8 Oct 2024 · 24m · Alison Beard, Jay Barney · 20 quoted facts
Generative AI is a tool available to every company, so using it for generic jobs like cutting costs or drafting code will not create a lasting edge over rivals. Jay Barney argues that sustained advantage still comes from resources that are valuable, rare and costly to copy, such as trust, brand, supplier relationships and culture. AI pays off only when it is tied to those strengths.
The tension is between urgency and durability. Barney says leaders should adopt AI quickly everywhere it helps, because failing to do so is a competitive disadvantage. But that adoption only buys parity. CEOs must work out what makes their firm special and point AI at it, while tempering spending expectations. Agility, the ability to absorb change, is itself a possible advantage.
Because any company can buy the same tools, cost cutting and drafting work are quickly copied by rivals. Skipping them creates a disadvantage, but doing them does not create a lasting lead.
Barney says a rival with a functionally equivalent dataset will find similar patterns. Sampling theory suggests a small dataset can reveal the same patterns as a huge one, so sheer size may not matter.
Barney points to socially complex assets like trust, brands and supplier relationships, things that take years to build, and things where the path to copying them is unclear. AI amplifies these but does not replace them.
Barney defines agility as managing change better than competitors. Large firms are often slow at this, so a nimble smaller company can apply AI in ways bigger rivals cannot.
Barney says functional managers should chase value from AI, while CEOs protect and grow the firm's real advantages. The CEO and CIO should constantly check that tech investments do not put the core at risk.
Barney says generative AI is available to every company, so any cost savings or efficiency you get can be copied by competitors. It keeps you level with rivals rather than ahead of them.
Yes. Barney says you should use it nearly everywhere you can, as quickly as you can, since it is fairly cheap now and not using it would leave you at a competitive disadvantage.
Not necessarily. Barney says if a rival has a roughly equivalent dataset, the patterns found will be similar, and a much smaller dataset may reveal the same patterns as a giant one.
Barney says to look for assets that are socially complex (like trust, brand and supplier relationships), that take years to build, or whose creation steps are unclear. Tying AI to these makes its benefits hard for rivals to copy.
Barney says it can, mainly through agility and unique business models. Large firms are not known for agility, though on average he expects firms that already have advantages to do better.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Jay Barney, a professor at the University of Utah's Eccles School of Business, co-authored the Harvard Business Review article 'AI Won't Give You a New Sustainable Advantage' with Martin Reeves of Boston Consulting Group.
“Jay Barney is a professor at the University of Utah's Eccles School of Business. With Martin Reeves of Boston Consulting Group, he co-authored the HBR article, AI won't give you a new sustainable advantage.”
Barney says firms are using generative AI to produce first drafts of code, which humans then refine.
“Firms are using GenAI to, right now, produce the first draft of code, and then a human being will go through and then update and make it make it more elegant or whatever.”
Barney says boilerplate contracting work in the legal profession is expected to be substantially affected by generative AI.
“in the legal profession, a lot of the sort of boilerplate contracting work and and other kinds of work that the lawyers historically have done is”
Barney compares building a general-purpose proprietary AI system to a company building its own word processor to gain advantage.
“It's kinda like deciding, Gee, we wanna get a competitive advantage from word processing, so we'll build our own word processing system.”
Barney argues proprietary AI software is unlikely to provide sustained advantage because competitors can build their own.
“if it one firm can develop proprietary software, then its competitors can also develop that proprietary software, and so even that is unlikely to be a source of, uh, sustained advantage.”
Barney uses the example of a 15-year database on customers, suppliers and employees to illustrate unique proprietary data.
“suppose that your company, uh, has developed a very long, very complex database that summarizes, uh, information you've collected about your customers, your suppliers, and your employees for the last 15 years.”
Barney says sampling theory suggests seeking the smallest dataset that reveals the patterns needed, and that research is under way on minimal datasets for generative AI.
“sampling theory suggests that, uh, we should actually be trying to figure out what's the smallest sample of smallest data set we need to have in order to generalize”
Barney says results from using generative AI feed back into its data, which creates a second-mover advantage.
“when you use GenAI to do something, the results of the analysis you've done then feed back into the data set, and so the GenAI becomes smarter after you've done your work.”
Barney says companies should use generative AI widely and quickly because access is currently inexpensive.
