Building the AI-Native Enterprise: Leadership, Software, and Compounding with Mahesh Rajasekharan - EP.286

0:00AI is such a fundamental shift. It's probably a hundred year shift. And so I think having an incredible sense of urgency is the most important thing because you're going to learn. At the same time, you need to have a north star. And at the same time, you should lead from the front as CEOs. I think that's where sometimes people, I think, make mistakes when they delegate to a chief AI officer. I'm not saying there's anything wrong with having a chief AI officer. The CEO should learn what problems they're solving and see what AI can do for it versus delegation.

0:34Alex Bridgeman: Mahesh, it's great to see you again. Thanks for coming on Think Like an Owner for a second episode. This podcast is all about how small companies are grown by ambitious owners. And you've grown quite a company in Clio. And I'm excited to talk about how you see software companies growing into the future and through AI tools and becoming a more AI first organization. It's certainly a bit relevant for what Rob and I are thinking about, too. But for those who don't know, can you tell us the story of yourself and Clio and kind of what the business does? And then we can get into your views on AI for software.

1:11Sure, Alex. I've spent more than 25 years in and around supply chain across strategy, planning, execution, and digital transformation. And I've had the privilege of leading Clio for a significant part of the journey. Clio today is a category leader in AI-driven supply chain orchestration. We serve more than 5 ,000 customers globally across industries, including manufacturing, logistics, wholesale distribution, and retail. At its core, what we do is actually pretty simple to describe. We help companies connect and orchestrate the flow of orders, shipments, invoices, inventory, and payments across the business ecosystems between themselves and their customers, suppliers, and logistics partners.

1:58And the reason I find this space so fascinating is that supply chain has evolved from what was historically viewed as an operational function into a major driver of business performance, revenue, margin, working capital, and ultimately customer experience. But there is still a fundamental gap in enterprise software, which is planning systems understand what a business wants to happen, but they generally cannot execute. Execution systems, on the other hand, are very good at processing transactions, but they often don't have the business context to determine the best action when reality deviates from the plan.

2:34So that's the gap we're focused at closing at Clio, bringing together integration, context, intelligence, and coordinate execution. And that's what we mean by AI native supply chain orchestration. And with AI, I think the opportunity to transform how these business ecosystems operate is greater today than at any point in my career. And that's a big part of what is currently so excited about the next chapter for Cleo.

3:00Alex Bridgeman: That's a great background. Thank you for sharing. the kind of, I think, crux of the subject you're excited about and have thought deeply about is how do we build better software businesses with AI? And a lot of companies describe being AI first as a key value of how they're building their businesses. But what does that actually mean? Maybe what does it mean to you? I think we have to be careful, Alex, about the term AI first, because today, as you know, almost every software company describes themselves that way. For me, being AI first isn't about putting a chatbot into your product or adding AI to every feature.

3:40It's much, much more fundamental. It changes how you build software, what you choose to build, how you operate the company, and ultimately how you produce outcomes that can create value for customers. So I'm incredibly bullish about AI. I think it's one of the most consequential technology developments of our careers. But I'm not particularly bullish about AI for AI's sake. The ability to access incredibly powerful intelligence is becoming ubiquitous today. The frontier models are getting better and extraordinarily. So simply having access to AI is not going to be a durable competitive advantage.

4:18The question I always keep coming back to is, what exactly are you amplifying? because that's what a billion AI fundamentally is. It's a tremendous force multiplier. So if you have a great product, if you have preparated data, if you have deep domain expertise, rich context, if you're trusted customer relationships and mission-critical workflows, then AI can dramatically amplify the value of all those assets. And conversely, if the underlying value proposition is weak, simply adding AI isn't necessarily going to create a great company. And so I do think there will be significant winners and losers in software.

