Artificial intelligence is changing how we live and work in ways large and small. It’s why Cold Call has tackled the topic several times. And it’s why today we’re sharing an episode of Harvard Business School’s Managing the Future of Work podcast, hosted by Professors Bill Kerr and Joe Fuller.
The show is all about the forces, like AI, that are reshaping the nature of work. In the episode “Microsoft’s AI perspective: From chatbots to reengineering the organization” from February 21, 2024, HBS Professor Bill Kerr talks to Jared Spataro, Corporate Vice President of Modern Work and Business Applications at Microsoft.
They discuss how the tech giant is experimenting its way from AI assistants to autonomous agents as it engages with stakeholders. The conversation also touches on the company’s relationship with OpenAI and ensuring the technology is employed responsibly.
BRIAN KENNY: If you’re a regular listener, you know that we’ve tackled the topic of AI multiple times over the past year. And for good reason, it’s a topic that is changing how we live and work in ways large and small. So, it won’t surprise you to know that we aren’t the only ones at Harvard Business School who are thinking about this. With that in mind, we’d like to reshare an episode of Harvard Business School’s Managing the Future of Work podcast, a show hosted by Professors Bill Kerr and Joe Fuller, that is all about the forces reshaping the nature of work. In this episode from February 2024, Bill Kerr talks to Jared Spataro of Microsoft, about how the tech giant is experimenting its way from AI assistants to autonomous agents as well as responsible AI, and upskilling. We thought this would be a great discussion to get you thinking about what AI has in store for us all in 2025. And if you have some thoughts on that, we’d love to hear from you. Email us at: coldcall@hbs.edu.
JARED SPATARO: It might sound counterintuitive, but one aspect of the propagation of AI innovation is absolutely the human element. People have to learn it. They have to learn not to be afraid of it. They have to learn that it can be beneficial for them as individuals and as, in the case of being a leader or manager, in those circumstances. And so that’s what’s emerged for me is, I would just say, “Hey, get after it. Get after it with alacrity, but don’t try and jump over and say, ‘We’re going to transform our entire firm from the ground up by reinventing everything.’” That, I feel like, is unnecessary tumult.
BILL KERR: It’s early 2024, and generative AI is fast becoming pervasive. Its impact will be both earth-shattering and mundane. While many organizations are experimenting with it, few are yet making wholesale changes to take advantage of it. What’s clear, though, is that AI will reshape the global economy and the world of work. The International Monetary Fund [IMF] estimates that 40 percent of jobs globally—and 60 percent in advanced economies—are exposed to elimination or change as a result of AI. The IMF also warns that the technology has the potential to increase inequality. And while AI is sparking innovation across the spectrum, dominant technology players have a distinct advantage in terms of their resources and market share. What can we expect from these leading suppliers of productivity tools as AI works its way through the enterprise and into our smart devices?
Welcome to the Managing the Future of Work podcast from Harvard Business School. I’m your host, Bill Kerr. My guest today is Jared Spataro, Corporate Vice President of Modern Work and Business Applications at Microsoft. We’ll talk about how Microsoft internally and through its investments in OpenAI is AI-enabling its ubiquitous products—from office apps to Teams collaboration software. We’ll consider use cases and, crucially, how Microsoft sees organizations adapting business processes, jobs, and tasks, and managing the risks of the powerful technology. We’ll talk about how the company is gearing up and skilling itself internally to carry out its AI strategy. And we’ll look at the bigger picture of ethical and regulatory considerations. Jared, welcome to the podcast.
JARED SPATARO: Great to be here. Thanks for having me, Bill.
BILL KERR: Jared, why don’t we start with a bit of your personal background and how you came to lead Modern Work and Business Applications at Microsoft?
JARED SPATARO: Well, sure. I’ve been here for a long time, 18 years at this point. And throughout my history at Microsoft, I’ve always worked on what we’ve called essentially our “productivity” businesses—so started with applications like Office and then over time has expanded into these areas like CRM [customer relationship management] and ERP [enterprise resource planning], kind of large areas where businesses are investing in technology. So it’s given me a really nice rounded view of how businesses use technology to run their operations.
BILL KERR: And with the pace of change being so fast right now, what’s your personal strategy for keeping up to date with what AI is doing and how it might influence Microsoft’s products?
JARED SPATARO: Interestingly enough, I use AI to keep up to date, and that I think is going to become one of the themes that you’ll hear from me in our conversation today. The pace of innovation, the pace of change, has picked up so much over the last few years that there’s no way that humans can keep up with it on their own. The old pattern simply of scanning the news is far from adequate. So these days I use AI-based agents to help me keep up with AI-based agents. I think it’s the only way to go.
BILL KERR: Sound advice. And so maybe we’ll ask you to continue and tell us, what are those agents saying are the biggest trends right now? And also, what might be around the horizon for us?