“Obviously, you should be using GenAI pretty much everywhere where you can, as quickly as you can, especially since access to GenAI is actually not that expensive right now.”
Barney states that sustained competitive advantage requires resources or capabilities that are valuable, rare and costly to imitate.
“you have to have resources or capabilities that are valuable, that are rare and costly to imitate.”
Barney cites Amazon's complex relationships with millions of suppliers and customers as a setting where generative AI can add value.
“In order for Amazon to run its business model, it has to have relationships with its suppliers and customers, and there's millions of them, and it's deeply complicated.”
Barney and Reeves call a firm's ability to apply new technology cleverly and manage change 'agility' in their paper.
“We we in the paper, we call it agility. Some firms are able to take that, uh, new technology and apply it in an agile and a clever and creative way”
Barney lists friendship, trust, supplier relationships and brand names as examples of socially complex resources that are hard to imitate.
“They're things like friendship, trust, relationships with suppliers, brand names, which are, uh, which are a promise to consumers about what your brand stands for.”
Barney says capabilities that take years to develop through sequential steps are likely sources of sustained advantage.
“You can't go from where you are to where you want to be just by working hard. You have to go through a series of steps and processes to get there.”
The Barney and Reeves paper uses the example of asking generative AI for seven new kinds of toothbrushes to illustrate work that yields only competitive parity.
“We use the example in the, uh, in the paper of, uh, toothbrush example, you know, asking GenAI to generate a list of 7 new kinds of toothbrushes.”
Barney says firms at generative AI conferences openly discuss cost-cutting methods, which he says helps competitors imitate them.
“I go to these conferences on GenAI, and a a firms are just talking about all the cool ways they pull out costs, uh, in various processes in their organizations.”
Barney advises that CEOs should focus on understanding and protecting their firm's sources of sustained advantage, while functional managers focus on creating value with AI.
“CEOs, on the other hand, should be focused on understanding, nurturing, protecting, growing these sources of sustained competitive advantage.”
Barney compares generative AI to the introduction of personal computers, which changed everything yet did not alone confer competitive advantage.
“I remember when PCs were first introduced, you know, this will change everything, and in fact, it did change everything.”
Barney illustrates that a firm of 10,000 employees might run about 20,000 generative AI experiments a day.
“we have 10,000 employees, that means there are probably 20,000 GenAI experiments a day, some of which have no value at all, but some of which might be quite valuable”
Barney suggests companies run generative AI competitions, with prizes such as a thousand dollars, to harness employee experimentation.
“you can have GenAI competitions where you say, Hey, if you come up a really cool application of GenAI, submit it to this people, this this group”
Barney surveys efficiency uses such as legal contracts and first-draft code, and notes the large societal implications.
“The most obvious ones are efficiency-oriented, that is, taking costs out”
Because AI is open to all, cost savings are copied by competitors, so generic use creates no lasting advantage.
“GenAI is available to any company, even though it creates value”
Barney argues that custom models can be copied and that equivalent datasets reveal similar patterns, so size alone is not decisive.
“the patterns that'll be revealed in that data are not likely to be sufficiently different”
Early adopters gain little lasting benefit, since later users benefit from the data earlier users added.
“there's a second-mover advantage because the second mover gets to see not only the the generic data”
Advantage requires tying AI to socially complex, path-dependent capabilities and to agility, as with Amazon's business model.
“you have to have resources or capabilities that are valuable, that are rare and costly to imitate”
Barney says agility and unique business models give smaller firms openings, though incumbents with advantages tend to do better.
“agility may not be among them, and in that world, uh, a smaller firm”
CEOs should protect core advantages while others pursue efficiency, and companies should harness employee experimentation.
“The CEO and the CIO, it seems to me, have to be in a constant conversation”
If you listen to IdeaCast for conversations and strategy, you'll wanna check out Founder's Mentality, the CEO sessions. In-depth conversations with top executives about the judgment calls that define their careers. Follow wherever you get your podcasts. Turn breakthrough ideas into real results at the virtual HBR Innovation Summit on November 12th with top CEOs and master classes on AI and growth. HBR executive subscribers attend free with exclusive programming November 13th. Learn more at hbr.org/innovationsummit. Welcome to the HBR IdeaCast from Harvard Business Review.
