 From around the globe, it's theCUBE with digital coverage of BizOps Manifesto Unveiled brought to you by BizOps Coalition. Hey, welcome back everybody. Jeff Frick here with theCUBE. Welcome back to our ongoing coverage of the BizOps Manifesto Unveiling. It's been in the works for a while, but today's the day that it actually kind of come out to the public and we're excited to have a real industry luminary here to talk about what's going on, why this is important and share his perspective. And we're happy to have from Cape Cod, I believe, is Tom Davenport. He is a distinguished author and professor at Babson College. We could go on, he's got a lot of great titles and really luminary in the area of big data and analytics. Tom, it's great to see you. Thanks, Jeff, happy to be here with you. Great, so let's just jump into it and getting ready for this. I came across your LinkedIn post, I think you did earlier this summer in June and right off the bat, the first sentence just grabbed my attention. I'm always interested in new attempts to address long-term issues and how technology works within businesses. BizOps, what did you see in BizOps that kind of addresses one of these really big long-term problems? Well, yeah, the long-term problem is that we've had a poor connection between business people and IT people between business objectives and the IT solutions that address them. This has been going on, I think since the beginning of information technology and sadly it hasn't gone away. And so BizOps is a new attempt to deal with that issue with a new framework, eventually a broad set of solutions that increase the likelihood that we'll actually solve a business problem with an IT capability. Right, it's interesting to compare it with like DevOps, which I think a lot of people are probably familiar with, which was built around agile software development and a theory that we want to embrace change, that change is okay, and we want to be able to iterate quickly and incorporate that. And that's been happening in the software world for 20 plus years. What's taken so long to get that to the business side because as the pace of change has changed on the software side, that's a strategic issue in terms of execution on the business side that they need now to change priorities and there's no PRDs and MRDs and big giant strategic plans that sit on the shelf for five years. That's just not the way business works anymore. Took a long time to get here. Yeah, it did. And there have been previous attempts to make a better connection between business and IT. There was the so-called strategic alignment framework that a couple of friends of mine from Boston University developed, I think more than 20 years ago, but now we have better technology for creating that linkage and the idea of kind of ops-oriented frameworks is pretty pervasive now. So I think it's time for another serious attempt at it. Right. And do you think doing it this way with the BizOps coalition, getting a collection of kind of like-minded individuals and companies together and actually even having a manifesto which we're making this declarative statement of principles and values, you think that's what it takes to kind of drive this kind of beyond the experiment and actually get it done and really start to see some results in production in the field? I think certainly no one vendor or organization can pull this off single-handedly. It does require a number of organizations collaborating and working together. So I think a coalition is a good idea and a manifesto is just a good way to kind of lay out what you see as the key principles of the idea and that makes it much easier for everybody to understand and act on. Yeah, I think it's just, it's really interesting having them written down on paper and having it just be so clearly articulated both in terms of the values as well as the principles and the values, you know, business outcomes matter, trust and collaboration, data-driven decisions, which is number three or four and then learn, respond and pivot. It doesn't seem like those should have to be spelled out so clearly, but obviously it helps to have them there. You can stick them on the wall and kind of remember what your priorities are but you're the data guy, you're the analytics guy and a big piece of this is data and analytics and moving to data-driven decisions and principle number seven says, you know, today's organizations generate more data than humans can process and informed decisions can be augmented by machine learning and artificial intelligence. Right up your alley, you know, you've talked a number of times on kind of the many stages of analytics and how that's evolved over time. You know, as you think of analytics and machine learning, driving decisions beyond supporting decisions but actually starting to make decisions in machine time, what's that think for you? What does that make you, you know, start to think, wow, this is going to be pretty significant? Yeah, well, you know, this has been a long-term interest of mine. The last generation of AI, I was very interested in expert systems and then I think more than 10 years ago, I wrote an article about automated decision-making using what was available then which was rule-based approaches. But you know, this addresses an issue that we've always had with analytics and AI. You know, we tended to refer to those things as providing decision support. The problem is that if the decision maker didn't want their support, didn't want to use them in order to make a decision, they didn't provide any value. And so the nice thing about automating decisions with now contemporary AI tools is that we can ensure that data and analytics get brought into the decision without any possible disconnection. Now, I think humans still have something to add here and we often will need to examine how that decision is being made and maybe even have the ability to override it. But in general, I think at least for, you know, repetitive tactical decisions involving a lot of data, we want most