In this compilation episode of The Parlor Room Presents: Hello AI, host and Harvard Business School Online Creative Director Chris Linnane gathers HBS faculty to share actionable advice for mid-career professionals.
Featuring HBS professors Felix Oberholzer-Gee, Nien-hê Hsieh, Sunil Gupta, and Linda Hill, the conversation explores how to stay relevant in an AI-driven workplace by embracing new tools, building human skills, and adopting a “wayfinder” mindset to grow and create value.
Resources
Catch up on previous episodes of The Parlor Room, featuring faculty from this compilation episode:
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Transcript
Editor's Note: The following was prepared by a machine algorithm and may not perfectly reflect the interview's audio file.
Felix Oberholzer-Gee: If you're doing a lot of the automated routine work yourself, I think you set yourself up for displacement.
Voiceover: If you're mid-career right now, AI isn't coming for your job. It's coming for the parts of your job you've been leaning on.
Nien-hê Hsieh: My view is that, no, no, actually taking on responsibility is important because that's something that AI can't do.
Voiceover: The person who raises their hand is still the most valuable person in the room.
Sunil Gupta: AI is just like another language, and this is going to be here for a while or perhaps a long time. So you have to learn the new language, and the only way to learn the new language is through practice.
Voiceover: You wouldn't ignore a language a billion people were speaking. Don't ignore this one.
Linda Hill: We need people who are wayfinders, not pathfinders, because there's no clear path to the future these days. There really isn't.
Voiceover: Welcome to The Parlor Room Presents: Hello AI.
Chris Linnane: Welcome to a special episode of The Parlor Room Presents: Hello AI. If you're watching this video, you'll see that I'm standing in the middle of the woods. You might be wondering why I'm doing that. I'll tell you in a second. This is a very special episode. It's a compilation episode of mid-career advice from four Harvard Business School faculty. We've done two early-career compilation advice episodes. This is the first of two mid-career advice episodes.
So, the reason I'm standing in the woods is that I wanted to go for a walk, listen to the episode, and see if there's anything I should cut or look for ways to enhance. And it just kind of struck me as funny that talking about AI while I'm walking in the woods and running out of breath, and I'm not sure I know where I am. That's got me a little concerned, but it's a long day.
I'll be fine. We are talking to, in this episode, Felix Oberholzer-Gee, Nien-hê Hsieh, Sunil Gupta, and Linda Hill. This first clip is coming from Felix Oberholzer-Gee. The first time I talked to Felix about something in depth was right after we finished filming his Business Strategy course, which is a really outstanding course.
And we were out in the hallway, this was years ago, maybe four or five years ago. We talked about TikTok a little bit. And I was saying, "Man, I don't know what this TikTok thing is. Why do people care about it?" And he was explaining to me how it's going to pretty much be a huge force, and how strong the algorithm is, and that's going to drive all this traffic.
And he gave me all these reasons why it was going to succeed. And I thought, "Eh, maybe," but turns out he was completely right, as always. So here's Felix.
For someone mid-career who's trying to stay valuable as AI spreads, what can they do? What mindsets should they have? Any advice for them?
Felix Oberholzer-Gee: So the first thing is, really, use the tools. If you're the one who stays away from AI, who doesn't use the tools, I think you endanger your career needlessly. So, you need to get familiar with the technology. It needs to become completely routine to use it.
And then I think as you're thinking about the longer-term future, think about which parts of your job require a lot of tacit knowledge, require a lot of judgment. Those are the things you need to do really, really, really well because those are the things that are not going to get automated.
And so, if you're good at using AI as a support system in order to help you be even better at the things that cannot be automated, then I think you're in the sweet spot.
Chris Linnane: Is there anything they should stop spending time on?
Felix Oberholzer-Gee: So I think because you want to stay away from AI, if you're doing a lot of the automated routine work yourself, I think you set yourself up for displacement.
Chris Linnane: This next clip is from Nien-hê Hsieh. Nien-hê is one of the calmest, smartest people I've been around. He has such a relaxing voice that I feel like he should be a DJ from 10 p.m. to 12 p.m. every night, talking about soft rock, and helping people get through their problems, 'cause he's such a calm, relaxing guy. But I think he might be too smart to just be a DJ. He could do both. He could be a DJ, and he could be a professor, and he'd be great at both of them. Here's Nien-hê Hsieh.
