In this episode of the Me, Myself, and AI podcast, host Sam Ransbotham speaks with Taylor Stockton, chief innovation officer at the U.S. Department of Labor, about how artificial intelligence is reshaping the workforce. Taylor emphasizes that AI is having an economywide impact, transforming tasks within nearly every job rather than affecting only certain industries or specific roles. He stresses the importance of helping workers and businesses adapt.
He also argues that AI literacy is becoming a foundational skill and should be prioritized alongside soft skills like relationship building, which will remain essential for differentiation in an AI-driven economy. Taylor calls for shifting the public narrative from fear to optimism, toward highlighting the ways that AI expands opportunity, mobility, and meaningful work, instead of deepening uncertainty.
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Transcript
Allison Ryder: In the summer of 2025, the U.S. federal government released its “AI Action Plan.” Today, we talk to one of the executives behind it, from the U.S. Department of Labor, to understand how the agency is thinking about labor trends nationwide in the age of AI.
Taylor Stockton: I’m Taylor Stockton from the U.S. Department of Labor, and you’re listening to Me, Myself, and AI.
Sam Ransbotham: Welcome to Me, Myself, and AI, a podcast from MIT Sloan Management Review exploring the future of artificial intelligence. I’m Sam Ransbotham, professor of analytics at Boston College. I’ve been researching data, analytics, and AI at MIT SMR since 2014, with research articles, annual industry reports, case studies, and now 13 seasons of podcast episodes. In each episode, corporate leaders, cutting-edge researchers, and AI policy makers join us to break down what separates AI hype from AI success.
Hey, listeners. Thanks again to everyone for joining us. I’m excited to be talking with Taylor Stockton, chief innovation officer at the U.S. Department of Labor. He leads the exploration of how AI and emerging technologies are affecting the labor market. He’s got [an] interesting background, and I hope we can get into it, but most importantly, he and I met when he was an undergraduate student at an introduction to IS course I taught. Because of this, I feel completely justified in taking full credit for any of Taylor’s successes. And I hope our listeners realize that’s completely ridiculous. Taylor, great to talk to you again after so long.
Taylor Stockton: Sam, it’s great to see you. I feel like I should still say, “Professor Ransbotham.” But it’s great to reconnect so many years later.
Sam Ransbotham: After this much time, I think we can go with “Sam.” Let’s start with your current role. You’ve got a great overview of the economy right now. What’s artificial intelligence doing to the economy?
Taylor Stockton: It’s such an exciting time to be at the Department of Labor because I think, in many ways, technology, especially because of AI, is reshaping what labor is more than any inflection point in American history. I think what we’re seeing in the economy is that AI’s impact is not specific to a certain sector or a certain occupation. It is truly economywide. And even if there are jobs that won’t increase or decrease dramatically because of AI, every job is being transformed. So I think our role at the Department of Labor is to say, “How do we have to evolve ourselves in terms of programs and policies to make sure that businesses and workers can really benefit from AI’s benefits and navigate its challenges as well?”
Sam Ransbotham: Well, that’s the crux of the whole problem, though — getting the good but not getting the bad there. Let’s get a little detail: What kinds of changes are happening? What kinds of things are you seeing?
Taylor Stockton: We’re working with a lot of different business and industry associations across the economy, and we really believe that AI can shape [the] benefits of productivity and job growth. So we want businesses to be able to have the tools and resources to adopt this technology and integrate it within their companies.
Part of the challenge that we are hearing from a lot of these businesses in terms of adopting AI is the change management. The barrier itself is not actually the capabilities of the technology. In many cases, it is traditional change management processes of how do you get a workforce to buy in to the benefits that this technology can bring to them, not just to the enterprise overall? But then how do you translate those benefits through to the different workflows, through to the different job descriptions and org charts?
I remember from my days in management consulting right out of Boston College, some of these systems for a large enterprise to incorporate might take multiple years, just for one system. And as you’re looking at AI, needing to reshape all of the systems within an enterprise, all of the workflows, all of the job descriptions, these are, in many cases, things that are going to take multiple years. But our role as the Department of Labor is to make sure that we provide the resources and the funding and the guidance that can hopefully help accelerate that a little bit, such that workers and businesses can see these benefits even earlier.
