On the 15th episode of Enterprise Software Innovators, hosts Evan Reiser (Abnormal Security) and Saam Motamedi (Greylock Partners) talk with Sanjay Srivastava, Chief Digital Officer at Genpact. Genpact is a global professional services company with over 100,000 employees that enables the world's largest companies to digitally transform. Today, Sanjay shares how companies should approach driving true digital transformation and his perspective on the tremendous potential of AI.
Quick hits from Sanjay:
On how technology fits into the business: "I used to be all tech, tech, tech. And I realized tech is no longer the long pole in the tent. It's about people, processes, data, orchestration, and change management, all great things that need to happen for tech to deliver results."
On how to think about applications of AI: “Often [people] think about AI, it’s these big massive things like autonomous driving…the reality is you have to put all that aside. Think about ‘small’ AI, like utilities and tool kits. It's things like NLP: NLP is ahead of human capability today. Computer vision is actually almost at human capability. Voice to text is pretty close if not at human capability. Think about day-to-day business processes and efficiency.”
On how tomorrow’s workforce can harness AI’s potential: “I always like to say that the world doesn't really need another machine learning engineer…what the world really needs is a finance and accounting specialist that understands machine learning; a manufacturing engineer that also understands computer vision; a data scientist that actually gets pattern recognition. It’s this idea of the intersection of sciences that becomes very important.”
On the difference between digital transformation and digitization: "Digital transformation is a big word these days, and it often gets used in the same way that digitization gets used. The reality is those two words, though interchangeable at times, couldn't be further apart in meaning. When we talk about digitization, it's about taking an end-to-end process, breaking it down into its components, and automating every single piece. You've got an end-to-end process that is faster, scalable, more efficient, and more reliable. But the work remains the same; it's just done faster. When you do digital transformation, you're redesigning the value chain, rethinking the experience, and delivering a much more sticky endpoint to a client. You are using new emerging technologies and get a redesigned end-to-end value proposition. The work that’s left behind is now different and new. Digital transformation is about orchestrating change in the dimensions of people, processes, data, and technology."
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Saam Motamedi: Hi there and welcome to Enterprise Software Innovators, a show where top technology executives share how they innovate at scale. In each episode, Enterprise CIOs share how they've applied exciting new technologies and what they've learned along the way. I'm Sam Motamedi, a general partner at Greylock Partners.
Evan Reiser: And I'm Evan Reiser, the CEO and founder of Abnormal Security. Today on the show, we're bringing you a kind conversation with Sanjay Srivastava, chief Digital Officer at Genpact. Genpact is a global professional services company with over 100,000 employees that enable the world's largest companies to digitally transform. In this conversation, Sanjay shares insights about how companies can drive a culture of innovation and how to best harness the potential of AI. Well, Sanjay, first of all, thanks so much for taking time to chat with us. I've been looking forward to this conversation, and every time we do one of these, I feel like I learn a lot and just feel really thankful for you spending the time, of course.
Sanjay Srivastava: It's our pleasure. Evan, thank you for reaching out.
Saam: Sanjay, let's get started. One of the reasons why we've been excited to have you on is you've had such an interesting career in technology, both on the small company side, building companies from scratch. Now at Genpact, maybe let's start by just sharing with our audience a little bit of background on you and how you've made your way to Genpact and then your role at Genpacked.
Sanjay: I started as a hardware engineer early in my career at Hewlett Packard, putting together the first set of wrist chips and Unix boxes, sheet metal and the like, and luckily ended up being in a business that grew from sixty thousand dollars to six billion dollars. And along the way, obviously, we learned a lot in that journey. I left HP to become a startup entrepreneur and built four startup companies. We built the first one around Edge Networking, which was acquired by Akamai, and I stayed on there for a little bit. The second one was in data center automation, which went to BMC. The third one was in what I would now call predictive algorithms, but I must admit I didn't even know the term back then, and that was acquired by SunGard. Now FIS, and I stayed on to run part of the business, and then I started a company in what we would now call the SaaS space, and it was an enterprise finance application that was acquired by my current employer. So we acquired my company as I came in as a startup CEO, and I've stayed here for the last ten years. We were a different company back then. When I came here, we decided to transform ourselves into a digital transformation company, of all things, and I got a chance to lead and help build that business. It's been a great experience. I must tell you. I got chopped off at my knees within the first year I got here. I used to be all tech tech, tech, tech, tech. And I got to jump back, and I realized Tech is no longer the long pole in the tent. It's the people and the process and the data and the orchestration and the change management, all of those great things that need to happen, really, for tech to deliver results. So I lead our digital, I built a digital business here. Now I lead our thinking around strategy and consulting. I work mostly with Fortune 500, CIO, CTOs and CEOs. And I informed them and helped them think through their digital strategy. So it's been a great ride, and I really enjoy what I do.
