Futurist Amy Webb on the Plausible Outcomes for AI and Work
MOLLY WOOD: That was Amy Webb. She’s a quantitative futurist and CEO of the Future Today Institute. And she looks at what business leaders can do today to prepare for a future, or present, with AI. There is of course no way to predict the future, yet, but Amy and her team are doing their best. Together, they use data to find emerging trends about the ways that AI will impact humanity. In today’s episode, Amy shares her most plausible outcomes for what the future looks like with AI, and what business leaders can do today to make sure their organizations are set up for success. And here’s my conversation with Amy.
[Music]
MOLLY WOOD: Set the stage for people who may not be familiar with your work. You are a futurist, what does that mean, in the context of business specifically?
AMY WEBB: So, futurists don’t actually predict the future. That’s not our job. We are really people who work in strategy. So we take signals in the present that help us identify trends—that describes what we can know. Uncertainties are areas over which no one entity has total control. So those are the things that we cannot know. So we combine the stuff that we know, along with the stuff that we can’t know, that’s going to be variable. That helps us create what-if scenarios. The scenarios aren’t the end of the work—they tend to be narrative, and sometimes they veer into something that feels or sounds like sci-fi, but they really are strategic. The whole point of a scenario is, if it’s done well, you’re extrapolating out but you still have enough data that you can help anybody see alternative futures. And what that allows a business to do is to work back to the present and make better decisions. So this is really strategy work. And I would argue, the fundamentals of foresight should be required of every leader, just as the fundamentals of strategy at this point are required of every leader.
MOLLY WOOD: We are taking it almost as a given now that AI is the future. And so I guess I want to start by saying, do you agree with that? And how much is it informing your work right now?
AMY WEBB: So the answer is, I do not agree. And that’s because AI is the present. This is part of the problem that I see organizations and leaders really struggling with. AI still feels like a frontier technology. AI has been with us for, you know, dozens of years. If you use a mobile phone, you are using AI. Molly, you and I having this conversation in two separate cities, using a streaming service, like, we are using AI. I don’t want to sound glib, but I do think it’s worth noting that some of the technologies that we’re hearing about in AI sound very magical, but they’re not magic. They’ve been in some form of development now for a very long time. Yes, they will be a part of us going forward, which is all the more reason why it’s very important right now to get very clear on what this technology is, what it isn’t, and realistically why it matters.
MOLLY WOOD: So what do you think, given that at least socially, conversationally, and probably technologically, we are at a bit of a tipping point… what do you think the next one to five years entail in terms of answering those questions—what it is and what it isn’t, especially?
AMY WEBB: Yeah, so the sort of moment that AI is having right now falls within the generative category, and specifically as it relates to language. What most people are familiar with right now is ChatGPT. The GPT stands for generative pre-trained transformer. And these systems need lots of data. And you have to train models on those data, basically telling them like, you know, if a system sees a picture of an elbow, like yes, this is an elbow versus no, that is not a knee, right, things that might look otherwise similar. This is the place that we’re at right now. The much more interesting aspect of this is, how that technology becomes an enabler of other technologies. So for example, imagine a robot arm, and imagine an array of packages and boxes and toys, just like a giant cluttered mess. Imagine being able to tell that robot arm, pick out the prehistoric animal—without having to specify “little toy plastic dinosaur,” but describing it more naturally using natural language. And that robot arm successfully picking the right thing. Previously, a researcher to train a robot arm would have had to painstakingly just over and over and over again, specifically measure, you know, that the exact size of that dinosaur, the placement and sort of tweak over and over and over again. The difference now is we’re teaching the robot arm to learn through repetition. And that’s why today’s chat-based systems are interesting. But what they enable going forward is the thing that I would keep my eye on.
MOLLY WOOD: I know you said you don’t predict the future. And yet, I do want to dig into the optimistic scenarios that you think are possible, and how we can get there. Because there is some magic. That’s the magic.
