We ended up talking to a bunch of insurance companies. A number of them said, well, we don't need those entry-level people anymore because we're having AI do the tasks that they used to do. And I would say, well, how are you going to get the experienced people of the future if you don't hire the entry-level people to do the boring, easy stuff? And they said, well, we're not quite sure. And every company that I've talked to has said the same thing for 12 or 13 years now. They don't have a good plan for that. Every week, another AI breakthrough hits the headlines. Every month, another company claims that the AI revolution will change everything. If you're feeling overwhelmed by the pace of change or skeptical about the hype, you're not alone. My guest this week is Tom Davenport, who's been at the forefront of technology transformation for over 30 years. He's a distinguished professor at Babson College, a fellow of the MIT Center for Digital Business, and the author and co-author of 25 books and more than 300 articles. I've read most of them. He helps organizations transform their management practices in the accelerating age of digital transformation. Hey, Tom, welcome to the show. Thanks, nice to be here. Thanks for having me. So I've written one book, a couple dozen articles. First question I have is, what keeps you inspired to keep going? Well, maybe if I'd chosen another field like epic poetry or something, I'd say, yeah, there's enough that's been written about that, but the field of technology, information technology, and how organizations use it keeps changing all the time. So I got to write about the new stuff. Now, there's a ton of hype. I mean, the technologies are pumping it out left and right. The algorithms are amplifying it as fast as they can. And it's pretty dystopian. It's gonna replace our jobs and some challenging things are gonna happen. As you look left, and right in the business landscape today, specifically AI, what's your take? Well, I don't think things are quite as dire as they're often portrayed to be. I sometimes say, if you're not concerned about AI and jobs, you're not paying attention, but so far, things haven't been too bad. We've been using various forms of AI for 60 years or so now and haven't taken too many jobs. And there's some really smart people derowing us a mogul for example at MIT, you just won the Nobel Prize says, yeah, you know, he thinks there'll be some small loss of jobs, but nothing quite substantial, not substantial enough to really increase the productivity rate much. So I keep telling myself, that augmentation of people by AI and vice versa is the best possible outcome. And I hope that more organizations reach that objective. But I do worry about it and I started writing some things on the sub stack about it. And that's really the only topic that I am writing about these days. But hard to get a whole lot of concrete evidence. It's mostly predictions and let's say about the predictions. They have one thing in common, they're all way wrong. What are the things that you cover a lot, especially when you talk about augmentation and your writing is around how AI fits in a larger process. It's embedded in a process when you look at large organizations. And having come from large corporate companies like Amazon and Microsoft, they're hard. I mean, minor change in these organizations is hard. And then you get into the hype cycle, you've seen a lot of technologies come in your career. They call it the internet or mobile or pick your technology. I'm glad you didn't say the electric light bulb. But you've seen this sort of storybook before where some technology comes, it's amazingly powerful. It is transformative, but how does transformation really work? Well, the speed of organizational transformation is definitely a lot slower than the speed of technological innovation. And I think now, for example, we're realizing with generative AI that despite the fact that, as you suggested, every day there's a new announcement about a cool new model that does this better than anything ever has. And we're right around the corner from artificial general intelligence. We realized that organizations are having a bit of a tough time finding value with it. And so I think it takes a lot of concerted effort and change management and some determined and also visionary thinking. I think by seeing your executives who are willing to see it through and eventually get something valuable out of it. But as you suggest, it's not nearly as easy as people suggest it is. And generative AI fools us into thinking it's gonna be easy because it is so easy to use by individuals, but I'm just finishing up an article with a couple of Stanford people on how really to get value from it, I think you have to have an enterprise level project to use it, not just tell a bunch of people, hey, maybe you'd like to write your blog posts with this or from hints forth, do your PowerPoint presentations with this generative AI tool. That doesn't, I think in general, create measurable economic value. So you have to look at the process, you have to redesign it, you have to upscale people, you have to figure out what the new process is and integrate it with the rest of your technology and that takes years in many cases. You mentioned leaders, we've seen the manifestos. I don't think they're coming out as much, but the Shopify CEO, DuLingo and a bunch of other, CEOs came out and said, we're AI first. And then they wrote a bulleted list of what that meant. To them, and some of it was actually informative saying, hey, everybody's gonna reskilling efforts or here's some areas that we're gonna start exploring where we think we can find value. When you look at these manifestos from CEOs, what are they getting right and what are they missing? Well, I think it's a good idea to be aggressive. I wrote a book along these same lines almost