JARED SPATARO: I tend to think of us being in an era that is characterized by AI as an assistant—an assistant to individuals, an assistant to groups. You see it as a standalone assistant in products like ChatGPT and like Microsoft Copilot. You see it as an embedded assistant into many different applications. We see just about every software vendor on the planet building these assistants into their interfaces. But, increasingly, I think you’ll see, with the explosion of these assistants, people scratching their head and saying, “Man, I don’t know which assistant to go to for what at this point. It’s too much.” And so I also anticipate that, in the coming months and years, we’ll see some consolidation.
BILL KERR: So, it’s almost like a meta layer that’s going to help crossing the chasm from the earliest users to the general mainstream and beyond.
JARED SPATARO: That’s right. I think the context is important. Almost 60 percent, based on our telemetry, of the average information worker’s time is actually spent in communication and coordination just to do the rest of their job. So, for instance, it’s in meetings or in chats, or it’s used in email. And that percentage is growing month by month, and it doesn’t look like it’s flattening out. So what people actually tell us in the qualitative work that we’ve done is that they say, “Man, I hardly have time to do the job I was hired for.” And that backdrop, that context, I think, is important, because the assistant era of AI is arriving just in time.
BILL KERR: Yeah. Neither of us are going to be able to see the listeners, but I’m sure a number of heads were nodding when you talked about the overload that some of these communication tasks have had for us. Is there any early data on the impact for individual productivity that comes with AI, generative AI tools?
JARED SPATARO: I think that there are two data points that really stick out to me there. We’ve asked users, for instance, “After you have used, in particular, a product from Microsoft called Copilot, do you want to go back to working without it?” And 77 percent of the people who had early access to Copilot as a product said, “I never want to go back to working without it.” The other one that has been particularly interesting in terms of sentiment is that the best users of Copilot are estimating that they save north of—more than—10 hours per month. That said, anyone who’s in business for a while knows that people lie, and they, in particular, really lie when they answer these types of surveys. So we’ve done some work to just go in and actually run experiments, where we have groups that use the tool, Copilot, and then control groups that don’t, and we give them a series of tasks working across a corpus of information. On average, users saved 29 percent of their time to get the work done, so they were 29 percent faster without any real degradation in quality. So that gives me hope. If you say, “I can give you 30 percent of your time back,” once you figure out how to use the tool across this broad swath of information-worker tasks, that’s pretty attractive—certainly attractive for individuals if they can harness the excess, the surplus, that’s generated; very attractive for organizations as well.
BILL KERR: Yeah. One of the things that a couple of academic experiments have also found is not only that productivity gain, but that it really accrues to the youngest workers or the newest to the job, that it really can flatten out parts of the experience curve. Is that something you’ve also found?
JARED SPATARO: Absolutely. We’re very excited about that, because it does level the playing field. It helps people essentially close the gap that experience ends up giving. But we would like to get that same productivity boost even for experienced workers. So that’s some of what we’re digging into right now.
BILL KERR: That’s great. So tell us a little bit about how Microsoft is viewing AI. What’s the strategy or the approach that it’s taking toward it? How are you staging building AI into the many products and services of Microsoft?
JARED SPATARO: Well, let’s take a step back for a moment. For a large part of 2023, there was this sense that, “Okay, I get it. You basically have a technology that can do things like summarize and answer basic questions.” But what we have found during that last year was not only can it do some of those key skills, but we really believe that what we have here is a general-purpose reasoning engine. And I always pause for effect when I’m talking with my customers about that, because we’ve just never had that before. We have computers that essentially reason over math-based formulations of problems, because they’re driven based on math, essentially. This is the first time that we have something that is a language-driven reasoning engine, and that’s really important. As we look at the basic implications of that, it means that it can become an incredible natural language user interface that makes a lot of sense. You can speak to computing resources going forward. But even more important than that, what we have found is that there’s a pattern that has emerged that many of our listeners will have heard of that’s called “RAG”—retrieval-augmented generation—and it essentially says this: “If you ask me a question, and you give me essentially a “scoop”—a bucket full of data—to reference as I answer that question, I can take the reference data and reason across that reference data to get you the right answer,” as long as the right answer is in that bucket full of data, we call that a “context window,” and that pattern is retrieval-augmented generation. It’s become, from my perspective, the single biggest advance for business that we’ve seen over the last 12 months. It’s very, very significant, and it means that you can take these large language models as they continue to get better and better at reasoning and really have them reason over your data, your finance data, your HR data, any type of data to help you make decisions. And that’s a very exciting area of innovation right now in business with generative AI.
BILL KERR: Wow, and that seems very transformative. You talked about saving time, getting those 10 hours back for yourself, which I’m sure everyone wants. But the capacity to have that superpower come toward your actual meaningful work, the things that you’re doing to learn from the data, seems significant. Do you also anticipate that level of improvement in the productivity of the work itself?
