About This Episode
Artificial intelligence has become one of the defining topics in business, yet much of the public conversation continues to focus on one question: Will AI eliminate jobs? In this episode of the Future of Work® Podcast, Frank Cottle welcomes Sebastian Fixson, Professor of Innovation & Design at Babson College and Founder of the Work Futures Lab, to examine a far more meaningful question: how AI is changing the way work itself is designed.
Rather than focusing solely on automation, Sebastian explains why organizations should rethink workflows, redesign value streams, and develop innovation capabilities that allow employees and AI to work together. The conversation explores how AI is influencing productivity, organizational structures, decision-making, leadership, entrepreneurship, hybrid work, and the future quality of work. Throughout the discussion, Sebastian emphasizes that technology alone rarely creates lasting value. Organizations realize AI’s full potential only when they redesign the systems surrounding it.
Whether you’re leading a large enterprise, building a startup, or preparing your workforce for AI, this episode offers a thoughtful framework for understanding how organizations can embrace innovation while creating work that remains productive, engaging, and meaningful.
About Sebastian Fixson
Sebastian Fixson is Founding Faculty Director of the DBA Program and Professor of Innovation & Design at Babson College. His research focuses on helping organizations build innovation capabilities through design thinking, organizational design, digital tools, and artificial intelligence. He also founded Babson’s Work Futures Lab, where he studies how emerging technologies are changing work, innovation, and organizational performance.
What You’ll Learn
- Why AI is redesigning work rather than simply replacing jobs.
- How innovation capabilities become a competitive advantage in the AI era.
- Why workflow redesign matters more than technology adoption.
- The relationship between AI, organizational structure, and productivity.
- Why experienced professionals often adopt AI faster than early-career employees.
- How startups and established companies approach AI differently.
- The importance of involving employees during AI implementation.
- Why organizations should measure work quality alongside productivity.
- How AI and hybrid work may complement one another.
- Why imagination may become one of the most valuable leadership skills in the future of work.
Trasncript
Sebastian Fixson
[ 00:00:00,000 ]Two at that time PhD students at MIT ran an experiment like that with a task that we could compare to writing rockets in copy. So a bound 20-minute type task that in involves some creativity, creating an artifact that used to be done only by humans. And in that experimental fashion, what they show is that if you add AI, in that case as a tool, to the person’s toolbox, if you will, they show on the task level productivity differences of about 30%.
Frank Cottle
[ 00:00:27,930 ] Sebastian, welcome to the Future Work podcast. We’re really excited to have you here today. Really? To explore the topic, everybody’s exploring, but with your expertise, uh, that’s AI, and the impact on the future of work. So thank you very much for joining us.
Sebastian Fixson
[ 00:00:44,880 ] Thank you for having me. I’m excited to be here.
Frank Cottle
[ 00:00:47,160 ] Okay, well, that’s a good stage. Right there, because most people are going, I’m just happy to be here, and you’re excited. Oh, that’s that’s that’s good. You know, most AI conversations today, that we see the big headline ones, are about job loss.
Frank Cottle
[ 00:01:03,700 ] About job loss, and your research focuses on something much more detailed. And it’s really how AI is changing the structure and the processes inside of jobs.
Frank Cottle
[ 00:01:15,330 ] That’s a really important and distinctive point that’s different than many people are making. What do you think that reveals on a broad basis?
Sebastian Fixson
[ 00:01:26,460 ] I mean, it’s not surprising, given how important work for everybody. It has a big interest in the subject, if you will. But depending on what level you are at looking at this question, you might have a very different perspective. Zoom if you will. Policymakers look at economies, entire job categories, industries.
Sebastian Fixson
[ 00:01:48,250 ] Company leaders look at firms and their sector, maybe, but individual employees look at their job.
Sebastian Fixson
[ 00:01:55,080 ] And these different layers lead often to very different outcomes in the analysis. So if you look at the range of predictions of job losses caused by AI, they’re enormous. And that doesn’t necessarily mean that one is right and the other one is wrong. It typically indicates that. Different factors flow into the analysis.
Sebastian Fixson
[ 00:02:16,310 ] And. Personally, sort of where my research is a lot more on the ground, trying to understand what happens in work. My background is studying innovation work.
Sebastian Fixson
[ 00:02:27,720 ] So my perspective is that’s where I can contribute. Something to the conversation.
Frank Cottle
[ 00:02:34,720 ] Well, you know. As we look back at history, and we’ve all seen a lot of, um, industrial revolution, technology revolution, etc. We’ve gone through a variety of major changes in the past. Which at first were scary to people. But in the end, I’ve written.
Frank Cottle
[ 00:02:59,110 ] For every one job, two new ones were created or the value of two new ones was created in many cases. Oh. A single truck.