I think a lot of what we are seeing is that there’s a lot of industries where AI’s capabilities increasingly can do different tasks that are core to certain roles. You see these knowledge workers such as accountants and legal professionals and management consultants, and suddenly, the new AI models are really good at reviewing long documents and summarizing documents and making small edits. And they can do these things in a much more rapid way than humans can. So I think the big question on people’s minds is “What does that mean? Does that mean the jobs are going away?”
I think what we’ve actually seen though is that the change … being experienced in the economy is that roles are shifting, and the tasks within each role and within each occupation are shifting. AI and these AI applications can increasingly take on some of these aspects of work and actually shift the roles that humans take on — our hope is — more meaningful and more fulfilling work that only humans can do.
Sam Ransbotham: I think that’s the hope here. Now the tough part’s always in the details. I’ve got kids that are in high school right now. We teach people in college. What should we be telling people to do now that’s different than what it was back a decade ago, when you and I met?
Taylor Stockton: I think the first thing that comes to mind for me, as someone who’s been in the startup world, is I think entrepreneurship and small business ownership are more possible and more feasible than any time before. I think the reason is a lot of these AI applications allow for some of the automation of back-office functions where, maybe in the past, entrepreneurs would have had to raise capital or take a lot longer to really build the infrastructure to launch a business. Suddenly, you can create a web page in 15 minutes. You can file the forms in a much more streamlined way. So I think I’m personally excited about some of that entrepreneurship and encouraging young people to see how feasible some of those paths are.
But what I would also say is that even though AI is transforming some of these roles massively, there’s a lot of tasks that I think AI can’t do that are more in this category of soft skills that people haven’t maybe focused on as much in the past, which is to say relationship building, trust building, all of these skills that are across a lot of industries and roles. AI can’t replace that. And I think it’s going to be only more important as AI automates other parts of the job. So I would encourage young people to think about, regardless of the industry that you’re working in, how do you make sure you develop those relationship-building skills and other soft skills that may be even more important in the age of AI?
Sam Ransbotham: Yeah, I buy that. At the same time, part of me also wonders about technical skills. When some new AI something or other comes out, I feel like people [who] have deeper technical backgrounds are going to be better able to assimilate the new thing going on. And that’s almost a complete counter to the focus on soft skills. How do we reconcile that? I don’t know what the right answer is on that.
Taylor Stockton: I think you’re right, and I don’t think it necessarily has to be reconciled. I think both of those things can be true at once. Briefly, looking back to the class we took together 16 years ago, part of what we looked at was the basics of Microsoft Excel, the basics of different software in a business context, as students thought about different paths in the business world.
I think a lot of those same types of views are relevant here to say, “What are the core AI literacy skills? What are the core AI skills individuals need to have in all areas of the economy, in a health care context, in a manufacturing context, in an accounting context? What [do] AI literacy and AI skills development look like to make sure that [people are very comfortable with] the tools and workflows that are increasingly common in the age of AI?” In my mind, it is both the soft skills as well as the ability to manage a lot of the AI tools that will increasingly be prevalent across the economy.
Sam Ransbotham: That’s a tough answer to everybody listening, because then it’s not A or B. What you’re saying is A and B. Naively, it’d be great if I knew everything, but you’ve got to make choices about where you spend [your time]. Let’s say you have one hour this afternoon to spend on something. Should you spend it on a soft skill, or should you spend it on a technical skill? Where does that incremental marginal hour go?
Taylor Stockton: You’re pushing me to prioritize here. I will say I know I started with soft skills, but I think the Department of Labor believes that AI literacy and foundational AI skills truly are going to be the gateway to opportunity in the AI economy. So if you force me to prioritize, I’ll go with the AI literacy skills, because we’re seeing so many new jobs [being] created, new forms of productivity [being] unlocked across the economy, but I think we recognize that a lot of that is only going to be possible for workers if they have those foundational skills. So it’s been a massive push for us to say, “How do we make those core AI literacy skills as accessible as possible across the economy?”
Sam Ransbotham: One thing that feels like a nightmare to me is all this stuff changes so quickly. I’m sure that everyone’s having trouble with that, but what kinds of things are you doing within the Department of Labor to try to keep your finger on that pulse?
Taylor Stockton: It’s a terrific point, Sam, because I think a lot of the conversations that we have [are] around the challenges of AI. And there’s a lot of headlines about some of this doomerism about mass job loss, which we’re not seeing in the data, and we don’t think it’s going to be the case. But to your question, a lot of times we note that the biggest challenge in our mind around AI and work is going to be the speed of change, because a lot of the cycles of education systems and workforce systems and enterprise transformation are so much longer than the speed of change of AI.
