Episode Transcript
[00:00:00] Foreign I want to expand our discussion of the super worker effect this morning after a week of discussing the super worker research with clients and journalists and other people. I want to give you some insights into what's going on in this massive transformation around AI.
[00:00:28] So when you read the report, and I encourage you to get it, what you'll see in the super worker research is that AI is not a technology to implement. It is an empowering, systemic type of technology which includes agents, assistants, platforms and soon, eventually autonomous systems to transform the way we do things, to transform business processes, to transform customer service, to transform sales, finance, HR operations and so forth. As a technology that allows you to transform, you need to think about it a little bit differently than we would buying a tool. And so as we describe in the research, there are essentially four stages. The first is you take your existing job as is, you buy a tool like the Microsoft Copilot or ChatGPT and you try to use it to do the same things you were doing before, a little faster. And you know you might get 5 to 10% improvement in productivity by doing that. But the tools aren't perfect and they're not tailor made for your application. So you have to rewrite things and you have to prompt it and you have to figure out how to prompt it. And you know, it summarizes meetings well, but it doesn't summarize emails well. So you know, you, you get incremental improvements in the current way you do business.
[00:01:57] At level two, you start to replace work and you say, you know what, in my workflow as an analyst, I'm not going to write outlines anymore by hand. I'm just going to let the AI do it or I'm not going to call people on the phone anymore, I'm just going to let the AI do it. And these sort of numbered level two enhancements might give you a little bit higher levels of productivity, 20%, 30%, because you're replacing time consuming work with AI. A software engineer that uses AI to write code as an example, I was talking to a software engineer yesterday actually and he said, yeah, it writes a whole bunch of code, but I got to go back and check it. So it's not a hundred percent replacement, but it does improve my speed sometimes. At level three you. So those are the first two levels and those are basically what we call redesigning work, not re engineering work. Then at level three you get into re engineering work. And now you're saying like for example, in recruiting I have an AI recruiter that can handle candidate inquiries. It can allow us to not spend a whole bunch of time on the phone with candidates deciding what job to apply for. It can streamline scheduling of interviews and it can do background checks automatically. So we don't need recruiting schedulers anymore. That job's unnecessary. And maybe meet for me as a recruiter, I don't need access to LinkedIn Recruiter anymore because I'm not doing sourcing. The AI is doing sourcing on my behalf. So I can reduce all those, you know, the training that I spend on that system and the money I spent on that system. And this is where you're now re engineering work. So you're sitting down as a team and you're looking at the way you recruit. Maybe you're mapping it out on a whiteboard and you're saying, listen, step one, step two, that can be automated. Step three, we're going to have to have a person, step four, we're going to automate with step five and step six and so forth. And now you're getting 100% improvement, 200% improvement, 300% improvement. In the case of recruiting, in the world of paradox customers, which is the most sophisticated AI agent system that we have in the market, you know, we're talking about taking time to hire from weeks to days or from weeks to hours literally because of the automation. And this is where the roles change significantly. And there are new roles to manage the AI, to monitor the AI, to train the AI to keep it current. And the people that were doing routine work are now doing customer facing activities of much higher value, or they're helping design the process to make it work better and better and better, as well as looking at metrics in the process. And eventually you get to level four, which we call autonomy, where the system's smart enough to kind of do a lot of this for you. Now there's very, very few autonomous systems in business yet. And I don't know whether that will ever truly happen because no two companies are identical. But a lot of business processes are pretty similar. And so if you had autonomous systems that could determine who the bad debt was going to be, or you know, who to send emails to first, or how to communicate with somebody as a salesperson, it would be very helpful. So the point I'm trying to make, and I've just made this point about 10 times with different organizations, is that this is not implementing AI. This is re engineering your work and your jobs and your roles around AI. And by re engineering I mean rethinking how you do work. Now it turns out that, you know, if you look back at the history of digital adoption back, you know, starting back around 1998 or so when digital started, a lot of companies don't like to do re engineering. They don't know how to do it, they don't understand it. So what they want to do is they want to buy something and turn it on, press a button and assume that everything's going to work better. And so they only get the, you know, the 10, 15% improvements and they won't become super powered organizations, super worker companies. And what we found, I, I, there's a research paper linked in the article that I put up that in the Digital Revolution, now that a lot of people have studied the transformations that took place in Digital, really only 10 to 20% of companies became high powered digitally enhanced businesses. It's becoming easier and easier now 20 or 30 years later because the tools are much more generic and the models of what to do are much more clear. But in the first 10 to 12 to 15 years of digital, the differentiation between the high performing digital firms and the laggards was