From Reactive to Proactive

Tullio Siragusa (00:12):

Good day everyone. This is Tulio Siragusa with Tech Leaders Unplugged, and we're going to get unplugged again today. Today I'm speaking with my guest who is Joel Neeb, VP of Execution and Transformation at VMware. Joel, it's great to have you on the show today.

Joel Neeb (00:29):

Thanks, Tullio. Appreciate it.

Tullio Siragusa (00:31):

We're going to talk about something that if you're a business leader who's been involved in any form of m and a, you'll want to listen to this show. This is going to be very informative, hopefully some, some insights you can get out of this. We're not talking about how to move from being reactive to proactive. Now, this could be applied to a number of topics, but we specifically will be focusing on transforming IT operations with AI to eliminate operational debt. Now, Joel, there's some confusion. Some people think of operational debt as technical debt, which is related to software. Can you clarify for us what that means? But before you do, please tell us a little bit about yourself, how you ended up where you are, and again, welcome to show.

Joel Neeb (01:13):

Thank you very much. Excited to be here. So, my background is I spent about 15 years in the United States Air Force as a fighter pilot. My last role in that in that regime was as the chief instructor pilot for all of air education and training commands, which means I was the instructor of the instructors and teaching for the entire Air Force Flying program and the pipeline that we use to create new pilots. And that really ignited a passion in me to build new leaders and scalabilities, and really I'm a teacher at heart. And so wherever I, I find myself for the rest of my life from a professional perspective, I want to be doing some type of learning and teaching at the same time and scaling out abilities in others. And so, after my time in the military, I went off and joined a consultancy called Afterburner, a 20-year-old company that is a global organization, an office in Australia office here in Atlanta. And I rose up to be the president and CEO of that company for the last five years of my time there. And after that chapter, I had the opportunity to join our biggest customer, which is VMware, where I'm at today. I've been at VMware for almost two years, but I've worked with VMware teams for about a decade. Also got a chance to work with every team from the big names in the tech industry, from Google to AWS to Microsoft, and got to watch all their transformations and test out their cultures. And, then when it was time for my next chapter it was an easy decision to join. What I found was, the culture I resonated with the most, and really just love this role that I'm currently in in the office of the CEO at VMware.

Tullio Siragusa (02:44):

Awesome. Well, thank you again for your service, first of all. And secondly, because you love to teach, we hope we learn something from this as well. I certainly get a lot of out of these interviews because I learned things that would otherwise require reading a bunch of Gartner reports. So it's fantastic. So let's talk a little, let's dig into the conversation today about operational debt versus technical debt. Maybe we can clarify the difference for those who may not know.

Joel Neeb (03:14):

Yeah. If you haven't heard of operational debt yet, which I wouldn't blame you if you hadn't, it's a relatively new concept. I promise you're going to hear about this a lot over the next decade. So let's define it quickly. We all kind of know what financial debt and technical debt are. It's, you know, having to pay for the sins of the past, right? The financial debt I'm going to borrow from someone so that I could pay it off in the future. Technical debt is when we have technical software or hardware that is antiquated and doesn't really interact well with the rest of the technology. We have the best example, I've, I've heard of this recently was one of the Fortune 50 companies still has an instance of Windows 98 operating in, in their technology. And so they have massive technical debt and they're just kicking the can down the road on updating their capabilities to match the current tech and for everything to talk to each other. Now, let's look at this from operations from a cultural perspective. And I'll give you an example that I've seen over the past decade. So for the past decade, anytime you ran into inefficiency in tech, it was easier to throw people at that process problem than it was to really structure a better process around fixing it. Here's what I mean by that. Money was free over the past decade. The demand was so strong for our technology that the time you spent building efficiencies was probably better spent just selling to the market and just building the next iPhone in the basement. And it was a market that was gobbling up our technology almost faster than we could develop it. And so it was a counterintuitive period when we had free money when we were doing massive acquisitions everywhere. And companies like VMware were telling the acquisitions, Hey, keep acting like you still run the business. Keep acting like you're the CEO and it's going to be great. We're, you know, you don't have to change your systems, your tools. We don't necessarily care if your culture meshes with everybody else in the rest of the company because it doesn't have to right now. If you want to go fast, go alone. And that worked great for a lot of groups in the past decade. But now we are in this new period where we want to go far together. And by we, I mean the tech industry at large, all of a sudden, everything changed around us. So the market has now valued tech more than the features and pieces and parts. And we don't have technology buying technology any longer. We have business leaders buying business impact that happens to include our technology. And so what does that mean? That means we have to show up much more cohesively, like a coherent organization instead of the pieces and parts and ingredients that we were pulling up in the past. And that requires us to clear out a lot of, and here's the word, operational debt. All of these times that we told organizations, Hey, you don't necessarily need to mesh and keep your silo going, just go fast. Now we're telling them we need to go far together. And there's a lot of untangling that needs to be done within operational debt at every company within the tech industry because I saw it over and over as a consultant.