4:57If your product essentially automates a relatively simple workflow, then increasingly capable models may be able to reproduce a lot of the value. But you think about a complex enterprise environment like supply chain, like I just described. So in that case, you need to understand the industry. You need proprietary operational data. You need to understand the context surrounding the supply chain transaction. You need complex workflows across the Balta enterprise ecosystem of multiple companies in a supply chain. You need governance. You need security. And ultimately, you need the ability to take action.

5:34So that's a very different problem. So I think something interesting happens as the underlying frontier models commoditize. So then context becomes more valuable, not less. And that's particularly relevant to Clio. So we, as you know, spent years building deep supplies and expertise and connecting companies with the trading partners, with the flow of orders, shipments, invoices, payments, and all of the interaction around them. So in our case, our opportunities in Sipri to put AI on top of Clio is to combine powerful intelligence with all of that accumulated data, context, domain expertise, workflow, and execution capability.

6:15And so ultimately, there has to be, in my mind, a very simple scoreboard for companies. And the scoreboard is, did we create substantially more economic value for the customer? And I don't particularly care how many AI features they launch. I don't care how many agents we can put on the slide. The question is, did we help a customer protect revenue, improve margins, reduce working capital, serve the customers better, and ultimately grow faster? And that's the gold standard. When I say AI first, I don't mean AI everywhere. I mean using AI intelligently throughout the company to build better products, operate better, and most importantly, dramatically better outcomes for customers.

6:59Thanks, Mash. And by the way, I just wanted to call out, it's great to talk to you as a podcast host now. We're used to seeing each other in the boardroom. You were on my board at Send for six and a half. years. I'm incredibly grateful for that. And yeah, good to be here with you again in this context. Related to that question, how has AI fundamentally changed the way Clio builds and deploys software? Yeah, Rob, I would go further than just saying AI has changed software development or, you know, basically how we build software. I think the act of producing software has been turned on its head.

7:39Think about the traditional model, right? In the traditional model, humans wrote the software, And all the tools helped the humans building the software, right? Now we're moving towards a world where humans are increasingly defining the problem, the architecture, the intent, the standards, and the desired outcome. And then the fleet of agents, AI agents can participate in actually producing the software. That was the profound change. We're already seeing it throughout Leo. We're using the highest quality frontier models we can access for coding and testing agents and other parts of the product development lifecycle.

8:13what we call the software development, the product development lifecycle. And the productive improvements we're seeing is just dramatic. It's extraordinary. Things that historically required significant engineering time can happen much, much faster. Ideas can move from concept to prototype to testing at a speed that would have been unimaginable just a few years ago. But also there's an important misconception here, which is generating code is not the same thing as building great software. In fact, once code becomes easier to generate, a whole different set of problems becomes more important. If feed of agents are producing code, how do you know they're producing the right code?

8:55How do you validate it? How do you test it? How do you know it fits your architecture? How do you secure it? How do you govern the agents? How do you make sure the resulting product is reliable at enterprise scale? So that's why at CREO we built something internally called the CREO Code Generation Harness, which we're actively using. And we are effectively building the infrastructure around our coding agents to make sure that as we dramatically accelerate the creation of software, we maintain and hopefully improve the whole quality of what we're producing. So to me, that's one revolution, which is we are redesigning the software factory itself.

9:33But I actually think that a second revolution is equally interesting and even more interesting. And that's what you choose to build. Because the ability to create an AI capability has now become remarkably ubiquitous, right? So you can build things today in days or weeks that were taken months or years or simply wouldn't have been economically feasible before. And so paradoxically, the scarce capabilities now become less about can we build this and more about should we build this? Where can AI truly create differentiated customer value? And that's a product management or domain expertise question.

10:08And so in our case, I really don't want the Clio team of forcing AI into every feature just so that it can call everything AI power. That's really not our objective. We want our product leaders and engineers, industry experts, and our customers, in many cases we work with customers directly, to identify the handful of places where AI-driven intelligence can fundamentally change an economic outcome. And then I want us applying extraordinary technology against those opportunities. So I actually think that means judgment becomes more valuable in the AI era, not less. When everything becomes easy to build, knowing what to build and what is worth building becomes incredibly important.