huge. And the same thing's going to happen here. You as a team, as a company are going to transform as fast as you feel capable of doing and you historically have done. Yet somebody in your group, your industry, your peer group is going to do it 10 times faster than you and you're going to look around and read an article or talk to us perhaps and find out that, whoa, holy smokes, we didn't realize we could do that. How do we get there faster? And that leads to the next few minutes I want to spend on the podcast, which is how do you as a company and as an HR team become an AI pioneer or an AI pacesetter as we call it in this world of very rapidly changing tools and technology and potential opportunity to have 200, 300, 400% more productivity. So here's what we found and here's what the academics found and there's a lot of research on this. If you go back and read the research on digital transformation, the first is of course, what's in the way of making major changes. As I talked about in the article and we talk about in the report is the fact that we have jobs. We have job titles, job levels, we have managers, we have mid level managers, we have a lot of infrastructure built around the way we do business today. And the way most companies do business today is we're big collections of people with roles and we scale by hiring more and more of those people. And giving them each tools to do their roles better. In the world of AI, when you start to move jobs around and change roles and get rid of roles and create new ones, it's all of a sudden throwing a wrench into this works of how things work today. So it feels very disruptive. Some companies, because they're either very big or they're very scalable or they don't have a lot of IT resources aren't very good at this because in order to change roles and jobs and re engineer work, you need to have skills in it. By the way, in the digital world, the research shows that the pioneers had two and a half times higher percentage of revenue spent on IT than the digital laggards. And I think the same thing will be true here. You're going to have to spend money on the platforms on understanding the platforms, on buying them, on licensing them, on testing them. That's IT spending, that's people to train the AI, et cetera. We for example, have much, much more focus and resources in our company on Galileo than we ever did before. We really outsourced most of our IT until the last couple of years. So, so that's number one. Number two is you have to have experience with re engineering and it turns out, you know, based on our research in the dynamic organization, that if you haven't re engineered before or you're not familiar with it, it's hard to do. The more you do it, the easier it gets. In fact, dynamic organizations are re engineering all the time, literally continuously. Because in a sort of non transforming company you think about transformation as a step change and you say let's hire a consultant, let's hire an advisor, let's bring the Burson guys in, whatever and then let them tell us what to do and then when they leave we'll be fine. That kind of isn't the way it works anymore. This is a never ending series of decisions about how to optimize as the technology gets better. And so when you look at the companies in, we call them pacesetters, excuse me, in the GWI research and we have very explicit research on how pacesetters are different in their skills and capabilities from the rest of the world and organizations, they have a lot of skills in change management, they have a lot of skills in training. By the way, the research from MIT shows that the highly evolved digital companies spend as much as nine times more on training. Nine times. That is a massive number. If you don't train people on not only just using the new tools, but understanding the new tools and understanding the human implications of having them, then the employees resist the change because people don't like to be told that they have a new job, frankly. And they are not always comfortable or confident that they can do the new job until somebody's really explained it to them. I've told this story before, but let me say it again. Back in the 80s, when I worked at Sybase, which was a database company which is now owned by SAP, we implemented Siebel. Siebel was an early version of what is now Salesforce. And our CIO and CEO did some sort of a big deal with them and forced us to use it. And so I was in the sales organization at the time and they, you know, we went to hours and hours and hours of training and, you know, had to redo our laptops and all this stuff and it was a complete flop, complete failure. No one used it, everybody badmouthed it. It was a massive waste of money and time. And it may be one of the reasons the Sybase was eventually sold, because they really didn't train, they didn't really think about the re engineering, they didn't probably think about the ROI model much, and they didn't train us at all on why we should use it or what would be our new role. They just said, hey, you got to use this system. Let's show you how to stick all your data in here. So there's training and then there's change enablement resources. You know, when you, those of you in HR know this, when you want to sit down with a group of people and change the way you're doing something, you have to stop, you have to plan a workshop, a series of workshops, a discovery session, a design session, a session with feedback, an exploratory set of brainstorming sessions. You have to do hackathons, you have to do experiments. This takes time. This is stuff that takes time away from running the business. And a lot of business leaders don't want you to waste time on some workshoppy thing that they're not sure what it's going to come out out of it, unless they've done it before. They've been in the consulting industry themselves. And so you have to have people who are specialized who know how to do this. Now, you know, you can go out and hire a consulting firm. And I talked extensively with Wall Street Journal about this yesterday, and I would imagine that the consulting firms there will be all sorts of new