of those I think to be at least recommended if not totally made by an algorithm or an AI-based system and that I believe would add to the quality and the precision and the accuracy of decisions in most organizations. You know, I think you just answered my next question before I asked it. You know, we had Dr. Robert Gates on the former secretary of defense on a few years back and we were talking about machines and machines making decisions. And he said at that time, you know, the only weapon systems that actually had an automated trigger on it were on the North Korea and South Korea border. Everything else, as you said, had to go through a subperson before the final decision was made. And my question is, you know, what are kind of the attributes of the decision that enable us to more easily automate it and then how do you see that kind of morphing over time both as the data to support that as well as our comfort level enables us to turn more and more actual decisions over to the machine? Well, yeah, as I suggested, we need data and the data that we have to kind of train our models has to be high quality and current and we need to know the outcomes of that data, you know, most machine learning models at least in business are supervised and that means we need to have labeled outcomes in the training data. But, you know, the pandemic that we're living through is a good illustration of the fact that the data also have to be reflective of current reality. And, you know, one of the things that we're finding out quite frequently these days is that the data that we have do not reflect, you know, what it's like to do business in a pandemic. I wrote a little piece about this recently with Jeff Cam at Wake Forest University. We called it data science quarantined and we interviewed somebody who said, you know, it's amazing what eight weeks of zeros will do to your demand forecast. We just don't really know what happens in a pandemic. Our models maybe have to be put on the shelf for a little while until we can develop some new ones or we can get some other guidelines into making decisions. So I think that's one of the key things with automated decision making. We have to make sure that the data from the past and, you know, that's all we have, of course, is a good guide to, you know, what's happening in the present and in the future as far as we understand it. Yeah, I used to joke when we started this calendar year, 2020 was finally the year that we know everything with the benefit of hindsight, but it turned out 2020 is the year we found out we actually know nothing and everything we thought we knew is not what we knew. But I want to follow up on that because, you know, it did suddenly change everything, right? We got this light switch moment. Everybody's working from home. Now we're many, many months into it and it's going to continue for a while. I saw your interview with Bernard Maher and you had a really interesting comment that now we have to deal with this change and we don't have a lot of data and you talked about hold, fold or double down. And I can't think of a more, you know, kind of appropriate metaphor for driving the value of the BizOps when now your whole portfolio strategy needs to really be questioned and, you know, you have to be really well executing on what you are holding, what you're folding and what you're doubling down with this completely new environment. Well, yeah, and I hope I did this in the interview. I would like to say that I came up with that term but it actually came from a friend of mine who's a senior executive at GenPact. And I used it mostly to talk about AI and AI applications but I think you could use it much more broadly to talk about your entire sort of portfolio of digital projects. You need to think about, well, given some constraints on resources and a difficult economy for a while, which of our projects do we want to keep going on pretty much the way we were and which ones are not that necessary anymore? You see a lot of that in AI because we had so many pilots. Somebody told me, you know, we've got more pilots around here than O'Hare Airport in AI. And then the ones that involve double down, they're even more important to you. They are, you know, a lot of organizations have found this out in the pandemic on digital projects. It's more and more important for customers to be able to interact with you digitally. And so you certainly wouldn't want to cancel those projects or put them on hold. So you double down on them, get them done faster and better. Right, right. Another thing that came up in my research that you quoted was from Jeff Bezos, talking about the great bulk of what we do is quietly but meaningfully improving core operations. You know, I think that is so core to this concept of not AI and machine learning in kind of the general sense, which gets way too much buzz, but really applied, right? Applied to a specific problem and that's where you start to see the value. And, you know, the BizOps manifesto is calling it out in this particular process, but I'd just love to get your perspective as you speak generally about this topic all the time, but how people should really be thinking about where the applications or I can apply this technology to get direct business value. Yeah, well, you know, even talking about automated decisions, the kind of once in a lifetime decisions, the ones that, A.G. Laughley, the former CEO of Proctor & Gamble, used to call the big swing decisions, you only get a few of those, he said in your tenure as CEO, those are probably not going to be the ones that you're automating, in part because you don't have much data about them, you're only making them a few times, and in part because they really require that big picture thinking and the ability to kind of anticipate the future that the best human decision makers have. But in general, I think with AI, the projects that are working well are, you know, what I call the low-hanging fruit ones, the, some people even refer to it as boring AI, so, you know, sucking