Episode Categories:
Resources:
Join the discussion: Got something to say? Let us know here
Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Connect with Jordan Wilson: LinkedIn Profile
Try Our Free AI Prompting Course: Register for our free Prime, Prompt, and Polish AI Course!
Unlocking AI's Potential: The Missing Link in Your Company's Strategy
In the world of AI-driven advancements, nearly every company is eager to harness the transformative power of artificial intelligence. Yet, despite substantial investment in technology and platforms, many organizations find themselves unable to achieve the full potential of AI. The missing piece? Operational muscle. Dive into this overlooked component and learn why building it is crucial for your company's AI success.
Understanding Operational Muscle
The concept of operational muscle encompasses the intangible aspects of operating AI at scale. While much of the focus today is on technology, such as platforms and models, successful AI integration demands much more. To truly leverage AI, companies must focus on building education, teams, and culture tailored for it. This involves establishing a center of excellence where researchers, scientists, engineers, and data scientists can collaborate effectively, each bringing their unique expertise to the table.
The Critical Role of People and Culture
When implementing an AI strategy, many businesses fixate on the process and technology. However, the people and culture aspects are equally vital. Effective AI deployment is not just about the hardware and software; it's about the humans operating them. Organizations must focus on enabling their teams through education and creating a collaborative culture. By doing so, companies can ensure that AI becomes an integral part of their operations, rather than a siloed initiative.
Pilot Projects: The Path to Big Wins
Success in AI doesn't come from trying to revolutionize everything at once. Rather, it's about starting small and iterating. Whether simulating half a room in a healthcare setting or analyzing a single aspect of a manufacturing process, beginning with manageable projects allows teams to learn and refine their approaches. Over time, these small victories compound, leading to significant competitive advantages.
Mechanisms of Education and Teamwork
A diverse team is central to building operational muscle. The future of AI requires professionals from different fields—developers, subject matter experts, and more—to collaborate. This necessitates new educational mechanisms that prepare and empower teams to work with unfamiliar technologies and people from diverse professional backgrounds. As AI continues to evolve, so too must the educational strategies that support its use.
The Future of AI: From Generative to Agentic
While generative AI like chatbots grabs attention, the future lies in agentic AI, where models not only understand but take action. This transition underscores the need for strong operational muscle, as AI systems will increasingly perform decision-making roles traditionally held by humans. Businesses need to prepare for this shift, ensuring their teams are equipped to integrate these advanced systems in ways that enhance—not replace—human capabilities.
Conclusion
Building operational muscle is not a luxury but a necessity for companies looking to thrive in an AI-driven world. By focusing on people, culture, and iterative growth, businesses can unlock the full potential of AI technologies, ensuring that they remain competitive and innovative. As AI continues its rapid evolution, the companies that invest in developing their operational muscle today will be the leaders of tomorrow.
Topics Covered in This Episode:
- Reasons for AI Failure
- Operational Muscle in AI Strategy
- Introduction of Andy Lin from Mark three Systems
- Importance of People, Culture, and Process in AI
- Need for Cross-functional Teams in AI and Digital Twins
- Challenges in Enterprise AI Adoption
- Generative AI vs. Digital Twin Applications
- Strategies for Starting Small with AI Projects
- Importance of Explainability in AI and Digital Twins
- Role of People in Multi-Agent Systems and Digital Twins
Podcast Transcript
Jordan Wilson [00:00:16]:
There's so many reasons why AI doesn't work sometimes. Right? There's so many use cases, so many, easy, you know, seemingly easy ways to just gain more productivity, to get more things done, but why does it fail sometimes? You know, today, I'm excited for a conversation that we're gonna be having about operational muscle and and what that means and how I think that might be the missing key to every company's AI strategy. Alright. So I'm excited for this conversation. And if you're new here, welcome. Thank you for tuning in. My name is Jordan Wilson, and this is Everyday AI. We are your daily livestream podcast and free daily newsletter, helping everyday people like you and me not just keep up with what's happening in AI, but how we can all actually leverage it to get ahead to grow our companies and our careers.