Frank Cottle
[ 00:03:09,820 ] In the early 1900s was able to deliver three, four, five times as much material.
Frank Cottle
[ 00:03:16,050 ] Speed-wise as a horse and wagon.
Frank Cottle
[ 00:03:20,400 ] Computers, we know how computers have evolved and how computing power has evolved and how that has impacted us. Same with communications.
Frank Cottle
[ 00:03:29,280 ] The invention of the telephone. All these things are breakthroughs. They’re scary.
Frank Cottle
[ 00:03:34,970 ] They all ultimately have contributed to growth and to improvement.
Frank Cottle
[ 00:03:43,100 ] Do you think we’ll see the same thing with AI, or what do you think the magnitude of the improvement? I guess, because you can’t not see it with AI? What do you think the magnitude will be? And I know you’ve done a ton of research at Babson with the word futures left. Um, is there?
Frank Cottle
[ 00:04:02,260 ] material uh empirical data they’re starting to show this or is it, is the the jury’s still out?
Sebastian Fixson
[ 00:04:09,040 ] A little think overall, the long-term jury, of course, is still out. No one knows right and in fact, that’s what researchers are trying to do to create projection based on certain assumptions. Right. So if you look at studies that talk about millions of jobs created versus millions of jobs lost. It’s not. Impossible that they’ll shape out in some sort of that way.
Sebastian Fixson
[ 00:04:35,780 ] However, there are lots of uncertainties around these estimates. That’s one, even in the aggregate level. But on top of that, the changes you mentioned from past technological revolutions had very different impact on different people. So if you take the job owner’s perspective, the one who loses the job is not necessarily the same who gains the new job. And so therefore that humans find that scary, I think, is completely understandable.
Sebastian Fixson
[ 00:05:02,600 ] That’s then where conversations end.
Sebastian Fixson
[ 00:05:05,200 ] Whether we need job training programs and how different. In terms of required skill set and competencies, are these new jobs?
Sebastian Fixson
[ 00:05:13,940 ] Um, if you You asked for empirical evidence. I think there are two types of evidence that are emerging, which point towards large-scale changes.
Sebastian Fixson
[ 00:05:24,540 ] The first one are studies that look at essentially in experimental fashions, trying to understand what the productivity impact of adding AI to someone’s work is. Yes. A couple of years ago, two at that time PhD students at MIT ran in experiments like that with a task that you could compare to writing a marketing copy.
Sebastian Fixson
[ 00:05:48,260 ] Bound. 20-minute type tasks that involve some creativity. Creating an artifact that used to be done only by humans.
Sebastian Fixson
[ 00:05:58,130 ] And in that experimental fashion, what they show is that, if you add AI in that case as a tool to the person’s toolbox, if you will. Yup. They show on the task level productivity differences of about 30%. Winch? On its face are enormous if you think about it.
Sebastian Fixson
[ 00:06:16,760 ] At the same time, in the wild, so to speak, the so far observed productivity changes are much, much smaller.
Sebastian Fixson
[ 00:06:26,400 ] And that suggests that, in reality, Can I ask a question on that?
Frank Cottle
[ 00:06:30,430 ] Um, if they’re smaller is that because it’s smaller per individual or is it smaller because the base of so many people that aren’t really embedded in the process.
Frank Cottle
[ 00:06:46,100 ] Dilutes it.
Sebastian Fixson
[ 00:06:48,220 ] Well, that’s a very good question and the answer is it’s both. The individual has that task and many other tasks like the job. And as it turns out, in that natural setting, things are a lot more complicated and the contribution of AI, at least initially, is less clear and less clean.
Sebastian Fixson
[ 00:07:06,710 ] Um, The other point you’re pointing out, we are in a transition phase and all of these adoption and diffusion studies, so it’s very uneven.
Sebastian Fixson
[ 00:07:16,370 ] across sectors, across age groups, across education level. So even though they are all growing in AI use, and the measure is typically, if I ask you, have you? How frequently in this week have you used AI?
Sebastian Fixson
[ 00:07:32,180 ] But then there may be this category: daily, weekly, or monthly.
Sebastian Fixson
[ 00:07:37,650 ] But that still doesn’t say, ‘okay, Frank, what?’ Task. Have you given to AI or in which way? Have you actually interacted? In creating the outcome. And that ultimately is the answer to the question: what the productivity improvement in real life jobs will be.
Sebastian Fixson
[ 00:07:57,300 ] So right now our measures are so coarse that we’re talking about users almost in a binary fashion yes or no). So we’re not there yet to understand what is actually happening to the way in which the work is changed.
Frank Cottle
[ 00:08:12,320 ] Well, you know it’s interesting.
Frank Cottle
[ 00:08:15,180 ] We have another company, Alliance Virtual Offices.