JARED SPATARO: We do. I noted earlier that we’re in this assistant generation, where we’re all about helping people—I would call it, almost incrementally improves their performance, their productivity. But the next generation moves us out of just individuals, and even small groups, into process-oriented—maybe you could even call it “function-oriented”—productivity. Many of the processes that are so common and so core to the way a firm operates today can be automated. So closing the books at quarter’s end totally can be automated. Reviewing sales pipeline, that can be automated. Answering customer questions, even finding the next best thing to do with customers, even unearthing the right people to target for new customers, all that can be automated. And much of that work—because it requires a logical progression, a reasoning over data—is done by people today. So that second generation, we believe, really moves us into process automation, where we do start to get some pretty important gains—not at the individual level, but at the functional level, in terms of how work is getting done. So, again, we start to see this technology used in an increasingly sophisticated way to drive returns.
BILL KERR: Another area that I know you’ve been thinking a lot about is collaboration. So tell us about how you’re seeing AI’s next generation toward collaborative work, recognizing the process functions that you were just describing have some element of collaboration, but there’s many things that would be cross-functional that would also be collaborative.
JARED SPATARO: Interestingly enough, what we’ve seen these digital tools do over the course of, let’s call it, the last two decades, is that, at the individual level, they have driven quite a bit of productivity. A spreadsheet is a great example. It helps me do things that I otherwise would’ve taken days to do in sometimes minutes, sometimes seconds. But the downside to them speaking as a purveyor of these tools is that they’ve made it incredibly easy, frictionless even, has been our claim—and it’s really true—to communicate with other people. And although that sounds like nirvana, we are over-communicating. We are communicating, and in some ways coordinating, too much. If a person has a question, instead of going to yet another person to ask the question, they could go to a bank of answers or an AI agent that would understand what’s happening across the firm, it actually would significantly improve what the efficiency of the overall processes would be. So one aspect of what we’re trying to do is actually get people out of the business of just having to service other people in very mundane ways. Think of how much time we spend servicing our inbox. One of the things we could do for collaboration, interestingly enough, is reduce how much we have to go to each other for some of the mundane aspects of work where we could be much more efficient.
BILL KERR: Well, let’s just continue on the other use cases for meetings. What are some of the pieces you’re envisioning for the future workplace and office?
JARED SPATARO: There are two aspects to meetings that have caught our attention over the last couple of years. The first is that the in-meeting experience can be much more efficient than it is. What we find with experiments and research is that, when meetings are facilitated by skilled humans who really know how to run a meeting, they are much, much more effective. They’re much better at reaching, for instance, decisions or pursuing outcomes. But the problem is, not everybody’s a skilled meeting facilitator, even though that’s a core part of what many information workers do every day. So one of the most exciting use cases that I’ve been involved in is building, essentially, a copilot for a meeting that actually helps the meeting to be more effective than it otherwise could be, playing the role again of that skilled human facilitator. And the fact is, we just don’t have enough of those folks. We don’t hire them. It’s not a job that we do. But it is a job that AI can do very well. Then, over the course of the pandemic, we saw meetings explode. So the time in meetings actually doubled for the average Teams user, the user of Microsoft Teams. And we saw large meetings start to balloon. More and more people are joining meetings—sometimes as a fly on the wall, sometimes to hear that five minutes of an hour-long meeting that is pertinent to their job. So the after-meeting experience—making it easier for people to not attend a meeting, but instead get the value that they would need out of the interactions—that’s incredibly important. You ask Copilot, “What decisions were made? What did David say? What did David and Sally talk about when it comes to this topic?” It can extract all of that out and summarize that. So those two things. I think meeting-goers should rejoice as they think about what AI is going to be able to do for them, but they’ll have to learn how to use the tech.
BILL KERR: Yeah, Jared. I agree that there’s far too many meetings at my employer and I’m sure at many other employers. There’s also a function or a role of meetings that’s not just about coordinating on a solution. It’s about all coming to terms that this is the way that we’re going to go forward and soliciting buy-in, and there’s these extra things. And as you think about a future that’s going to have more of this AI collaborative role in organizing stuff, do you anticipate that there is going to be resistance from people that will look at the outcome and say, “Wait a minute, that’s not what I would’ve agreed to or bought in”? Will we also learn to lean in more on these types of prompts and directions for seeing the path forward?
JARED SPATARO: I think what these tools will do is that they will shine more of a spotlight onto the why of a meeting—“Why are we having this meeting, why have we invited the people that we’ve invited?” And it will take some time. That has been one of my biggest observations is, as the technology makes us in some ways much more proficient, maybe much more able to dig into the details of our work, then it does push us to, not gloss over those details any longer, but really ensure that we’re surfacing them. So to be very specific, I think there are meetings where it’s about building consensus and support around a particular direction. But we often don’t say that’s the reason we’re meeting. And I think we’re going to start to want to state that, “