Some of these cycles in an enterprise — [for looking at] strategy or enterprise transformation or systems — change maybe once a year, maybe once every few years, but there [are] new AI models and new AI applications every six weeks. So I think the core capability that we are both encouraging businesses to think about — but also the capability that we are trying to think about ourselves — is agility.
To your question of specific projects and initiatives on our side, the big initiative that we’re about to launch is called the AI Workforce Hub, which is really going to be a little bit of an R&D lab around how we support workers in the age of AI with a core capability … being how we collect the right data around how AI is impacting the labor market, what we’re seeing from an AI adoption standpoint, [and] what we’re seeing from a productivity and time-saving standpoint. To your point, a lot of these metrics have not been available in the past, certainly not at the speed needed to truly measure in the age of AI. So we’re super, super excited to launch that initiative and make sure that we’re able to support businesses and workers in this faster-moving economy.
Sam Ransbotham: Talk more about that initiative. When is that happening? How long does that process take? Give us some details.
Taylor Stockton: It’s been in the works for a while. It was originally announced in the White House’s “AI Action Plan” that came out in the summer of 2025, and then, now early 2026, we’re looking to launch as soon as possible. I think the overall vision is to have that type of agility that can not only take in more information in real time about how AI is impacting work [and] not just have it be a passive research exercise [but to] really be something that’s a research and innovation exercise to say, “Let’s translate the data that we’re seeing into new policies, into new types of resources, and into funded innovation pilots to say, for example, if there are challenges that we’re seeing in the data around entry-level workers and the types of skills that perhaps entry-level workers need in an AI-driven economy, let’s also have a set of funded innovation pilots that further explores new models to support those individuals.”
So I think that type of research and data collection is one part of it. But that muscle of action, whether it’s through policy, through guidance, or through experimenting new models, is something that we’re super, super excited about.
Sam Ransbotham: It does seem like something that would be very nice to do at a national level, given that we don’t want everyone out there making these idiosyncratic duplicative efforts, and that’s exactly the sort of role that we would hope [for] a common good.
Taylor Stockton: What I would also say is that part of the vision is trying to address a challenge that we see right now, which is that the narrative around AI and work feels very fragmented. It feels very speculative. Everyone has their own thought leadership that they’d like to share on LinkedIn of what they think [will happen with] the AI workforce in five to 10 years. And that’s OK. I don’t want to discourage that or look down upon that. But it is also something I think businesses and state workforce agencies struggle with sometimes, to say, “How should we make decisions when there’s so much noise around the possible outcomes for workers and for businesses?” I think our goal is, to your point, how do we use our role as the U.S. Department of Labor to really be the signal through the noise and really be a central source of truth that businesses, that workers, and that state and local governments can come to really understand what’s happening and the possible levers to support workers?
Sam Ransbotham: I like that because at the core of this, there’s a lot [of] new [things] going on, and we’ve focused a little bit here on the speed at which it’s happening, which I think is important, but there’s also just the fundamental problem of: It’s new. One of the analogies that I think about is, we’re very good at measuring stuff like how many things we make. When we’re talking about how many cars we make, how many X we make, we’ve got great metrics in our world about counting how many of those we make, and we know how much we sold a car for. But when a company makes an open-source AI model that it provides to people out there that literally billions of people use, we don’t have good metrics around how much value that’s creating. If we fail to measure these things, then it’s going to be hard for the Department of Labor to figure out if an initiative is working or not.
I’m going to switch back to your comments about entrepreneurship, because I think those are fascinating. When these tools are available to everybody, how does one entrepreneur differentiate themselves from everyone else?
Taylor Stockton: I think it’s a great point of the two things that are true in the age of entrepreneurship and the age of AI, which is that it’s easier to get started and perhaps easier to get off the ground. Perhaps that’s more commoditized, to be able to get going, build a web page, launch a product, get initial feedback from the market — but to your point, others will be in the same position. So there will still be difficulty and challenges in scaling up and further differentiating.
Perhaps, counterintuitively in a certain way, in an age where technology is so abundant, and AI-generated content and products and services are so abundant, it may come back to humanity and relationship building to say in many products and services [that] consumers and enterprises may still prefer the solution that, yes, has the great AI-generated content but also has someone you can go to that is a human you trust and you’ve actually built a relationship built with and built a rapport with. So I’m hopeful [that] among the different aspects of differentiation,[there] will still be the human element.