Tullio Siragusa (06:07):

That's an interesting observation, Joel, having been I think in three post-m and integration efforts, there's also the concept of leaving money on the table. So there, you know, we could argue run autonomously, just chase the market, but also there's this concept of leaving money on the table because you don't have the full breadth of capability at your disposal. So let, let's dig in a little bit because we talked about the topic of how AI can help in this issue. What's the thought process there? I mean, this is a behemoth challenge, especially for large organizations like VMware. How does AI help in?

Joel Neeb (06:45):

Yeah, so about that. It, it, it, you know, it's we're all, everyone's talking about gen AI right now. I'm with a group called YPO, Young President's Organization, which is a collection of CEOs all across the world. And I was the chapter president for the group here in Atlanta. And I'm, I've been on the board for the past five years and we just had a meeting last week with our CEOs and it was only supposed to last an hour and it went over two hours. And the reason was that everyone was asking how all these different businesses could leverage AI and start planning on how to use gen AI as a resource strategically in the next 18 months. And so it was a fascinating conversation. And so, you know, there's, there's no shortage of new ideas coming out about how to leverage it. I'll talk a little bit about how we're considering it from an operational debt perspective. But before I do that, I've got to talk about what we did prior to, to, you know, capturing this AI concept. Because this is relatively new for everyone. So at VMware what we did was we said, we told all these leaders to act like they're CEOs of their own business unit, and we acquired all these different products. So we looked a little bit more like 40 different companies instead of one cohesive company. Once again, , it was great because, our customers were buying the products. They weren't necessarily buying the cohesive outcomes for the past 10 years. And, the world has changed now. And so what we've had to do behind the scenes is build a common mindshare and a common framework to view how we show up in front of the customer. And the way we've done that is through an overlay of OKRs objectives and key results. So for our entire VP plus community vice president above, they built out their objectives and key results, and they all funnel up into the top priorities for our company, which of course are helping us to better serve the customers in this new era. And they also allow us to map out during this work-from-anywhere period where we're rarely in the office and just like all tech companies, we rarely have that opportunity to interact around the water cooler. How we can in this environment build a complimentary horizontal alignment that needs to occur and build, bring cross-functional teams together to build their objectives as one group. So that's what's taken place over the past couple of years at VMware. We're aggressively going after this one Mindshare approach where we all have a clearly defined common goal where we have accountability towards driving towards that goal for the customer. And it's done wonders to help build out the capability. So now we think about how, you know if we're answering some of these questions on operational debt, what is AI and answer that, I, I think we got to go back and look at just the mess of information that we've had over the past 20 years. And we're, we're all in the information age right now, and we can ask, you know, the Google machine a question and we can get a pretty good answer most of the time to that question. So this is the, but I would argue that we're not any wiser than we've been in the past, you know, when I was a kid, just to think that I could have just about any question answered, do you think we, we, we got to be the most intelligent generation on, on that's ever been on the planet, but we can all see that we are not necessarily wiser because of it. So it's the information age, but not the wisdom age. That's all changing as we enter this new period where it's not incumbent upon us as these simple, fallible humans to make sense. Out of all this piled-on information, now we can put this into algorithms to build linear regression models, track trends, and to figure out what is causing something and what we can predict for the future. And so when you think about all this data that we have at VMware for the objectives and key results that's exceeded, the ones that were challenged, what the teams looked like, all this data that would've been a bit of a set it and forget it approach in the past, we're now able to tap back into that and look at how we can predict for the future. So, we can look at the past, and look where we're successful, we have AI algorithms that are going to be able to predict our strategy moving forward and how to best set ourselves up tactically to be successful in that strategy. I mean, it's mind-blowing opportunities. You can, you can imagine, I think it was Mark Andreesen who said recently there's going to be leaderless organizations soon. Imagine an organization without a CEO because you don't need that strategic decision-making to be done by a person. You have the trends that are analyzed to a much greater capability than a human could and then to predict the next steps that we should logically take.