I'm Alison Beard. It's pretty basic business strategy. Find something that your company can do better than any others out there, focus your investments, and reap the rewards of competitive advantage. For anyone leading an organization right now, the big question is how generative artificial intelligence might change the game. Those who are quick to incorporate it into their business operations are certainly finding gains in terms of efficiency and even creativity, while those not using it yet are probably falling behind.
But today's guest notes that early adoption of widely available new technology rarely yields sustained momentum. Instead, long-term success with GenAI will depend on applying it to existing advantages. He's here to talk more about that. Jay Barney is a professor at the University of Utah's Eccles School of Business. With Martin Reeves of Boston Consulting Group, he co-authored the HBR article, AI won't give you a new sustainable advantage. Hi, Jay, welcome to the show.
Hi, thanks for having me.
I want to start with some of the ways that companies are currently using AI to gain a bit of advantage. What are you seeing as some of the most successful early tactics?
Well, actually, it's it's hard to keep up. Almost every day, some new, wonderful thing that GenAI can do is is described in the business press. We know everything from writing reports, uh, creating new products, improving online customer service, creating new drugs, even composing new music. The most obvious ones are efficiency-oriented, that is, taking costs out and making processes that used to rely heavily on human beings now less so. And there are some sectors where the expectation is at least that, uh, that there there will be substantial impact of GenAI on, for example, in the legal profession, a lot of the sort of boilerplate contracting work and and other kinds of work that the lawyers historically have done is
just perfect for GenAI. Another area where it seems to be taking off very, very quickly is in the area of computer coding. Firms are using GenAI to, right now, produce the first draft of code, and then a human being will go through and then update and make it make it more elegant or whatever. But there's every reason to believe that a lot of basic coding will be done by GenAI going forward. Right now, a lot of that will be created mostly by cost reduction and increasing efficiency.
The broader societal implications of that, of course, are are pretty profound and deserve a lot of discussion.
But why is that not enough to give competitive advantage over the long term for these first movers who are doing it really well, creating their their own internal systems, even using it for more creative tasks?
The the problem, of course, is that GenAI is available to any company, even though it creates value, even though it reduces costs, it reduces costs for anyone who applies it, uh, in in sort of a generic kind of way, and those thus, we don't expect to see a lot of sustained competitive advantage being had there. That doesn't mean it's impossible for GenAI, when linked with other aspects of organization, that it will be able to generate competitive advantage in those settings.
Many leaders would think that, okay, well, the obvious next step, you know, I can't create any sort of advantage using the publicly available models, but can't I build a proprietary model or, you know, layer my own information on top of an existing LLM? Why isn't that enough?
First of all, building your own proprietary system, it's unlikely if it's just general purpose, uh, artificial intelligence, that a particular user will have the scale and expertise that some of the other firms that are out there that have been doing this for a long time will have. It's kinda like deciding, Gee, we wanna get a competitive advantage from word processing, so we'll build our own word processing system. You know, I'm sorry, probably shouldn't be trying be producing a general AI system for yourself, but proprietary GenAI might be possible, it might be necessary.
For example, if you have unusual data requirements, or if the patterns you're trying to recognize are slightly different than other patterns. But thing about proprietary software is that if it one firm can develop proprietary software, then its competitors can also develop that proprietary software, and so even that is unlikely to be a source of, uh, sustained advantage. The bigger one that people actually talk about a lot is proprietary data, that is, we have data that no one else has, and so our data is is unique, and thus therefore, it can be a source of competitive advantage as we apply our data to, uh, uh, analyze it with GenAI.
Again, that's possible, we're not denying that it's possible, but it it turns out that in order for for proprietary data to be a source of advantage in this space, it also has to be unique in some sense. So, for example, suppose that your company, uh, has developed a very long, very complex database that summarizes, uh, information you've collected about your customers, your suppliers, and your employees for the last 15 years. If that's unique to you, no doubt about about that.
But if one of your competitors has collected its own database about its own customers, its own suppliers, and its own employees, and it's done so in a way that, uh, is, uh, what we're saying work is functionally equivalent to your data set, then while your data set may be unique, the patterns that'll be revealed in that data are not likely to be sufficiently different to be a source of competitive advantage. And other people another thing people talk about.
I have a good friend who's a, um, a three-time entrepreneur, and I was explaining to him the thesis of this paper, and he says, But but Jay, some of these companies have 10,000, 000,000 data points. Isn't that a the source of competitive advantage compared to a firm that says data has only 50,000, 000, or whatever the smaller number is? And my response was, Eh, not so obvious, actually. If the patterns that you're looking for, or that that the software is looking for, are revealed with 50,000, 000 just as well as they were with 10,000, 000,000, then, uh, that 10,000, 000,000 sounds like a not not helpful, not that necessary.