AMY WEBB: Yeah, totally. So maybe let me go backwards. I was meeting with some of our clients in the healthcare space. And I think those in healthcare are looking at this new technology with both excitement and concern. Excitement because it does promise to automate some routine tasks that are just enormous cost centers. But concern because some of the folks who have maybe spent, you know, a decade in school learning how to do something specific, like oncology, are concerned about what that means for the future of their jobs. So I took a publicly available P&L for a hospital that I found online, I hit, you know, copy, and I pasted the text into ChatGPT. The P&L for this hospital was a disaster, the hospital was bleeding money. They were clearly in crisis mode. And I was imagining the executive leaders of that hospital having crisis meetings trying to figure out, how do they shore up their operating budget. So I dumped the data into ChatGPT and asked, using a prompt, how can I reduce operating budget by—I think I just picked a random number—8 percent year over year without reducing headcount, which would be the typical place that a company or a hospital would usually look. And within 27 seconds, it spit out a very detailed analysis of many other ways to trim costs, without having to cut back on essential services, or reducing headcount. Now, here’s the thing. There’s nothing in there that was surprising. But what it did do was, the 80 percent of the work that would have been a cost center for that team, they would have had to spend a ton of time and energy and effort and resources to just say, yes, these are the obvious things. So what’s kind of amazing about this, I think going forward, is, that system or one like it, can get that stuff out of the way and allow that executive team to focus 80 percent of their time instead on creative alternatives, which is what, frankly, they should be doing anyways. So to me, that’s emblematic of what we might see going forward. But what’s interesting here is that if you ask anybody in that field, what do you think the future of AI is? They immediately think about reducing headcount. I don’t think that’s actually the case.
MOLLY WOOD: It’s such an interesting way to sort of shift that narrative to say, what if you actually use this technology to specifically choose to save jobs?
AMY WEBB: Some of the big reports that have come out, with detailed numbers about how AI will generate all of this economic growth while at the same time eliminating, you know, hundreds of thousands or millions of jobs. I think those numbers are wrong. The forecasts that we put together show something very different. And listen, this is not, I’m not being a sort of cheerleader for AI. It’s not that at all. I’m a pragmatist. There are technical reasons why a lot of the jobs that are being forecast to go away, it’s improbable that that’s the future, which means that leaders are probably looking at their future the wrong way. Most of the executive leadership that I talked to, regardless of industry, are looking at AI as a way of managing bottom line growth, which is really a story about efficiencies, getting more productivity out. The better way to look at this is, how does AI increase top line? Meaning, where are your new work streams that didn’t exist before? How can you do things that you were not able to do before because you didn’t have time? Again, I think that’s one of the huge benefits of this that nobody’s talking about. Some of these tools, what they do is they generate time. And that’s the number one thing that I hear from every executive that they just do not have. And that becomes an excuse for why they don’t innovate.
MOLLY WOOD: Yes, you just go back to the same old well over and over and over, and unfortunately that well is often headcount. But on that point, your book, The Big Nine, is about the world’s most important companies when it comes to the future of AI. Microsoft is one of them. And as you said, you’re a pragmatist. There are lots of scenarios, not all of which are good. So what is your advice to these companies?
AMY WEBB: AI is a technology. It’s an umbrella full of technologies. It’s kind of a strange metaphor, since the technologies would fall down from the umbrella, but I think you understand what I mean—the bucket full of technologies [Laughs]. And I think if leaders of organizations have the right understanding and background, and they’re not making decisions based on fear, then I think that growth is highly plausible. So, I see a lot of upside there. What we’re also hearing about, which is true, is how this technology creates geopolitical challenges and potentially further divides society because of misinformation or any other number of things. What I will say is that some of the companies in the AI space—Microsoft, I think, is a leader here—have really been working hard to think through plausible futures, and ways in which those serious challenges are abated. Maybe we head them off in advance. But I don’t see every company doing that.
