26 Days to 3: How Zoetis Is Building an AI-Powered Operating Model

Help me think through how this episode applies to my situation. Start by asking what I am trying to change. Separate the episode's claims from your suggestions, and say when the notes do not support a claim. Use the transcript to find passages, then check the audio before quoting. Transcript: https://yuvalyeret.com/podcast/episodes/26-days-to-3-how-zoetis-is-building-an-ai-powered-operating-model/transcript.md ## Published episode notes Zoetis is reducing a 26-day infrastructure workflow to 2-3 days with a three-agent pipeline. One agent designs, another produces infrastructure as code, and a third executes. I talk with Kumar Venugopal, CTO of Zoetis, about moving from personal AI productivity to organization-wide impact. We cover the four levels of AI integration, the operating-model changes behind faster delivery, and why design thinking becomes more important as agents get faster. Kumar also explains how fast prototypes change build-versus-buy decisions, which skills employees need, and why Zoetis is doubling down on Agile while delivery cycles compress. "The real opportunity with AI isn't strictly automation. It's how you embed it into how we make decisions." - Kumar Venugopal "We are doubling down on Agile. Even the agentic approach has to be built with a minimum viable product approach, with proper user stories. The cycles are a lot faster, but the process doesn't go away." - Kumar Venugopal Chapters 00:00 Introduction - from 8086 to the AI era 03:01 Why this AI wave feels systemically different 05:30 AI in veterinary diagnostics and decision-making 08:12 The four levels of AI integration 11:01 Infrastructure automation - 26 days to 2-3 days 13:57 Design thinking as the missing AI skill 16:29 Build vs. buy - can vibe coding replace SaaS? 19:26 Who does this work and what is the real constraint? 21:58 Three competencies every employee needs 25:11 Why Agile is more important now 27:45 Scaling AI beyond the technology organization 30:30 Kumar's three-step change model for leaders Links and resources Kumar Venugopal on LinkedIn Zoetis CTO on AI operating-model change Scaling AI: From Activity to Impact Yuval Yeret on LinkedIn ## Transcript This transcript was edited from automatic speech recognition for readability. Speaker turns may be wrong or absent, and it may contain recognition errors. Check the audio before quoting or attributing a passage. Episode: https://yuvalyeret.com/scaling-ai-podcast/26-days-to-3-how-zoetis-is-building-an-ai-powered-operating-model/ ## Why this AI wave feels different Can we build an agent here? Can we really get this role that you're doing to be done by an agent so you can go off and do more important things for the organization? And that's the step where we are today, trying to get people to move beyond personal productivity. Now we tried to get them to the point where they're able to say, you know what? I think that's a good idea. I think if we had an agent that could help me here, then I'm not so intimidated by that. I understand what that means for me. I'll still be a human in the loop for now. I could leave that and go do something else. That seems to resonate pretty well. Hi, Kumar. Welcome to the Scaling with Agility podcast. Hi, Yuval. Good to see you. Yeah, it's been a while. Would you share a bit of what you're doing, what's your background, what are you up to do this? Yeah, absolutely. I'm the Chief Technology Officer of Zoetis, the world's largest animal health company, which spun off from Pfizer in 2012 or 2013. But most of my time has been spent almost 30 years in technology. starting in the.com era and leading all the way to today, which is the AI era. Very interesting ride. I got into computers when I was 12 or 13 years old in my first 80, 86 computer. Never in my wildest dreams that I think I'd be still doing this so long after, almost 40 years after. But that was the, that's been the great journey. So it probably gives you a good vantage point on all of these transformations and ages that we've gone through. If I look at my background, the other day I was reflecting on my first billable job doing something related to computers was building applications. I was working with the local computer store in Israel and he had clients which needed some applications which today somebody would vivcode probably but we were using paradox which was like magic to to do inventory and kind of spare ball and things along those lines. So, you know, we've both seen a lot. So let's start with how different do you feel what's going on right now with the AI era versus is it more of the same waves that we've seen over the years? It feels different. I can tell you that the.com era felt different than this one. It feels more systemic. feels more revolutionary. It feels like it's going to impact not just technology, but technology as a means to transforming other areas. So it feels very different. It actually feels like it's actually going to change things, not just be an add-on. Like the internet became an add-on, e-commerce became an add-on. We don't shop in brick and mortar stores. We now shop online. Okay, but we were still shopping before. We're still shopping now. But this one feels like tomorrow's version is completely different than yesterday's version. And that's maybe just