In fact, I've had someone who's used to teach statistics for a living, sampling theory suggests that, uh, we should actually be trying to figure out what's the smallest sample of smallest data set we need to have in order to generalize, uh, and to understand what patterns exist in a in a data set. So, large, uh, in fact, there's a lot of research going on right now in in GenAI, trying to figure out what is the smallest data set we can use in order to generate the patterns that we need to see.
So, 10,000, 000,000 sounds like a sounds like a winner, but actually, it may not be all that special.
So, it sounds like you're saying that GenAI is going to be the same asset for everyone, you know, like word processing
Pretty much.
or the internet. But isn't there an advantage to being the company that's experimenting with it first, finding out what it can do, or is it such that just that the pace of development is so fast that the gap between the first mover and the fast follower is just minuscule?
I mean, I'm not going to pretend that there aren't examples, possible examples of some first mover advantages that last a little bit of time. You know, one of the ironies, of course, of GenAI is that when you use GenAI to do something, the results of the analysis you've done then feed back into the data set, and so the GenAI becomes smarter after you've done your work. So, in some sense, there's a second-mover advantage because the second mover gets to see not only the the generic data, but also whatever data you added to the data set.
I'm not suggesting that firms need to, or can, or should, sort of delay and delay and not use GenAI. Obviously, you should be using GenAI pretty much everywhere where you can, as quickly as you can, especially since access to GenAI is actually not that expensive right now. At least. I I just I think right now that makes a lot of sense, but it's unlikely that those are gonna be sources of sustained advantage. That just based on the GenAI now.
We've talked about linking GenAI with other aspects of organization. Now that's that's more likely to be a source of competitive sustained advantage rather than GenAI per se.
Okay, so let's talk about that. How do you ensure that the way you're using GenAI is focused enough that it it will propel you sort of further ahead than your competitors because you're leveraging it in a better way?
Y- you're leveraging it in a way that is unique to you. The general story here is to gain a sustained competitive advantage, you have to have resources or capabilities that are valuable, that are rare and costly to imitate. So, what you have to do to really get competitive advantage out of GenAI is link it with some attribute of your firm, of a firm's resources and capabilities, that are, in fact, rare and costly to imitate. In order for Amazon to run its business model, it has to have relationships with its suppliers and customers, and there's millions of them, and it's deeply complicated.
This is exactly the world where, uh, GenAI can be of great value. Is you put GenAI your systems through GenAI, it's going to suggest all sorts of ways that you might be able to reduce costs and increase effectiveness. But because you have a, um, virtually unique business model, those insights are only valuable to you. They're not valuable to anyone who doesn't have that same business model that you have. Then your use of GenAI to improve your the the execution on your business model could be a source of sustained competitive advantage.
But that's not because of GenAI. What GenAI does is it can tee up, suggest to you new, exciting ways to, uh, improve your business processes and and, uh, gain some competitive advantages, at least in the short term. But firms differ in their ability to take advantage of those. We we in the paper, we call it agility. Some firms are able to take that, uh, new technology and apply it in an agile and a clever and creative way, uh, in an agile way that their competitors can't.
Uh, now, in the long run, will the competitors catch up? Probably. But in the short to medium term, that that kind of agility can be also be a source of, uh, of competitive advantage. But, again, it's not the GenAI per se, it's the GenAI plus the ability to change and manage change effectively. It turns out that when you're trying to discover these resources and capabilities that are rare and costly to imitate, the things you want to look for are resources and capabilities you control that are socially complex.
They're things like friendship, trust, relationships with suppliers, brand names, which are, uh, which are a promise to consumers about what your brand stands for. Another thing you want to look for is is, uh, resources and capabilities that really take years to develop. You can't go from where you are to where you want to be just by working hard. You have to go through a series of steps and processes to get there. Those are also likely to be sources of sustained competitive advantage.
And then, finally, things that are where it's unclear what steps you would take to create the advantage. So I can know that my competitor's advantage is fully dependent on their unique organizational culture, and that I don't have that organizational culture. But it's not obvious what I should do to change my culture. I mean, that's not an easy thing to do. It is, uh, it's not a simple thing to say, Hey, get rid of my old culture, build a new culture, and move forward.