Evan: You and Your Role, rice you've Seen both digital transformation kind of inside the company, right? You guys have 100,000 employees? I'm sure things have changed a lot in the last ten years since you've joined. But also, like, you work with A Lot Of big enterprises on, kind of, their digital transformation. Do you mind sharing? Kind of like, how do you even define digital transformation? And let us hear your perspective on what that means and how it's kind of changing the world.
Sanjay: Yeah, digital transformation is almost a big word these days, and it oftentimes Gets used in the same Way that digitization gets used. And the reality is, those two words, though interchangeable at times, couldn't be further apart in meaning. You know, when We Talk About digitization and we've done a lot of it, all of us have it's really About Taking an end to end process. You break it down to its components. You Take each part of the process, and then you automate it. You automate every single piece. Then You Pull It Back together, and You've got an end to end process that is faster, it's scalable, it's more efficient, it's More reliable, arguably, and that's digitization. But at the end of all of that, the work remains the same. It's just that it gets Done faster. Now, in contrast to that, when you do Digital Transformation, it's a completely different game. You're redesigning the value chain. You're rethinking through the experience. You're Delivering a much more sticky endpoint to a client. And in doing so, you're using new emerging technologies, some that weren't even available when you first put your technology infrastructure in place. And on the back of all that, you end up with a much better redesigned, reimagined, end to end value proposition. And that's true digital transformation, except once You're Done With that, the work that gets left behind to do is now completely different. It's completely new. And so if you Think About digital transformation, for it to Be really successful, for you to get to outcomes, what you really have to do is you have to Think about the people. And by people, I mean change management. I think about operating models. I think about resourcing. I think about skilling. You have to think about process and what the redesign process needs to look like. What the reimagined value proposition looks like. And then how do you actually make that all come together? How do you integrate it with the back end? How do you integrate it in the front end? So it's a very different end to end process. And so a lot of digital transformation now is actually about orchestrating change on the dimensions of people, process, data and technology. And in many ways it's super different from digitization. And so it's really important to understand that context and that difference. And I think as you approach it, then the way you approach it with transformation becomes very different than perhaps the way you would have approached digitization in the past.
Saam: So I assume for companies that are optimizing and kind of transforming themselves, step one is building this set of metrics and orienting on this set of metrics. There's likely also an important piece around culture and driving a culture that's oriented on being very process driven and challenging oneself and one's company to continue optimizing and rebuilding these processes and systems.
Evan: Do you agree with that?And what have you seen the best companies do on the cultural side to drive this kind of transformation?
Sanjay: Actually, some culture is the key to transformation. What are the technologies aside, culture is the one critical success factor that makes it happen. Our view, and certainly my view, is that there are probably three aspects to that that I think are something that I see very consistently across companies that do it well. It's customer obsession, that's a big one. It's agility, another big one. And then I think the whole bit about talent and learning and let me just double click into each of those. Right? So customer obsession is a super important thing. I mean, in our own business, as an example, in the early days we serve semiconductor and other sort of pop up the supply chain kinds of industries and you could see very clearly what they were going through and what the long term impact would be. The ability to sort of be obsessed about how to serve those customers better allows you to make business decisions that you want as you continue your day to day operations otherwise. Right? And so this is where customer obsession allows you to sort of think ahead of where your customers are going to be and really try and think about technologies that are going to change the game for them, not because they need it today, but you can see the arc of the curve as you serve them, and that customer obsessiveness becomes super important. The way you think about your own business. I think the other one was agility, and I said that earlier, I think agility is super important. I'll give you one example. I think you mentioned this. We're 110,000 employee company in 30 plus countries around the world. We have a great workforce, an amazing set of colleagues. But I must say, back in the day, I don't want to say we're a hierarchical organization, but every organization has a management mechanism. And you and I've been around this. I mean, you sort of have town halls and you go around and visit. You don't have meetings with lots of people and you have a management layer that goes from leaders to the next layer down to this, that to the managers that are managing a group of 20, 30 people. And we classically train them through an HR discipline and so forth. And so fast forward to the Corvette. Everyone's working from home. There's no methodology and mechanism for someone to come into the office and be able to interact with managers and manage that environment. And you're at best interacting with colleagues and Zoom calls and other things on a one on one basis out of different locations. How do you run HR? How do you know where the pulse is? How do you know where the hot spots are? Across 110,000-employee corporation? We like for it to not be this way, but the reality is they'll be hot spots, right? And how do you know Bangalore in the data engineering group is where it is? Or how do you know in Bucharest in the compliance group is where the problems are? How do you know that every employee that joins six months after the joining date is really when they're most vulnerable to this time and the other? But with AI in a chatbot, you can do that. You can train it. You can get it to go and trust back. You can get it to react and act with all of your employee base and then actually be able to tell you at a moment's notice sort of where the trouble spots are, what the trends are, where the hot spots are. And so we've been able to use that. And