Episode Transcript
[00:00:00] Hey, everyone. I've had a week or so in Europe on business and some personal stuff, and there's been two or three huge things going on. I want to give you some perspectives on today following our big launch of Jupiter. The first, of course, is all the discussions about AI safety, the swarms of bots jumping outside of their cages, attacking things, cheating, lying, falsifying, et cetera.
[00:00:29] And the second is I want to talk about HR 2030 and where we're going with it, because I had a lot of meetings with clients this week and got some interesting feedback.
[00:00:39] So, on the first topic, I've written a substack piece on this and I'll probably write another article. But essentially the situation as I see it is the frontier. Labs are going to have to operate like aircraft manufacturers, where the safety of their product has to become engineered in. They can't look at safety or alignment, as they call it, as a research project.
[00:01:08] It needs to be an engineering project.
[00:01:11] And I think there's a strange cultural thing in those companies where they call themselves labs and they call their engineers researchers.
[00:01:20] But this is engineering, this is product, this is liability, and this is quality.
[00:01:30] Now, I'm not saying I know how to do this, but I remember in my earlier years at IBM when we sold CICs, which was the network transaction processing system that IBM developed, or DB2 or IMS, the database systems.
[00:01:48] And I'm sure Microsoft. Actually Satya and Adela made of a lot of these same comments.
[00:01:53] We didn't ship anything that didn't work because if it didn't work, somebody's bank would lose money.
[00:02:01] They just tested it like crazy. And they had really good safety engineering. You know, in an airplane or a jet engine, I'm a mechanical engineer.
[00:02:12] The engineers know that the airflow over every blade of the engine. And they know what will happen if the airplane flies into rain or dust or snow or hail or birds, because they've tested it and they've engineered it.
[00:02:32] So why do these labs seem to think that it's a research project to figure out whether their product is going to misbehave?
[00:02:45] Now, I'm not saying this is an easy problem. There's all sorts of probabilistic statistics going on here. And there's a huge conversation in this industry about AI being unpredictable and unknowable, which I kind of understand that. But to me, that's an immaturity problem. We need to know it, instrument it, watch it, monitor it, whatever. I mean, there's a million ways to deal with these engineering issues rather than just write articles about how dangerous they are.
[00:03:16] And there seems to be a lot of political jockeying by the two big vendors, regulatory capture they may be trying to accomplish.
[00:03:26] And then of course the really underlying issue is that there's $3 trillion of investment debt, expectations of going public, billionaires flying around on private jets and they don't want to upset the apple cart. So the last thing they want to do is slow down or re engineer their products to make them safer because then everybody will not be able to cash their checks. Well, sorry guys, that's your job. If you're going to sell this stuff to corporations or businesses or governments or militaries or individuals and it's not predictable or observable or safe, I don't think you're a legitimate business. I think you're just a research company and you don't want to be a research company. I know that. So there's all of that. And the only thing I would say to you as a corporate buyer if you're interested in this, I'm going to post some really fascinating things worth listening to just to sort of teach you about the process of alignment and how complicated it is.
[00:04:32] But assume that when you get your hands on an agent that you have to align it and you have to tell it what your rules and values and behavioral constraints and must dos and must not dos are because it may or may not behave the way you think it's going to behave. That's pretty much what's coming out anyway. Okay, so I'll stop there. There's a ton to talk about on this and be happy to talk to any one of you guys. Maybe we'll do a webinar on it.
[00:05:03] Second topic, the future of hr. Agentic hr.
[00:05:07] So we're having just a heck of a Fun time with HR 2030.
[00:05:13] The implications of this are massive.
[00:05:16] And the story I've told many times, I won't repeat it, but basically we're giving you a roadmap or what we call a reference blueprint on what HR is going to look like four or five years from now to help you plan your product strategy, purchase strategy, vendor strategy and internal operations and development strategy. And a lot of these agents. And there's 161 agents and eight super agents in the architecture. And we're not publishing the tool we have, but we have an interactive tool so you can look at how the agents all work, you can see the data structures they have and you can actually apply up to 220 business use cases against them. And you can see how they would operate conceptually.
[00:06:01] So when you go to your vendors and you buy something or they get, you know, they show you a demo, you'll be able to ask them some intelligent questions about how you wanted this all to work.
[00:06:12] So, of course, this is very new for everybody. And most companies barely know what to do with AI yet. And so there's a lot of getting started going on. And we're more than happy to help you with that. I really think that's the business we're in is to just help you prioritize and strategize and build an architecture. But there's another side to this coin, which I'm going to talk about at Day Rising and a few other places starting in a few weeks.
[00:06:39] And that is what I would call business 2030. The benefit of HR 2030 is dynamic enablement for growth.
[00:06:49] Dynamic enablement of individuals to grow and the company to grow by allowing the company to have a flexible operating model to reskill, redeploy, hire, loan, borrow people to move in new directions more quickly. And right now, mid-2026, every company is either planning, expecting, or in the middle of a massive transformation around AI. Not everybody knows how it's going to impact their company yet, but rather than wait and sort of cross your fingers that the AI transformation is going to go well, we advise the opposite is that you plan to use AI in the business strategy that you want to improve.