10:48And that's one of the things that excites me most. The best software companies aren't simply going to build the same products faster. They're going to build better products that weren't previously possible.

11:01Alex Bridgeman: And one thing we've heard, Mahesh, you say often is enterprise software is entering a new era. What do you mean by that? You know, I think we're really moving from systems of record to systems of action. And, you know, for decades, for decades, enterprise software primarily recorded what happened, right? Very much about after the fact systems of record. Then we added analytics, we added dashboards, we added applications on top. They really added increasingly sophisticated systems of intelligence that helped humans understand what happened and make decisions. The key is humans understood and made decisions, right?

11:39But the operating one remained fundamentally human-driven. A person logged into the software. They looked at information. They interpreted the dashboard or the information. They made a decision. They clicked something. They initiated the action. it. Now, if you think about the AI world, AI fundamentally changes that model, right? Increasingly, agents will participate directly in those decision-making loops. They will discover information, understand the context, evaluate alternatives, make recommendations, and with appropriate controls, and this is important, you know, control and governance and harness becomes important.

12:16With the right, appropriate controls, the agents can increasingly take action. So I think we're really going through a paradigm shift. We're moving from what I would call human-led technology-assisted work towards human-supervised technology-driven operations. And that doesn't mean humans disappear. It's very important. Humans move up the stack. They focus on judgment, strategy, innovation, relationships, and ultimately accountability. And that changes how enterprise software itself means really design, right? Historically, we optimized software for a human sitting in front of a user interface.

12:51That's how UX is designed. Increasingly, and we're doing it clear every day, which is we are looking at software that needs to be agent-friendly. It has to expose trusted data, business context, permissions, governance, and secure ways of taking action. And that's also why I don't believe the simplistic argument that AI makes enterprise software irrelevant. Actually, AI provides extraordinary intelligence, but that intelligence alone isn't enough. You still need context. You need trusted data. You need deep understanding of workflows. You need controls. You need security. You need domain expertise.

13:26You need the ability to maintain and the ability to expect. And ultimately, you need the ability to execute. And that's where I think this next generation of enterprise software really gets interesting in the AI age. What do most executives misunderstand about AI today? I think there are several things executives misunderstand. You know, the first is really confusing the ability to generate code with the ability to build great software. Touched about that. AI is making code and coding dramatically cheaper to produce. That's an extraordinary development, no question. But enterprises and customers don't buy code, right?

14:07They buy outcomes. They want reliability. They want security. They want governance. They want accountability. And most importantly, they want trust. So those responsibilities just don't disappear because an agent wrote the code. That's most important. If anything, they actually become more important. The second misunderstanding is believing that because you can put AI into everything, you should. I think that's going to create a lot of basic investment. So the question is not about where can you use AI because the answer for that is almost everywhere. You're using AI. The better question is, where can AI produce a materially better outcome?

14:44than what you're doing today? And that's a much harder question, right? And there's a third misunderstanding, which is the AI conversation is often framed almost entirely. And I would say this based on all the discussions I'm having. The AI conversation almost always is framed around replacing human beings, replacing people. Of course, jobs and roles are going to change, but every major technology transition has done that, right? This is not new. If you look back at the history of technology, I think the opportunity that I'm most, excited about is something really different, which is now AI is going to replace friction on people.

15:21Think about how much time really talented people spend searching for information, reconciling two systems, investing in exception between ERP system and the CRM system, for example, following up with someone else, manually moving information between applications. That isn't the highest use of the extraordinary human capability. So I think if AI can remove enormous amount of that friction, then people can operate at a much higher level using the judgment, creativity, strategy, innovation, and customer relationships. So I find that future incredibly exciting. But getting that requires real leadership, right?