because we're in it, but in the 30 years that I've seen it feels different. So how does that show up in Zoetis? What are some of the signs, the signals that you're seeing? So it shows up in our veterinarian products, first and foremost, the ability to use AI and leverage that to do better diagnostics, to do better genetics, to do better everything in almost every product line that we sell to our customers is the number one impact. Within Zoetis, it really shows up in every department, in every conversation we have. It's not just about going out and buying a product, Salesforce AI, Agentforce, right? It's not just about getting the things bought, but it's really about how do we implement this? How do we get this to change our business process? And what are we trying to do with our workforce? What's our strategy around the workforce? What are we gonna do with them today? What are we gonna do with them tomorrow? Are we gonna prepare the technology folks for the transformation? How are we gonna prepare the business folks for the transformation? We think people are still critical. They're not gonna replace people. The company can't be run by agents, but there is gonna be a big impact on how people work, how people function and operate. So you wrote something interesting, that resonated for me a couple of weeks ago. It was something I think about the opportunity, the real opportunity with AI isn't strictly automation. it's how you embed it into how we make decisions. So let's unpack that. Can you give me an example of how you see AI changing, how you'll make decisions inside Zoetis? Or how is it already doing that? Or how do you envision it driving change? Yeah, so let's take diagnostics. We wanna take our pet to the vet. It has an issue and the vet puts in the sample. Yes, today we can give a diagnosis pretty quickly. It's not a big problem, but could we accelerate that process? Could we make the decision making faster? Could we provide an easier process for the vet? Could we even avoid the vet? Is there a ways to do that without needing a vet? So first of all, there's a whole new market, whole new thing to think through. How will we do that in the future? The second thing is just within the company. How do contracts work? Do we need to do contracts in a specific way? How is our decision-making enabled for those contracts? Are redlining done by humans? Can they be done by the AI? And then when we have a decision to make the pros and cons, the risks, it's really enabled by generative AI today. It's personal productivity today. It's not ingrained into business processes. But if I'm looking at here's my five risks that I've come up with for this particular item. And then I go into the chat GPT thinking model or any of the others. And I figure out that this is a prompt I want. It tells me what's an additional set of risks that I haven't thought about. It tends to help me think holistically, it helps me think in more detailed ways, and that's the decision making that I was talking about. It's really about making right decisions and having the right information in your hand. So, okay. So I guess one of the, when I talk to leaders like you, Kumar, and I asked them, what are you doing with AI? Something like that is they see a lot of the potential of how to integrate AI into their organization. ## Where AI is already changing decisions And then we have a conversation on what are the levels of integration of AI into how we're doing things, both inside our products, inside the veterinary products in your case, as well as in our key business processes. And we see a couple of different levels. There's augmenting human thinking and human decision making. making, that's chatting with Claude, chatting with ChatGPT co-pilot, whatever environment you're in. That's a good start that a lot of people typically start with. The next step level I see is we're still augmenting human beings, but we're doing that in a much more structured way. So for example, if we take your contracts example, it's not just that every time somebody in purchasing or legal needs to do something, they need to feed the contract and the prompt and they need to do prompt engineering. We take it to the next level of we create a project for them, an environment where they only need to drop, you know, an additional contract and all of the contacts, all of the data is available and it's almost automated. That's the next level. The next level after that is what people might call Claude Code, Codex, which is you start to work in an environment where there's the ability to actually let the agent think on its own and develop stuff. And there's full-agentic which people don't necessarily need to be in the loop. are only in the loop to code to agents and to fine-tune the agents. What's your vision or roadmap for where do you see Zoetis right now? Do you already see people that are taking you to some of these higher levels? Do you see that coming? What's the constraint, I would say, in getting your I usage to these agentic levels. Yeah, that's a great point. So we have personal productivity very high. People are using AI every day, making themselves more productive. Let's just say 20% more productive. So lawyers are 20% more productive. Scientists are 20% more productive. I think that's pretty well happening. It's not consistent. And that leads