Frank Cottle
[ 00:08:20,020 ] Employee base about 125 people there. And the company’s been around for a while, and they’re very stable. Base of him. Executives and managers and staff, very stable company. And what’s interesting that I find is the people with the most experience in business.
Frank Cottle
[ 00:08:43,390 ] The most gray hair, maybe. Uh, as well are the ones that are reaching out to AI.
Frank Cottle
[ 00:08:52,770 ] Oh. On their own, the fastest.
Frank Cottle
[ 00:08:56,870 ] They’re the ones that are saying, ‘Aha, I can now do this extra thing that I used to have to rely upon someone else to do.’ Or this task used to tell me, ‘Take me two hours,’ and I can now do it in one hour. We’re seeing more. Natural take-up. Uh, from the people with the greatest experience, so I wonder if that’s just because they know how long they’ve known how to apply themselves to a work product where a younger generation is still trying to figure out their job. A little bit, and doesn’t really, and doesn’t have the confidence to reach out to a new tool as quickly. Are you seeing anything like that, or in most cases, are we just an anomaly?
Sebastian Fixson
[ 00:09:44,280 ] No, I think. I think number one, um, as I mentioned, the uptake or diffusion of radicals is very um, that. varies a lot across Um, both education level, but also leadership levels, and the more the higher the leaders, the higher the use is.
Sebastian Fixson
[ 00:10:06,630 ] I think one of the explanations for that is that the experience gained through the professional life puts you in a position to judge the outcome. What’s that? fairly high degree of confidence. You know if you get a report from an employee or an output from AI, what it’s worth. How good is it? How much to trust it. That expertise, however, you have built over decades—either in your job or in various forms of appointment, if you will. And that is actually one of the big challenges for people at the other end of the career arc. We’re entering a job where someone would like or who are asked to have that expertise to have good judgment, to evaluate the output of AI tools. But they don’t have that yet because they haven’t spent. Enough time in this type of work to know how good looks like.
Frank Cottle
[ 00:11:02,730 ] Well, I think that’s. That in itself is a really critical understanding. That people need to have in terms of the judgment capacity.
Frank Cottle
[ 00:11:13,430 ] What we’ve done and what we see is that—uh— first we’re going full AI native. We’ve decided we made that decision increases in productivity, increases in value as a result of higher employee and security, safety for job security, and higher compensation through higher productivity. We think we should be giving people raises rather than fire.
Frank Cottle
[ 00:11:38,280 ] Okay, so that’s our attitude towards AI. So we’re going full AI native. Um, and in doing so, the first thing we recognized is exactly what you said. Is that some job functions what we need to do is redefine the job itself.
Frank Cottle
[ 00:11:56,170 ] And then build AI into a process or into a multitude of processes to help the person. Who may not be able to make the judgment, but were purely an accelerator. As opposed to someone who may be able to make the judgment that’s going to be pulling in. Data and reporting and forming opinions and making high-level decisions. But you really have to reshape each of the job descriptions in order to integrate AI within a company in my opinion.
Sebastian Fixson
[ 00:12:26,950 ] Yeah.
Sebastian Fixson
[ 00:12:28,420 ] I would expect that we have to go actually much further. Because if you think about Jobs are functions inside of an organizational structure. An insight of more or less well-defined processes throughout. Whatever the organization does.
Sebastian Fixson
[ 00:12:46,470 ] And if you, to use the lean terminology, redesign value streams.
Sebastian Fixson
[ 00:12:51,670 ] And if you redesign them in a significant way, you are effectively redesigning your organization.
Frank Cottle
[ 00:12:57,160 ] Well, can you explain what a value stream is in your definition?
Sebastian Fixson
[ 00:13:02,130 ] So in the my my my original home department is operations management and the lean discussion that emerged 30 years ago. From the quality movement and the manufacturing.
Sebastian Fixson
[ 00:13:14,960 ] At its core, if you go back to the early writings, they talk about the value stream. And what that means is: The very first task for a leader is to identify what is the value that we want to create, that ultimately customer value.
Sebastian Fixson
[ 00:13:30,230 ] And then align all the activities, the stream, towards that goal.
Sebastian Fixson
[ 00:13:35,720 ] And I think, on the a very fundamental level, that is still the task that we have today with every organization.
Sebastian Fixson
[ 00:13:43,310 ] However, if AI allows fundamentally restructuring value streams, that means we’re looking at very significant organizational change questions.
Sebastian Fixson
[ 00:13:54,530 ] And those of us who have tried this throughout their career, I’ve experienced how difficult organizational change is. For a whole host of reasons— legacy cultures, legacy IT systems, people, and their own mindset—there’s a whole host of questions.
Sebastian Fixson
[ 00:14:10,880 ] But in order to tap into the true potential of AI, that is probably necessary.
