There’s a better future as we transition through a tumultuous period

As companies struggle to understand the implications and applications of generative AI (gen AI), one thing seems clear: AI and its future iterations are not going anywhere. In the McKinsey Podcast, senior partner Kweilin Ellingrud and partner Saurabh Sanghvi share findings from McKinsey’s latest report on gen AI with editorial director Roberta Fusaro and explain why companies must pivot to embrace the technology itself and the deep and lasting changes it may create. The McKinsey Podcast is cohosted by Roberta Fusaro and Lucia Rahilly.

Edited excerpts from the podcast.

Roberta Fusaro: Why this report and why now?

Kweilin Ellingrud: Two influences have changed things. One is we’re emerging from three years of COVID, where there has been so much turmoil and change in the workforce. And two is generative AI that burst onto the scene about six months back. Together, they’ve changed the nature of work and jobs and inspired us to ask, “What’s different now?” and, “What can we expect in the future?”

Saurabh Sanghvi: I would add that the labour market looks different from what we’ve typically seen. We think that some of that has to do with the pandemic, and some of that has to do with workers’ needs and preferences changing. We’re seeing all kinds of unprecedented technical change even beyond generative AI. If we go a step further, we’re seeing a record level of federal investment in infrastructure and efforts to reach net zero. It’s this confluence of factors that we wanted to cover with this new piece around the future of work and generative AI.

Roberta Fusaro: When you add automation and the specter of gen AI to the mix, what effect do we project that’ll have on the labour market?

Kweilin Ellingrud: The impact of gen AI alone could automate almost 10% of tasks in the US economy. That affects all spectrums of jobs. It is much more concentrated on lower-wage jobs, which are those earning less than $38,000. If you’re in one of those jobs, you are 14 times more likely to lose your job or need to transition to another occupation than those with wages in the higher range, above $58,000, for example. But it also does affect the jobs on the higher end of the wage range. Writers, creatives, lawyers, consultants, everybody is going to need to work differently, because parts of it will be affected by gen AI. For some, it will be a more fundamental elimination of the job. For others, it will remake how we spend our time.

Roberta Fusaro: I know there are a lot of great opportunities associated with gen AI. What are the mechanisms by which we can ensure that there is a way for lower-wage workers to move up the ladder?

Kweilin Ellingrud: Twelve million occupational transitions are likely going to need to happen between now and 2030, with 80% of those in those four occupations that I mentioned: Customer service, food service, production, and office support. How do we make sure that workers in those jobs can reskill and upskill? A lot of that depends on individual companies to do that at scale, not in the hundreds but in the thousands of workers. I think public-private partnerships between the federal government and educational institutes could help to train and build the skills of our workforce.

Roberta Fusaro: Are there any other forces cited in the report that could affect the labour market in the next two to five years?

Saurabh Sanghvi: Some of the other factors that could impact the labor market are, one, we have an aging workforce. That will have an impact on everything as it relates to retirement and the fact that we’ve seen quit rates at an all-time high over the past two years. We’ll see an impact on demand for healthcare in the US as well.

Another finding that was probably my favourite of this report is that, over the past three years, we’ve seen 50% more occupational transitions than the previous three years. This is positive news in my mind because we’re seeing the occupation transitions result in workers disproportionately being in higher-wage roles.

Kweilin Ellingrud: One other element in terms of our workforce is that immigration has been quite low for an extended period. As we combine the higher quit rates with early retirements due to health and other concerns, and then lower immigration — all of these things exacerbating the talent shortage that we were describing earlier—when you combine it all, it’s a very challenging job market for employers more so than for employees.

Roberta Fusaro: What was the most surprising finding from this research?

Kweilin Ellingrud: The finding that shocked me most was that people in lower-wage jobs, below $38,000 a year, were 14 times more likely to need to transition occupations than those in the highest-wage jobs. I thought that it would be more egalitarian in the impact of automation and generative AI. I knew it would have a bit of a disproportionate effect. But 14 times is quite stunning to me.

Saurabh Sanghvi: As Kweilin mentioned earlier, our overall automation number in the midpoint scenario went from 21% of activities being able to be automated to 29% of activities being able to be automated. But one of the really surprising parts that’s not been covered is the number before gen AI was 21%.

Roberta Fusaro: If there were one message or sort of one silver lining that you could share with our listeners, what would that be?

Kweilin Ellingrud: Looking at the upside, looking at the increasing number of jobs, higher wages over time, yes, there are a lot of occupational switches that we’ll need to transition through, a lot of upskilling and reskilling at scale. But the GDP growth, the upside is that more jobs at higher wages gives me a lot of comfort that there’s a better future as we get through that tumultuous and challenging transition period.

Saurabh Sanghvi: One of the findings from our report is that it’s much more a story of augmentation. The huge kind of potential for this technology is, how can we help augment professions and help free up time so that it can then be repurposed. In the case of a teacher, it would allow them to spend more time directly with students to help improve student outcomes. That’s just one example, but it has a lot of analogs in other professions.