15:56You just cannot delegate an AI transformation to an AI team and sit in the corner. The CEO leadership team have to be deeply engaged because you're redesigning how the entire company actually works going forward.

16:11Alex Bridgeman: You've talked about software features to business outcomes and moving in that direction towards outcome. Can you explain how you mean and what do you mean by that? Yeah, this is something I believe for a long time. And I think really AI makes it even more important, which is the ability to focus on business outcomes. Customers don't wake up wanting software features. I go and really sit down with a lot of the C-level executives in the supply chain and information technology all the time. And almost all our conversations are done about software features. They have businesses to run. These executives want to grow revenue.

16:52They want to improve margins. They want to reduce working capital tied up. in a new tour. They want to serve their customers better. They want to enter new markets faster. They want to operate more reliably. So our core philosophy at Clio is start with the economic outcome and engineer backwards. And AI makes that discipline critical because today is remarkably easy to build something that looks impressive, right? You can create a fantastic AI demo in afternoon. You've got young product managers who can just wipe code and show amazing things. is helpful because you know what it looks like. But there's an enormous difference between an impressive demo and a demo of whiteboard software and a production system that creates true economic value every single day.

17:38So let's take a simple example. Let's take a late shipment, right? There's an order from a retailer and you ship late. The shipment is going late. Telling somebody the shipment is late is visibility. It's useful, but it's limited, right? You're just sharing information. But now suppose the software understands that shipment in context, which order is it connected to? For which customer? Is it a large retailer or a small retailer? What commitment did we make? How much revenue is potentially at risk? And what inventory alternatives exist? Can I ship it from a different warehouse? Can I redirect something which is due later to a customer who needs it right now?

18:17What are the downstream consequences? So now we're actually moving from data and information to context, right? At this point, intelligence can now determine potential responses. And finally, the system can coordinate the appropriate action across the companies and systems involved. So I think the right progression is data leading to context, which powers intelligence, powers action, and powers outcome. So you're going from those progressions. And when you go from that progression from data to context, intelligence to action, to ultimate business outcome, the outcome is ultimately what matters.

18:54That should become the scoreboard for enterprise software companies, not how many transactions did we process. It's how many, and not even how many alerts we generate. What business results did we change and did we make the customer incredibly successful? That all sounds like orchestration. why do you believe supply chain orchestration represents the next major enterprise software category? Rob, I've spent more than 25 years around supply chain and there is a real structural problem that has fascinated a long time why people never solve, which is you have these multi-billion dollar planning systems that do forecasting, supply planning, inventory planning, and people have spent billions and they keep on trying to upgrade to future versions of those planning systems.

19:45Those systems understand what the business wants to happen, right? Understand the intent, understand the context. And also companies have spent billions on execution systems, ERP systems, transportation systems, warehouse management systems, and so on. And those execution systems are very good at processing transactions. But there is an enormous gap between the two, right? Which is planning understands the intent, but generally cannot execute. And execution systems can act, but oftentimes they don't have the business context to determine the next best set of actions. And so we have a gap, we have a coordination and synchronization gap.

20:27And also the real world never executes perfectly according to plan, right? You have something, there's a tariff related, you know, hold back, you know, at customs or the state of hormones got closed or you cannot get through the Red Sea or there is a strike. So the real world never perfectly acts according to plan. And by the time you get back to planning, it takes days. Let's look at an example. The supplier is late. Okay, so inventory isn't where you thought it was. A shipment misses a connection. A customer changes an order, which happens all the time. Customers change quantities. They delete a line item.

21:03They want a different date. So something happens every day. And so we define this as supply chain orchestration. You need to orchestrate between planning and execution. You need to connect the execution data with the business context and increasing the intelligence. So now let's look at an alternative reality where you imagine a system that understands the complete context surrounding an order. It understands the customer who plays the order, the supplier, the inventory positions, the shipment, the logistics provider, the invoice, and all the economics surrounding it. Is this going to be a late fee?

