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AI Founder of the Month: Sriram Muralidharan 10-21-25
00:14:16
In this episode, Sriram Muralidharan, shares insights on the evolving world of enterprise AI, the leadership principles that drive adoption and trust, and the importance of balancing speed, quality, and purpose in innovation.
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Transcript20 lines
- [00:00]
A
This is Scott Becker with the Becker Business and the Becker Private Equity podcast. We're thrilled today to be joined by an absolutely brilliant co founder and CEO of an enterprise level AI startup. We're joined today by Sriram Mr. Lee Dharan. And Sriram is a brilliant leader who spent time at both Tesla and Kumir and several other startups. He is this month's AI Startup of the Month and at Becker Business and Becker Private Equity. Shiram, we are so excited to have you on. Can you take a moment and tell us about your background and what you do?
- [00:36]
B
Sure. Scott, thank you so much for having me on your podcast. Love being back. So for me, I mean, I started my journey at Tesla. I worked on the Model X program, then I spent over a decade with various hypergrowth startups in the Valley, building and scaling enterprise AI products to millions of users. And for me, leadership is all about not it's not about deploying AI, but having the conviction to pursue greater outcomes. When you pursue greater outcomes at scale, you create an environment for people to embrace change and not fear it. And I like turning chaos into organized chaos and eventually into scalable motions. And that's really how I ended up in the world of AI. I've been there for like close to a decade now.
- [01:21]
A
Amazing. So you're an early starter at it. You're also near and dear to my heart that you're a Midwestern college person, University of Wisconsin, so a badger as well. Take a moment and tell us, where are you seeing AI create the most meaningful business impact? Where are you seeing this have the impact and where is it overhyped?
- [01:44]
B
Sure, Scott. I mean, candidly speaking, Scott, I mean, I think enterprise AI is struggling. I mean there's a great disconnect between what we see as in terms of optics and how the internal metrics really are. I mean at scale, we have to start measuring adoption correctly. For enterprise, it's all about quality and reliability. Nothing else matters more. And we're moving from AI as a feature to AI as a foundation of how companies roll. And that means how the AI becomes foundations of how companies operate and compete globally. And to me, I mean, once you start deploying AI with intent for AI adoption, the conversation shifts from like what you call is it operating models of today to like new operating models. I mean, we starting to see that in like customer service. A good example is we went from like chatbots to context aware, like chatbots to personal assistants. I mean, if I can think of an example, I mean in the banking space, let's say when you get connected with an online chat assistant today from your chase or your BofA app, I mean the first thing you'll figure out is have you lost your card? Have you had fraudulent transaction in the past? And if those are the cases, I mean the assistant takes actions in terms of freezing the card, like I mean could send you a new card. And what that means is, I mean from me as a bank exec, I can rely on IT users. I mean as a banking person, I mean as a banking user, I can trust it. And it also scales faster. Then suddenly you have a whole new operating model which reduces a lot of time and has got new business impact.
- [03:36]
A
Talk about, you mentioned on the enterprise side that it's been a little bit slower, having the impact that people think it should have. But the flip side is generally you're seeing it touch everything in different ways. Talk a little bit about the difference in the enterprise side and how this will sort of evolve, would evolve in waves that we see more and more AI involved in everything or what do you see there?
- [04:00]
B
I think we'll move from like what you call AI is a single point tool to more sort of context awareness systems. It becomes embedded into daily workflows. That's how I see on, on the enterprise side. The chat assistant which I gave you is a very good example of that. And we'll see more and more outcomes. I mean another example I could give you about is the Waymo. I mean we went from taxis to Ubers. The user experience of a Waymo is pretty natural, as natural as possible. So that's where we see the world moving towards.
- [04:37]
A
Thank you. And what are you most focused on and excited about currently?
- [04:42]
B
I think, I mean, I see it in two ways. I believe that the most context aware systems and the human AI collaboration layer will win. The next will be the next set of AI products or companies coming out to break it down. I mean on the technical side, I mean, I believe, I mean like I said, AI has to understand context at a very deep level, not just what like chat assistant or chatbot. So what, what that means is AI is to understand what someone is asking, which is and how, why they're asking for it. Have they tried this before? And what does success look like to them? And that means the strongest AI products will have memory, it'll have the ability to learn from every interaction, it'll have a judgment of its own. And the chat assistant which we talked about is a very good example. I mean that brings a very new operating model. On the human side, how Does AI naturally embed into human life? Early on, AI products were designed for technologists. So we had some adoption challenges. But now they're starting to feel more natural extension of how they already work and not foreign technologies which they have to learn. And companies that figure out how to embed intelligence into workflows and make it feel like using less of AI and will define the next decade of how AI is going to move.
- [06:18]
A
Right. So it feels more just part of the natural workflow than particularly pushing a button to do AI this or AI that, more just naturally part of the workflow. And that makes great sense to me. When you think about leadership and organizations adopting AI, how do you think about that and what does good and great leadership look like as organizations try and adopt and utilize AI?