that's all about agility because you want to go after problems fast. And the third one is talent. And I think the big thing on talent we're finding out is we no longer know the skills we'll need tomorrow. Not because we're not good business people, we actually do a pretty good job at what we do. But the reality is the world is changing so fast in the business I'm going to be in tomorrow is actually going to be very different from the business I'm in today. And I hate to say it this way and don't take it the wrong manner, but I actually don't know the skills I'll need tomorrow. And that is okay. That's the world that we're in. And so we're hiding for attitude. We're hiring for curiosity. We're hiring for this desire to learn because we know that workforce is going to approach change. It's going to approach the next generation technologies and business environments in a very different way. And so how do you think about talent? How do you skill your existing employees? How do you recruit better with a very transparent view to what life in your corporation looks like, I think becomes really important. And companies that are getting ahead of the game in my mind are companies that are doing those three things right, being very customer obsessive, being very agile in the work they do, and then really focusing on talent as a game changer for them. And I think you get those three right. You are well ahead of the pack.
Saam: AI is an area of strong personal interest to Evan and I and unsurprising to you, when we have guests on the show, it's top of mind for every enterprise around the world. There's a lot happening on the scientific and research side even in the last several months as we see the impact of these large foundational models across new types of tasks, but help us ground that in real business. Impact. I'd be curious from your vantage point where you see AI having real impact in terms of driving transformation today, and for the organizations that are seeing the most success with AI, what are a few things about how they're approaching the use of AI in their environments to help make that success possible?
Sanjay: The way to think about AI often you sort of wake up in the morning, you think about AI, you think about these big massive AI things like autonomous driving and you try to make a call whether it's there, when will it get there? The reality is you have to put all that aside. You have to think about small AI. And by small AI I don't mean quote unquote small, but I mean utilities and toolkits. It's things like NLP. NLP is ahead of human capability today. Computer vision is actually almost at human capability. Voice to text is pretty close, if not at human capability. So when you think about that, right, and you think about day to day business processes, you think about efficiency, the amount of paper that we're all dealing with, all of the information that sits in different places and unstructured. File formats, the ability to actually, for the first time, call out the data from PDF files and other unstructured documents that are lying all over the enterprise, and be able to classify them, categorize them, and then actually auto decision them means that you can actually get to touch less computing in many different areas. Think about transactional finance, think about supply chain planning, think about regulatory reporting. A lot of that depends on heavy text, human analysis, read out, re-reporting and writing and all of that actually, as we speak, is changing with the use of NLP. We look at computer vision or we look at voice as an example. We do a lot of work for Fortune 500 corporations around call centers and actually addressing CX and customer support and the like, or even things like banking as an example, looking at credit cards as an example. And they're the ability for a voice AI capability to sit alongside and be able to listen in, not necessarily to sort of score how you're doing, but to augment your intelligence with next best action recommendations or prompts to be able to take the conversation one way or the other. We're seeing that significantly changed the arc of the conversation and thereby the value that's getting delivered through that. Just imagine you're on a call with someone on a support issue and instead of answering your question just before you hang up, it says, well listen, the reality is that most clients that have this question actually come back in a couple of weeks and ask this next question, do you want me to walk you through that? I mean, the whole idea of call centers was how do you get rid of calls as quickly as possible and resolve them and close them out? Then your model is how do you actually address the client satisfaction in a way that they don't even need to call back that second time. And all of that is happening because there's data that you can use to do pattern analysis and use voice AI to be able to convert all of that and then be able to provide those recommendations. And then of course, pattern analysis. I mean, the amount of data that we're touching. Fraud analytics is a lot of work we do around digital banking. As an example, you can't even get started trying to do fraud analytics through sort of human approaches. It's just this the volume and the scale is so high. And so I actually think these utilities, these tools that are coming through an artificial intelligence are now exponentially sort of changing this working environment in large corporations and the driving outcomes and driving value. I think the thing with AI in particular is that it doesn't automate, it transforms. And to the point I was making earlier, because it transforms, the work is now different. The skills that are required actually changed, the way the process functions is actually not the same. And so you can actually have the best AI utility in the world if it doesn't integrate into your existing, quote, old unquote infrastructure. And you don't change your operating model to take advantage of all of this new stuff you're getting, and you don't redesign the process to be able to accommodate that. It's actually good for nothing. And so AI by itself is massively beneficial. But the benefits only come if you orchestrate in AI, AI in a manner that you can integrate it properly to the upstream processes, into the downstream processes, that you change your operating models. You can take advantage of all this. And there's a whole talent piece to it. Not just the talent for AI, but the talent that can use AI in other meaningful ways. And so those components become very important. And so the companies, I believe, that are doing well in deploying AI and the mainstream operations are ones that are not only good at AI itself, but that are good at thinking through operating models, redesign processes, and being very purposeful around the change they need to drive.
