18 Mar 2025 · 26m · Sam Ransbotham, Shervin Khodabandeh, Raffaella Sadun · 22 quoted facts
Harvard Business School professor Raffaella Sadun argues that getting value from AI depends less on the technology than on how a company organizes itself. Firms need a clear business reason for AI, tailored experiments, and a plan for changing how people work. Her team experiment suggests AI can stand in for some cross-functional collaboration, producing good ideas faster.
The central tension is whether AI widens or narrows gaps. History from the computer era suggests firms that adapt their organization pull ahead, and a few firms get far bigger rewards. Sadun also sees a hopeful possibility that AI could empower workers with middling expertise. The hosts worry that if AI substitutes for colleagues, people could end up working alone with machines.
Sadun says the technology and data layers are prerequisites, but change management is equally important and often neglected. Her doctoral work on 1990s computer technology found the same pattern: payoffs differed by how well firms reallocated tasks and reorganized work.
Because knowledge about how AI adds value in specific businesses isn't yet written down, Sadun says returns are high for firms that form a hypothesis, test it, and feed the lessons back into the organization. She sees little attention paid to that last step.
In a consumer packaging company's R&D and marketing hackathon, one person with AI produced ideas rated as good on average as those from teams. The AI played the missing function, so each person's ideas moved toward a blend of technical and commercial thinking.
Teams with AI were disproportionately better at placing ideas in the top 10 percent of submissions. Sadun concludes that for exceptional outcomes, people working together with AI are still needed.
A speculative National Academies chart shows AI helping people with some context more than novices. In a study with blue-collar workers, job satisfaction was rising halfway through, though Sadun says it is too early to know if that lasts.
Sadun says it comes down to organization and change management, not just technology or data. Firms that clarify why they use AI, run tailored experiments, and get people to change how they work do better.
In Sadun's experiment, one person working with AI produced ideas as good on average as human teams, and faster. But teams with AI were better at producing the very best ideas, so humans together still add value at the top.
Sadun expects more divergence between firms that adapt well and those that don't, as happened with computers in the 1990s. She notes that a brand-new organization might adapt better than an older one that must retrofit.
Sadun sees a possible alternative, where AI empowers workers with middling expertise, such as store managers, by giving them more decision power and more interesting jobs. She stresses this is not yet proven.
Sadun says we don't get off the wheel, which is why lifelong learning is discussed more. Sam cited research that skills now have a half-life of under five years, meaning half their value fades in that time.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
MIT Sloan Management Review and Boston Consulting Group have jointly researched and published on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies.
“Together, MITSMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies”
Raffaella Sadun's first doctoral dissertation paper examined why computer-related capital helped some firms far more than others.
“my first paper of my doctoral dissertation was all about this.”
Sadun says computers and software in the 1990s changed the task content of jobs, replacing some tasks and complementing others.
“With the introduction of computers and software in the nineties, these technologies were changing the task content of jobs”
Sadun found that information technology helped some firms more than others depending on their ability to reallocate tasks, hire different workers, or reorganize work.
“it all came down to their ability to adopt this technology, which in turn came down to their ability to reallocate tasks or hire different types of workers, or change the ways in which work was organized.”
Sadun says data link the emergence of superstar firms and widening disparities in outcomes across firms to differences in the ability to implement new technology.
“there is data out there that traces back the emergence of superstar firms and this disparity in outcomes across firms at the macro level.”
Sadun speculates, based on very preliminary evidence, that new organizations may use AI better than older firms that must retrofit their operations.
“a new organization that is born today would be better equipped at using this technology and getting exponential returns from these technologies than organizations that have to retrofit what they do to adopt the technology.”
Sadun says tight labor markets after the COVID-19 pandemic led companies to rethink reskilling.
“after COVID, tightening labor markets really scared companies, so they started thinking about reskilling in a different way.”
Harvard Business School's Digital Reskilling Lab is working with fifteen companies in Europe, Latin America and the US.
“it's called the Digital Reskilling Lab, and we're working with fifteen companies now, some of them quite large, some in Europe, some in Latin America, some in the US”
Sam Ransbotham cites research indicating the average half-life of skills is under five years, and under two and a half years in some fields.
“some of your research pointed out that the average half life of skills is now less than five years, and in some fields was less than two and a half years.”
