doesn't exist to do hr. HR exists to optimize and improve the performance and culture of the company. And all of the programs and practices and innovations and skills and things that we do. And you know, we have 94 of them in our capability model, are really there for that single purpose. And of course, we understand how complex this is, as do all other professionals. But I think we sometimes do lose track of the goal. The goal is to make the company grow, to help the company innovate and improve time, to market and solve customer problems and meet customer needs and improve healthcare outcomes or deliver food or whatever it may be. And the people are one of the most important elements in that strategy. And if you just think about it at that level as a workforce or employee or human capital optimization problem, then AI is going to completely revolutionize what we do and in a very, very interesting and powerful way. And a lot of jobs in HR are going to change and a lot of jobs in HR are going to go away, but there'll be new ones. Because with what we call our agent architecture of agents and super agents, there will be many, many opportunities for the HR professional, the recruiter, the business partner, whatever role we call it, to tune and tweak and modify and adjust this new agentic human capital system that we're building. Now. Let me just dive in a little bit. We have to assume that over the next four to five years, the pace of innovation is going to continue just as much as it has.
[00:02:30] So if you Fast forward to 2030, we will be living in a world where the technology and the data knows everything about the workforce. We will know people's jobs, we will know their skills, we will know what they're talking about in meetings, we will know what they're sending email messages about, we will read their documents, we will know their location, we will know how many hours they worked. And of course we will know their demographic data, a lot of their educational history, their skills history, their performance ratings, and all those other things. And through the use of outside data sources, we will also know their match to different roles and careers and interests. And I won't go into the details of all of these things, but the reason I'm very, very convinced this is going to happen is because what we've seen with Galileo and our digital twin in our company, so this idea of assessing somebody's skills with a test will still be useful for operational and safety and compliance, but it won't really be that necessary for many other things. We also know that in 2030, the AI will be able to generate content and experiences at a hyper personalized level. So the tools that we give to employees, whatever they take, will be like personal coaches, personal tutors, personal advisors, personal mentors. And so we as the leaders of this particular profession or this function, have enormous data and personalization capabilities that we've never had. I mean, there's been endless projects to build Personas and journeys and career paths, et cetera, to try to make all of our stuff relevant to each person. That's going to happen at an individual level. Third thing we know, and this is something a lot of you probably have not thought about, is that the AI itself will be autonomous, like the autonomous car. So the AI system, the new agentic HR system, whatever we end up calling it, will see in its own data trends. It will observe trends in performance, in productivity, in people coming in late and people working late, in people leaving the company. It will observe trends among new hiring staff who has come to market and come to productivity quickly, who has come to productivity slowly. It will identify different managers, performance relative to their teams, diversity in pay. It will see inequities or imbalances or discrepancies or changes. As a system, we won't have to run surveys, do people analytics projects, or do interviews and walk around the company all the time to see what's going on. Now of course it won't see the human side of work, it won't see the emotional side. It won't see the empathetic or non empathetic side of different situations, although it might, I'm not sure yet it could, but certainly we'll see all those other things. And so a lot of the HR practices that we built to assess performance, identify performance, evaluate performance.
[00:05:35] We may not need this idea of a performance management process where everybody writes down their goals and they get evaluated once A year. I don't know if that makes any sense. When the AI platforms know so much about what's going on and rather than evaluate the person on a 1 to 5 scale, the AI could see that somebody is falling behind in a particular area and could give the person coaching and adjustments. Not just an annual review with dynamic enablement, which we know we can do in the learning platform. So the way the AI monitors and observes the company is going to be significantly different. And then there's the issue of rules and culture and decision making rubrics. Today, whenever a decision is made about a person, who to hire, who to promote, who to put into leadership, who to move to a new role, who to let go, et cetera, there are cultural and personal characteristics and behaviors that are unique to your company. You might have very clear definitions of that stuff, or you might not, or it might be regional or it might be local to a manager. Well, the AI doesn't necessarily know what those things are. So we will be giving our AI platform rules and rubrics to follow. For example, I've talked about this in a podcast I did a week ago. Imagine we have a talent redeployment or talent allocation super agent that's talking to lots of other data agents and it's trying to optimize the performance of a business unit that's underperforming and is and has been told by the humans to reduce labor expense by 10% because the business unit is shrinking and we're probably going to sell it for some other reason. The super agent could look at all of the individuals job titles, job roles, their performance ratings, their skills, their individual performance activity coming from other systems and give us a recommendation recommendation on the optimum way to reduce 10%. It could also decide which one of those people needs to or could be redeployed in a high growth area of the business and map out a career path and opportunity for them and decide which of the people in that business unit are likely to be redundant and probably should be terminated or could be moved to functional roles in another part of the business. Then it could look at the pay levels of this underperforming business unit and it could analyze the pay across the company and across the industry because it'll have that data and say one way to reduce 10% is to tell everybody we're going to have a one year pay freeze to deal with the low performance because we think this is going to turn around, but it won't know which of those scenarios to take unless we tell it so we are going to be telling or advising our agents how to make decisions. They will have lots of data, but we need to give them the frameworks and the rubrics in some sense. I was joking about this in a meeting. It's like, like sitting there with a bunch of steering wheels where the car is driving along and we're just grabbing the wheel and pulling it from place to place periodically based on what we want to accomplish and where we want to go. And let's suppose we don't really know what the right approach is to reducing this 10%.