Sebastian Fixson
[ 00:14:18,110 ] And if you look at the current discussions that Ethan Mollick and the kinds of have talking about it— capability overhang that points that out that the narrowly measured the tools are far ahead of what we currently in our current organizational structures can actually realize.
Frank Cottle
[ 00:14:35,450 ] Yes.
Sebastian Fixson
[ 00:14:36,450 ] Change to change the organization, that would still take years.
Frank Cottle
[ 00:14:41,070 ] Well, this goes— a fundamental question I’ve got, and I don’t know the exact number, but I’m going to make a number up because I’m good at that. I’m going to say that 70% of the people in the United States today work for Fortune 1000 companies.
Frank Cottle
[ 00:14:59,350 ] Excuse me, 30% of the people work for Fortune 1000 companies. 70% were first— small to medium enterprises.
Frank Cottle
[ 00:15:06,450 ] And I think that number, from the Bureau of Labor Statistics, is pretty close. Um, that means entrepreneurial companies— those smaller companies— outweigh in terms of number of employees the larger companies. Yet it seems most, if not all, the studies are done on the larger companies and smaller organizations, just like turning a ship or an airplane or a car. You know, Ferrari handles a lot faster than a big SUV.
Frank Cottle
[ 00:15:35,280 ] Okay.
Frank Cottle
[ 00:15:36,980 ] Smaller organizations can react faster.
Frank Cottle
[ 00:15:40,930 ] Yeah, that’s a principle too. Level of adaptation. So in terms of pure numbers of individuals, I think adaptation from that group will outweigh the others, and it will also happen faster. Other thing all.
Frank Cottle
[ 00:15:55,960 ] Bring up is. I was recently at a meeting in New York that was a whole variety of investment banking companies and investment funds.
Frank Cottle
[ 00:16:08,550 ] And it was really funny because I was speaking in one of the you know there there’s a there was a little system where people were texting around he says oh great We get to listen about the future of work from an old guy with a beard.
Frank Cottle
[ 00:16:23,170 ] So anyway, it was an interesting meeting. But the one thing that struck me is when you talk about investment criteria for new companies and where the capital, the venture capital is looking. It’s not just an AI company. but they really don’t want to invest in companies that aren’t working towards an ai native structure operationally anymore They want to see how companies that they’re going to invest in. will be applying AI to gain a competitive advantage. So, if this is the case, capital resources will be limited to companies that don’t manage this change.
Frank Cottle
[ 00:17:08,680 ] Are limited to companies.
Frank Cottle
[ 00:17:11,180 ] Limited. Companies if they don’t manage this change.
Frank Cottle
[ 00:17:15,760 ] Is that something that you’ve seen, or is there any research that you’re seeing on that, or is it just anecdotal from the Dumb meeting I was at in New York the other day.
Sebastian Fixson
[ 00:17:26,619 ] I mean, I’ve heard, in the investment perspective, that I’ve heard similar arguments.
Sebastian Fixson
[ 00:17:32,470 ] The hypothesis behind this is obviously that if AI allows much faster scaling, then you would assume that the capital requirements to scale are smaller. And so that should be an indicator of the potential of a company to grow fast.
Sebastian Fixson
[ 00:17:53,380 ] I think the pressure in an established company is probably a different one, through more stock market general assessments.
Sebastian Fixson
[ 00:18:01,990 ] Um.
Sebastian Fixson
[ 00:18:03,200 ] But I think it’s also a fundamentally interesting question, if the Um, financial the capital raising force. That you mentioned. It’s disciplining all. small companies in the same way.
Sebastian Fixson
[ 00:18:17,970 ] On a theoretical level, you might think that’s true. On the other hand, to go back to your metaphor of the many small ships. Um, If we compare the AI to the wind, then yes, every small boat will align. with it in the same way.
Sebastian Fixson
[ 00:18:36,730 ] On the other hand, The. lower requirement for capital search might also be an advantage for a small company to do things differently. Than VCs or other private equity people would expect.
Frank Cottle
[ 00:18:50,080 ] Well, I think you’re right.
Frank Cottle
[ 00:18:53,400 ] Asset Light was the flavor of the month several years ago, still is, and it’s part of the built-in requirement now you have to be asset light, have a high level of agility, if you’re going to seek and gain capital. Um and New large companies are they tout that light now? Large companies seem to be touting the benefit they get from immediate AI as a reduction in hit count.
Frank Cottle
[ 00:19:24,489 ] And all of a sudden they’re quarterly profits look better and so their stock takes a temporary jump. I think that’s a band-aid— A terrible band-aid. By the way. It’s a reactive structure as opposed to a progressive structure. Um, but we’ve seen different requirements start being laid out and then they become a standard.