Evan: One of the really common themes we hear about in AI is that there seems to be a big gap between the promise of AI versus the outcomes that actually get delivered. What do you think causes that gap? Is it the operating model or kind of people thinking that's just one component, nothing? Holistically I've heard your thoughts about why that tends to be the common experience in the first generation of AI applications.
Sanjay: Evan I spent half my time talking to CIOs, Fortune 300, Fortune foreign companies all day long. I spent the other half of my time talking to CEOs of startups and VCs and others that are really investing in these state stuff. And the reality is that there is one disconnect that's very obvious to me, which is the CEOs of AI startups have an amazing view of technology and the ability to solve a very specific problem. The CIOs of large corporations are looking for something else. They're looking for something that integrates into an existing process that works with the rest of the infrastructure, that actually combines. Think about IoT as an example and the whole promise of manufacturing ford, Auto and many of us would say that we haven't seen anything close to all of the stuff that's been promised. But the reality on the ground, if you're an operations manager and a planned shift, you've got a set of manufacturing assets. By the way, a manufacturing asset has a much longer lifespan than an It asset. So now you’ve got a very complicated piece of machinery and you can't take it down to be able to then upgrade and put all of this stuff on it. So the physical reality of how you instrument AI in the world, in the actual mainstream corporation, is actually a whole different thing. And so I always like to say that the world doesn't really need another machine learning engineer by the way we do. But just stay with me for 30 seconds. We don't really need another machine learning. What the world really needs is a finance and accounting specialist that understands machine learning, a manufacturing engineer that also understands computer vision, a data scientist that actually gets pattern recognition. So it's this idea of intersection of sciences that becomes very important. And so to get back to your question, I think part of the reason some of this amazing technology isn't really and I agree with you, isn't translating the results in the mainstream is the fact that they don't really get integrated, the right use cases don't really come through. And the change management that needs to happen at the back end to integrate it, to utilize it, to deliver on it, and to really get to actual outcomes isn't there in a mainstream way? And so my advice, my input, my learning actually has always been that technology is one part of the equation. But how you instrument it, how you architect it, and then how do you change, manage it through the process in the business is a really important part. And I'll just say the last part, which is, look, this is why I think what you're finding now is there's a new sort of leaders that are business leaders that happen to have technology. That the, kind of, the old intersection between the It group and the business group. That line exists no more. Business is becoming It and technology has become business. And I think the new set of leaders that we're seeing across the enterprise that lead digital or tech or data, you can see that they come from a very different background. They're thinking about business first and then thinking about how technology enables business.
Saam: Completely agree, both on the startup side and just when we talk to enterprises, so much of the focus is on the technology and then people voice frustration around this trough of disillusionment we're in. And I think there isn't enough recognition of, to your point, technology is a piece, but a lot of it is the process, people, systems and how that changes and marries with the technology to drive change and transformation. Given your vantage point, you must see many interesting examples of this. And I'm wondering if there's one or two specific examples you could share of how you've seen, like AI, technology and real process change come together to drive an interesting or maybe surprising business outcome.