Nien-hê Hsieh: So if it's a mid-career person, I would ask them first to reflect upon what they believe it is that has enabled them to succeed thus far in the context of their work. Let's just lay out in a very simple way, either sort of at a very concrete level, whether it's certain skills, whether it's certain resources, whether it's a network, whether it's things that they're good at, to a much more abstract level, sort of thinking about, "Well, actually, how is it that I got to where I am and what is it about me that I bring to the table?"
And my hope is, in that kind of exercise, each of us will find, in some sense, those things that are distinctive and unique to us. Some of those may be hard skills, but other things may be things about what we do, or how we act, or how we lead and engage in an organization. And my guess is a lot of those things that have enabled people to succeed thus far, if we focus on those, we'll find in there things that are distinctively, not just unique to us, but also sort of more on the human side that couldn't easily be replicated by AI. And then sort of double down on those. I think that's sort of one piece of advice.
The second one, as I mentioned before, is to actually actively seek out responsibility, and actively taking on that kind of responsibility, I think, is something else that I would imagine would be important as well. Also, interestingly, depending on how far people's careers are, it turns out that AI augmentation is very effective for people who are actually into their careers, like say engineers, for example, who are a few years in, then it really takes off in terms of their ability to use AI because they have that kind of tacit knowledge and things like that, and in a way that's different actually from entry-level jobs as well.
So, thinking in terms of what's brought me to where I am, what's distinctively me, and actually embracing AI in ways that allow you to be more effective than other people who are in the entry-level positions.
Chris Linnane: So when you're saying taking on more responsibility or taking on responsibility, you're talking about AI responsibility?
Nien-hê Hsieh: Or generally, more generally as well. I think that's right. Yeah. So I think that's the kind of advice people normally give anyway. So sort of lean in, show up, take on more responsibility. I think it's a general kind of thing, but I think that's kind of...
I think there before it was like, "Oh, well, you can sort of demonstrate that you can do the job better and things like that." My view is that, no, no, actually taking on responsibility is important because that's something that AI can't do.
Chris Linnane: Back with me in the woods, I'm not going to lie. I think I'm getting more lost as we go. This next clip comes from Sunil Gupta and uses a wonderful analogy of language, and it really is a language that everyone's trying to get used to hearing with AI. So, without further ado, here's Sunil.
Sunil Gupta: So I think this is just like another language. AI is just like another language, and this is going to be here for a while or, for perhaps, a long time. So you have to learn the new language. And the only way to learn the new language is through practice. Another analogy I'll use is: You can't learn swimming by sitting by the side of the swimming pool. You have to jump into it.
So my first advice to the mid-career people is, rather than making an argument why AI is not the right thing to do for my job, just jump into it, see what it can or cannot do, and then you have to learn to adjust as to what role AI plays in a job. And maybe you can be more productive doing that. There are a lot of rote work that every job has that you would rather let the technology do for you.
And that's what we and you all need to figure out. But at the same time, companies need to give these mid-career people some space to learn and fail. And that's the biggest challenge, that if you are evaluated in the same way that we are evaluated before, because the company's not giving you space to learn and fail, then obviously there is a bit of resistance to try it out.
Chris Linnane: Now, that analogy you just used of not being able to learn how to swim by sitting on the side of the pool, that hit home for me because I used to teach swim lessons, and I teach it to five-year-olds. And you wouldn't believe how many five-year-olds think they can swim by just sitting on the side of the pool. I always had to throw them in the water, and I felt so guilty.
I feel like you almost have to experiment on your own, too, not even in a workplace environment. You just have to, on your own, try a bunch of things. And I think that's maybe where, if you just exercise your tasks through work and your responsibilities at work, then maybe you're not seeing how you can bring other things to your job as well. I've noticed that just trying a bunch of different things has helped me in my day-to-day as well, silly things, but they tie it back.
Alright. Back in the woods, I think I'm okay. I just saw a woman and her dog, and I was too embarrassed to ask her where we were. Instead, I just said, "Hi, how are you?" And she said, "Good, how are you?" And I should have said, "I'm scared," but I didn't. I just said, "Oh, good." And I kept walking. Anyway, I think I'm going to be fine. If she's here with a dog, we can't be too far from civilization.