Frank Cottle
[ 00:19:45,610 ] Okay. Asset light agility is now a standard. Period. Okay, now. What’s the next standard? Thank you. AI native, uh, AI integration, uh, some of the level of new level of revenue to personnel ratios that are different than they used to have to be. How do you see that playing out?
Sebastian Fixson
[ 00:20:08,060 ] Well, I mean, if you look at the The newest Fable. Animal, the one person unicorn.
Sebastian Fixson
[ 00:20:16,070 ] There you have your ratio. That’s you.
Frank Cottle
[ 00:20:18,130 ] That’s me.
Sebastian Fixson
[ 00:20:20,530 ] Of an enormous ratio of revenue per headcount. But I think directionally, that’s certainly what many entrepreneurs are aiming to— to create an organization that can do a lot more with fewer people. And, that one of the reasons why I’m looking into this problem specifically or this question is— Startups obviously don’t have a legacy. They don’t have a legacy in the organization, they don’t have a legacy in in existing IT systems or accounting systems, or— establish some power structures. If you form a company from scratch, Things could evolve in different ways and at least initially, startups behave that way. Right. The job descriptions are not clearly delineated as they are in more established organizations.
Sebastian Fixson
[ 00:21:12,620 ] I think the interesting question to me is, will certain legal requirements force them to. So I can tell you, I had a I’ve been a couple of Roundtable discussions. Two months ago. And. Interestingly enough, they’re on the table where company leaders and they were in one of two camps.
Sebastian Fixson
[ 00:21:36,820 ] One was that we need to get our people to use AI more and faster.
Sebastian Fixson
[ 00:21:41,550 ] But the other, Kim said, when. We are worried about losing control.
Sebastian Fixson
[ 00:21:46,470 ] It’s sort of bring your own tools and people do a whole host of things where is the data, what’s the risk, control, right? Who is responsible for compliance.
Sebastian Fixson
[ 00:21:56,460 ] Those things are. Unanswered and some of the people at the table were really worried about that problem. You can see that startups without any experience in those spaces might create organizations.
Sebastian Fixson
[ 00:22:09,270 ] Where they have to retroactively figuring out to keep control on some of these items. Whether it’s IP, whether it’s liability questions, etc.
Frank Cottle
[ 00:22:18,890 ] Well, you know, the old story about a leader is a leader always has to look back over their shoulder to make sure somebody’s still there. All right.
Frank Cottle
[ 00:22:27,990 ] I think you’re on now. Corporate development, entrepreneurial development. A lot of times the brain of the entrepreneur is always out in front of the practicality of operations.
Frank Cottle
[ 00:22:45,330 ] You know, a lot of times. This is true.
Frank Cottle
[ 00:22:48,840 ] Very few people could go forward and stay.
Frank Cottle
[ 00:22:52,630 ] operationally attached at the same time and smaller companies moving faster, this becomes more of a problem. But you know, we’ve solved that in accounting, we’ve solved that with technology, we solve that with personal use of devices, and all of that we’ve solved all of those problems in the past.
Frank Cottle
[ 00:23:11,569 ] Is there real fear that we won’t be able to solve it this time?
Frank Cottle
[ 00:23:17,410 ] Uh. Is this just another time that we uh just something we have to do, have to deal with that— an ordinary course of business issue?
Frank Cottle
[ 00:23:27,120 ] Oh.
Sebastian Fixson
[ 00:23:28,120 ] Interesting question. One of the participants made that comparison and said, ‘Well, that’s when spreadsheet ended our world and suddenly data was copied from here to there and we no longer knew where the spreadsheet was and week. Got better control of that over decades of work. And tightening up IT systems.
Sebastian Fixson
[ 00:23:45,830 ] And maybe that is the answer. I don’t know.
Sebastian Fixson
[ 00:23:49,680 ] But it’s also.
Sebastian Fixson
[ 00:23:51,670 ] People in my generation think about information systems as as ordered and structured in some way— who has access to which kind of data, etc.
Frank Cottle
[ 00:23:59,370 ] you’re mentioning.
Sebastian Fixson
[ 00:24:01,900 ] But one of the inter- one of the entrepreneurs that I interviewed. Um, as essentially the AI be the—memory. Slash brain of his organization for people. and all user data interviews they sort of feed into this. Thank you. Each one has conversations.
Sebastian Fixson
[ 00:24:24,880 ] with the system for the questions that there are. currently working on. And on one level that sounds very organic.
Sebastian Fixson
[ 00:24:32,580 ] and very productive, low barriers of entry. finding things is left to the device of the AI system to feedback to you. But at the same time, you don’t really have you don’t really know. where the data is and who has access to which data once you have to stratify that.
Sebastian Fixson
[ 00:24:52,270 ] Perhaps in the future, AI systems will build in these kinds of. control layers. But it’s not clear to me that at this point they exist.