A recent National Academies report speculated about the impact of AI on the labor force, according to Sadun.
“There was a recent report from the National Academies, and they were speculating on what's the impact of AI on the labor force.”
The National Academies report speculated that AI would have limited effects on the least expert workers because they lack the context to use it productively, a pattern shaped like an inverted U.
“The report was speculating that AI would probably have very limited effects on the super naive and less expert workers because they don't have the contextual knowledge to really use these technologies in the most productive way.”
In Sadun's ongoing study of AI for blue-collar workers solving machinery problems, job satisfaction was increasing at the halfway point.
“We are halfway through the study, and it seems like job satisfaction is increasing.”
Sadun's experiment on AI and teams was run with a consumer packaging company and involved research and development and marketing staff.
“In particular, in this experiment, we have R and D and marketing. So we worked with one company. This is a consumer packaging company.”
The experiment compared individuals alone, individuals with an AI persona acting as their counterpart function, teams without AI, and teams with AI.
“One is individuals working on their own, the second is an individual working with AI, and the role of AI is to be a persona.”
The researchers ran the experiment as a hackathon designed to mimic the company's normal innovation process.
“In the context of a hackathon, we tried to mimic as much as possible what the company does normally.”
In the experiment, individuals working with AI produced ideas whose average quality matched that of teams and teams with AI.
“The quality of their output is on average as good as the quality that comes out of teams and teams with AI.”
Without AI, participants' ideas reflected their own function, with R&D people proposing technical ideas and marketing people proposing commercial ones.
“when people work by themselves without AI, their ideas pretty much mimic their functional expertise. If you're an R and D person, you're going to propose a very technical proposal.”
Teams with AI were disproportionately likely to place ideas in the top decile of submissions in Sadun's experiment.
“we find that teams with AI are disproportionately better at placing these ideas on the top decile of the submission.”
Participants using AI, alone or in teams, produced better solutions on average in a fraction of the time.
“The individuals plus AI were, and also the teams with AI were able to, on average, come up with better solutions, but also at a fraction of the time.”
Sam Ransbotham says widening divergence between firms resembles the Matthew effect, in which the rich get richer and big companies get bigger.
“It speaks to the Matthew's effect, where the rich get richer, the big companies get bigger, the small companies get smaller.”
Shervin Khodabandeh says it took about a decade and a half for computer vision to surpass human performance.
“If you look at how long it took computer vision to be better than human, it took decade and a half, I think.”
Shervin Khodabandeh says generative AI went from grade school math to complex mathematical proofs in a matter of months.
“how long post generative AI, it took for generative AI to be able to do the grade school math few months, how long did it take it to do complex mathematical proofs?”
Sadun says change management is as important as technology and data. She also stresses the need to know why a firm is adopting AI.
“the organization and the change management part is as important and often neglected”
Her early research on 1990s computer technology shows that organizational adaptation, not the tech itself, explained which firms benefited. She links this to the rise of superstar firms.
“it all came down to their ability to adopt this technology”
Firms are more interested in reskilling than before, driven by tight labor markets and aging populations. The hosts raise how quickly skills lose value.
“I don't think that we get off the wheel.”
A speculative inverted-U idea suggests AI helps people with some context most. Early blue-collar study results show rising job satisfaction.
“AI would probably have very limited effects on the super naive and less expert workers”
Sadun describes a four-condition hackathon at a consumer packaging company, comparing individuals and teams with and without AI.
“Let us experiment with four conditions.”
With AI, specialists' ideas converge toward a blend of functions. Still, teams with AI were best at producing top-tier ideas.
“So there is still hope for humans.”
Sadun names the democratization of expertise as AI's biggest opportunity and warns about confident but stupid output.
“what excites me is the democratization of expertise knowledge”
Sam and Shervin debate whether work will change fast or slowly. They also worry about AI replacing colleagues.
“We don't have a back of the book to look at here.”
Stay tuned after the episode as Shervin and I discuss the key points from today's guest. We hear a lot about augmenting humans with AI. But what if a cross functional team was 100% AI? Would companies be more innovative? One researcher shares her conclusions on today's episode.
I'm Raffaella Sadun from Harvard Business School, and you're listening to me, myself and AI.