Tullio Siragusa (10:59):

Yeah, it's, this is interesting. We've seen how machine learning has been used for the automatic maintenance and monitoring of different applications, typically operational-related applications. But what you're saying is that can also be used for not only predictive maintenance of a piece of machinery but also being able to predict anomalies coming up in the business and being able to essentially analyze massive amounts of data that can input or inform better strategy based on trends in the market based on how the organization is working across the market. So what I'm hearing is that if you do it right, you can improve decision-making, strategic decision-making. How far away are we from that being in reality? I know we, we have generative ai, you can use it as an analyst, you can ask questions and it gives you amazing input, but then, you know, you still have the human element of an organization, right? You still have the nuances that come with people who have emotions, egos, et cetera, right? So yeah. How does that shift happen, do you think, to enable this to be more effective?

Joel Neeb (12:12):

So I've, I've actually this morning just demoed this new capability for the system that tracks our OKRs and the system that we use. It's an outside vendor called Workboard, and I'm happy to plug them here. They're a great organization that's been the single pane of glass for us to look into the strategy for the entire company. Let's map that out for the VP plus crowd. We're able to see where the synergies are, where our progress is taking place, where we have headwinds, where we have tailwinds. And so I looked this morning at what they're building from a gen AI perspective, and it's fascinating. Some of the things that they're developing are saying, you know, build out the next version of the strategy. Tell me based on the historical precedent, what's the strategy that we should use moving forward? And then they would build out the strategy and the objectives and the key results. And then they'd say they had to have the option to say, now make it a little more inspirational. Make it a little more aspirational. Let's go after a bigger goal. Tell me what that could look like. And then they would say, give me some leading indicators for success based on how we've framed this in the past. Gimme some lagging indicators for success in other, those, in other words, those things that we should see take place after we're successful. And the AI was able to come up with some really fascinating ideas on what this could be. As you can imagine with all AI right now, with chat G p t, if, if you're playing with that, it's, it's good at developing a first draft, right? When I use it, it builds like the 70% solution and I have to make some changes and, tweak it, to bring it up to a hundred percent. This is really to your point that you know, humans will be involved for the time being. And I think they'll always be involved as, as a fighter pilot. I remember when our version of Gen AI came online with the drones. And we were concerned that gosh, do we just work ourselves out of a job? Are we no longer going to be flying? And of course, here we are 20 years later from that when that journey started with me. And we have plenty of pilots in the sky and there's probably never going to be a time where we have completely pilotless aircraft, certainly in the commercial airlines. But it's, it's augmented everything that we've done. And once again, it's helping us to transition, whether it's in the flying world or in tech, from the information age to the wisdom age where we can start to whittle down the wealth of information into insights that we can leverage to be successful. And the last example I'll give you is just coming to mind. I hadn't planned on talking about it, but think about the movie that came out from a Michael Lewis book. So whether you read the book or watched the movie or both about 15 years ago called Moneyball, do you remember this one? Tullio, did you see this? And it was, it was tracking how it was some unique aspects of team members in, in the major league baseball that were allowing teams to be really successful and do it in an inexpensive way. And it was things they hadn't necessarily thought would be relevant, like the batter's height and, and how that got them on base at a much higher percentage when they're shorter. Because they had a smaller strike zone, right? And this was all stuff that was done with the person at Jonah Hill in the movie who was, was playing the role of, you know, kind of this, this tech geek who was really into the data and he was able to extract information that nobody else was able to do. Well, well now we have AI to do that for us. We don't have to go find this person to go extract this information and it's going to develop a wealth of new insights and take us in directions that intuitively we probably wouldn't have gone. Because once again, just like in Major League Baseball, our minds go in one direction because we see the veneer and we see, you know, the superficial. Whereas the data is always going to lead us to the, the better answer that that's trending over time that we're going to miss.

Tullio Siragusa (15:34):

All right, so let's just quickly recap a little bit. This is very interesting. When it comes to operational debt, one of the key things is to implement a continuous, this is what I've heard so far continuous learning and improvement mindset in the organization. Looking for ways to coordinate activities in a common way. OKRs are a fantastic way to do that. We also use OKRs. It's really probably the most effective way to get everybody aligned and then improve decision-making by using AI to supplement some of the assumptions that have either been made through historical trends or experience and translating that knowledge into some kind of actionable wisdom. You know, using the power of being able to look at massive amounts of information and analyze data quickly in such a way that can inform better decision-making. So it begs the question, and I think it's happening and we're going that direction. And I had a conversation recently with someone, a VC, head of a VC, and I said, where's AI going? And what is, what is the next thing after AI? And she said I think people are going to need to learn how to think again. So, what is your thought process there? I mean, is there a danger with these kinds of things where we are so reliant that some of that intrinsic human creativity that we've, we've tapped into for years goes away? How do we, you know, balance that? What are your thoughts?