data out of a contract in order to compare it to a bill of lading for what arrived at your supply chain. Companies can save or make a lot of money with that kind of comparison. It's not the most exciting thing, but AI, as you suggest, is really good at those narrow kinds of tasks. It's not so good at the really big moonshots, like curing cancer or, you know, figuring out what's the best stock or bond under all circumstances or even autonomous vehicles. We made some great progress in that area, but everybody seems to agree that they're not going to be perfect for quite a while, and we really don't want to be driving around in them very much unless they're, you know, good in all kinds of weather and with all kinds of pedestrian traffic and, you know, that sort of thing. Right. That's funny. You bring up contract management. I had a buddy years ago that had a startup around contract management, and I was like, and this was way before we had the compute power today and cloud proliferation. I said, you know, how can you possibly build software around contract management? It's language, it's legalese, it's very specific. And he's like, Jeff, we just need to know where's the contract and when does it expire and who's the signatory? And he built a business on those, you know, very simple little facts that weren't being covered because there were contracts from people's drawers and files and homes and Lord only knows. So it's really interesting. As you said, these kind of low hanging fruit opportunities where you can extract a lot of business value without trying to, you know, boil the ocean. Yeah, I mean, if you're Amazon, Jeff Bezos thinks it's important to have some kind of billion dollar projects. And he even says it's important to have a billion dollar failure or two every year. But I think most organizations probably are better off being a little less aggressive and, you know, sticking to what AI has been doing for a long time, which is, you know, making smarter decisions based on data. Right. So Tom, I want to shift gears one more time before we let you go on kind of a new topic for you, not really new, but, you know, not the vast majority of your publications. And that's a new way to work. You know, as the pandemic hit in mid-March, right? And we had this light switch moment. Everybody had to work from home and it was, you know, kind of crisis and get everybody set up. Well, you know, now we're five months, six months, seven months, a number of companies have said that people are not going to be going back to work for a while. And so we're going to continue on this for a while. And then even when it's not what it is now, it's not going to be what it was before. So, you know, I wonder, and I know you teased you're working on a new book, you know, some of your thoughts on, you know, kind of this new way to work and the human factors in this new, this new kind of reality that we're kind of evolving into, I guess. Yeah, and this was an interest of mine. I think back in the nineties, I wrote an article called, a co-authored an article called Two Cheers for the Virtual Office. And, you know, it was just starting to emerge and some people were very excited about it. Some people were skeptical. And we said two cheers rather than three cheers because clearly there's some shortcomings and, you know, I keep seeing these pop up. It's great that we can work from our homes. It's great that we can accomplish most of what we need to do with a digital interface, but, you know, things like innovation and creativity and certainly a good, happy social life kind of requires some face-to-face contact every now and then. And so, you know, I think we'll go back to an environment where there is some of that. We'll have times when people convene in one place so they can get to know each other face-to-face and learn from each other that way. And most of the time, I think it's a huge waste of people's time to commute into the office every day and to jump on airplanes, to give every little sales call or give every little presentation. We just have to really narrow down what are the circumstances where face-to-face contact really matters and when can we get by with digital? You know, I think one of the things in my current work I'm finding is that even when you have AI-based decision-making, you really need a good platform in which that all takes place. So in addition to these virtual platforms, we need to develop platforms that kind of structure the workflow for us and tell us what we should be doing next and make automated decisions when necessary. And I think that ultimately is a big part of BizOps as well. It's not just the intelligence of an AI system, but it's the flow of work that kind of keeps things moving smoothly throughout your organization. Yeah, I think such a huge opportunity as you just said because I forget the stats on how often we're interrupted with notifications between email, texts, Slack, Asana, Salesforce, the list goes on and on. So, you know, to put an AI layer between the person and all these systems that are begging for attention and you've written a book on the attention economy, which is a whole nother topic, we'll say for another day, you know, it really begs. It really begs for some assistance because, you know, you just can't get him picked, you know, every two minutes and really get quality work done. It's just not, it's just not realistic. And, you know, and I don't think that's the future that we're looking for. Not great, totally. All right, Tom. Well, thank you so much for your time. Really enjoyed the conversation. I got to dig into the library. It's very long. So I might start at the attention economy. I haven't read that one in to me. I think that's the fascinating thing in which we're living. So thank you for your time and great to see you. I mean, pleasure, Jeff. Great to be here. All right, take care. All right, he's Tom. I'm Jeff. You are watching the continuing coverage of the Biz Ops Manifesto and Veil. Thanks for watching theCUBE. We'll see you next time.