Sam Ransbotham: I was reading about your registered apprenticeship programs and some of these things. Like you say, there’s a change management process both in industry and in society. I don’t know how all these things are going to play out. How long does that take? How is that going to happen?
Taylor Stockton: We put out a big report as the Department of Labor alongside the Department of Education and the Department of Commerce called “America’s Talent Strategy.” Part of what we outlined in that strategy is that the traditional idea of different pathways to economic opportunity is broken. And we need radical change to really make sure that individuals are able to see pathways into the workforce. We think that this notion of the “college for all” movement that was true for a while didn’t work. Higher education and four-year degrees will still be a great path for many people, but there’s also a lot of other paths that may make sense depending on their interests, depending on their context. I think part of the value of registered apprenticeships is that you’re learning on the job, but you’re also getting paid from the very beginning.
You’re not only not taking on debt, you’re getting paid. And there’s also not the risk of this mismatch that I think we so often see, whether it’s higher education or a boot camp or training program. Sometimes, individuals get to the other side, and they figure out the hard way that the skills that they’ve built, unfortunately, still aren’t fully connected to the skills that employers may be looking for. So the beauty of work-based learning models like registered apprenticeships is that you’re learning on the job, and you’re learning those skills that are so deeply intertwined with where the workforce is moving. That’s one of the reasons that we’re really investing in that model, to make sure more workers and more businesses can benefit from it.
Sam Ransbotham: You’ve alluded to a couple of times [with] some of your past consulting and [when] you phrased one of your jobs as “Hey, that’s before AI took over for that.” Take us [through] a little bit of history of what happened since Boston College. What have you been doing? How did you end up at the U.S. Department of Labor from an intro to IS class?
Taylor Stockton: Well, again, I credit any future success back to Professor Ransbotham.
Sam Ransbotham: Got that on the record.
Taylor Stockton: I fell in love with the idea that education is one of the greatest levers to unlock economic opportunity and this idea of the American dream. As I looked more into education and the concept of the American dream, I looked at this dynamic of technology reshaping the workforce in such a profound way that really requires us to totally change and transform the way that we approach education workforce development. So coming out of Boston College, I did start in consulting but with a specific focus on education and workforce projects.
From there, I actually moved to South Africa for a couple years at an education startup that was thinking about what is the future of K-12 education, and how do we make sure that we’re embedding technology in the way that we prepare students for the future workforce? From there, I did get my MBA at Harvard Business School, but while I was getting that MBA, [I] started the Future of Work Club [and] started a future of work blog that may or may not be somewhere still on the internet.
I helped start a workforce tech company that partnered with government agencies at the local and state level to address some of these issues and [say,] “Hey, how can we actually use technology to better match job seekers [who] are looking for jobs, looking for retraining, and businesses that are hiring in the economy?” So I was thinking about a lot of these issues, [which are] outside of government in the private sector, but because we were partnering with government agencies, I began to see the tremendous role and influence that government agencies have in really shaping the type of innovation that’s possible to support businesses and workers. So I was really grateful for the opportunity, especially from the deputy secretary of labor, who’s leading on a lot of these AI issues, and [I was] able to have conversations with him about leading a portfolio here, to really double down on the areas that the Department of Labor has focused on [in] the past and really make this a core part of how we think about the agency’s future.
Sam Ransbotham: One of the phrases I liked from your startup, too, was a “GPS for your career.” I like that, because earlier I was kind of pushing you on “If I have one incremental hour, where do I spend it?” I worry about that a lot, and I think about that a lot. It’s uncertain for me: What’s the best next thing for me to do? There’s one thing that I could spend an hour [with] this afternoon that would really help me, but what is that? If I could have that GPS, it feels like that’s a great mission.
Taylor Stockton: One of the pillars that we spoke about in our big workforce report, “America’s Talent Strategy,” is worker mobility. I think part of what we observed and reflected on is that individuals need to be able to move through the economy, perhaps so much more than they did in the past. A lot of people [are] examples of how much they’ve moved in their career compared to their parents. My mom worked for a real estate company for 42 years. I’ve already had more jobs than her, still feeling relatively early in my career.
So I think because of that, the need is navigation. There’s a need to say, “How am I able to see the different possible pathways that I might be able to take?” But, also more critically, not just what those pathways are, but how to get there, and how do I further equip myself with the right skills and right resources to better set myself up for those future options that I’m really interested in? What our startup did — and many other startups are doing — is say, “How do we use technology and AI to personalize that navigation experience to really support people in those future endeavors?”