21:40Do I have to deliver on time? And if I don't deliver on time, what is the penalty the retailer is going to charge you? At that point, when reality deviates from the plan, that system can detect the problem. It can understand its impact, determine possible responses. Now you can coordinate across the entire ecosystem, take action, and learn from the outcome. So to me, that's much bigger than automation. People use the word automation too loosely. For me, automation makes individual tasks faster. Orchestration makes the overall outcome better. And there's another reason I really find supply chains particularly interesting because no single company owns the entire process.

22:20Now, everything is outsourced with multiple participants in the supply chain. Your customer is involved. Your supplier is involved. Your logistics provider is involved. Sometimes the banks are involved. But ultimately, you're not just trying to create an intelligent enterprise. You're trying to create an intelligent business ecosystem because you're getting across multiple supply chain participants. And so this is where AI combined with orchestration makes it increasingly possible. And that's why I believe this will become a very, very important enterprise software category around supply chain orchestration.

22:56Alex Bridgeman: I would imagine as a follow-up to that, that AI would make pulling information across all of those different groups along the supply chain and summarizing it and organizing it into a single clear picture a lot faster and easier than a version of that process, you know, five or 10 years ago too. Yeah, absolutely. Because if you think about any supply chain, just imagine there are probably thousands of signals coming at you, right? From ships, from ports, from, you know, custom solutions, from ERPs, customers in the orders, cancellations. the first order of decision making is which is true signal, which is noise, and be able to filter out the noise and focus on signals.

23:44And then even within the signals, humans can only process so much, right? So you need to then decide what the payoff is. If I go and make this shipment on time for Walmart and I earn the trust, I'm going to get more volume and I can go from the meat section than Walmart or the daily section of Walmart. It's a much bigger payoff than a long-tail retailer, Fritz Sack. It really depends on the decisions you want to make. But codifying those payoffs is what's going to allow the human decision makers to operate at a higher level. So in other words, we're trying to really make the shift from sort of very reactive firefighting mode to proactive decision-making mode in the time you have to make those decisions.

24:30Alex Bridgeman: How should other CEOs think about building with AI and balancing those investments with smart capital allocation, especially as there's more and more models available to use across from open way, open source to the latest and greatest, most expensive models and a whole lot in between? Yeah, they're not slowing down. If you look at the frontier model, they're getting increasingly better and better. But I think CEOs have to hold two ideas simultaneously. The first one is, AI demands tremendous urgency. You need to act. You cannot think. You have to act. But urgency cannot become an excuse for abandoning the economic discipline.

25:13So one thing we're learning with Clio, and this is the last couple of years of being deep into AI, is there's a very different economics depending upon where AI is being consumed. So let me give you a very concrete example. When you're building software, and our software processes billions of transactions in hundreds of billions of GMB flows for a platform. So in our software, we want to access the highest quality frontier models. The way we build, we're using sophisticated models with coding agents, testing agents, increasingly large fleet of agents participating in the software development process.

25:52And in that environment, we're not focused on and not particularly interested in minimizing token usage. We'll just spend the money. Now, suppose I spend significantly more on model consumption, but I'm enabling an exceptional engineer to produce much better software or dramatically increase development velocity. That's an extraordinary economic trade-off. So in other words, we're perfectly happy burning tokens and expensive models at human scale is human scale to build great software. But now you move to transaction time, right? That's completely different, meaning what we put in our product for our customers to use.

26:31Then the question becomes, imagine in our case, AI operating inside high volume, high velocity supply chain processes across enormous number of orders, shipments, invoices, and other transactions. Now the economics matter tremendously. You don't necessarily want to spend or send every problem to the most expensive frontier model. You want the right intelligence for the right problem and the right economics. So sometimes it's a frontier model because the workload is sophisticated. Sometimes it's a smaller purpose-built model. Sometimes it can be an open-weight model, such as the models of Lama family, where we can combine the model with clear domain expertise and procreate the context.