to the second part where we do have focused efforts on taking departmental work or workflows that are cross-functional in nature and building out solutions that are a little bit more defined and also on the one hand like fixed on the other hand has some flexibility. So that way a team of medical writers can collaborate and become not 20% personally individually productive but 40, 50% productive as a department. So we have a lot of initiatives in that realm that have shown good results. And we're continuing to push the boundaries on that. On the Agentic front, the third and fourth category, we're still in the early stage. So just one example would be you've all infrastructure. I believe we can have a very modern AI first infrastructure to do server provisioning, cloud provisioning, things that are anyway templated, but also to do the identity access management, to the firewall access management, to do all the 25, 30, 40 tasks that happen when you do that. I believe all of that is agentic capable now. So we're in the process of doing a bold decision to just make that fully agentic sooner than later. If we can do it this year, early next year, we'll start with taking our current workflow, which takes 25, 26 days. We'll try to get it down to two to three days because there's still humans in the loop, cybersecurity, and there's some critical steps we can't miss. And we challenge ourselves to get to two to three days. Once we hit that this year next year, then we have to work on the next step to try to get more efficient. So I think there's areas in the company that are very liable for that. But we're still early days. I don't feel the need to take the plunge on it today. We will wait. So let's maybe dive into that example. It's an example I at least feel more comfortable talking to the veterinary experience. My background is more infrastructure than pets. So I hear you talking about that example of optimizing and automating and giving agency in the the world of infrastructure and I'm thinking, how does that relate to infrastructure as code? Is infrastructure as code a prerequisite for enabling agentic AI infrastructure? And let's say you have infrastructure as code, what is it that agentic AI is really providing? Like what's the added value that you see beyond infrastructure as code? So we have projects that are starting. They need design services. Actually, the business customers and or the functional IT, the application site, they don't know what they need. They don't know what resources they want to build. They don't know what Azure services they want to bring in. So there's a whole host of Agentic exploration on just the design component, setting up the blueprints, getting it organized. Then you have the second piece, which is the, what you're talking about, the execution mode. set of preconceived infrastructure's code, template-driven approaches that are very manual in effort today. Those are kind of guardrails for the agent. So in my mind, you get the design blueprint from the design server, design agent, and then you have a human in the loop to check the hat. And then you from that build an actual infrastructure code, like a codecs or a cloud code kind of a system that actually pulls out the actual build scripts that are based on your templates. And then you have a third agent after some human review that is able to execute on all of the above. That's the three step process we're thinking about. In the end, for the customer, they should feel, this is my need. These are my project documents. This is the SaaS solution I'm buying. This is the integration I need. And the design components able to say, Well, based on that, I know you need an API gateway, you need REST APIs, you need this and this and this. And based on that, I can build the actual code to actually develop those. And based on that, I can actually now execute with humans in the loop, checking it every step of the way. That's a complete overhaul of how infrastructure is done. So I'm hearing that and I'm thinking of something similar to how when you think about a software, you vibe code. There's the transition from just telling cursor or lovable, I want an app that will do whatever, to taking a step back and planning and what's called spec-driven development these days, creating these specs you're talking about and infra oriented aspect of that spec fascinating. So how does that fit into the wider need? So infrastructure is one aspect. Are you thinking about how that infra connects to enterprise architecture and cybersecurity and even the functional needs? and are there similar agents that will take care of these aspects? Is that part of the vision? Yeah, we have some beginning aspirations. So AI ops is an area of intersection. So we have observability platforms today at 2 AM in the morning. If something happens in the US time, we may or may not, we have a global team, but we have call lists and things like that. But both in the integration and application space, we really are hoping to combine that. There's eyes on the sky, let's say 24 by 7, and give it freedom to actually initially execute defined runbooks, but then to allow it to execute corrective actions that are not defined, and that's where people begin to get nervous in production systems. And that's a whole nother aspect that we have to explore, that we