So, uh, those are the things you really want to look for are resources and capabilities that you that you control that are socially complex, that are path dependent, and then that or the or the steps to create these new resources and capabilities are not obvious.
It sounds a little bit like what you're saying is that GenAI is gonna help winners keep winning, when I think a lot of us think of it as this great democratizing technology that allows sort of the young startup to to do something more quickly and more efficiently or more innovatively to compete with the big guys. How should I reconcile that? Are there still opportunities for the non-Amazons of the world to use this new tech to their advantage?
Remember my example with agility. So agility is a so a socially complex resource or capability. It's, uh, one firm is able to manage change more effectively than another. You know, large firms are not known for agility, right? So, uh, they may have, uh, other advantages, but agility may not be among them, and in that world, uh, a smaller firm, uh, who is maybe more agile, may be able to take and apply AI in a way that, uh, a larger firm cannot do.
It is the case, however, all that said, on average, we expect that firms that already have sources of sustained competitive advantage embedded in them somehow and are able to then leverage those more effectively with GenAI, those firms will should do better than firms who don't have anything special and then try to use GenAI to make them special. There are a lot of small firms out there, so it's not just large firms are gonna benefit from this.
A lot of small firms, very innovative, many unique business models that GenAI could apply in that world to be generate insights that aren't available to other the other organizations. So agility, um, a unique business model, all those can can link with GenAI and be potential sources of competitive advantage. But you gotta start by knowing what your organization's core competencies actually are, and then figure out how to leverage those core competencies with GenAI, and that's likely to be a source of competitive advantage.
So all the other work that you might do, the sort of cost cutting, maybe brainstorming for marketing or new product development, all of that is periphery. What really needs to happen for any business is you need to figure out how to apply GenAI to the one thing that makes you special.
I think all that other work that you described, e- efficiency enhancing, making, um, we use the example in the, uh, in the paper of, uh, toothbrush example, you know, asking GenAI to generate a list of 7 new kinds of toothbrushes. All that work is f- fine, and you should use GenAI for all that, but that's a source of competitive parity. If you fail to do GenAI and and to use GenAI to to do that work, you could put yourself at competitive disadvantage.
So we are not arguing against doing those things. We're only arguing that they are not likely resources that sustain competitive advantage. Now it is the case that sometimes firm gets firms get so excited about all the cool things GenAI can do that they fail to do the other the work of trying to figure out how to really leverage GenAI to generate a sustained competitive advantage. Uh, I know in in in my world, in the academic world, uh, I go to these conferences on GenAI, and a a firms are just talking about all the cool ways they pull out costs, uh, in various processes in their organizations.
And I'm just sitting there s just mystified on on 2 dimensions. First of all, they're telling all their competitors how to imitate them. I just don't understand that. I mean, it it show so sooner or later, the competitors will figure this out on their own. But why don't we just delay that as much as possible and keep quiet about what we're doing? So that's the 1st thing. It mystifies me. The 2nd is there's there's no conversation about how we can leverage GenAI to be a source of sustained competitive advantage.
Now, by the way, ironically, you could actually talk about that source of sustained competitive advantage because the kinds of resources and capabilities that you leverage with GenAI are not easy for your competitors to imitate. But just pulling costs out, or just, you know, that's not hard, and everyone will do that quickly. Firms need to managers need to temper their expectations about what it is they're going to get out of GenAI. Suppose you do an analysis and discover from your analysis that the kinds of sources of sustained competitive advantage you might have are not easily or not obviously leveraged by the application of GenAI.
That means GenAI is not likely to be a source of sustained competitive advantage for you. That's not a disaster because there are other ways to get sustained competitive advantage. It does say I'm not going to s I I probably don't want to spend millions and millions and millions of dollars trying to build a proprietary system that's not going to be a s source of sustained competitive advantage for me. So I gotta temper my expectations, which temper my investment.
So, 2 leaders out there, you know, what advice do you have in terms of how they need to be thinking about GenAI in the next 6 months and then over the long term, you know, say 5, 10 years?
