Episode Transcript
[00:00:01] Speaker A: Hello, everyone. I'm recording today from Dublin, Ireland, where I just finished a rousing two and a half hours with the learning and development institute here. About 100 learning and development people talking about the transformation and disruption and organization and AI going on in training. But I want to talk about actually something completely different today. And that is two really big announcements that I think are going to echo across the business community. The first was UKG's announcement of the layoff of 14% of their workforce, more than 2000 people. And then followed quickly by Intuit's announcement of the layoff of 10% of their people, several thousand people. And the reason I'm bringing this up is because we've been a lot of worries that AI was going to eliminate jobs.
But in both of these companies, the reason they're letting people go is actually something different.
[00:01:08] Speaker B: AI costs a lot of money.
[00:01:10] Speaker A: Not only do you have to hire engineers to build AI solutions, but these are both software companies and they have.
[00:01:16] Speaker B: To deploy AI solutions.
[00:01:18] Speaker A: And the deployment of AI solutions costs a huge amount of money. Why do you think Nvidia is a trillion dollar market cap company?
These companies, UKG and QuickBooks, Intuit and others, now have to shift resources from human investment in call centers and salespeople and other service professionals to AI systems that actually cost a lot of money to run. And so what both CEO's mentioned, and.
[00:01:49] Speaker B: I talked to the UKG CEO in.
[00:01:51] Speaker A: Detail, is that they need to reduce spending on labor to increase spending on capital. In other words, AI. Now, theoretically, the reason the AI industry exists is that it's supposed to be improving productivity, but it doesn't improve productivity if it costs more money to produce and sell and deploy than it does to save. So even if you increase productivity, if.
[00:02:20] Speaker B: The AI platform costs a fortune, you're.
[00:02:23] Speaker A: Not going to be better off and this whole game is over. So even though, of course the cost of AI will decrease, right now it's very expensive. And both of these companies have said that. So it's funny, but the AI loss of jobs is completely different than we thought. It wasn't really about AI taking over somebody's responsibility. It was about not being able to afford to keep the people on the payroll.
Now, the second issue that came up, which I think is even more interesting and maybe more important, was the comment from the CEO of Intuit that of the two or 3000 people that were laid off, about 1000, in fact, precisely 1050 were underperformers. So I have a whole bunch of questions about that. First of all, if 1050 people and that's a very precise number are underperformers. I have to wonder what Intuit's doing about their performance management process in general, and why they would suddenly lay them off when they have managers who are supposed to be managing this process. So that's a capitulation that maybe their management team and management process isn't working, but maybe that's not actually what happened. Maybe what actually happened is something a little more interesting.
And that is, let's assume that the staff that are being let go are in roles like sales, customer service, marketing, operations, jobs that require a lot of manual labor. Whether you call this high skilled or low skilled, it's white collar labor. And let's suppose AI comes along and automates 25 30% of that work. So if you have ten salespeople, one salesperson is hitting it out of the park, way over exceeding their quota. Eight salespeople are at quota or close to quota, and then one or two are really underperforming. In a normal environment without AI, the way a manager would deal with that team is they would congratulate and celebrate the person who's really hitting their numbers, look at what they're doing to succeed, and teach the others, particularly the people that are underperforming, what they need to do to reach mid level or higher levels of performance. That's called increasing talent density. In other words, not letting anybody run in the company who isn't adding value to everybody else. And of course, if that process is unsuccessful, you either move that person to a new role, or give them a developmental assignment, or ask them to leave. Now, suppose AI eliminates 35% of that work. So the person who's overachieving their job, great, now we're doing even more work, and maybe the AI is less expensive than their salary, so we're making more money. By the way, if AI is costing more than their salary, then maybe you wouldn't turn it on. You may not use it at all. The mid level performers, you're going to say, okay, same thing applies. These guys are doing pretty well. We'd like them to do better. So let's give them coaching and development and show them how to use the AI to be more productive. The lower level performance, now, performer, you're saying, well, wow, these guys, or this person is maybe barely hitting their numbers and they're underperforming. We think a third of their work can be taken away by AI. We need to cut them immediately because.
[00:06:05] Speaker B: Their added value is almost near zero.
[00:06:08] Speaker A: In other words, what AI does is it raises the base performance for every individual job. And now everybody has to perform significantly above the level of performance by the AI itself, because in some cases, the sales client or the customer service client may interact with the AI first before talking to the direct human. And that means I, we may not need so many humans. And so in some sense, AI is forcing us to increase talent density in the company, which is exactly what everybody was worried about at the meeting I just finished.
[00:06:53] Speaker B: Most of the L and D professionals.
[00:06:55] Speaker A: Who I talked to at the end of the day were concerned about their own skills. How do I learn more about this stuff? How do I stay up to date? How do I make sure I don't become irrelevant? I think all of us are going to have to ask that kind of question of ourselves, of course. But of our teams, if we automate 25 or 30% of the work, what.
[00:07:17] Speaker B: Are the rest of the people doing.
[00:07:18] Speaker A: To justify their added cost above the work of the AI? Now, there's certain things that AI simply cannot do, for example, like training itself or marketing itself or improving itself.
Of course that's going to come. But, you know, a lot of the.
[00:07:35] Speaker B: Care and feeding of AI has to.
[00:07:37] Speaker A: Do with data management and curation and performance management and tuning, et cetera. That is basically human work. But I think these two layoffs are really important to consider as a business person, as an economist, and as an HR person.
Now, as it gets to HR, I have a few other questions that come to mind. In a company like Intuit that has 20,000 or 25,000 people and 2000 or so are laid off, I think 2800 might have been laid off.
