Rethinking Work for the Age of AI

As AI evolves faster than we can learn, how must work adapt?

In this MINDWORKS Mini, host Daniel Serfaty and Prof. Joseph Fuller of Harvard Business School discuss how artificial intelligence is reshaping the workplace—changing how we hire, train, and retain talent.

They explore why continuous learning is essential, how AI coaching tools are catching up to human mentors, and which skills will define the workforce of the future.
 

Transcript

Mini 5.2.4 Rethinking Work for the Age of AI

Daniel Serfaty: In terms of designing the workplace, what are some of the biggest challenges, not necessarily the physical workplace, but we talked a little bit about that in terms of creating an environment when humans and AI complement each other as opposed to compete or even trying to eliminate each other?

Prof. Joseph Fuller: Well, I think there are several. I am finishing now a very extensive project with my friends and collaborators at the research branch of Accenture, the high-tech giant, appropriately named Accenture Research, where we're looking at how the application of AI inside entire organization structures will lead to those structures changing. Now, we can only begin to add on a sense for that systems dynamics effect that we talked about earlier.

But even without the ability to forecast that accurately, several implications are pretty clear. In a number of industries, you're going to have a need for fewer entry level and particularly what are called individual contributors. So, you're through the entry level job, but now a member of technical staff, for example, in the software company, where you'll have a very significant improvement in productivity, which changes the ratio of those people to their supervisors.

That suggests that in the future, organizations are going to have a much, much greater focus on retention. Why? First, because we need people that have the judgment who can either see the three-sigma insight or detect the well-formed confabulation, but also because if I have fewer entry level workers, filling those roles with people who have that insight will be harder. And if I tolerate a high turnover rate in middle level, let's say senior software engineers, or I compete with some large companies that hasn't understood this until it's too late, and therefore, reduced to just throwing money at people, not at the extent that Mark Zuckerberg or Elon Musk are throwing it at AI geniuses. But I'm late to this party and now I must steal the other's talent, I'm going to pay a big price.

Let me add another thought, which is the rate of technological advancement is at this point, kind of asymptotically approaching the time it takes to master a technology. By that I mean the half-life of a typical technology of a version of a technology. So, I'm not talking about generative AI versus machine learning. I'm talking about, of course, machine learning is integral to generative AI. But going from ChatGPT 3.5 to 4, to 4.0 to five, the half-life of those quite different models that have to be handled differently is approaching how long it takes to learn that. So, we've never been anywhere close to that.

Historically, what we've done is evaluate candidates for jobs, especially white-collar jobs based on their credentials, mostly their academic credentials, field of study, performance, selectivity of institution, maybe some work experience either as a student or a first job. And once we've hired those people, we've let them learn how to be productive in our companies basically through on-the-job learning. In fact, corporate formal learning budgets have been being reduced for quite some time in the United States.

Well, neither of those two things work anymore. I have to do much more learning internally to get people to keep up with the rate of change. I can't let them rely solely on learning on the job or I won't be getting the most out of the technology until I'm all moved at the next technology and it's hopeless for the education establishment to keep up with this type of velocity. These institutions are not built for speed.

Daniel Serfaty: Not designed for that yet.

Prof. Joseph Fuller: Not at all. I tease my colleagues here that certainly at Harvard College, our undergraduate institution that Oxford Don from the 15th century would basically understand the structure of Harvard College and its governance. He would be speaking a very confusing English to us and be astonished by things like smartphones. But if you showed him an organization chart, he'd know what you're talking about.

So, we're going to have to have much greater work-based learning, apprenticeships, co-op programs from university. So, employers can help evaluate the young workers, a rent-to-own model, but also give them experience that's going to give them a background, allowing them to be sufficiently productive fast enough that every job doesn't turn into a mastery job.

Daniel Serfaty: That's fascinating. In a sense, one of the implications of what you're saying is that if we're looking at the nominal 40 hours week job, because of that need to learn, learn and do, learn and do loop, the much larger proportion of those 40 hours will have to be built-in and designed for learning on the job and not expecting necessarily an immediate productivity out of it, but the future productivity will be a multiple of what it is. That's a pretty drastic implication, as you say, in the way we hire and how we hire folks that will be able to sustain and benefit from that kind of learning.

Prof. Joseph Fuller: Yes, indeed. And we'll be looking for people with a number of attributes. One is intellectual curiosity and a demonstrated capacity to learn. And as I mentioned earlier, we're looking for people who will have higher order, what are called social skills, the ability to work with other humans. That goes well beyond EQ, by the way, to communication skills, negotiation skills, comfort with dealing with strangers.

By the way, women are increasingly dominating higher education in the developed world. 58% of all current college enrollees in the United States are females. And women generally outperform men on social skills by between 25% and 33%. So, high social skills people with a proven capacity to learn that mix is going to be increasingly women. And when I say this to some, let's say, reunion classes here at Harvard Business School, I'll make a estimate of the number of women in the room, the number of men in the room, and I'll say it's 40% women, 60% men. I will say, I'm about to say something that 60% of you will find unbelievable, and 40% of you think is so startlingly obvious that I ought to be embarrassed as a Harvard professor for mentioning it to an audience.

Now, there's another factor at work here, which is every single one of these dilemmas is a market signal to innovators and to entrepreneurs come up with an AI solution for this. So, AI coaching applications now are within single digit points of the effectiveness of human coaching in multiple dimensions. Why? Because human beings tend to make errors of the same type in a recurring fashion. We all have our personality footprints and they come to the fore, and our quick and twitch reactions are going to be similar transaction-to-transaction. So, it's easy for the AI to get a sense of that.

So, I think there will be AI bots that understand Daniel's learning modality versus Joe's. There'll be 8 or 10 archetypal learning modalities for people in a consulting firm with this background, with this job description, with this academic background, and it will be able to detect where you're likely not to grasp what it does or you're likely to make an error, be on the lookout for it, capture data, maybe prompt your supervisor, maybe prompt you, and we'll see the same thing, I hope, in certainly grade 4 through grade 12, if not grade 2 through grade 12 in the K through 12 system, where not only can I help young Daniel and young Joseph overcome things that are hard for them, don't come naturally.

But also, I can tell their instructor, "Daniel needs help on this. Joseph needs help on that." Or your entire class, we've taught something last week, but none of them actually understand it. You better review that, because you're building on a foundation of sand on that core concept.

Daniel Serfaty: I think that last point is very important, because I see that in both K through 12 education. But also, in professional training that one of the big benefits of those AI agents or bots is the individualization of instruction and the individualization of feedback just because a single teacher or single trainer cannot take care of 20 people all over the learning curve and specialized AI bots could.