- [06:43]
B
I mean it's about what you call is creating a psychological safety for change. I mean, I should feel comfortable trusting AI. That's how leadership is built around in the space of what you call in the world of AI. See, most organizations today, they don't fail because the product or the AI things that don't work because they fail, because end users are not willing to adopt it. And I'm not able to trust it. I'm not going to adopt it. It's as simple as that. I mean, if I go sell something to my mom, my mom is saying, would this even solve my problem? That's, that's how they would see it. So if I have to break it down, I would break it down into like the three parts to leadership here. Translation, trust and transformation. In terms of like translation. As a leader, my job is to translate AI products and solutions from buzzwords into something tangible, more humane. I mean, how does, what does voice AI could do for a call center admin who's at the front line of fronting customer calls? And you will have to make invisible things visible. Like how do you save time? How do you sort of prevent burnout? How do you prevent, like reduce admin burden? How do you sort of add value to users? And the next is the obvious trust factor. Like I have to build credibility by being transparent about the limitations of AI. If you over promise AI perfection, you're going to lose people. And if you show them progress, if you show them iterative progress, they'll come along for that. The key is understanding what those most important measurable outcomes are and show them the pathway towards those outcomes. Now you have customers then who will trust you and will also understand you as a leader. And finally comes the transformation. I mean, I strongly believe to leadership I mean like I said in the earlier part of our conversation, it's not about deploying AI tools, it's about continuously learning from within the team, from your customers. And that's how organizations thrive. And we should not see AI as a one time implementation, but it's more of a living system that evolves with your user workforce. So it comes down to these three things. You have to be able to translate, you have to customers and you'll have to build trust and you'll have to transform how this operating models work.
- [09:16]
A
Right. You ultimately have to make the transformation, you have to have trust and you gotta be able to translate it both into the systems and with people so that people understand what we're trying to do and then actually make those transformations. Talk a little bit about sort of the investment world and AI. Obviously this year a tremendous amount of venture capital has gone into AI related companies and uses. What do you see going forward? Do you have a feel for that? You see our investors are looking at AI driven vehicles.
- [09:46]
B
Being a foreigner, be careful at this. But I think investors are tired of just another tool. I mean they want purpose built AI which can solve problems at scale and not and offer pathways to solve problems. I mean like to my earlier point, it has to be around natural embedding into the workflows rather than feels artificial of using an AI. I mean that's, that's where investing investors minds are and back it up. I mean in terms of customers, customers they're more open to spending on AI. So which means it's such a natural sort of transition where investors are also willing to spend on and they're taking their hedges on various companies at that point?
- [10:28]
A
No. Fantastic. And when you look at attracting great AI talent and top tech and data talent. Any advice there or thoughts there on attracting the best and brightest?
- [10:41]
B
I mean that's a very good question, Scott. This is my personal belief. I believe AI talent of today, they just don't want just a paycheck. I mean they want a purpose, they want extreme ownership. So I mean what I tell my peers and when we talk about this, we want to focus on the mission and we want to tell the prospective hires of how it helps them. Not just the technical details, but how much how their technical work kind of translate into more impactful outcomes for the end users and to back, I mean to do that, I mean we have to master, we have to unlock mastery as a culture in. Obviously retention becomes a challenging problem. So I mean retention happens, I mean internally within a company. If an employee as an employee. If I feel that I'm growing faster inside corporation A versus where I could go out and grow elsewhere, that means, I mean we just have to kind of give them the visibility, be transparent, give them visibility into the big picture, room and space to learn, make mistakes and also give them an opportunity to experiment. And as a result, I mean the best teams today will come out building internal flywheels of curiosity. People who constantly push either push each other to learn and shift. I mean a good example is the high performing sports team. Like there's a lot of learning which as what you call business leaders, we can learn from high performing sports teams. And one last thing, Scott, I mean here, like in the Valley, I mean I've seen a lot of emphasis on speed while sacrificing quality metrics to just to ship faster. And it's, it's a common theme with a lot of hypergrowth startups in the Valley. I mean if speed becomes your company's moat, then you don't really have a moat. Speed should be part of your culture, not a feature of what you're offering to customers. Top leaders who come out of this, the next set of execs will come out of this, will focus on a balance between quick iteration and what real outcome looks like for customers.
- [12:54]
A
Thank you. Talk a little bit what industries you're working with currently and about how you guys work with customers and clients and what you're doing.
- [13:03]
B
I'm working on enterprise and I'm pretty focused on embedding workflows. That's all I could say about it today because we still kind of in the stealth space, but I'm working keen on building predictive intelligence for enterprise.
- [13:20]
A
Fantastic. Sriram, it is amazing to visit with you. Before I let you go, is there anything else that you'd like to share with the audience today that we didn't get a chance to share? And what a pleasure to visit with you. Brilliant, brilliant leader. Thank you for joining us. Anything else you'd like to share with the audience today?
- [13:39]
B
I think, I mean I'm going live with our startup pretty soon so folks should keep tuned to our LinkedIn and see what we're doing. What is buzzing with us. That's what I want them to see. Pay attention towards.
- [13:52]
A
Thank you so much. Shuram is our AI leader of the month for the month. A brilliant leader. Shiram. I can't even tell you how much I appreciate you joining us today on the Becker Business and the Becker Private equity podcast. Brilliant. And thank you. And keep working on hard, hard things which is what you're doing. Remarkable what you do. Thank you for joining us.
- [14:11]
B
Thank you so much. Cartoon.