[00:08:52] So we ask the AI, what approach do you advise we do based on the historic pattern of underperforming businesses in our company that we've experienced before. And it would probably be able to do a pretty good overall assessment of the pattern of change we've been through and which of the many tuning knobs we should apply. So there will be rubrics or rule books or cultural manifestos that we, constitutions as anthropic would call it, that we will use to teach this human capital system what to do. But there will be times when we have to advise it to do things differently. Sometimes we want to override the corporate rules and just do something that's unique. Then it will also know what people know because it will see the experts and high performers in detail.
[00:09:42] So when there's a new project or we hire somebody who's really a very high value employee or an activity or a customer or a need, it will be able to find the experts and show us who they are and bring them together to solve these problems. So expertise and capability at an individual level as a super worker will be very valuable. I mean, I've always felt, at least in my career, that many experts and many high performers are buried in the organization underneath layers of management who have political reasons not to expose them. Well, that will go away because everybody's data will be available to the HR systems and managers as well, will be evaluated on a fair and data driven way. So there'll be a much more egalitarian performance based meritocracy in the company because all this data will be so transparent. Now at the senior levels, business unit heads and executives, this new AI powered infrastructure will help us with planning and strategy. Now most of you know, certainly if you're in the tech industry, that the dynamics of the markets we serve are moving very fast. Customer demand, business cycles, regulatory changes, technology changes, there's all sorts of things that happen, even a fire or a tornado. You're in healthcare or retail have huge impacts on your company. And so we have lots of risk management and various insurance policies and things to prevent and enable us to deal with these changes. Well, once this human capital agentic infrastructure exists, not only will we see these things because the system will have history and it will understand how the company's adapted in the past, but it will be able to give you scenarios. So if you're sitting around in the boardroom and you're evaluating a merger and acquisition, by the way, this agentix system in company A can be connected to the agentix system in company B. So we could evaluate the merger and acquisition deal we're trying to do too, from a skills and alignment standpoint. But let's suppose we're trying to make a decision to get into a new area. Should we build it, should we buy it? Should we buy a big company? Should we buy a small company? Should we focus on the product or the channel or the sales or the marketing or what? We should be able to have human analysts look at these scenarios and build them into the human capital system, because this agentic human capital system is going to be coupled to the agentic financial system. Now, I'm not a guru on financial operations, but this, everything we're talking about in the human side is easier in the financial side because we don't have these weird human dynamics going on. So if the level of innovation I'm talking about in HR happens in the finance function, then the finance function and its agents will be coordinating with the human capital agents and we'll be able to see the financial implications, the supply chain implications, the customer service implications of all of these decisions. Now we're talking about four years from now and things are going to change a lot. But I clearly see this happening. The other thing that's going to be very different is we're going to operate our companies as horizontal workflows, not vertical. The vertical job function, job family world we live in sales, marketing, engineering, customer support, consulting, finance, hr, it, et cetera to me is sort of a relic of the industrial businesses of railroads and manufacturing companies. And it even goes back to slavery, to be honest. And then we grouped human capital and human skills into these functional areas. But as AI automates a lot of the routine work that we do, these vertical functional areas can cross because the actual value of a company is in the horizontal. When you go to a customer and you identify their needs and you solve them or sell them something and then they like it and they buy more and then you support them and you give Them answers to questions or sell additional products. That's a horizontal process that crosses sales, marketing, finance, support and all sorts of other things we do. So the customer doesn't see the verticals, the customer sees the horizontals. So with AI we can optimize the horizontal workflows without worrying so much about the vertical workflows. The reason we have the job families is because it was easier to set up managers and it was easier to set up service delivery functions in those vertical functional areas. But I don't know that we're going to need that. So there'll be much more cross functional jobs, roles and business areas, business units that will look more like pods, as we call