27:15And we have the rags of retrieval augmented generation to provide the right governed Clio or customer context at inference time, right? So at the inference time, we can provide the context and where appropriate, we can specialize or fine-tune the models for a particular class of problems. So then you add intelligent model routing, right? So that question asked is, what is the complexity of the problem? What quality is required? What latency is acceptable? And what's the risk? And that's critical because then you get towards economics and then you accordingly route it. It's a class of what we do called model routing and optimization.

27:53So the principle we use at Clio is at design time, which is when you're building software, they're optimizing the intelligence. At transaction time, when people are accessing the platform, we optimize the intelligence per dollar. And I think that's an incredibly important distinction for CEOs because the answer isn't to use the cheapest model everywhere or avoid using frontier models. and it isn't to use the most expensive model everywhere. What's important is to understand the economics, the outcome you're producing and there's an interesting strategic implication. For example, the raw model intelligence is really becoming more ubiquitous.

28:30But as that is happening, the proprietary context you have as a company actually becomes even more valuable because more context and more indexing you can provide, you don't need the most sophisticated model, right? So everyone has access to the same good intelligence. What is your differentiation? The question for software companies is your differentiation now moves towards who has the best context, who has the deepest domain expertise, who owns the workflow, who has the trusted data, who has actually first-party data or normalized second, third-party data, who can safely take action, and who can learn from the outcome.

29:07So that is a very interesting world for Clio where we have spent years building those capabilities. And so now, as we think like an owner, I simply don't ask, how much are we spending on AI? I want to know what economic value I'll be creating for every dollar of intelligence you consume. And that to me, ah, that's just capital allocation. So in those examples, you're talking about train-offs and the decisions you make around those train-offs, whether it's different kinds of resources, people, capital, time, and then the principle that you bring to bear to actually make that decision. What other leadership principles become even more important in building an AI-native company?

29:54Yeah, Rob, I think about this a lot. And I think AI makes several traditional age-old leadership principles even more important. You know, to me, the first is clarity of purpose. You know, when technology changes this rapidly, there are hundreds of interesting things you could pursue, right? A leadership team needs a very clear true north or the organization will chase everything. So that's first, clarity of purpose. You know, what's in North Star? For us, it's orchestrating supply chains so we can create tremendous economic value by closing the gap between planning and execution. That's a North Star.

30:30and that we believe is a 1.6 trillion market problem and we unpack individual problems like child-backed prevention which are all in the 50, 60 billion per problem we solve. So not so important. The second leadership principle is really people. I've always believed that great companies are ultimately built by exceptional people and AI actually makes exceptional people more valid, not less valid. I think it makes them more leveraged, right? A great engineer equipped with AI can accomplish things that previously required a much larger team. A great product leader can explore and test ideas incredibly quickly.

31:07A great salesperson, which is actually really important, great salespeople are now able to get information across various sources, whether it's Salesforce, whether it's Gong, whether it's different customer interactions, emails, and really leverage AI for account planning and prepare for customers at a completely different level. But there's a flip side, right, which is when execution becomes easier, actual judgment becomes even more important. So getting the right people in the right roles is the main fundamental. And the third is something we talk a lot with at Clio called divine best content, which is on one hand, be proud of what we accomplished, but never become satisfied with it.

31:45Just push the ball race. And Clio has reinvented itself multiple times over the years. And AI now requires us to renew ourselves again. As CEO, I cannot stand on the sidelines and tells everybody to become AI native. I have to understand these technologies. I actually sit down with our developers and they show me every week what problems they've made, what interesting things they're working on. I have to use them. I have to build my own agents. I have to change the way I work. I have to ask better questions. And fundamentally, you lead the transformation as a CEO from the front. And finally, I think great leadership in the Zeta requires what would sound almost contradictory, which is have a long-term direction, but you need short-term intensity, right?