three years ago called All In on AI about companies that are really aggressive in their use of AI. It was mostly then what I call analytical AI, not generative AI, little bit of generative AI in the book. So I think it's good to be aggressive and it's good to have a vision. Think big, start small, I often say with these technologies that you're not that familiar with. But I think it's a bad idea, a really bad idea to say, we don't think we'll need half of the people that we have today because that really obviously does not encourage people to sort of try to figure out how to use AI to be more productive. It sort of suggests if you're too successful, you're out of here. So I think that part that they get wrong, I think you have to say more than we're all in an AI or AI first, you have to kind of specify the areas of the business that you're going to focus on and maybe even give a sort of high level vision of how you might go about it, but don't start getting people worried about losing their jobs yet. I saw an article, and I'd love to see somebody do research on it, but an article about how the companies that are saying, hey, it's going to replace jobs or the ones that are selling the technology. As a sales tactic, using the hype cycle of job replacement seems to be a way to help sell the ROI until a leader gets their hand on generative AI and realizes it's a lot harder than it seems. Yeah, well, in some cases, that's true. The CEO of IBM said it and the CEO of Anthropics said it. There are some other companies that have said it, like the CEO of Klarna, the Swedish pay for your internet purchases company, but he's backed away and said, nah, we couldn't get rid of as many people as we thought and we've hired some back, so you might be right in general. There are some CEOs like the CEO of Ford who said we will only need half the white-collared people that we have today, and he said it not just for Ford, but for the United States. But as I say, these predictions have all been wrong so far, and I suspect they'll continue to be wrong. There was a couple of articles that you've written in a cluster around process, and specifically around process mining. If I'm, you know, even in a mid-size company or I'm just an individual, I think it scales from an individual all the way up, and I have things that I'm trying to accomplish to be productive or to create value out in the world. Process mining's been around for a long time. You used to be RPA, and before that it was something else, and then there was a guy for it who used process improvements to build Model T. So it's been around for a while. How do you break it down and help people understand how to think about process improvements, process mining in a world where this technology is something they need to go all in, as you said, or experiment with? Yeah, well, I have a pretty long history with process. In the early 1990s, I wrote a book called Process Innovation about business process re-engineering, this idea that we can radically redesign our processes with information technology. It was the first book, not the best-selling book, I will admit, but I think that's coming back, and you know, in those days we were thinking, well, we need to redesign our processes to be enabled by SAP or be enabled by the internet or whatever, but now it's to be enabled by AI. And I think if you're not thinking simultaneously about, gee, AI stuff is really quite cool, and gee, we have a business process, where in the workflow can we fit it in? How will that change things? Can we make some radical improvements in it? Then I think you're not going to get the value that you would like to see. And again, I think a lot of organizations got rid of their process and provers and re-engineering largely died, I think maybe because of some overly aggressive head-cutting that was disguised as re-engineering, but I think it's coming back, and I'm not one to say, oh, bring back these ideas that I worked on a long time ago, but I do think that's a pretty important one. We have these tools like process mining that can tell you everything you know from your transactional system logs about how they're actually performing. You don't have to anymore, have these crazy little post-it notes on whiteboards the way we used to do it. So lots of opportunity, I think, for using these technologies to change the way work is done, but that still doesn't mean it's an easy, easy thing to do. When you're talking to your colleagues about data AI technology, there seems to be this battle around the C suite of titles and the person that's in charge, whether it's a CIO or the head data officer or the chief AI person, and everybody needs to sit in direct report to the CEO in order to lead this change. And I even read an article that HR now manages all the engineering and because they're gonna be managing agents and humans together. And HR needs to be in charge. And how do you think about organizationally to advise any company, whether it's a mid-sized company or a large company, that's trying to make sure that their leadership team, their SLT is set up in a way that is able to have conversations and make decisions? Yeah, I have done some work on this too. I thought that I had an idea that was fairly controversial. So I did a survey on it of data and technology leaders. And that idea was that there were too many tech chiefs out there as you say, chief information officers, technology officers, data officers, analytics officers, AI officers, security officers, blah, blah, blah, digital officers, don't forget them. And so I did the survey and it turns out that a number of them said, there are too many and we don't collaborate in the way we should. And we'd be fine with the idea of a single tech and information and data leader who manage all of this, who reported to the CEO. I mean, obviously if you have six or seven of these