Alright, this next clip comes to us from Linda Hill, and it's so appropriate. She talks about two things that she's created: wayfinders and pathfinders. And right now, I would take either. I love how Linda breaks down the complex into something so simple to understand. So here we go, Linda Hill.
So Linda, question for you about mid-career professionals and the avalanche of AI talk every day in the office place. How do they put themselves in a position to succeed in AI if it's something that potentially is intimidating them right now?
Linda Hill: I think you just have to start using it. And you have to start using it in your work and in your life, and get some sense of what it is and how it works. So, I have written my last two books with someone in their 20s. And they start off with me when they're sort of 20, 21, right after college, whatever age they get out, because I really think I need to have a different point of view than my own.
So I always work with a young person and an executive when I do my work now. And I would say if you are mid-career, you need someone or something to nudge you along, because unless you use it yourself... So they forced me to use different large language models, ask the same question of three or four of them, and see what comes out.
And so, I've been doing that ever since they nudged me along, and they're nuisances as I call them, but they really helped me understand that I don't know that I would've done it so much without them. And then one of them encouraged me to take a vibe coding, I guess that's what it's called, course. Learned a little bit about that. She's still vibe coding a lot. I cannot say that I am, but I did do it. I understand what it was about and why this was what was different about it.
So one is, I think you just kind of have to use it and try to get comfortable with it a little bit, just to know what it is. And actually, with a little bit of experimentation, you have all these questions about it. So one of the things related to the mid-career person, stay curious, or if you're not curious, get curious.
So it is true that when we went and asked, "What do you need to be to be an effective leader today?" One is that you need to be curious. You need to be curious, you need to be adaptable, you need to be able to have comfort with ambiguity. This is what we were told, and I think that all of that is uncomfortable making, but until you get in there and use it a little bit.
And then I think you need to talk with some people who have begun to deploy it in their organizations across more than one person, if you will, but as some kind of tool, and understand what they...understand sort of vicarious learning what's working, what's not.
One of the leaders that I'm studying right now has begun to identify a few sets of those companies that he thinks are actually creating value with, and then, I guess, there are not that many in some ways, in his mind, with gen AI. And he's called them up, and he wants to visit them, and spend...They're not direct competitors, so he's set aside some days to go do a little tourism as—what does he call it?—education tourism, or something that he calls it, to go see what's happening and have some conversations.
I think it's an important time. The other thing I'd say to the mid-career person, what happens to organizations over time, and probably to us as individuals in our own way, is that we stop the outside-in look. We're looking in and force yourself to be open and curious and look out. So one of the things we ended up writing about in our new book was that we keep looking for pathfinders, people who can be our pathfinders.
And we ended up writing in the epilogue of our book about how we need people who are wayfinders, not pathfinders, because there's no clear path to the future these days. There really isn't. So, consequently, it doesn't mean that you don't want to be visionary and all of that, but we've never been good at forecasting, and I think we're really not so good at it right now.
And the wayfinders, those were the people who were willing to get out into the ship and see, "Is there another... And maybe the world's flat and maybe it's round, but let's go see what's over there." And they didn't have the fancy tools we had. And we now have all these technologies that can help us. They had the sun, the stars, the way, whatever.
And these wayfinders are people who are like explorers, who are willing to go out and see, and they're going to have to pivot a lot, and they're going to get lost. Then they're going to find their way, or they're going to find their way to a destination they didn't know that even existed.
So, I think the wayfinder mindset, as opposed to the pathfinder mindset, is one that we all need to have. And so I'd encourage you to kind of be a little bit more of an explorer at your own... I'm a physical coward, so for me to say this to you, but that is what we see. These are people who are wayfinders, who are not looking...the path isn't there. We can shape it.
Chris Linnane: Okay, so this concludes our episode. Thank you so much for listening and watching. I should say that I can hear the road. It's right over there. I'm going to be fine. There's a house over there, too. I might stop and ask them for water, and maybe they could call my wife and have her come pick me up. That would be great. Thank you again for listening to and watching this episode. And if you like what you're listening to, please be sure to subscribe and/or follow wherever you get your podcasts. Thank you again.