32:29You have a north star measured in years, but when the world is changing this quickly, you have to operate today with enormous sense of urgency. And I don't see those ideas as being in conflict. I think the best companies do both.

32:43Alex Bridgeman: So with that rate of change, how do you allocate your own time and capital across acquisitions, new talent, innovation, markets. It's a lot going on. How do you think about those decisions? Yeah, look, I mean, for us, it's, you know, it's a lot of growth at Clio. And so one of the biggest transitions you make as a CEO, as a company gets larger, not just AI, generally getting larger. And of course, AI has been a profound shift is really realizing that almost every significant decision is ultimately a capital allocation decision, right? Hiring another 100 people is capital allocation. Building a new product is capital allocation.

33:26Entering a new geography is capital allocation. Acquiring a company and doing sort of inorganic growth is capital allocation. So it's really also choosing not to pursue something, especially in AI, when there is a lot of pressure on people to do something, choosing not to pursue something is also capital allocation. So my orientation has been always towards long-term compounding, and we present the Clio compounding blueprint. Every year, we bring it up in our vision summit and we talk about this is a long-term compounding. And I'm not trying to optimize Clio for one quarter or even one year. The question we always ask is, how do we build a company that becomes structurally stronger every year for a very long period of time?

34:10And when you look at companies, many of those are most important assets compound, customer trust compounds, domain knowledge compounds, Data and context for us compounds a lot in supply chain. Product capability compounds. Because in our case, we have a platform and all of the context intelligence we build on the platform. So more signals and more transactions flowing through the platform allows us to build better products, especially in the AI era. Talent absolutely compounds. We have an internal form system and we grow people. Of course, we hire great people from outside, but our real goal is to just grow people from internally upwards.

34:46Go to market capability compounds and culture compounds. So what's really interesting about a major technology transition like AI is that occasionally something comes along that allows you to unlock those accumulated capabilities in ways that weren't previously possible, right? That's one of the reasons I'm so excited about where Clio is today. We've spent years building many of these capabilities and now AI gives us an opportunity to absolutely amplify. So when we're evaluating an investment, the one question I find useful is, Does this investment simply produce a result? Or does it allow us to build a capability that can help us durably compound, continue producing results?

35:27And those are two very different things. The second one can compound, right? And that's alone how I think about acquisitions. So we're absolutely open to acquisitions when they accelerate something strategically important, like technology, talent, data, domain expertise, go-to-market capabilities, market access, better than we can accomplish organically. But we're not just doing acquisitions simply to, you know, get bigger, right? So we really believe in building businesses that can come on our time. And the same is true for new markets also. You always look at another market you can enter as a new industry, getting to channels, getting to Europe, for example.

36:08But growth adds enormous complexity. So if you're not smart, it can distract the organization and take capital away from a much higher return opportunity, which isn't necessarily good for growth. So you also need the ability to say no. And this is where an owner mindset becomes incredibly useful. If it's your money, would you spend it on any initiative? So the whole question then reduces to, you know, this is entirely my own capital and I had a 10-year horizon. Would I put the next dollar here? And I think that question for us at Clio removes a lot of noise because ultimately capital has an opportunity cost.

36:46Every dollar you invest in one opportunity is a dollar you cannot invest somewhere else. So our job as leaders is to continuously move towards and move resources and dollars towards opportunities that we have the highest and the greatest combination of strategic advantage, customer value, and long-term compounding protection. And that's really how I think about capital allocation. You mentioned compounding long-term time horizons. So looking ahead 10 years, what will enterprise software and leadership look like? And what advice would you give founders and CEOs like Alex and I building for that future?

37:29Yeah, Rob, I'm always a little cautious about 10-year predictions per se, because the world has a wonderful way of humbling great people, great CEOs who make those predictions. But I think there are a few things I have pretty high conviction about. First, I think enterprise software becomes increasingly agentic. Agents will participate in virtually every important business process. Agents will gather information, they'll understand context, they'll evaluate alternatives, they'll make recommendations. And I believe increasingly, with appropriate governance and human accountability, they will execute.