Sanjay: I'll give you an example from medicine. We work with so many amazing companies. But one of the things I got exposed to is the area of clinical trials and how you can use AI to actually go from the old model, which is clinical trials used and actually probably still do in one physical location in one city. And so you got to manage data in a very contained fashion. And that's now moved in the back of AI and the foundation of data into distributed trials. And so as you open up, as you unravel, as you transform that world, one of the things that's happening is those clinical trials are no longer restricted to the zip code of Seattle, Washington and a specific set of demographics that now actually have access to it. And instead now these clinical trials become available across the world, actually across the United States as an example. And so you have a much more inclusive methodology, which obviously makes for much more equitable care when you think about people that have access to it. But just as importantly for these pharmaceutical companies, allows them to design therapies better, that is more inclusive, that is more broad, that can actually deliver more comprehensive results. And that's happening because they're building a foundation of data, they're digital training kind of what that process looks like, and they're applying artificial intelligence to it.
Evan: Exactly. I know for both me and Thomas why we got into technologies, because it's fun to kind of imagine what the future could be and then bring that to life. I love to just hear your thoughts on what are some of the surprising ways that AI can enable digital transformation, really kind of improve the customer experience in ways that maybe kind of most people might not be able to envision.
Sanjay: I was at a game with Seattle Seahawks, and there's a lot of retail is obviously changing, as is every industry on Earth, changing significantly. And the sort of touchless purchase, right, which is the idea of walking into a store, picking up what you want, basically walking out with it. It's such an amazing piece of technology, but when you put it into a game or a stadium environment, who wants to line up for 25 minutes to buy a hot dog and miss something? And so you think about that sort of an application where you can just jump off your seats and a small break, run to the bathroom, stop at the pizza place, pick it up and walk out. Such an amazing technology. And I sort of a lot of times, actually, you scan your hand and the amount of thinking that went behind how you pay for it, I double clicked into this. And what was really interesting to me is there's a lot of fraud that's possible because it can basically take a photograph of your palm and then potentially use it. But that scanning technology not only looks at your hand, which, by the way, is a very non intrusive thing to do. So it's not facial, it's not biometrics, it's not fingerprints, et cetera, is actually a palm, but it actually looks at the sub skin level and looks at blood flow in your veins to actually decipher the fact that you are a real person, not a photograph of your hand. That's an amazing example of how AI is at play and so important to make that process secure and fraud proof for it to be pervasive and usable. That's one great example just from the weekend that sort of comes into play. Our son is a sophomore at a university, and he's gotten into a generative AI. He just this weekend, or actually last week, sent us a piece of art that he had created, the new stable diffusion capability that allows you to actually describe in natural language and be able to very quickly produce a piece of art associated with that. And I look at what's happened, and so I sort of didn't know much about it till he actually sent me this photograph. And then I spend all this time trying to learn about it, and I think about what's going on over there, which is in the last two or three months, there's just been an explosion of the ability to do that. And, yeah, it's a lot of fun, and kids are playing with it. And they're having a great time, etcetera, etcetera. By the way, just won the Colorado State Fair. So obviously this thing is getting mainstream. But I fast forward to what's going to happen with that technology. And you think about ads and you're looking to buy a car and none of the stock photographs of cars that you're seeing in Tahoe or something like that. How about being delivered a video of the car driving down in your neighborhood and you're being served that personalized ad as opposed to some stock photograph? It is going to change the way we think about consumer marketing. It's going to change the way we think about personalization so there are some great examples of things that are happening with a long list, but so amazing to see.
Evan: If you kind of went back ten years ago and you asked technologists, what are some of the first applications that next generation will go tackle? Right? They probably wouldn't have said creating art writing code, right? We're seeing it today with stable diffusion and GitHub copilot. And so as you kind of work with large enterprises to really drive digital transformation, what are some of the ways you think the world will change, maybe in ways that other people might not expect?