Joel Neeb (17:10):

Yeah, it's a great question. You know, we're going to have to reinvent ourselves yet again, I, I know there's a lot of angst and anxiety around that. If you look at Reddit today you'll see on the front page this big concern that jobs are going to be taken away by ai and it's going to, you know, remove the ability to be creative because songs and art and everything else are going to be created by, by AI to a greater extent than we could. And, you know, I, I connect with that. I understand in the short term where that's a concern. But to be honest with you, I'm excited about it. And even if that means that jobs like mine are really relegated as obsolete in the short term. Because once again, this is only going to allow us to inject ourselves as humans into the work that we like the most, right? It's going to take away the menial, it's going to take away creating your first draft of this information. I think that people get so caught up in what it's going to, you know, what, what jobs are going to go away that we miss the fact that we keep up-leveling our roles to more and more creative positions, which is where we really get fulfillment as humans, right? I don't want to do the rote, boring, menial thing. If you can get an executive assistant for me or an AI tool that's going to do a lot of that for me and get to me to a first draft, that's fantastic. And that's, that's, you know, where we need to start thinking somebody on stage at a, a recent r and d event that VMware said it really well, he said, pretty soon intelligence is going to be outsourceable. And I would argue that we're probably pretty close to that point right now, right? I mean, we all kind of remember a person, or at least people like you and I who are, have, remember the advent of the internet agent Google. Remember there was a time period when we all knew a guy who just happened to know all the trivia, right? And, if you wanted to know who won the 1963 World Series, you would say, hey, let's call up Carl, and let's ask Carl. Cause Carl knows all that stuff. He's just a big sports buff. Well, Carl's phone hasn't rung in a while because we don't need him to answer these questions for us anymore. We have the information at our fingertips, intelligence is outsourceable, and pretty soon building those trends is going to be outsourceable as well. But it will still require a human, and this was the person's point on this event, it will still require a human to present that consciousness because consciousness is not outsourceable. And so we have to think about how we show up and add value and inspiration and consciousness, which are really the fun parts of leading in the first place that we always want to do. And so we have an opportunity to, to offload everything else and only focus on that.

Tullio Siragusa (19:33):

Now here's another interesting question we're coming up on time, and I, and I have to ask this because we have seen historically, for example, when a massive software came to the market, let's say 30 years ago in the e r p space or even in the CRM space, many of those implementations failed. I think the number was 70% of those implementations failed because the companies adopted the software but didn't customize it, didn't map it to their business, and basically thought it would just work. Do you know? It's a well-known fact, for example, SAP struggled with that. 70% of the initial implementations failed until the big four consulting figured out, Hey, we have to map this stuff to how you run your business, right? So is there a danger that some CXOs might look at this as like, Hey, I have an opportunity to reduce costs? I can put all these AI engines in place and moves too quickly and it could turn into a scenario where it fails because of the lack of understanding that you still need the human interactions to validate, to make it conscious. What do you think is going to happen? Do you think we're going to repeat some of that? Or+ do you think that maybe we'll be smarter this time around in realizing you've got to adopt technology in such a way that's consumable? What are your thoughts?

Joel Neeb (20:51):

Yeah, I definitely don't think we'll be smarter this time around. I think that you know, humans in humanity have a very short attention span and memory, right? And so the lessons of the past become the sins of the past that we, we, we tend to forget. And so, it's going to be on a spectrum, is going to be a group of people who dig in their heels and say, we're not going to use this AI because it threatens our employees and, and threatens, you know, the, the, the workforce. And so, we're just going to stay away from that to their detriment. And then there's going to be a group of people on the other side of that spectrum who say, we're only going to use AI and I don't need to hire a content team anymore. And I don't, I don't have a use for any of the employees or maybe let's say 70% of the employees that were doing this work in the past. And I think that's going to be to their detriment. We need to start thinking about how we can leverage AI to augment what we're currently doing and explore new areas where we can gain a competitive advantage. And I like to talk about this from the perspective of operational debt and why operational debt and, and curing that is a competitive advantage right now. And you think about, you know, in the past 10 years, operations and efficiency in the tech world, it's about as sexy as brushing your teeth, right? It's, it's hygiene, you know, it's something that you kind of have to pay attention to it, but it's really downside risk mitigation. It's just making sure that we don't have such poor operations that we fail as a company. There are very few companies that would argue that their operations are what allowed them to succeed, though they would say it's our innovation, it's our, it's our technology, it's our product market fit that's made us so successful. But I think that world is changing, I think for the next 10 years because money now costs something because we now can't throw people at process problems because we're going to have AI that's going to help to build trends for us and to help to answer some of these challenges for us and, and get us ahead even faster than the better our operational agility and rigor and efficiency behind the scenes, the better and more successful our companies will be. In other words, it's not necessarily going to be the company with the best tech that marches to the front of the pack for the next 10 years. It's going to be the one that best articulates their value, has the simplest path to that value, and is able to integrate seamlessly with the customer. That's very different from the past. In the past, you know, I stayed outside of Best Buy, you know, 10 years ago waiting for the iPhone to be released and, you know, slept outside for that. We had an iPhone released six months ago, nobody even noticed, right? And it, but I'll tell you where I do care about it. When I went and bought a car recently sitting in the car, and I, and I'm talking to the person who's showing me this great display, and it's bigger than the monitor I'm looking at right now, and he's bragging about how the Fidelity's so high in the map in this car. And I had one question for this individual as I'm looking at this map and he's telling me how you can plug in an address on there and just punch it in while you're driving. What do you think my question was for him, Julio, as I'm looking at this map and the technology and the car that I might buy, any guess?