Welcome to Me, Myself and AI, a podcast on artificial intelligence in business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, professor of analytics at Boston College. I'm also the AI and business strategy guest editor at MIT Sloan Management Review. And
I'm Shervin Khodabandeh, senior partner with BCG and one of the leaders of our AI business. Together, MITSMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate. Hi everyone, thanks for joining us today. We are speaking with Raffaella Sadun, professor of business administration at Harvard Business School.
She's also the co-chair of Harvard Business School's project on managing the future of work and the co-principal investigator of the digital reskilling lab. Raffaella, welcome to the show. Thank you for having me. It's my pleasure to be here. It's wonderful to have you, Raffaella. You have a long history of researching and publishing on innovation, technology, AI, and the impact on organization and nature of work. And that last piece, organization and nature of work, is actually one of the most important ones in the work that Sam and I have been doing and talking about quite a lot.
We see that most companies struggle to get that part right, and that's where the real, you know, hidden value is in getting full impact from AI. Share with us what you're seeing in your own work and what are some of the key highlights.
So let me start by saying I am doing research on AI, but my ideas on AI are also shaped by research that I did prior to the AI era, more generally on technological change and adaptation, how firms adopt new technologies in their organizations. I think a lot of the attention now is focused on the technological layer or the data layer, because it's clear that these are essential prerequisites for AI to work. You need to know and you need to have enough information to be able to start a prediction process, which is most of what these technologies do.
And you want to have enough of a technological layer to be able to interpret and understand what the algorithm is doing for you. So that I take for granted. What I'm observing in organizations is that the organization and the change management part is as important and often neglected. And so where do we start? Well, first, you need to understand what is the business application. We know that from the latest surveys, a very large number of US workers are engaging with these technologies, but when it comes to the adoption of these technologies in the firm, you first have to ask yourself, why am I doing it?
And what's the value add? That, in itself, I think, is one instance where I see tremendous heterogeneity across organizations. Because just to be able to ask that question and answer that question, you have to have in mind how your organization adds value relative to competition. And so you want to understand what is your competitive strength and how this technology may affect your competitive strength.
Strategy with and for AI, exactly, yes.
Exactly, and this is where I come to my second point. My sense is that we are at a point where it is similar to the onset of a new technology paradigm, where a lot of the knowledge about how this technology adds value and whether it adds value to specific verticals or specific businesses has not yet been codified. So this is a time in which there is tremendous return from experimentation, and in particular tailored experimentation within the firm.
And this is where I see organizations diverge. Are you able, as an organization, to formulate a hypothesis, to set up the experimental context that would allow you to test that hypothesis? But also, are you able to codify the learnings that are coming from your experiment and feed it back into the organization? I don't see a ton of attention on this step, but I think that this step is really, really, really important for the adoption process. And this is the third part.
At the end of the day, your massive change management task is to convince people to do things differently than what they're doing now.
And it's a double-edged sword, right? In some ways, you can argue that if you go back ten years ago, where AI was doing what AI could do, and only AI could do, which was massive amounts of data and seeing trends and predicting and optimizing, and human was doing things humans could do only, and only humans could do, which is sort of smooth around the edges, consider the strategic options, be creative, negotiate, even I would go as far as saying like, show empathy, those kinds of things.
And you'd say, yeah, there's human and AI, and they each do their own pieces. And I think what we're also seeing now is AI is doing things that humans could do, and sometimes is doing it better. So I'd imagine the speed with which organizations have to evolve and change is also drastically changed. And what concerns me is that the rate of change for those who are doing this well and with momentum are creating a real gap in every sector.
How do you see that evolving?
It's helpful to look back at what happened with the ICT revolution, because there is an analogy there. With the introduction of computers and software in the nineties, these technologies were changing the task content of jobs, right? So we were having some tasks that were being replaced by technology and some tasks that were going to be complemented. Now, this is what happens on average in the economy. But then, going back to your point, when you observe how this technological change ended up affecting firms, what you observe in the data, and actually, you know, my first paper of my doctoral dissertation was all about this.
So why is it that ICT capital at that time was having very positive effect in some firms and actually not so much in other firms? Well, it all came down to their ability to adopt this technology, which in turn came down to their ability to reallocate tasks or hire different types of workers, or change the ways in which work was organized. Because some firms remained behind and they were never able to really keep up with the speed of adaptation on the organizational side.