[00:07:37] Everybody's got things about their company that are strategically doing great and things that could be doing better, and things that you'd like to accelerate and things that you like to decelerate, and all of that is really how you use AI.
[00:07:50] But once you figure that out and you decide to do a big AI project, a customer experience thing, or a sales thing, or a new product or a new service, or some way to improve operations or give employees or customers better service or, you know, many, many, many options. Then you've got to figure out the people and redeployment operations behind that.
[00:08:14] And I had a really good meeting yesterday with a woman who works for a large energy software company. And they sell basically like an energy ERP type of system that allows a utility or an energy company to completely transform its customer operations to radically improve its customer service and really, really successful company. And she said, the problem we have is the software works great, but the companies aren't ready for it. So we've got to transform the companies. And a lot of them are just stuck with their operating models and their siloed structures and their cultural issues and they can't use our tool, so they buy the tool, but it doesn't really get deployed or used correctly. That scenario is everywhere.
[00:08:56] And AI is giving you a lot of opportunities to do amazing things, but your structure of people and roles and skills tend to get in the way. So. And this is all by the way, detailed in our research, the Dynamic Organization. So we have, you know, lots of dimensions of this to teach you about if you're dealing with these issues.
[00:09:15] But my point is that HR 2030 is the facilitation of Business 2030. Business 2030 is a company that is operating with AI as a resource that can be dynamically configured and dynamically used for capacity. It's a company that redeploys and reskills people as needed. It's a company where employees become superworkers and they have bigger jobs and bigger responsibilities because they're managing teams that include agents and so their capacity level is higher. It's a company where data quality and data integration is far more available and easy and so you can think about customers across their life cycle instead of within each functional silo of your operation.
[00:10:05] It's a company that experiments and delivers new solutions, new products and services very, very quickly. It's a company that is no longer talent constrained, but is really idea constrained because, you know, we will have enough people, but we won't have enough creative ideas on how to use this AI because within five years the AI is going to be, I don't know, 10 times smarter and more capable than it is today. And remember that the AI will be ubiquitous. One of the big things that's coming is embedded AI. AI in your glasses, AI in your phone, AI in your clothing, your devices, your cars. And so business opportunities are going to be very, very, very interesting. So business 2030, which we're building out now into more of a sort of a research framework for everybody, is a, is a set of new ways of thinking about how your company operates. And this means that hr, whatever, if we keep calling it that, and I guess we will for a while, is an enabler of a better business model, of a better business operating structure and operating model. And what that means for all of you in HR who are bogged down in the training department or the recruiting department or the payroll or whatever it may be, is you will still have to do that kind of work. It'll be a little more automated than ever before, but you're going to be thinking about the business all the time.
[00:11:36] You're going to be thinking about the company, the growth, the competition, the market all the time. And so the skills and the capabilities and the opportunities for all of us in HR are going to be very business centric.
[00:11:49] In fact, in my case, what I now find in my personal experiences with Chros and a lot of companies is I don't talk so much about their HR problems anymore. I mean we talk about it a bit and then we get into the issues of going which copilot should I use and why do I have so many tools and you know, what you recommend we do about this or that.
[00:12:09] But before we do that, we talk about the business issues and the company strategies. I mean one of our big clients that is actually you'll learn more about them is a big beverage company that just is merging cold beverages and coffee. They bought a bunch of coffee companies. So they're in the hot beverage business and the cold beverage business.
[00:12:29] And the cold beverage business is a manufacturing type of operation and the hot beverage business is a farming type operation. They gotta go out there and they gotta get beans and they have to roast them and they have to get them into the stores and turn them into coffee. Different, quite a bit different, even though it's something you drink. So they're going through a business restructuring of how do we operate the coffee part versus the cold beverage part and what are the things that are different, what are the things that are the same and many, many org structure, kinds of interesting things to think about.
[00:13:03] And Galileo of course is actually a spectacular tool for this because it's helping them a lot and we're trying to help them directly. But you know, that's the kind of stuff you need to be thinking about in business 2030.
[00:13:15] So I think it's going to be a lot of fun. I think you guys are going to have tremendous career opportunities and lots and lots of new ways to think about your role. Whether you're a recruiter or training person, an OD person, payroll, tech person, data person, whatever it is, it's going to be very, very interesting over the next decade.
[00:13:35] So anyway, that's something to think about. And I'll be talking about all of this at Unleash. I'll be talking about it at Workday, Rising, at the SAP Connect Conference and a few other events between now and the end of the year. If you want to talk to us about any of these things, please reach out and check out the Jupiter release of Galileo. You now have Galileo on every major AI platform. You can license it and it'll be everywhere and you'll get the access to 25, 30 years of research 1800, 1900, 2000 industry examples and case studies. Benchmark data on turnover, benchmark data on salaries, benchmark data on skills. A whole bunch of new data coming out on frontline work integrated into your experience. So every employee, every manager, every HR professional has access to this as part of their core system to help them with every project that they're working on and every personal project that they want to do for themselves at work. Have a great weekend, everybody, and talk to you later.