Tullio Siragusa (23:31):

Who's keeping an eye on the road?

Joel Neeb (23:33):

Yeah, exactly. Well, it really comes down to you, does it integrate with my phone, right? Because the last thing I'm going to be doing is typing away on this thing while I'm driving. But I will get a text from somebody and they say, Hey, go here, I'm going to put that in there and hopefully, that just integrates with the, with the map. And the person said, no, it doesn't integrate with the phone yet. We don't have Apple CarPlay set up. I didn't buy the car because of that. It didn't matter how great the technology was. And here's my point here, it's about how it integrates right now. We're less enamored with the tech. We're coming out of that stage where the tech is just so fascinating to us that we'll wait outside to get it. It's more important now how it integrates with our lifestyle. And we're not, we're not just seeing that at an individual level on a company level. The question is not what's the tech, but how does that integrate into business outcomes for us? And can I see that clear line of sight to that path?

Tullio Siragusa (24:18):

I think that's where Apple and Google have failed, by the way, to realize that they could come up with a chip that they, gets plugged into various devices. So wherever you go, it just follows you including the car. But that's a topic for a whole other day. So what I've heard is simply Joel uses AI to level up. That's really the key. It's not a replacement strategy, it's a level-up strategy. You want to improve your operational debt, understand where it is, get better at planning for it, and get better at strategizing to infuse it into your OKR processes. Get the organization to level up. Use the intelligence to see patterns that you perhaps didn't think of or opportunities that you didn't even think of. It's really what it sounds like to me, the smart company, the thing is going to use it to really level up their teams. So we're up on timing. What last words do you want to share with us before we announce the coming up guests?

Joel Neeb (25:18):

You hit it out of the park. That's exactly what it is. Tullio, how do you level up with AI? And right now, it's all about how we can augment what we're currently doing and map out the next focus less on what jobs it's going to take away. And she'll make a few obsolete, but we really don't know that. And jobs will become obsolete no matter what takes place. So that's, you know, circuit City's gone. Best Buy is on the way out. Blockbuster video is gone and that's all good. We accept that because we see how technology and new things have taken his place. Think about how we can augment our lives with ai, use it a way that helps us to create that first draft and advances us even faster into the future.

Tullio Siragusa (25:55):

So, to my producer, soon enough, we'll have an avatar, an AI-driven avatar that will do these interviews and there won't be any need for me to do these. So there you go. That's going to happen. It's only a matter of time. Thanks for being with us, Joel. We appreciate you. Just stay with me as we go off there in just a second. We got another guest coming up tomorrow. We have Bob Rogers who, the CEO of Oii and we're talking about the amazing utility of digital twins. That's going to be an interesting conversation and the role of digital twins in today's world, especially with remote working. That's a fantastic thing. If you haven't considered that. Come and learn about that tomorrow. And then next week we have another full week. On Tuesday we're going to be speaking with Tony Sumpster, who's the CEO at Worksoft. On Wednesday, I'll be speaking with Prashant Sarode, who's the SVP of Automation and LPL Financial. Thursday, another VMware guest. We're going to talk with Amanda Blevins, who's the CTO and VP of VMware and Friday Piyuk Malik, who's the CTO of Veredict. So we got a full week next week. Two of those interviews I will do from San Francisco at Ascent. I am also hosting three brain dates there. So if you're going to be at Ascent, please let me know. Look for me, I'll be hosting three brain dates on how to use design thinking to improve sales. So stay with me in just a second, Joel, as we go off the air and everybody else, thanks for joining us, and see you again tomorrow.