We're not talking about reinventing the technology. We're really talking about making sure that the technology works for you. Over time, we have seen a massive divergence, at least in terms of productivity between winners and losers. And I think that there is data out there that traces back the emergence of superstar firms and this disparity in outcomes across firms at the macro level. So we're really thinking this is having big impact on the economy, to the differential ability to implement new technology.
So I think I agree with you. I mean, if history repeats itself, I think that we might see very well is further divergence across firms, which is going to be influenced by their ability to experiment and adopt.
That divergence seems scary, though. It speaks to the Matthew's effect, where the rich get richer, the big companies get bigger, the small companies get smaller. How do we keep that from exacerbating inequality?
My sense looking at AI adoption right now, it's very preliminary evidence, but my sense is that precisely because these technologies require a very different organizational setup, it might well be that a new organization that is born today would be better equipped at using this technology and getting exponential returns from these technologies than organizations that have to retrofit what they do to adopt the technology. So my worry is more about which type of workers are going to be able to adapt to this type of changes.
That's why I'm so interested in how do you reskill the population, and in particular, maybe because I am becoming a middle-aged person, that you know, how do you convince an adult, somebody who has been working for a long time, to change their way of doing things and maybe switch into new occupations or changing what they're doing? And I think that that's actually a very, very tough problem.
Do you think, compared to prior milestones or landmarks in technological evolution, do you think that the extent of change in workforce and reskilling is higher, lower, or similar?
My sense is that the interest of firms in reskilling is higher. And I attribute this heightened interest to a couple of things. The first one is after COVID, tightening labor markets really scared companies, so they started thinking about reskilling in a different way. That you might say that, you know, labor markets have almost gone back to normal, so that is probably not so much of an incentive right now. But there is one thing that will keep happening and is happening, which is we're all getting older.
And, you know, in some parts of the world, the aging of the population coupled with the need to adopt new technologies is a real concern. We have a lab here, it's called the Digital Reskilling Lab, and we're working with fifteen companies now, some of them quite large, some in Europe, some in Latin America, some in the US, and they've invested massive resources for these purposes.
The pace seems difficult here because I think some of your research pointed out that the average half life of skills is now less than five years, and in some fields was less than two and a half years. I mean, that's a very rapid. How do we get off the hamster wheel where we're retraining just in time for the new technology to change and again, a constant reskilling, never able to use those skills?
Yeah, I mean, look, I don't think that we get off the wheel. Perhaps this is why we are hearing more and more about lifelong learning as being an idea. And maybe the change, which I agree with you, part of this change is scary, you know, do you have to reinvent yourself every so years? So I don't think that that's something that you get out of. That's where I think AI is particularly interesting. There was a recent report from the National Academies, and they were speculating on what's the impact of AI on the labor force.
And remember, there is this very interesting graph. I think it's entirely speculative. I don't know. I don't think anybody has tested it yet. But it was think about an inverted U. And on the X-axis is the level of expertise of workers, and on the Y-axis, it's the potential returns from AI. The report was speculating that AI would probably have very limited effects on the super naive and less expert workers because they don't have the contextual knowledge to really use these technologies in the most productive way.
So people who know something about the context, they have some basic knowledge, but they're not the super experts. And I think that the reason why I think this is interesting is because it's true that the cost for those type of workers are quite high. You know, if you ask a person that has already started on an occupational pathway to change, and this is costly. And we're doing some studies with companies that are introducing AI for blue-collar workers.
At the beginning of the introduction, they started with a lot of concerns about the technology and concerns of what the technology could do to their jobs and whether they would be comfortable using it. We are halfway through the study, and it seems like job satisfaction is increasing. They are experimenting with the technology. These are blue-collar workers who are using technology to solve problems on machineries. I think it's too early on to see whether this is a one-off and whether the effects will be permanent.
Maybe it's a transition period, but I definitely find that specific aspect of AI very interesting and different from past technological waves. What we are talking about here is something that could potentially empower this middle expertise workers rather than replacing them. So, for example, how can this technology change the job of a store manager? Right? Could you decentralize more? Could you give them more power and make the job more interesting? I think we are in a stage in which we don't know yet whether this is true, but I think it's an interesting possibility, and that's why I'm studying it.