38:02So software itself changes. We in Clio call it as autonomous supply chain operations. The ultimate holy grail is a ship rerouting itself. But even without a ship rerouting itself, so many decisions can be made agentically. So I believe the winning platforms won't simply have the best user interface. They will provide trusted data, rich context, domain intelligence, governance, security, and really the ability for humans and agents to work together and take action. And human beings will increasingly move towards the things we are uniquely good at, which is strategy, judgment, innovation, relationships, leadership, accountability.

38:43But if I were talking to a founder of a sample company today, I actually wouldn't tell them to start with, what's an AI strategy? I would start with a much more fundamental question, which is what important and enduring problem are you willing to spend the next 10 or 20 years of your life solving? Because the technology will change. The models, the frontier models that are so is that over today, won't potentially go obsolete. The architectures will change. Today's breakthrough will become tomorrow's commodity. But important customer problems endure. So I would recommend to founders, the CEOs, to pick an important problem, get extraordinarily close to the customer, understand the problem, develop deep domain expertise, and then start in this AI age, build appropriate data and context.

39:29That's your own, right? Create capabilities that become stronger with scale. So with scale, you need capabilities that just compound and become enduring and actually create an amplifying effect. Surround yourself with exceptional people. And when you do that, whatever the best technologies available, use it to deliver extraordinary outcomes. And that's really how, you know, I think about Clio. and we've been building this company for a long time. And we've gone through multiple technology generations, right? And this happened over time. And we've had multiple chapters of reinvention. But I think AI is probably the biggest opportunity we've ever encountered.

40:09And one of the things I've learned is that the job as a CEO isn't to perfectly predict what the world will look like 10 years from now. The job is to build an organization that's capable of renewing itself for the next 10 years. So I would say have a long-term North Star. Surround yourself with extraordinary people. Stay incredibly close to your customers. They will teach you or they'll tell you if you're doing something incorrectly and reward you if you're doing it right and show you what to do next and be willing to challenge your own assumptions. Be humble. Move with tremendous urgency because the world is changing and especially in the world of AI, you know, a mistake becomes an advantage and a benefit because you want to learn from that quickly.

40:48The systems learn. And so the key is really maintaining the discipline when everybody else gets caught up in the hype and never confuse technology with the algorithm. Because ultimately, great companies aren't built around a technology cycle. They're built to compound through technology cycles. And that's really what we're trying to build at CLEAN.

41:13Alex Bridgeman: Mahesh, this has been awesome. Is there any question we should have asked you that we haven't so far? I think you touched on pretty much everything. I would think at this day, just to reinforce, I think that the key really is AI is such a fundamental shift. It's probably a hundred year shift. And so I think having an incredible sense of urgency is the most important thing because you're going to learn. At the same time, you need to have a north star. And at the same time, you should lead from the front as CEOs. I think that's where sometimes people, I think, make mistakes when they delegate to a chief AI officer.

41:50I'm not saying there's anything wrong with having a chief AI officer. The CEO should learn what problem they're solving and see what AI can do for it versus delegation. So I think that's the one thing I'll leave with, which is get actionable, get in the thick of things, learn, make mistakes, and recover faster than a competition does.

42:12Alex Bridgeman: Amazing. Mash, thank you. Always love getting to chat, and especially on the podcast. So thank you for sharing more of your time. Awesome. This is fantastic, Alex Rob.

42:25Alex Bridgeman: Thank you for listening. I hope you enjoyed today's episode of Think Like an Owner. If you enjoyed the show, please consider leaving us a review and telling a friend to help more folks find Think Like an Owner. For full episode transcripts on our weekly newsletter, please visit our website at tlaopodcast.com.

42:50Transcription by ESO. Translation by —