haven't gotten to yet. Maybe we will feel comfortable someday, but not yet. That's one aspect. The other aspect is really just can we replace SaaS applications through vibe coding, through the same approach for infrastructure, not just infrastructure as code, but really software as code, building some replacements. And we're actually exploring a couple of options today that if we could get rid of some basic SaaS applications, subscription based, that we no longer need, could we then use that same approach to build out that software, implement it, get it out. And I'll just use an example. Monday.com is an example. For me, that's a very generic piece of software. And we could literally develop that. Why do we need to pay $30 a user per month per license? And as a corporate, you have hundreds of those. Why do we need to do that anymore? Yeah, I think it's a good question. ## From personal productivity to workflow change What's the mode for these applications? I don't know what's the mode for Monday.com. It's a question I've been thinking about. I've never understood where's all that enterprise value coming from. But it does raise an interesting question, which is, okay, so whether it's build versus buy and vibe coding, whether it's even if you have Monday. How do you use it via MCP? Just as an example, via MCP or APIs to achieve optimization and automate things, who's responsible for all of this? Who are the, so first of all, who are the people that you see doing all of that work, exploring, discovering where's the value, building it, validating it, maintaining it over time? And you're not, let's start with that. Who do you see as the people in Zoetis that will do this work? Well, I mean, I'm trying to spearhead this kind of thinking, you know, within the organization as the technology person. But I would say there's almost everybody's interested in joining this effort. There's almost nobody who says, yeah, AI, I really don't care about it. I don't want to deal with it. I pretend to just do my cobalt programming and I'm good to go, right? There's nobody in the company that does that. Nobody in the technology function at least. So I would say we've got a good variety of people. believe it or not, not just developers that are interested learning about vibe coding or interested in learning about how to automate infrastructure. So cross functional teams of people that are incredibly interested in how to make this happen. And that's been a shift that business people can use vibe coding to develop solutions. It's no longer a developer. It might just be a prototype, but it's a start, right? So that means business analysts on the IT side can do stuff that they couldn't do before. And that's been a really powerful shift. We have ideas, we have a ton of people ready to execute on those ideas. We have tools like GitHub Co-Pilot and others that we could use to get them access and get them to execute. And all we need to do is buy tokens. And that seems to be the big constraint is to have enough to go around. So for the listener, we do have some histories. in a previous organization where we've worked on the transformation, I recall you had power ops, right? We didn't have AI at the time. You had AI for other things, but not gen AI. So there were power ops. There was this vision that the business users, the people that are doing research and development, thinking about how to commercialize drugs. They will use power apps rather than need IT and technology to do everything for them. It was very hard, as far as I recall, to get traction with these sort of internal products. I can guess why it's different in the world of AI, but I'm really curious about your perspective. It's whether it's different and why. It's different because Power Apps is really still a technology tool and still behaves very much like a technology tool. If you want to integrate it to anything, all of a sudden you need much more sophisticated skills than just building a couple of forms. That's easy, but nobody wants that. That's useless, absolutely useless. InfoPath and Microsoft and Google Forms can do all that simple UI work. AI is different for us because we can offer the complete set of services. We can provide for them integration, databases, calls. I mean, those are things that are not easy to do in the past world that are still not easy, but becoming easier by the day. And that seems to be a big difference maker. Just to give an example, with intent from engineering, if you know what you're doing, we could build that we did build a replacement for Monday.com in a span of a couple of days, working prototype. Does it have bugs? Yes, of course. Does a developer do that? Yes, but actually it's just prompts. So actually, the developer didn't do anything. You know, could a business person have easily done that with training? Absolutely. They still need IT to integrate it, but I think the power of it is completely different scale, completely different setup. So you mentioned training and we're not talking about training the LLMs, we're talking about training people. I guess the question that's top of mind for me at least is, okay, beyond tokens and access to tooling and the permission and the interest for people to actually do this, which we understand there is, what's the competency that you need in order to effectively build