Because if that turns out to be feasible and economically productive, I think that this has implications for how the technology affects work. And, you know, I want to go back to Sam's initial concerns about inequality. This is one way through which potentially you could counter these concerns.
I like the idea of the store manager to make that concrete, that this is a particular person that if there are lots of instances of store managers across the nation who could then learn from each other in a way that they've never been able to learn from each other before. The you can see the appeal readily right there, versus a replacement argument. You've recently had an experiment about compositions of teams, where some parts of teams are artificial intelligence, and some parts of the teams are traditional humans.
Tell us about that experiment and what you're learning.
Yeah, so I mean, this is really going back to understanding how AI can affect the nature of work. And as you know, especially knowledge organizations work today is organized in teams. You have different functions, different experts coming together to produce. In particular, in this experiment, we have R and D and marketing. So we worked with one company. This is a consumer packaging company. Innovation is pretty central to what they do, and their concern is that their way in which this innovation, especially in the early stages, the way in which was organized was not optimal, because they always needed to have teams, R and D and marketing, to work together.
And they were concerned that it was hard to get people together in the same room at high frequency, but also they were concerned that perhaps there was a little bit of inhibition. You know, the R and D people going crazy, the marketing people telling them we have to make money or or, you know, or vice versa. So the experiment that we ran was what if instead of organizing, you know, the status quo is teams without AI.
Let us experiment with four conditions. One is individuals working on their own, the second is an individual working with AI, and the role of AI is to be a persona. So to be either if you're an R and D person, to be your marketing counterpart, if you're a marketing person, to be your R and D counterpart, relative to teams without AI, which is the status quo today, and teams with AI. And then we put them in an experimental setting, we asked them to innovate.
In the context of a hackathon, we tried to mimic as much as possible what the company does normally. And then we had the outcome of this process evaluated by experts who applied this process to look at the quality of the submissions or the quality of ideas. What we find are a couple of interesting things. First of all, AI and individuals, so this is the person with an AI expert. On average, they do really well. The quality of their output is on average as good as the quality that comes out of teams and teams with AI.
So this is telling you that, at least in this context, the AI potentially substituted for some of this team expertise. And if you look at why that happened, it's actually one graph that I love in the paper. You see that when people work by themselves without AI, their ideas pretty much mimic their functional expertise. If you're an R and D person, you're going to propose a very technical proposal. If you're a marketing person, it will be very commercial.
And you should imagine two distinct peaks that overlap a little bit, but not so much. When you put an individual with AI, you see the convergence of the ideas that they propose. And so an R and D person becomes more like a team of R and D and marketing, and a marketing person is more of a team of R and D and marketing. And I think that this is fascinating. In fact, we find that teams with AI are disproportionately better at placing these ideas on the top decile of the submission.
So if you care about the averages, this AI human team is fine. If you care about the exceptional outcomes, you still need teams of people with AI to work together. So there is still hope for humans.
Well, I I hope that translates also into the idea. Like in the universities, we tend to think our classes are the most important thing in the university. But deep down, there's a whole lot that goes on in the university educational experience that is not related to what goes on in the classroom. And so, by analogy, that's what's going on in organizations and teams as well.
Yeah, and Sam, as you were saying this, as I was listening to Raffaella, my mind went into what happens in a team or in a discussion. And so much of it is around knowledge and understanding, increasing, codifying knowledge, and there AI clearly has a ton of advantage, and particularly with more agentic forms of AI, where, as you were talking about R and D and marketing, now you can have different personas and bring in a ton of different personas with different biases, all that.
And I wonder whether, in a roundabout way, AI will make us all much more inspiring and motivating and and and
I hope so.
and interesting people.
Or even, you know, I I agree with you that this is a possibility, but even giving us more time to spend our precious time, which is really the limited resources on things that we only can do.
In your experiment, was there any difference in the speed?
So that's the other thing. The individuals plus AI were, and also the teams with AI were able to, on average, come up with better solutions, but also at a fraction of the time. It's a productivity effect, if you want to read it that way, that I think is also quite interesting.
Yeah, that seems huge. We have a segment where we want to ask you some rapid fire questions. So just answer the first thing that comes to your mind.