MOLLY WOOD: So it sounds like you’re saying, let’s hone in on the business leader, kind of, tactical advice. Specifically, it’s, do not stick your head in the sand about this, right? There is a lot of hype. And it is your job to not ignore it and not buy the hype, right, to try to chart that middle path.
AMY WEBB: Yeah, and you and I are like, hey, just, like, be reasonable, everybody. [Laughs] I mean, that is really, really, really hard to do right now. This is the most complex operating environment I’ve seen since I started doing this work 20 years ago. You need to have lots of partners to make all of this work. We had a client who was very, very interested in generative AI, and they wanted to get to strategy, they wanted to go three to five years in the future. They wanted a plan, they wanted the strategic direction and everything else. And we asked them a very basic question: when was the last time you did a data audit? And the answer was, we don’t know. And we said, okay, no problem, who is the person in charge of doing the data audit in your organization? They don’t know. And we said, okay, whatever, you’re a huge giant global corporation, your C-suite people… we’ll figure it out for you. Who do we call? And the bottom line was, they want a future where they’re going to reap the benefits of AI. They don’t have their internal infrastructure shored up yet. And you can’t leap to an AI future without having some of that internal stuff taken care of first, which again, you know, fear and FOMO are very powerful forces. And it’s—this is going to be a tough road ahead—to put those aside, set your eye on where you think AI helps your business grow, you know, and then do a gap analysis. And then you’re just, it’s strategy and execution, which every leader knows how to do.
MOLLY WOOD: I want to ask you about human collaboration. You know, we’re coming out of this very weird time. And now we have this idea that we’re going to interact with AI for information. How are you thinking about the future of human collaboration?
AMY WEBB: So if I think about the times, personally, that I’ve been the most excited, invigorated, working on projects, it’s when the stuff that just takes up time where you feel like you’re trudging through mud, like, that’s out of the way. And then you have the foundation that you need to really do the true collaborative, exciting work. I think there isn’t as much collaboration, because people just don’t have time anymore. Our lives become really complicated. So for me, personally, a future in which I can use a trusted AI resource—and trust here is very big, that’s a big deal—but if I could use a trusted resource to get the, you know, even half of the stuff that I have to get through on a daily basis as a CEO of my company, if I could just get that stuff out of the way… and again, this is like decision making. Can I just get a summary of the thing I have to make a decision about? Can I trust that summary, you know, without having to go through and read pages or multiple spreadsheets or whatever it might be, that opens the door for me then to work with my senior leaders and collaborate on the next things in our pipeline or other things that we want to do. So I think this unlocks that opportunity for collaboration. It also means, like, maybe we wade into areas that we just haven’t been before. I think when people talk about AI and creativity, they immediately think of visual effects or music or art. I think there’s a huge amount of untapped business creativity potential that we’re going to see unlocked sometime in the next few years.
MOLLY WOOD: Okay, so as leaders start to think about this, what are the kinds of futurist thinking frameworks that they should put this planning into? Because I love the idea of saying to people, think about what could be unlocked here instead of what can be lost. It’s the abundance mindset.
AMY WEBB: So we have a framework that we always recommend to everybody—it’s open source, it’s available online, at just about anywhere. It’s called a time cone. So, in a lot of organizations, when thinking about the future as happening, companies tend to use a line, right, and basically a line tends to mark whatever, two, three years in the future. And the issue with a line is—a timeline—it doesn’t account for uncertainty. And although it may feel like the future has been set in stone, given where we are with AI, the truth is, there’s an enormous amount of uncertainty, just huge amounts of uncertainty at how a lot of this will pan out. For that reason, a cone is a better shape. So in the very present—this would be on the, sort of, you’re thinking about this, on the left hand side where the vector is, that’s today—the further out in time you go, the more that that cone opens up. And in the present, we have the data that we can observe and the perspectives that we have. So we can make decisions that are more tactical in nature. The further out in time you go, you have less certainty, you have more variables, therefore the cone gets very wide. It doesn’t mean that we don’t make decisions, you just have to make different types of decisions. So that cone, imagine, has four segments—the farthest out, which is the farthest out in time, that represents transformation. So imagine 10 years in the future, and AI has transformed your business, your work stream, your industry, the world, right, whatever it might be, what does that transformation look like? And given what you know to be true today, what decisions would you need to make in order to win, to sort of play and win in that future? The second segment in from transformation is long-term strategy. So again, if this is the longer term future, then what are the longer term strategic decisions that would have to be made? And that tends to have to do with organizational changes, investments, M&A, things like that. The next one in is old-school strategy. That’s your next two years. Therefore, what do we need to do? And then the present day one is tactics. What is nice about this time cone is that it forces your team to make decisions in sort of four time horizons, related to anything, but in this case, AI. It also asks you to think very near-term and long-term at the same time. That’s the number one tool that I would recommend.