people, they're not all gonna report to the CEO. So if you have one who very business change focus, not a kind of a keep the lights on person at all, but one person who orchestrates all of these other things, which of course still are important and still need to have somebody leading them, then they would be happy with that. And I found a number of what I called super tech leaders who were already playing that role. They had several different functions. Some of them had five or six areas reporting to them that are tech oriented and even operations in many cases in financial services. So I tried to sort of describe what these people are doing. I don't know that it's had much of an effect yet. Super tech leaders like to be written about, but that's what I tell organizations they need to do. A lot of people find it very controversial. If you're a chief data and analytics officer or chief AI officer, you think, oh, you know, those CIOs, they don't even, they can't even spell data. Many of them can obviously, but I think it depends on the, if you have a talented executive who works well with other senior leaders, I mean, that's, you know, managing up is a really important thing in that role. And who can sell the case for IT enabled technology, enabled data and AI enabled business change. You know, it's a great person. At least you have somebody on the senior leadership team, whereas you might not have anybody if you split this role up into six or seven parts. Now let's go down into the organization and talk about reskilling. Your book, only humans need to apply. You talk about AI turns us into citizen developers. And I've been, you know, for the past many months been playing around with Bolt and lovable. And I've never hit compile in my life. I'm not a developer. I'm a non- You've been vibe coding. I've been vibe coding. But I'm a non-technical business leader. And to be honest, I've been blown away by what I've been able to create by describing it. And these tools are getting significantly more advanced by the week. But when you say we're all developers now, you used to be only developers could create a, even a webpage or a little web app. You had to spend hours and hours and have a lot of expertise to be able to build that. But from your book and you think about augmentation, help us understand that. Yeah, well, just to clarify, I did write a book called, Only Humans Need Apply, but that was not about citizen development. That was about what are the different roles that people play with respect to AI? That one was a while ago, it was like 2015 or so. The book that you're describing was called All Hands on Tech. And it came out more recently, I think 2023, maybe something like that. I'm up to 27 or 28 books now. So I'm keeping track of it all. I'm telling you as a podcast host who tries to spend a lot of time doing research. I appreciate the effort. And let me tell you Paul. The breadth of articles and books. And by the way, they're all super helpful. A corpus of work out there that has really, it's got a lot of trends and it's helpful. Well, thank you. Well, let me answer your question. I do think that it's important for business leaders to start to exploit these capabilities for citizens or vibe coders or whatever you want to call them. I kind of wish the term vibe coding had been created before. I wrote this book rather than after because it's a cooler term than a citizen developer. But I do think that it's important to sort of, restrict the domain a little bit. You don't want your vibe coders to be developing a payroll system or do it yourself, payroll system, or you're a banker or a demand deposit accounting system. These things are really mission critical for the enterprise. And if you screw them up, it's gonna bring your business down. So you have to make sure that the particular types of applications that people are working on are appropriate. I think Shell was the first to create a sort of a red, yellow, green kind of structure. Red, forget about it, doesn't make any sense. Yellow can be possibly done by a citizen, but you need to have some pretty strict governance. And then green, fine, go ahead. It's just for you or your little department or whatever. And then we talked in that book about the need for organization-wide. I really don't like the term governance, but in some cases it does need to be governance or heavy-handed. No, you're not gonna write a payroll system. But also things like guidance and guardrails and just things that make it easy to do the right thing for users. And more and more vendors are able to say, oh, by the way, somebody's developing something using this data, you might wanna check and make sure that it's right, or maybe you just send them a message. Some guidance to educate people on how to do this effectively. So we found a surprising number of companies, my co-author Ian Barkin, and my son helped out on this book too, Chase Davenport, but a surprising number of companies that already had a pretty well-established program in place. Shell is one of them. They have thousands of citizen developers. Microsoft, I think probably 90% of all development happens by citizens at Microsoft these days. They don't even have a corporate IT organization anymore. Satya Nadella is a huge fan of citizen development, so he encourages everybody to do it. But I think there are some cases where people need that kind of help and oversight. It was one of the most amazing things I experienced in my time at Microsoft, I was there over 13 years, was the hackathon when Satya came, and the entire company shut down for a week. And to shut down a company of over 200,000 people, intense, massive, massive tents were built around the world. And the idea that everyone sat there, maybe it was a developer, maybe it was people in all sorts of different organizations hacking, trying to figure out how to use the latest