Sanjay: I think the first thing that most large enterprises are working through right now, and I think this is where the state of the play is, is actually a foundation of data. You can't get started with AI, you're dead on arrival unless you have the right data foundation and building an enterprise data platform and getting all of that right. Not the technology piece, which I think is a little simpler, but actually the process and the ownership and the governance and getting all of that in place. Data is a new asset class. Most was in the enterprise used to think about data as a byproduct of automation. It's the sort of thing that comes out when you automate stuff. You keep it on if you need it, you do fun things with it. But that's about it. It was a byproduct, right? It was a collateral. I think now the world has changed and data is its own asset class. It is helping companies completely change the efficiency component of what they do. It is helping companies think through new business models and new value that they can drive on the back of data. And increasingly more and more companies I work with are thinking about monetizing the data along with other ecosystem partners to drive even more value. And so data is a new asset class. It needs its own chief data office or its own office. It needs a seat at the table, at the board. And that's a massive transformation. It sounds grungy. It sounds like a lot of, kind of, grunt work. But the reality is, unless you get that foundation done, you really can't even get started on the AI ML story. So that's. A big one. I think once you get past that, I think that many of the fortune 500 corporations I work with are really thinking through how to use AI in specific use cases, not broad based AI. There's no such program as how do we AI enable the whole corporation? But you take something specific in the pandemic as an example, many of my clients were suffering with rolling capacity, people, employees in different cities and locations. In the early days of the Pandemic, there was a reason componentry to it, right. Happened in China, and then I moved to California and I moved somewhere else. And so you had this kind of population dispersion across the world with differential access, with differential COVID effect. And so in that environment, how do you plan for PPE or for essential equipment? And you have 50 manufacturing locations, 200 different suppliers, 30 different trade lanes. And now you've got to actually think about, as the epidemiologist predict, the curve of that transmission, how do you shift your manufacturing? Where do you utilize? Which employees you're asking to come into work, which ones you ask not to, how do you distribute the workload? All of that is so poor, so essential. And if you didn't get it right, you don't have parts, you don't have products, you can't serve the population. And that's one thing. If you're selling and I hate to use the toilet paper example, but you're selling an essential equipment like a medical supply, it's a whole of the game. And so this is where AI has been really, really useful in being able to predict and be able to plan and able to do that dynamic planning as the world has changed so much around that.
Evan: Sanjay one thing. We like to do a quick Lightning round, just get kind of like the one tweet version, write a couple of things. Sanjay, do you want to maybe kick it off for us?
Saam: Definitely. Sanjay maybe to start, how should companies measure the success of the CIO?
Sanjay: I started my role not as a CIO, but as a chief digital officer for the company I serve. When I set my own well, I didn't my boss and I set the success metrics of my job, and we defined my job as being successful when we don't need me. And that's not to be cute or symbolic. I think the idea there was that every company needs to transform. And to transform, you need to incubate and build a new business model and your business and a new set of talent and a new approach to life and a new approach to work. But when you do that, it can't sit as incubated mini satellite within an organization that it needs to infiltrate and needs to become the business, and the business itself needs to become digital. And so we basically said, listen, we've got to run the business, and while we're running the business, we got to change the business. And so why don't we go change the business as a digital initiative and then go off in the corner and work? And I kind of mean that figuratively, but then when this is done, when this is right, when we will have achieved whatever success we think we can out of faith, then actually we're no longer going to need this role because we will merge it back into the rest of the company. And so I actually think the Chief Digital officers and it's not even merging into the company. We'll make the company digital, right? So not every part of the company becomes digital. And in my mind, the best measure of success for a Chief Digital Officer.
Evan: What's the upcoming technology that you're most excited about?
Sanjay: I think not one, but a basket of technology. I think Web3 auto is actually going to be very transformative. Of course, it's very clunky, it's very difficult, it's not mainstream, lots of challenges and issues. But look, I've been long enough in the business, unfortunately and fortunately. And I remember the early days of Web One and the early days of Web Two, and for me it feels no different. And yet on the other side of Web One and Web Two, we see the world has completely transformed. I think the same is happening with what will happen with Web3. So I'm super excited about what that does for us.
Saam: What's the most common mistake you see a newly minted CIO or CDO make? What's the most common mistake they could make in their first 90 days?
Sanjay: I think not leaning into the business and thinking through the change management, across the process and the new operating model for the digitized version is a big mistake. And you end up with great implementations of technology, but with very poor outcomes. And that's the number one thing to watch for in my mind.
Evan: That's super. Clear and I think a comment that we've heard from different guests will, Sanjay, I do sincerely, really appreciate you making time and it's been great chat with you and I hope we can catch up again soon.
Sanjay: I really enjoyed it and look forward to it.
Saam: Thank you, Sanjay.
Evan: That was Sanjay Srivastava, Chief Digital Officer at Genpact.
Saam: Thanks for listening to the Enterprise Software Innovators podcast. I'm Saam Motamedi, a general partner at Greylock Partners.
Evan: And I'm Evan Reiser, the CEO and founder of Abnormal Security. Please be sure to subscribe so you never miss an episode. You can find more great lessons from technology leaders and other enterprise software experts at Enterprise Software Vlog.
Saam: This show is produced by Luke Reiser and Josh Meer. See you next time.