them in the healthcare industry, to solve customer problems. And you know what this means when you go into an operation that's a fairly serious medical operation, the nurses, the anesthesiologist, the doctor, the assistants, the orderlies, they all come together and they do what needs to be done. That's the way great stuff happens, that's the way value is created. And these artificial barriers of functional job families are there to support people and develop them, but they don't add that much value. They sort of get in the way. So we'll have much more cross functional agile work teams. And then the human part of work. Are people happy? Are they engaged? Do they feel inspired? Do they understand what the company's trying to do? We have all sorts of tools today, surveys, engagement, culture tools, assessments, et cetera, to try to do that. Well, a lot of that's going to continue because humans are very complex. But I think it'll be very different. I already think here, right now in 2026, surveys have kind have become obsolete. I don't know how many surveys you guys take, but I ignore most of them. And I assume that even if I take one, it's going to go nowhere because the survey doesn't really ask enough questions, nor do you have enough time to tell it everything that you want to tell it. But an AI that you're probably working with all day, that's embedded into your computer or your phone or your headset or your glasses can ask you questions and listen to you and get your feedback in a very personal, dynamic way. So if there's a change of policy or a new deal that was announced or something that changes in the company and you have an opinion about it, you could tell your company AI what you think and it could collect information from a hundred others at the same instant in time and inform the leader, hey, this wasn't Such a good idea. Most people think it should have been that done this way versus that way. So this whole area of the human side of business, the engagement surveys, the psychology, the psychological safety, is likely to much, much more visible to us than ever before because we can get it at a personal, real time level. And those are tools that are just emerging. But I think we can also pick it up from the sentiment of people's language, of their emails, their conversations. You know, one of the most sophisticated part of our digital lives is the advertising industry. And the advertising industry, as much as we hate it, is really good at figuring out what's on your mind. I don't know why that technology wouldn't be applied to work, because the proposition at work is so much bigger than the value proposition to sell you a new pair of ski pants where we want to know why somebody's underperforming. So again, thinking four years ahead, at the rate of technology advancement we have, I think there'll be a lot of good insights into the psychological and cultural health of the company. And you know, those are the things that are probably the most important of all in a big organization and a small organization as well. Now, you know, another thing that I just want to mention before I sort of wrap this up is we're going to have to sort of let go of a lot of things that we've had for a long time. Non box grids, ranking, force ranking, five point scales, engagement surveys, linear salary bands.
[00:17:39] You know, some of that stuff is there because we didn't have this kind of power, we didn't have this kind of data, we didn't have these kinds of intelligence systems. So we built what I would call simplifications of very complex areas, but those could disappear. And little by little we'll probably rebuild a lot of the human capital practices that we're currently using today. So HR 2030 is a big change. Now what are we doing here? We are all over this. We've been working on a technical blueprint for agents for HR 2030. We have an operating model for HR that we're working on, an outgrowth and an extension of what we call systemic hr, which is looking at HR as an integrated system, not as a bunch of service delivery groups. And we're doing lots and lots of work with clients and vendors. So if you're listening to this and you're scratching your head and saying how do we get involved and understand this and apply it, you can join this HR 2030 collective with us right now as we get started. The most important thing of all is to run these ideas by you and listen to your case studies. So we will be talking to many of you whoever would like, and we'll be discussing this at our conference in June. We're talking about a couple of big partnerships here that will help this effort. And we will also be introducing in June a really big new education program that we've been working on now for a couple of years, actually, on how to understand all these multidimensional aspects of HR in a more depth. And I'm not the only one, and we're not the only ones thinking about this. There will be many, many others as well. So because we're so clued into the rest of the industry, we'll keep you up to date on other ideas and innovations as they come. But it's a pretty exciting time and I just want to get the ball rolling and inform you what's going on and where we're going and how exciting our future careers are going to be in the human capital part of business. I hope you enjoyed listening to this. Stay tuned for more.