technology to solve problems. And I forgot what the number was, but a lot of those things did make production or did make customer-facing debuts. And so it was an amazing thing to watch when you talk about empowering citizen developers. When you think of augmentation, how are you using AI to augment Tom? How is Tom being augmented with AI? Just give us a little peek behind the curtain. Well, all my books starting in the mid-90s were written totally by AI. I had nothing to do with them, so just kidding. I had to. There's someone who's read part of your books and written a book, I believe that not to be true. You find that hard to believe. I guess I should take that as a compliment. I'm not terribly much of a role model in this regard. It's funny, I just tried. I'm always trying to have AI do the scut work for me. So I wrote an article, it's about AI governance platforms, and they want the references in a certain style. And I read just this morning that some academic was doing all of her references. I hate references, you know, putting them in the American Psychological Association style. And so I asked GPT-5 or whatever model it sent me to. You know, you don't really know so much anymore to do that for me, and it just couldn't quite figure it out. I had links in it, a hypertext links, but I said, go to the link, find what the source is, put it in, put it at the end, put it in the text, et cetera. Couldn't do it. And last week, I actually, I'd written a novel draft several years ago, like 2020 or something. Maybe it was my COVID project, I can't remember, but it was on Robot Football at MIT, Robot American Football, that, you know, where we developed robots that could throw footballs and others that could catch it and run for touchdowns and get tackled and so on. And a lot of my friends had read it and they said, eh, you know, my wife was nice about it, but the friend said, needs a lot of work. And, you know, I kind of went back to writing business books, which I knew how to do, but I said to GPT-5, right after it came out, you know, clean up my book, figure out what it needs, make it more interesting, et cetera. And it did okay, but still a lot of work by me is required. So I use AI, fair amount in teaching. I tried to get it to do grading, I hate grading too, like every professor, but it turns out it's not very good at it. Basically it gives every student the same grade. It gives them copious amounts of feedback, but the grading I had to do on my, and you know, I still gave them some of my own comments too. I don't want them to think that, you know, they're not getting their money's worth, but it didn't do a great job of that. But figuring out how to teach with, you know, these capabilities that the students have so that they learn something and don't just issue a prompt and turn into papers really, really quite challenging these days. So I want to talk about what's next. And again, we'll go to yet another book, and I think I've got this one, right? About agents and artificial intelligence. I do a bunch of training sessions. So one of the things that I do is I do a prompt lab where I try to help business professionals just do basic prompting and just kind of get comfortable with generative AI. And every time we do a thing of, hey, what are you interested in learning about? And all of the pins go to AI agents. Every single time. So help me understand if I'm a business professional and over the next five to six, 10 years, how should I think about agents? Give me a construct to think about them. What's Buzz? What's real? We have co-pilots today. The definition of agents is, you know, it's kind of coming together, but it's different when you hear different people talk about it. So give me a one-on-one on agents. Yeah, I did co-author a book with a number of other co-authors on agentic AI. And I think I was the, among the authors, I was the one saying down boy, down boy, because I don't think they're ready for taking over our business processes, maybe small tasks within our business processes. But if you have a lot of little chopped up tasks, that's not gonna yield a whole lot of value. I think they will certainly improve, become more reliable. But, you know, there've been some studies, Anthropic did this study of a little store in its headquarters building, having an agent run it, and it really screwed things up and started to give stuff away for free. And the Carnegie Mellon professors did this little study of what things in a business could agents do without making errors, and it was like 24%. So I don't think we're ready for transactional capabilities for the most part. I personally think that we need some sort of, as long as we have probabilistic AI as the primary component of an agent, there's a chance that we'll get something wrong, call it a hallucination or just a bad prediction. I do think some mix of probabilistic and deterministic AI is gonna be really necessary for that to be successful. Some of the RPA vendors, UiPath in particular, are moving quite aggressively into that space. So Gartner was suggesting, maybe they've moved a little too quickly in abandoned RPA, but I do think that it makes sense to have the orchestration done by some deterministic technology, be it rules or something similar. I do think that they're gonna get better. I do think they'll be able to do a lot of things without much human supervision. And frankly, I think, you know, that's what's got managers excited. Hey, I don't need all these people around anymore. I hope that it takes away the boring work, like I was mentioning up trying to use AI for, that we don't have enough help for. And so we humans can do something of more value, but what worries me is we're eventually, we're gonna take away all the boring work and there won't be a whole lot left to do. It's interesting, we talk a lot about boring work. And I think about it