Sebastian Fixson
[ 00:25:02,260 ] Some other firms for that reason, are very, very careful in handing data. customer data into these systems for that reason.
Frank Cottle
[ 00:25:10,880 ] Well, you know, it’s interesting. As I look back uh um Not too far. Companies have their own knowledge banks. They have their own, that were internal to them, where people could go and find out things, etc. Now, companies are building, we’re building our own LLM tied to our own company, all of our own company. everything in our company is fed into. a single bacon than that.
Frank Cottle
[ 00:25:42,000 ] our own. private.
Frank Cottle
[ 00:25:44,920 ] model. for AI has the ability to access. that now we do segregate that we do segregate you know you you can’t get Credit card information. You can’t get certain things that would always be secure anyway. But in terms of everybody knowing everything they need from within the company’s history, customer base, customer experience, business plans, et cetera. We think that that’s important.
Frank Cottle
[ 00:26:11,920 ] Overall. Because decision-making is the most valued element within any organization.
Frank Cottle
[ 00:26:21,930 ] How fast can decisions be made and how accurately can they be made and how can they be then implemented. That’s what drives companies forward. More than anything else, and AI is a massive contributor to that. If. It’s reading the right information, which is part of your issue.
Frank Cottle
[ 00:26:40,990 ] If it’s reading everything, well, it may not be apropos, but if you could keep that funnel— or what it’s reading— then you really can accelerate decision-making. And I think that’s what a lot of companies are starting to do.
Sebastian Fixson
[ 00:26:54,120 ] Hmm.
Sebastian Fixson
[ 00:26:55,490 ] Yeah. I think the the fact that we are in a transition phase you can see in some other measures, right? So there are certain model performance measures that develop exponentially. Right? I don’t know if you know the company meter who Hmm? Measures sort of how long it takes an eye system to do a task that a human would take, let’s say, 30 minutes, and how much that number is expanding. That is, it is um developing exponentially.
Sebastian Fixson
[ 00:27:22,840 ] But then, if you read between the lines, they typically have measures that. The system is 50% accurate. They have another one that is 80% accurate and it’s still growing exponentially, but slower. But the closer you get to 90%, 95%, 98%, and that depends on the reliability requirements of your process.
Sebastian Fixson
[ 00:27:44,310 ] The more the Progress looks a lot more linear than exponential.
Frank Cottle
[ 00:27:49,910 ] if that’s the case, then I would say, what data do you have that says for that same accuracy and time thing. Where the human is doing it. Because in my experience, humans are rarely more than 50% accurate themselves.
Sebastian Fixson
[ 00:28:07,240 ] Fair enough. If that’s a comparison, then I think.
Sebastian Fixson
[ 00:28:12,270 ] The replacement. What? The augmentation of humans with AI. Um, this. Pretty obvious.
Sebastian Fixson
[ 00:28:21,250 ] I do think there are processes where you probably need higher reliability, also consistency over time, you get the same answer. And so those are requirements that in any probabilistic system are harder to meet.
Sebastian Fixson
[ 00:28:33,090 ] And that’s why the performance progress is slower than in these. edge measures if you will.
Frank Cottle
[ 00:28:39,440 ] Yeah, no, that could make sense.
Frank Cottle
[ 00:28:42,540 ] Imagining work. initiative overall.
Frank Cottle
[ 00:28:46,760 ] You start with the premise that most jobs today didn’t exist 50 years ago.
Frank Cottle
[ 00:28:52,740 ] Hundred years ago, where those are all certainly the way we do them. How does that?
Frank Cottle
[ 00:29:00,450 ] Perspective, that historical lens. Change the way we should be thinking about AI right now. You just said. You corrected yourself and went from replacing to augmenting.
Frank Cottle
[ 00:29:13,580 ] You know, what’s your thought on? New jobs will be created. That we have no idea yet.
Frank Cottle
[ 00:29:22,620 ] And with that. Is going to lead out for the future.
Sebastian Fixson
[ 00:29:27,870 ] Well, I think that The fact that we have so little idea of what that might be makes it obviously hard to project or extrapolate that. And if you look in historical terms, um, there have been massive changes. Technology introduced and used right so, for example, Post-World War II. When the shipping container was invented, it changed completely how harbors operate.
SPEAKER_2
[ 00:29:51,110 ] Sure, development.
Sebastian Fixson
[ 00:29:53,540 ] entire logistics chains, which the majority of people were physically unloading and loading ships. And now they control cranes, computers, trucks, all kinds of devices.
Sebastian Fixson
[ 00:30:06,520 ] To move many, many more goods.
Frank Cottle
[ 00:30:09,550 ] That was the creation of just-in-time inventory and the whole intermodal system. That was the foundation for the whole intermodal system. Yes.