consulting firms that will sort of claim to be experts on re engineering around AI and some of Them will probably be great. But my experience with this is that high performing companies do it themselves. Because the problem with hiring an outside contractor to do all the work for you, what you want is a consultant who understands how to facilitate your work, not to do it for you, is that they might do a lot of things and make a lot of changes. But if your leadership team and your culture does not adopt and understand and learn from these changes yourself, they may or may not stick either. And there have been so many stories of consulting led IT transformations that went sideways because until the people in the company come to grips with how the technology works and how to maintain it and what it can and can't do and how it's going to change our roles, the consultants are really not going to do much for you. So really high performing companies have consulting teams in the company, oftentimes in hr, by the way, with IT to lead these AI transformation efforts. And so, you know, if you're a medium to large company, you're going to want to take somebody in hr, one of your lead consultants or a really senior business partner type person or somebody who's gone through our consulting masterclass and you're going to want to partner them up with it and you're going to want to sort of put them in the business of working with the functional areas that are adopting or considering AI to work with them to do some re engineering and discussions of new roles and jobs around these AI platforms. And you know, the way I look at this, as a, you know, fairly senior, rather older person who's been around for a while, this is a trillion dollar reengineering effort. And I mean it, I mean we're going to spend hundreds and hundreds and hundreds of hours and dollars and platform money on rethinking the way we do things in companies.
[00:15:40] Now some of this will come out of SAP, Oracle, Workday, but not that much because the ERPs are really locked and loaded in how their systems work. All the workflows and things as you built into those platforms are more or less institutionalized. This is probably going to come out of, you know, your work with new vendors, maybe ServiceNow, but probably a lot of others that will have innovative approaches to solving problems that use the four models I mentioned before. And we're going through this in Galileo. The reason we wrote the 100 use cases document in Galileo. And I really urge you to read it because it's really about the business processes of hr. It's not about Galileo at all. Specialists will be able to help you think through the finance department, the sales department, the marketing department. I don't think off the shelf tools are going to necessarily do this for you. At this stage of the technology evolution, we're really immature in the level of experience with these tools. The vendors are moving very, very fast. So part of the transformation is not only having change management training, IT facilitation skills, but also having a team of people that can evaluate and learn and test these systems. Because the systems are changing so quickly that you can't really tell from the websites or the salespeople what's going to be the best fit for you. Now, you know it may sound intimidating, but honestly the reason we wrote this research is I want you to be inspired. This effort we're going to go through for the next decade or more is going to be inspirational for individuals, for workers. We might not need as many mid level managers. By the way, that was one of the conversations I had yesterday too. I don't know if we'll flatten our organizations even further. We probably will, but there are going to be very exciting new jobs in AI management, in training, the AI in corpus management in Customer service, in Listen employee facing customer facing roles are going to become even better. If you get in a ways waymo car and you let the car drive you around from place to place and you have a problem, there needs to be a human being to help you. So there are still people involved even in the most autonomous systems we have. So we're more than happy to help any one of you that are going through thinking about this. The Global workforce intelligence research GWI which we've done now 8 or 9 industries has explicit examples of the roles and skills that are needed to facilitate these re engineering changes. So that's my quick podcast for today. I encourage you to read the Super Worker research. Get your hands on it. You can get it through Galileo or the JBA or through a corporate membership and join me on my webinar that'll be taking place in about two weeks. Call us directly if you'd like to Just talk about your company and your opportunities and think about this as an uplifting job creating technology opportunity. By the way, on those lines, the reporter I was talking to yesterday was asking me a lot about this. If you think AI is a technology to eliminate jobs and simply reduce headcount, you're going to be in the 5 to 10% of companies who really don't see great benefits. That is not what's going on. I know the press likes to write about that stuff, but honestly we're going to be building even better roles and opportunities on top of this stuff in business processes that can scale an order of magnitude or more maybe two orders of magnitude faster than they could before and so we will still have jobs for plenty of people but our companies are going to get more efficient we're going to grow we're going to be able to reach new opportunities faster. Time to market is going to go up quality is going to go up we'll be more innovative. I mean I really really do believe we're in the dawn of a new era of business process re engineering even though that's an old term. It's going to be a really exciting opportunity for the next couple of years. I look forward to talking to each of you about your situations. We'll be talking a lot about this at our Irish Resistible conference in May. Come to the webinar get your hands on the research and we look forward to talking to you. Thank you.