MOLLY WOOD: Okay, listener, pause here if you need to and write this down, because even if it’s not planning for AI, useful, right? And now, back to Amy and what else can create AI abundance in your organization.
AMY WEBB: The other one is simple. It’s called ADM. We use this all the time. And if you’re a fan of Adam Driver the actor, I guess this is a good way to remember it. Adam is not spelled like a-d-a-m though. It’s spelled ADM. So act, decide, monitor. Every time you hear something new about AI, make sure that the source is correct and things aren’t being overblown. Then put it into a category: is this something we need to act on today? And, truly, without some type of action today, we get disrupted, we lose market value, we have a communications problem, whatever it might be. The center one is decide. This is somewhat near-term, it rises to the level of, we’re going to have to make a decision, we have to position ourselves. The last category is monitor, which is, this caught my attention, so it’s important enough, but we don’t need to do anything with it right now. But we still want to keep paying attention. The act of categorizing, when it comes to something that’s very emotional at the moment, like AI, gives you a sense that there’s forward momentum. And it organizes yourself and your team to take action when the time is right.
MOLLY WOOD: Right. I love it. So to be intentional, be thoughtful, apply frameworks to keep you from doing anything too quickly. Smart. All right. Final question for you, Amy. As you mentioned earlier, AI has the ability to save us a lot of time. What have you been doing with your extra time?
AMY WEBB: So, this is a true story. I’ve automated some of my work. And I’m a competitive cyclist. I have managed to eke out, you know, between 15 minutes and maybe an hour a day. And so now I no longer have an excuse to not do my core workout that I wish—I conveniently said I didn’t have enough time for before. And now there’s no excuse. So, thanks to AI, I have to do more core workout.
MOLLY WOOD: So what you’re saying is you’re a futurist and you run your own company and you are also a competitive cyclist.
AMY WEBB: But I’m bad on the hills. So there’s that. I’m a sprinter.
MOLLY WOOD: Amy Webb is a quantitative futurist and the CEO of the Future Today Institute. Thanks so much for being our guide today.
AMY WEBB: Thank you.
MOLLY WOOD: And that’s it for this episode of WorkLab, the podcast from Microsoft. Please subscribe and check back for the next episode, where I’ll be talking to Sam Schillace, corporate vice president and deputy chief technology officer at Microsoft, about AI consumer product culture and the next phase of productivity. If you’ve got a question or a comment, drop us an email at worklab@microsoft.com. And check out Microsoft’s Work Trend Indexes and the WorkLab digital publication, where you’ll find all of our episodes along with thoughtful stories that explore how business leaders are thriving in today’s digital world. You can find all of it at microsoft.com/worklab. As for this podcast, please rate us, review, and follow us wherever you listen. It helps us out a ton. The WorkLab podcast is a place for experts to share their insights and opinions. As students of the future of work, Microsoft values inputs from a diverse set of voices. That said, the opinions and findings of our guests are their own, and they may not necessarily reflect Microsoft’s own research or positions. WorkLab is produced by Microsoft with Godfrey Dadich Partners and Reasonable Volume. I’m your host, Molly Wood. Sharon Kallander and Matthew Duncan produce this podcast. Jessica Voelker is the WorkLab editor.