solutions, valuable solutions with AI. Everybody can build something. But how do you, what sort of training, what sort of acumen, what sort of experiences do people need in order to build effectively? I found that design thinking helps a lot where you're not thinking, you're thinking in the overall structure, you're thinking in the design language. Of course, prompt engineering is a critical skill set. It sounds easy. I'm just going to ask the LLM, but how you ask the intent. That's very difficult to frame correctly. It's very difficult to put it the most optimal way. So design thinking prompt engineering. The third that I've told people all the time is to learn how LLM's work even at a middle school math level. So how do they form these neural networks? How How do they tokenize? What is the self attestation Google paper that transformed the world of neural networks? I ask people to learn that because without it, I don't think they can really understand how to do it right, how to use it well, how to integrate it with the proper. So those are the three pieces that I always encourage people to get digital AI fluency. Learn how these models work really very detailed. detailed as your math skills allow you to go because I'm not that skilled at math. I go only go as far as it allows me softmax functions, vectors. I mean, you lose me at that point. So I learn as much as I can. And then the second is really learning prompt engineering from that, coming out of that. Okay, how do you influence the model to get what you want out of it? And then the third is design thinking. How do you think in a design language format? How do you think in a way that's holistic? And a lot of people get into the details of their application Instead of thinking holistically and to describe their intent, they can't do it. They can't translate that into a holistic picture. Why is that so important to think holistically? What are the anti-banners that you see when people are too focused on the solution? Yeah, you get a lot of pigeonhole stuff. You get a lot of stuff that with AI that doesn't work. You get a lot of forms and cludgy user interfaces. You know, I played with it for a couple of weekends, right? And I just for fun just left the overall intent, didn't tell the prompt what it was. Just wrote exactly what I wanted and totally different than what I expected came out, right? I give it the proper focus, the proper intent, the proper design thinking. ## What agentic infrastructure might actually mean I even took out the designed language from tools like Duolingo. And I said, you know, I want to use this design language. I want you to develop this. It was much more accurate. without just going to tactics brings a completely wrong solution. And people will throw it away because they'll go, that's all I want. And then next thing you know, they lose confidence in that. So I have an hypothesis. So as you're talking about it and I'm not surprised, I'm hearing user stories, right? I'm hearing talk about outcomes, recall some of our earlier conversations about IT people that need to start to think about what they're building not as, you know, tickets that come from the business people but products and they need to think about the holistic product experience and the experience that people actually need, not what they claim they want. And I'm wondering how do you see the relationship between creating an organization that is more product oriented, more agile and iterative and the ability to deliver value with AI. Is Agile useless at this point? Is it given? Is it the prerequisite like infrastructure is code? How are you thinking about this? And what are you doing in that regard? We do not think Agile is useless at all. In fact, we are doubling down on Agile and we are trying to be not just working on developer productivity, but even the The agentic approach has to be built with a minimum viable product approach with proper user stories. You feed those into your prompts. You build step by step because even AI cannot build sophisticated tools overnight with just one prompt. It takes step by step by step building progression. The cycles are a lot faster. So the ability to integrate and innovate, the ability to deploy and run again, it's a lot faster. It's hourly. It's minute by minute. Like every 15 minutes it can regenerate. So that requires us to change maybe the agile cycles, the cycles of sprint. But I don't see the process going away. The PI planning, the thinking about what you want, being able to put that into proper epics and stories, being able to get that fed into a model, being able to get that QA tested by AI, by building scripts that are related to test scripts that are related to to the functionality based on the stories, testing it, running it. I see the cycles shortening significantly. And you know, you were in the GXP world with me before you. Well, that would take us six weeks, seven, eight weeks to run through all of that, document it, put a trace matrix together in the GXP world. In this new world, I think we could do that within, I don't know, one week, max. And that changes the cyclical nature of it. And I like that. I think that makes it a significantly higher velocity. I mean, it wouldn't be a surprise that to you or the listener that my belief is that you need to hedge on my change, but agility