Frank Cottle
[ 00:30:18,720 ] Think how Amazon would exist today. If the intermodal system didn’t exist. It would. Yeah, it probably wouldn’t. It could not.
Sebastian Fixson
[ 00:30:28,030 ] So I think that tells me two things. One, one is that we have to train our muscle of imagination.
Sebastian Fixson
[ 00:30:36,200 ] What could be. Traditionally, for example, in business education, in the entrepreneurial realm, we do this a little bit. But I think we can do this a lot more. Um, helping. People envision futures that don’t exist and that are further is a different in a way that from the traditional extrapolation, like if you add, I don’t know, now the lane of highways will make the cars faster. Here’s what we could expect. And so, how do we learn to think well? What if something was completely different. And there are tools and techniques that I’m sort of exploring and want to test a bit more. How? We can train our students and lead us in being more imaginative. You’re right— at that point, it’s still an imagination. I think the second step to that has to be: Once you have articulated or identified a desirable future or at least aspects of a desirable future, the following question then is: What needs to happen to get there?
Sebastian Fixson
[ 00:31:37,930 ] Or. From a different perspective, if you backcast, what needs to be true for this future to have a chance.
Sebastian Fixson
[ 00:31:45,840 ] And I think what that means is that it also requires us to think about that the future is not determined.
Sebastian Fixson
[ 00:31:51,920 ] It’s being built. by people.
Sebastian Fixson
[ 00:31:55,460 ] And so the more we can. Train people in their own.
Sebastian Fixson
[ 00:32:00,030 ] Efficacy, if you will, that they have agency in the process. The more confidence they have, they can contribute. Towards a future that is more desirable for them. That sense, I think, this is a larger question not only on a comfortable level but on a society level.
Frank Cottle
[ 00:32:19,170 ] Do you think government’s impact on trying to regulate? All of this.
Frank Cottle
[ 00:32:25,110 ] The way the tools will work, the uh things of that nature that There’ll be a. Restrictive element in some areas that will actually reduce our capacity to take advantage of the technology.
Sebastian Fixson
[ 00:32:44,820 ] I think That’s a good question. I don’t have an answer. I’m not a. I’m a legal expert, but I think that bigger and more important focus is on the outcome.
Sebastian Fixson
[ 00:32:55,530 ] Can we agree on how do you Um, How does decent work look like? What are those elements? Bye. And I think we need, for instance, better measurements. So um, Just one small example.
Sebastian Fixson
[ 00:33:12,110 ] Um, Sarah Cooper, who writes for the Financial Times. In her recent book, as an example of the what happened to the work of translators. Right, with AI. And the shift was that the AI did the 80%. Translation and The agency would give you out the work. Now I ask the translator, well, can you check, can you verify, can you polish it a little bit? And as it turns out in that setting. AI has now taken what used to be enjoyable for these translators.
Sebastian Fixson
[ 00:33:44,640 ] In other settings, coding is mentioned in another way. The AI took the grunt work and now the problem articulation and the formulation and the checking is actually what the people enjoy more. So, a similar replacement of parts of the work had very different outcomes on the purpose of the job holder, if you will.
Sebastian Fixson
[ 00:34:06,630 ] And to me, that suggested. We don’t really have good measures and need a different form of organ to discuss.
Sebastian Fixson
[ 00:34:13,409 ] What is the quality of work? In addition to the productivity value.
Sebastian Fixson
[ 00:34:19,709 ] Of course, in our area. Economy, something that is utterly unproductive will not survive.
Sebastian Fixson
[ 00:34:25,630 ] Economic sense, it may be a hobby or something like that.
Sebastian Fixson
[ 00:34:29,489 ] But at the same time, our evaluation measures are very coarse.
Sebastian Fixson
[ 00:34:34,739 ] Can add to this some measures of work quality, I think that’s where we have to have this society conversation. I’m not so sure if we can. Meaningfully.
Sebastian Fixson
[ 00:34:44,940 ] Specified technical aspect of AI. That as a whole.
Sebastian Fixson
[ 00:34:50,080 ] Another can of worms that I’m not gonna eat. Not a fight to talk about, but.
Frank Cottle
[ 00:34:54,199 ] Not a can of worms, my friend. It’s a bucket of worms. I know I agree with you there. Shifting gears a little bit, there’s so much talk about flexibility in the future of work. Flexible work structures, remote work, hybrid work, asynchronous work, etc.—all the things that have to do with agility. Um, do you think AI accelerates that or do you think AI mitigates it? Do you have any ideas about physical work structure as it relates to AI and how that might change?
Sebastian Fixson
[ 00:35:34,360 ] It’s interesting that you say that.
Sebastian Fixson
[ 00:35:36,490 ] During COVID, I had the pleasure to serve as the Associate Dean of her quality programs and I wasn’t in that ministry for. Um, and that was challenging. I will admit that.