a lot because I go back to my career and think about a lot of the work that I've done that would be considered boring. But then I think about all that I learned from doing that work. And so there's this sort of disconnect around, hey, we're gonna have jobs where people start there, you know, graduate from college and you just don't do boring work. The more you look back at your career, at least in my case, and I'd like to get your opinion, the tedious stuff was part of thinking. You know, it was part of creating, it was part of learning what was valuable, what was not valuable. And I don't know that there's another way to learn that because what is valuable in my journey to create value is very different than what's on your journey to create value. Yeah, now I agree. And it's certainly the entry level workers that I am most concerned for. And I was doing some work, I think in 2013, 2012 and 2013 on automated decision making. And we ended up talking to a bunch of insurance companies for this research who were already doing a lot of automated decision making about underwriting and claims and so on. And a number of them said, well, we don't need those entry level people anymore because we're having AI do those, and this was largely rule based at the time, we're having AI do the tasks that they used to do. And I would say, well, how are you gonna get the experienced people of the future if you don't hire the entry level people to do the boring easy stuff? And they said, we're not quite sure. And every company that I've talked to has said the same thing for 12 or 13 years now. They don't have a good plan for that issue. And that issue is upon us now. We're seeing it with coding hires. Who knows exactly how many people who were turned away from coding jobs are turned away because of AI or maybe companies were just hiring too much during the pandemic or whatever. But I think we're gonna see it in a lot of different areas and we need very quickly to say, okay, how do we take an entry level person and make them an experienced person if they're not gonna have those entry level tasks to do anymore, big issue. I was talking to a friend who is at a big law firm and you talk about paralegals. And even paralegals are that entry level law work which you might call boring or tedious or is a way to learn the law, the practice of the law. And the only way to learn that is by actually trying it and doing it and having those collaborations on a lawsuit or a contract or whatever it is you're trying to accomplish. Yeah, what are you gonna have somebody fresh out of law school jump into the courtroom on day one and start making heroic arguments to the jury seems unlikely. But that's the path we're on. Yeah, I think it is. Before we go, I've got a couple of rapid fire questions that I wanna ask you. So I've got three. What's one myth that you would delete that's in every board deck right now? So every CEO is sitting in front of their board and they have a myth. What is that? We'll be able to chop out 50% of our white collar workers. Those discussions are happening at board room level. They're not really happening too publicly in most cases as we were discussing, but I know they're happening at the board level and not gonna happen. Those CEOs and board members will be retired or dead before that takes place, I suspect. Next question. What's one boring workflow that you think everybody could try to automate in business today? I'm tempted to say things like accounts payable, maybe accounts receivable would be better because accounts payable, somebody was telling me the other day, they used to have a consultant client who would say, okay, whenever I get a letter asking for money, I just put it in a drawer. Then if they harass me later, okay, I'll pay them. If they don't, great, I've saved a lot of money. Accounts receivable is a harassment game, basically. So I think automating that makes a lot of sense. You're starting to see that in some areas. I know I told somebody into it who is doing it with QuickBooks now and accounts payable. You see it in healthcare a lot where you have systems that will call your payer and say, when are you gonna pay that bill? It's actually, have your robot call my robot because hardly any human ever gets involved in the discussion. But I think a lot of that will eventually be automated and it's not terribly satisfying work. If you're advising a business leader today, let's say somebody calls up and says, hey, Tom, I've read your everything about process and data and transformation. What's one habit I could adopt today? I'd say sort of persistence. My latest book is called The New Science of Customer Relationships about using AI to improve customer relationships with a guy named Jim Stern. And it struck me as I was writing that book with him that the examples of companies that have been doing this for decades. And first they got somebody in the CEO's office who really cared about it and then they started replacing their transaction systems and then they put it all in one database and then they hired some really smart analytics and AI people and then they started to do loyalty modeling and then pricing modeling and so on. And it just takes a really long time for that sort of thing to happen. You could really transform the company but nobody would ever think of it as an overnight transformation. So be patient and persistent, I would say. That's great advice, especially from someone who's been in the game this long and continues to write as perfectly as you do. Thanks for your time. I know you're a busy man and thanks for your insights. And for those of you listening if this conversation resonated with you, make sure to share it with a colleague who's trying to figure out the reality versus the hype and maybe get some insights on how to navigate the next few years ahead. Until next time, everyone, stay curious.