Hm, okay, I'm not good with those ones, but go ahead.
What do you see as the biggest opportunity for artificial intelligence right now?
Oh, what excites me is the democratization of expertise knowledge.
What's the biggest misconception that people have about AI?
That it's only about the technology.
What was the first career that you wanted? My first career Hm.
I've always wanted to be an economist. I know it's very sad, but since I was twelve
Oh, do they have the Fisher Price Little Economist set or
they have the economist Barbie. I don't know.
okay? When is there too much artificial intelligence?
I mean, I think when it's stupid, and this is a real risk that it says things that look right and are just stupid and you can't stop it.
Hm. What's one thing you wish that artificial intelligence could do right now that it can't currently do?
I think it would be, Ah, that's a good question. Well, I don't know the answer to that one. Hm.
That's a good answer.
Thank you for taking the time to talk with us. I think it's really interesting to think about these huge macro effects you're talking about. It could be faster or slower, it could be, you know, there are good mechanisms behind each one of these possible reasons going on, and we're just in a big learning process to figure this out, and I think we're all gonna have to figure that out together. Thank you for taking the time to join us.
Thank you. My pleasure.
Thank you. This was wonderful. A really interesting conversation with Raffaella, Sam. I particularly liked the discussion on there's a transition period between, like, where we are now with AI and when we will be, as it's beginning to pervasively change the nature of work and what people do and how people do it and all that, right? And sort of the analog to industrial revolution and electric motor and all that. And I like the contrast of, you know, it could go really fast because
We're building on all this experience.
you can go faster, right? Cause exactly, we have all this data we never had, it's we got us this knowledge, and and we've seen how fast. In fact, we do see that in the tech curves, right? If you look at how long it took computer vision to be better than human, it took decade and a half, I think. And then how long post generative AI, it took for generative AI to be able to do the grade school math few months, how long did it take it to do complex mathematical proofs?
Another few months. So, like, we see that happening. But in terms of changing the nature of work, then the other spectrum was, oh, it could go very slow because it's gonna encroach on what's uniquely human. And
Also, with a population that's not been subject to this change before. You know, the the idea that it's no longer them, but us is a big deal.
hm. And so I'm thinking, like, how do you think? Because this is like, the the forces at work here are clearly the economic forces that are at work here. But I've got to believe there's gonna be regulatory and social forces at play here too.
Yeah, we're just not used to things changing that quickly, honestly. Our moon still goes around once a month, our sun still goes around once a year. We we're sort of locked into a lot of patterns as people that just, you know, just because the technology can move faster, I'm not sure that we can.
The other thing I liked was the experiment that they'd run where collaboration with human and AI in two different fields, like R and D and marketing, it's as good or better, right? That was the conclusion, which is positive in a sense that you have clearly no conflict, cause you can have really conflict with AI, but doesn't it also have the really, really scary effect that why do I even need the other side? And why does the other side need me?
And then we each go to, like, our own little thing, and AI just becomes the
Hm. The proxy for all of this. Hm. Yeah, that seems a little somehow sad in a the the vision of everybody working in their little room alone with the machine. That makes even me sad with my hermit-like tendencies.
Well, I mean, then what she said is, look, the rate at which AI will do the things that takes you a long time to do is so much faster that then you get to do the things you want to do, which is true. But then I think about in a regular person's, you know, forty-hour-a-week job, how much of their work is really, really the things they they just love to do. I mean, like, so much, the the work is the things they have to do.
Well, I know you think that the podcast is the the your favorite part of the week, I know it looks like everything else
Yeah, me too. Yeah, yeah, exactly right. Like, I, I work a lot better with my AI co-host.
exact yeah. But you know, I think also her point, though, that it's not just about that particular idea. And they recognize that their study is a short-term effects of just on an idea that there's a whole lot that goes on in organizations. And and maybe the bigger point here is that we're all learning about this stuff at the same time. No one's got the answer of how the human world transitions through this rise of machines. We don't have a back of the book to look at here.
And you know, the experiment that she's doing and that millions of other academics and businesses are doing are all part of that, and we're hopefully we'll figure it out.
Thanks for listening today. Next time, Sam and I speak with Linda Yao of Lenovo, please join us.