Sebastian Fixson
[ 00:35:51,690 ] And at that time I thought it was very challenging. Given the structure in higher education, et cetera.
Sebastian Fixson
[ 00:35:56,870 ] Now I’m thinking actually COVID was just. The training came for us. I think AI will make bigger changes than we’ve seen so far.
Sebastian Fixson
[ 00:36:10,290 ] But again, I think the devil is in the detail. So is remote work for every work, for every person, for every organization the best mode? I don’t know.
Sebastian Fixson
[ 00:36:21,390 ] I think every organization, every person has to figure out what they prefer and what works for them. And then the organization has a difficult task to figure out how to put that. those people together in a productive way. Yeah.
Sebastian Fixson
[ 00:36:35,180 ] Fuck.
Frank Cottle
[ 00:36:36,070 ] I think you’re right. It is very individual or very discipline-oriented for a particular activity. Um, but my view— and maybe this is a bias, because I come from the flexible workspace industry— is that. Some variation of flexibility, and let’s just call it hybrid. For the moment, is here to stay, period.
SPEAKER_2
[ 00:37:02,980 ] Hmm.
Frank Cottle
[ 00:37:03,790 ] And people’s the design of their jobs.
Frank Cottle
[ 00:37:08,440 ] And the augmentation that AI provides.
Frank Cottle
[ 00:37:12,050 ] Redesign of jobs will allow.
Frank Cottle
[ 00:37:15,710 ] A better work-life balance as a result of combining AI with hybrid work. Uh, etc, etc. And will this be a new way that we will learn to do things with those— the combination of those two together. AI shouldn’t just create more productivity. It should also create. A better work-life balance. All right. Hybrid work creates a better work-life balance, particularly in environments where long commutes are part of the process, etc.
Frank Cottle
[ 00:37:48,420 ] So I. you know I think that that uh I hope that’s the way it works out at least you know, and without a little hope and a little faith, we don’t make much progress anyway. For people that want to implement, for people that say, ‘Hey, I want to get on this path.’ I know about it. I understand it. I’m dabbling with it. Seems to be, what’s the first major step that an entrepreneurial company or even a large company should be taking? What can we leave our listeners with as the first thing to do? To really get on this path in a productive way.
Sebastian Fixson
[ 00:38:26,250 ] So, I mean.
Sebastian Fixson
[ 00:38:28,140 ] Let me take an example or one data point from a recent research study that was COVID-focused, not AI, but I think I can make the point why I believe the same is true for AI. We followed a couple of companies, how they went through COVID and came out of COVID. Same industry, same kind of work, very different responses.
Sebastian Fixson
[ 00:38:47,650 ] Not only did the work arrangement differ—[ they landed up two years out].
Sebastian Fixson
[ 00:38:54,450 ] But more importantly, the way the employees understood and accepted the new setup was different. And so we went back into our data and were trying to understand why is that the case. What happened and what we identified is that, even though the companies are in the same industry. They’re very different underlying cultures, and very specifically different ways of involving employees and finding the new Let me call this work arrangement the hybrid work and flexible work.
Sebastian Fixson
[ 00:39:25,730 ] And that made a big, big difference in the way companies now in the post-COVID world operate.
Sebastian Fixson
[ 00:39:33,360 ] So. taking that insight into the AI world, I would say. Do the change with your employees. Not only do they know much better in detail on what work needs to be done. But you also want to have a system on the other side of it. That everybody agrees on and is happy with.
Sebastian Fixson
[ 00:39:52,060 ] I’m not saying everybody needs to be 100% happy, but overall, use the creativity. To create a system that people enjoy working in that generally. comes also with bio productivity anyway.
Frank Cottle
[ 00:40:04,510 ] Yeah, no, I, I think that, uh, middle going both ways in a company.
Frank Cottle
[ 00:40:10,770 ] is better than top-down. Bottom up is better than top down. All these ways that are inclusive. and gain the I won’t say consensus, but the understanding. Actually, transparency.
Frank Cottle
[ 00:40:27,230 ] transparency of Thank you. to the customer that you serve, to the people that are on the teams and the employees that are inside of the company. to the shareholders transparency of benefit.
Frank Cottle
[ 00:40:39,140 ] is critical. It’s critical. Well, Sebastian, thank you so much for your time today. I know you’re incredibly busy with everything that you’re doing over at Babson.
Frank Cottle
[ 00:40:48,680 ] amazing work that you’re doing from a research point of view and I’m just so grateful to you for for joining us today. Thank you.
Sebastian Fixson
[ 00:40:56,130 ] Thank you very much. It’s my pleasure.
Frank Cottle
[ 00:40:58,890 ] Take care.
Sebastian Fixson
[ 00:41:01,210 ] Bye