My 1st advice, and I'm talking to to CEOs here, uh, because functional managers, people who are, uh, running, uh, large, complex processes and need to simplify them and pull costs out, those people need to focus on creating value with AI, even if it's not a source of competitive advantage. CEOs, on the other hand, should be focused on understanding, nurturing, protecting, growing these sources of sustained competitive advantage. Once you understand that with a clear view, um, then you can then start leveraging your your GenAI to help that help take advantage of whatever that is, what those resources and capabilities are.
In terms of 6 months over the long term, I think, uh, in this technology world, 6 world, 6 months is a hard prediction. 5 to 10 years, a really hard prediction. I will say this, I am quite confident that 3 or 4 years from now, another new technology will come along. Uh, this ha- this happened many times in my life and career. You know, I remember when PCs were first introduced, you know, this will change everything, and in fact, it did change everything.
But the great irony is because it changes everything, it's unlikely by itself to be a source of competitive advantage. So I think it's important to not get caught up in the enthusiasm of these new technologies to recognize, yeah, they can be very valuable. I no doubt about that. We should use them to create value. But that sources of sustained competitive advantage are usually found elsewhere.
But given how fast this new technology is developing, how do you how do you advise CEOs that you work with to stay current, to make sure that they're on top of it, using it in the best way they can to sustain that competitive advantage that we're talking about?
If I had the resources, I would hire someone whose job it was to make sure that GenAI is being used in in as efficiently as possible in my organization. F- from a cost reduction, efficiency point of view, I would go to those conferences, I'd listen to what those people are saying, I'd duplicate it, uh, whenever possible, and I would continue to focus in that area. And then don't lose track of the core, keep that most in your mind and then make sure that the technology people, the gen and people, don't lose track of that as well and actually continue to force, though, that conversation.
The CEO and the CIO, it seems to me, have to be in a constant conversation about, are we engaging in technology investments in GenAI or other technology investments that put our core at risk? That sort of m reduce the value that could be created by our our core, uh, competencies, or are we actually using C- uh, and GenAI in a way that actually allows us to leverage those more effectively? That's a conversation that the CEO and the CIO need to be have constantly.
And is there anything that lower-level managers should do, you know, either on the front lines or in the middle, to ensure that the organization is focusing on how GenAI can be applied to the core competitive advantage?
Yeah, super question because, uh, you were talking earlier about the democratization of of business associated with GenAI, and part of that democratization, to the extent it exists, is that people at lower levels of the organization can apply GenAI just as well as people at the high levels of the organization. This really goes back to this question of agility that I talked about earlier. One way to think about GenAI is that all GenAI should be approved by the IT department in a very systematic, bureaucratic way.
Another way to think about GenAI is we have 10,000 employees, that means there are probably 20,000 GenAI experiments a day, some of which have no value at all, but some of which might be quite valuable and that no one in IT has thought of before. How do you, uh, marshal that enthusiasm and power and experimental knowledge? That's really interesting problem. That's agility, you see?
Hm. So how do you harness it, though? All those experiments?
You can do lots of simple things like you can have GenAI competitions where you say, Hey, if you come up a really cool application of GenAI, submit it to this people, this this group, and we'll evaluate it along with everyone, everyone else does it, and we'll give a thousand dollar prize to everything that works. The challenge with GenAI and the opportunity with GenAI is that everyone becomes their own source of new information and new creativity, and and, uh, and so we have to find ways culturally and through systems and other processes that you can put in place to try to take advantage of that.
But I said earlier, agility is a potential source of sustained competitive advantage because not all firms can do this. All some firms are just locked in an old mindset and, uh, and a c- in a command and control mindset that is pretty inconsistent with with GenAI.
Well, I appreciate you being with us today. Thanks, Jay.
Thanks for having me.
That's Jay Barney, professor at the University of Utah's Eccles School of Business and co-author with Martin Reeves of the HBR article, AI Won't Give You a New Sustainable Advantage. And we have more episodes and more podcasts to help you manage your team, your organization, and your career. Find them at hbr.org/podcasts or search HBR in Apple Podcasts, Spotify, or wherever you listen. Thanks to our team, senior producer Mary Duke, associate producer Hannah Bates, audio product manager Ian Fox, and senior production specialist Rob Eckhart.
And thanks to you for listening to the HBR IdeaCast. We'll be back with a new episode on Tuesday. I'm Alison Beard. If you listen to IdeaCast for conversations and strategy, you'll wanna check out Founder's Mentality, the CEO sessions. In-depth conversations with top executives about the judgment calls that define their careers. Follow wherever you get your podcasts.