There's a lot of questions to be asked.
Of the people that were underperforming, why were they not dealt with earlier?
Of the people that were laid off? To save money for all these new AI projects, who's going to reorganize those.
[00:08:28] Speaker B: Teams, and how are they going to be reorganized again?
[00:08:30] Speaker A: That's work that HRP should be involved in. Of the people that remain, are their salaries going to stay the same, or are they going to go up based on the cost reduction? Are those people happy or unhappy based on the announcement from the CEO? The way these layoffs have taken place in the UKG case, there's a lot of grumbling from the people that have been let go, that they got an email and it was a very unsympathetic.
[00:08:56] Speaker B: Process, at least from some of the.
[00:08:57] Speaker A: People I've talked to.
That's not typical of UKG, but that seems to be what happened in the.
[00:09:03] Speaker B: Case of intuit, they were very careful.
[00:09:05] Speaker A: And very kind that they told people that if their job was eliminated, they were going to get paid through the end of July when some of their options had vested. But how are these people being communicated with, and particularly the people that were underperforming? Were they told in advance? Did they have the opportunity to improve themselves? I suppose those kinds of questions will be asked by those people and if there's lawsuits, we will see them. But then I ask myself, isn't the same thing happening to the HR function itself? And the answer is yes. In the case of L and D, where I believe there's going to be a massive amount of automation and reduction of work, if the L and D team learns how to use these new tools and replaces the amount of energy and time being spent on instructional design, for example, or content editing and curation, well, what are those people going to do?
[00:10:00] Speaker B: They should be working on performance consulting, they should be working on knowledge management. They should be working on training the.
[00:10:07] Speaker A: System to be smarter.
[00:10:08] Speaker B: If they don't know how to do.
[00:10:09] Speaker A: That, they won't be there anymore. So all of us in the white collar professions are going to be forced to upskill ourselves because of AI. And again, I'm ignoring the economic effect that the AI may or may not be cheaper than human.
[00:10:29] Speaker B: Ultimately, by the way, from a business.
[00:10:31] Speaker A: Perspective, I don't care if we're delivering services through AI or human services. Either way, if the human services are cheaper than the AI, we're not going to use it. So just remember that for those of you that are selling AI tools, if you get too greedy on how much you charge for stuff, nobody's going to buy it, even though it is kind of cool.
Final thing that I think is interesting along the lines of this whole situation.
[00:10:57] Speaker B: Is the general effect on other industries.
[00:11:00] Speaker A: The way most CEO's operate, and I know fair number of CEO's, and I am one, is that when interesting news like this comes out, especially if it's.
[00:11:11] Speaker B: In your industry and these two companies.
[00:11:12] Speaker A: Are both in the software industry, you read about it, maybe you know specifics about it, or you know the company itself, or you know the CEO, or you know people there, and then you immediately scratch your head and say, what does that mean about us? Are we in the same boat? Should we be thinking about the cost of AI? Are we putting enough money into this? Are we underestimating the expense and the capital investment we need to make? Are we also suffering from hell and down city problems? Do we need to improve our performance management process, that is all going to start happening at an accelerating rate.
[00:11:48] Speaker B: And again, I'm not saying these robots.
[00:11:50] Speaker A: Are going to eliminate jobs, but they're going to force us to be very serious about the skills, capabilities, and output of the people we have, which, by the way, is good business anyway. And if you don't agree with me or understand this, imagine what's been happening for the last 50 years to manufacturing workers. The first time a company buys a machine that automates, you know, stamping of metal or bending or assembly or anything else, the people on the assembly line.
[00:12:23] Speaker B: Sit there looking at it and say.
[00:12:24] Speaker A: To themselves, oops, what just happened to my job? And if the person who's on the assembly line has no other skill except.
[00:12:33] Speaker B: Tightening a bolt and using the torque.
[00:12:35] Speaker A: Wrench to make sure it's at the right torque, and a machine now does that in a fraction of the time.
[00:12:40] Speaker B: He or she is going to have to reskill themselves.
[00:12:43] Speaker A: And they've already done this to learn about maintaining the equipment, operating the equipment, or other services that the equipment that fall around the automation of the equipment itself. And in some cases, they're not going to make that transition, and they're going to be out of work and they're going to have to go back to school, learn something different. Another interesting example of this is McDonald's. You know, McDonald's, when I worked there in the seventies, I was actually worked at McDonald's for three years. We had, in our particular store in California, a lot of employees. I mean, I don't think you could open the store without a minimum of five people. And oftentimes there were ten or 15 people working in the store at a time. There were people at the front counter. There were a few people at the grill. There were a few people in the back room preparing the condiments. There was somebody manning the french fries. There was somebody working on the shake machine, and there were some other people cleaning up. So we had very high levels of staffing compared to today.
[00:13:44] Speaker B: You walk into a McDonald's today, and.
[00:13:46] Speaker A: Most of the employees or most of the customers have purchased on an app, they've gone to a kiosk, and behind the scenes, there's some number of people operating the equipment, I would imagine those people are making more money. I made $1.45 an hour when I did it. Of course, that was a long time ago, but there's fewer of them, and the jobs are probably higher level jobs to some degree. So I think the same effect is taking place here, where every one of us has the opportunity to upgrade our skills and add more value in the sort of threat or opportunity delivered by AI. So let's see where this goes. I think this brings back to the surface the topics of performance management, talent density.org design, upskilling, and of course, pay and employee motivation. And I will keep you in the loop on more that I learn about this topic. Bye for now.
[00:14:40] Speaker B: Having a great weekend everybody.