is something that you need even more in this space. But the question is, if you start to look at, let's go back to these business people. So the people in IT, maybe they already know how to work in agile, maybe they don't, you need to train them. It's, you know, we know how to do that. When you get to the business people, they don't even have a development mindset, right? They don't even think in, I'm going to build something. I need to think about how I build it. A lot of the business people that I've seen from their perspective, building is the role of technology. We say what we want and it magically appears, even getting them to these stakeholders that are involved in this process. It's been a challenge in the Israeli Air Force in the 90s and it's still a challenge in FHARMA companies in 2026. So how do we get them to say, you know what, this is on us. us, we need to step away from our operational work, from what we do for a day job, let's say, use the productivity gains from AI to free up some time to actually work on the work to start to develop things and to start to care about these processes that you're talking about. We need to train them to do design thinking, to thinking stories. How is that going? You see that as a natural organic progression or do you see that as a challenge to get business people to think this way or people in the business. It's not business people. It can be doctors. It can be finance people. There's enough anxiety about AI that people are interested, even if they're lawyers, to understand something. productivity is like the easiest thing to get them to understand. But what you're talking about with agility, trying to learn how to do this in a new way, I don't feel like we've gotten there yet. It's not like the resistance comes from the fact that I have to change. It comes from the fact that will I have a job tomorrow? If I'm able to get rid of majority of my redlining work as a lawyer, what am I going to do? That's a fear that comes from within. But at least the interest is there. Most of the people I talk to within Zoetis are eager to learn more. And if I tell them, you know, learn design thinking, learn how the NLP models are built, learn how to do proper prompt engineering beyond your personal space. Let's try to work on putting together a product that can help you accelerate contracts. I don't get too much resistance. There's a lot of openness. They realize their skill sets have to change and are blending with technology. There's no way around it now. You cannot be a business person and say, my role is not impacted by technology. Maybe as a final thought, if somebody's trying to figure out how do I scale AI beyond the confines of the technology organization? How do I get this mindset of productizing my work using AI to help me improve my function, the business process that I work on? thinking about using AI in an energetic way. This shift from my role is to just run the business processes. I'm hearing you describe the role, and I fully agree. They're all everywhere, even for lawyers and other business people, is to become architects, to become developers of better and better legal functions in organization, even the equality people. The real role would be to think about GXP processes. How can we run those faster? Not in order to enable the organization to run GXP without quality people. They're always needed, but to enable accelerating the whole machine of getting the teranerian products, more of them to the market, experiment more, and delivering more value to the world with the same capacity. What's your advice to other leaders who are navigating this cousin, this transition between AI as a technology and AI as driving this cultural change, I would say, how people even see their role? Yeah, that's a very, very difficult question to answer. What I've learned is that education is first and foremost the key. You've got to really teach yourself, which I had to do. And you got to teach your people, You got to learn with them, business people or technology people. So we've hosted classes to say, how do NLP's work? How do you build a model? If we had to build our own, how would we build one? Not that we're going to. What would that look like? So education is step number one. Step two, be open about the change. Explore how it looks like. Your role could be different tomorrow. Let's explore what that looks like. ## The operating question underneath the tools And then step three is really to see what's in it for them. And usually I try to tell people, here's what's in it for you. If you learn these skills, you become marketable tomorrow. You don't want to get left behind. You don't want to be the one who doesn't know how to do it properly. College kids come out with innate knowledge. You need to now keep up with that. And part of that is learning technology, reading technology, understanding how it relates, using people like me and other people in the company to help you. And generally speaking, they're very open to that. And we do all of this before we get into departmental workflow conversations, conversations that begin to shift work in the organization. So that's been the last, I would say, year. Now we can begin the conversation of, can we build an agent here? Can we really get this role that you're doing to be done by an agent? so you can go