Sam Ransbotham: That personalization level seems critical. Let me transition a bit here. We have a segment where we ask you some short questions, rapid-fire. What’s the first thing that comes off the top of your head? What’s moving faster or slower about artificial intelligence than you expected?
Taylor Stockton: I think part of what’s moving faster about artificial intelligence is the underlying model itself, the capabilities itself. It feels like there’s a new model [or] there’s a new feature every week, every two weeks, especially with the competition. But the challenge or the slower speed is the infusion within the enterprise. Again, I think there’s this hope by technologists that enterprises will just automatically adopt every new feature, whereas in reality, there’s a lot more cycles to getting to full usage.
Sam Ransbotham: What about artificial intelligence frustrates you the most?
Taylor Stockton: I think I am a perfect example of someone who constantly gets in arguments with my [large language models]. I am very pro-AI. I think there’s going to be a lot of benefits, but they are certainly not perfect yet. They certainly still make mistakes, and I may have even raised my voice a few times when interacting with them.
Sam Ransbotham: I think we’ve all been there. How are people approaching artificial intelligence wrong?
Taylor Stockton: I wouldn’t necessarily say “wrong,” but I worry. One of the things that I worry about is that there are still too many people and too many businesses, especially small businesses, that are sitting on the sidelines and that are waiting, doing a wait-and-see approach to say, “Let’s see how this evolves before I jump in.” This is a wave that is not going to slow down. It’s only going to accelerate. So I would encourage all individuals and all businesses to, even if you’re busy, even if there are other priorities, [ask] how you [will] make time to really make sure you’re building the skills to make sure you’re not left behind in a lot of the benefits that we’re going to see.
Sam Ransbotham: That’s great because we’re all busy. We all fill every day. It’s really hard to take that incremental hour to do anything new and different. I find that true. What do you wish that AI could do better?
Taylor Stockton: I think a lot of the areas of AI that I’m most excited about are in scientific and medical research. I think there is so much promise to really address issues that affect a lot of people’s lives, and I think it’s something that we’re seeing some investment by AI companies, but I wish the emphasis and the prioritization would be even more. There [are] sometimes new features or new product offerings that they launch, and you say, “Is this really the greatest impact option for humanity?” I won’t name any, but perhaps your listeners can think of a few. [On the] medical research side, again, there [are] so many people [who] suffer from long-term diseases that I think AI, if used the right way, can save lives. And I’m really hopeful about that.
Sam Ransbotham: I agree with that. That makes sense to me, because if you think back a hundred years ago, we were still sort of barely doing surgery. We were barely using anesthesia. We’ve had massive advances over the last hundred years. It’s kind of exciting to think what the next hundred could bring.
What should I have asked you? Is there anything you wanted to cover that we didn’t get in?
Taylor Stockton: I would just say that one of the biggest things on my mind right now is how we shift the societal narrative around AI and work. Even as the job data and productivity data looks positive, I think the reality is that there is a public sentiment question here that we have to talk about, that people are fearful and people are skeptical and people are uncertain.
A lot of what the Department of Labor is trying to do is say, “How do we shift the narrative from fear to optimism?” and make sure that everyone is able to benefit from the jobs, from the productivity, and from the meaningful nature of some of the shifts that AI can bring. I think it’s going to be a long journey. And I think the Department of Labor is up for that journey, but we’re looking for private sector partners and others to partner with, to make sure we are telling the story in the right way to benefit American workers across the country.
Sam Ransbotham: That’s great. In the big picture, it makes sense, but in the small picture, it’s really important, too.
This has been great. It’s been fun catching up. I hope maybe in 15, 16 years we’ll catch up again, and we’ll see what other wonderful things you’ve done. But thanks for taking the time to talk with us today.
Taylor Stockton: Thanks again, Sam. Really important conversation and appreciate you having me on.
Sam Ransbotham: Thanks for tuning in today. On our next episode, I’ll speak with Jacqui Canney, chief people and AI enablement officer at ServiceNow. Please join us.
Allison Ryder: Thanks for listening to Me, Myself, and AI. Our show is able to continue, in large part, due to listener support. Your streams and downloads make a big difference. If you have a moment, please consider leaving us an Apple Podcasts review or a rating on Spotify. And share our show with others you think might find it interesting and helpful.