off and do more important things for the organization. And that's the step where we are today, trying to get people to move beyond personal productivity. Now we try to get them to the point where they're able to say, you know what? I think that's a good idea. I think if we had an agent that could help me here, then I'm not so intimidated by that. I understand what that means for me. I'll still be a human in the loop for now, but maybe in the future, I could leave that and go do something else. That seems to resonate pretty well. So that'd be my advice. educate, talk to them about the current role, envision the future role, and begin this work of personal productivity, moving them towards getting comfortable and getting them to the more agentic future state approach. That's a change process that could take a year and a half for some people. That's great. Where do you get your inspiration? What are you following? What would you recommend the other CTO, CIOs, chiefs of AI, follow, read, listen to these days. Yeah, so one of them that I enjoyed, although it's not always consistent, is the All-In podcast by Chamat and a few other people. And I think that's a very interesting podcast to talk about AI and what's happening in AI, economic impact of AI. It's been kind of helpful to help me think through how these things operate. The second is just talk to the people in the industry, right? You and I have not spoken, but it's been on my list to catch up with you. What's happening in the product space? What's happening with AI for products and product engineering and for safe and agile? How do we think about it in this new world? Talk to people in the industry. And third, I do a lot of reading. I mean, I've literally spent all the time trying to figure out the implications, not so much the technical aspects of AI, although that's interesting. implications of that for an organization. How do you change the workforce? How do you make it more productive? How do you bring things that are relevant outside? And that could be, I try to avoid conferences. I mean, I try to do a lot of books and or podcasts or blogs that I find relevant. And lastly, Wall Street Journal for me is a good overall indicator of where we stand. That's like my number one go-to source in the tech section. Where do we stand with AI as a general society. And that's been helpful to think through issues. I like that list. I find that there's much more information about the technology aspect of AI than the organizational aspect. And maybe that's an opportunity. Like, this is where I'm trying to take this podcast and interview people that are thinking about this and provide insights that relate to this, but I imagine that it's only natural. People are just starting to, you know, bringing the technology, people on the bleeding edge like you are starting to think about what does it mean organizationally. So yeah, I think that's the whole right of time. We're so enamored with the technology, we're having a hard time figuring out how to make it fit. We're walking around with a brand new golden hammer, and we're trying to look for nails and it's not apparent to where the nails are. Yeah, I think of AI as also something else. AI is activity, but it can also be impact. It's not just artificial intelligence. Good point. So that might be the new name of the podcast, the AI activity to impact. So thank you Kumar for taking the time to share your perspective on what's going on. if the listeners want to follow what you're following and the insights that you're sharing, you're a fresh voice, I would say, at least on LinkedIn, where a lot of the posts are posturing and the I-written slop, I really enjoy following the post that you share from the trenches. They're not that often, but it's good. They're real when they come. I only share when I have, I only have share when I have something important to say. If I don't have important things to say, then why bother sharing? So I read out of 10 things I read, I might share one thing that's about it. That's good. Yeah, it's like Einstein. You probably remember that story. That the... No, I don't actually. So the story goes that Einstein only started talking, you know, at age three, four, whatever, until then, you know, it didn't really talk. And the first thing he said is, you know, the soup is cold. This is something along those lines. People asked why did he say I didn't have anything important to share until this point. So, exactly. So I'm also trying to find important things to share and the conversations, the interactions, beautiful that are doing it in the trenches. That's where I get my inspiration. So thank you for sharing your stuff and we'll share the link to the LinkedIn profile in the show description. So again, thank you Kumar for joining. Thank you, the list, dear Lee, for hanging in here with us. I hope you get some insights that help you navigate how to scale the use of AI in your organization and maybe a little bit about the role of agility as part of that. Thank you and see you again soon at this podcast. ## Source boundary These are the published show notes from the podcast feed. They are a starting point for discussion, not a verbatim record of the conversation. The transcript is machine-generated and may contain errors or unlabeled speakers. Check the audio before quoting anyone.