Jordan Wilson [00:01:07]:
And that starts here on this podcast and livestream, but it's literally starting here at NVIDIA GTC. So, yeah, if you're listening on the podcast, we are technically right here at GTC, with NVIDIA. I think one of the most exciting, tech conferences in the world. So bringing a lot of great, NVIDIA partners, to you on the podcast. So, enough about that. You know, if you haven't already, please make sure you go subscribe to the newsletter, youreverydayai.com. We're gonna be recapping today's conversation and a whole lot more. But enough chitchat.
Jordan Wilson [00:01:39]:
I'm excited for today's guest. So please help me welcome Andy Lin, the VP of strategy and CTO for Mark three Systems. Andy, thank you so much for joining the Everyday AI Show.
Andy Lin [00:01:48]:
Thanks for having me. Appreciate it.
Jordan Wilson [00:01:49]:
Alright. So can you tell everyone a little bit what is Mark three Systems? What is it you all do?
Andy Lin [00:01:53]:
Absolutely. So, Mark three, we're an NVIDIA lead partner, and we specialize in working with large organizations, including Fortune 500 companies, industry, research institutions, universities on building their AI, Gen AI, modern HPC, and digital twin centers of excellence.
Jordan Wilson [00:02:09]:
So, yeah, we're gonna dive into all of those different things, but I I wanna start at the top. Tell me about this this concept of of operational muscle and and how this can really be, a missing piece for enterprises that maybe are still struggling with AI adoption.
Andy Lin [00:02:24]:
Yeah. So operational muscle is a term we sort of termed, to talk about really the intangible aspects of operating AI at scale. Most talk in the industry today is all around technology and models. Right? It's the idea of platforms, GPU, software, how you train models, etcetera, PyTorch. But, actually, not a lot of talk is is thought about education, around how you build teams, how you build culture, specifically for large organizations that are looking to do that if this efficiently at scale. When we talk about the center of excellence, it's the idea of being able to have a centralized platform, right, anchored obviously by our partners at NVIDIA. But to be able to enable researchers, scientists, engineers, data scientists, folks training models to be able to use that tooling in a centralized way, but be able to maintain individuality and to focus on their work first and foremost. Because everyone's working on a different type of problem.
Andy Lin [00:03:18]:
Everyone's doing their life's work completely separately. You really can't slow these folks down. So how do you bring these two things, know, sort of perfectly in line with each other? It's actually a really tricky thing. And then when we talk about operational muscle, just to bring it back to that term, it's all around the idea of being able to enable the people process culture part of the equation to make sure you're at equilibrium with the technology. So I think when people, you know, are are dissecting AI and and and how they can make
Jordan Wilson [00:03:45]:
it work in their organization, maybe the process part of those those three things, pops into their mind, but maybe not necessarily the people and the culture. Explain why those things are maybe just as important as the technical side.
Andy Lin [00:03:59]:
Absolutely. Yeah. I mean, we've got it's funny. You know? People is actually probably the most important part of the equation when enabling an AI strategy. Right? You're talking about artificial intelligence, but it's actually the human part of it and how to build teams and how you enable a a mechanism to distribute education in a practical way, I think, is really the key that will determine success or failure. You know? When you talk about an organization, it's what I call the idea of me and us. Right? The organization that do it the best are when you have a community of researchers and data scientists and folks training models. And then on the other side, you have the technology teams who are focused on enabling platforms to serve those folks.
Andy Lin [00:04:39]:
You have an equal amount of me me versus us for each of those each of those groups. And, to be able to enable that is really key. To be able to talk about specific teaming aspect of it when you talk about people and culture is that when you talk about AI and digital twins, more than ever before, in order to enable a successful strategy at scale, you have lots of different types of people working together. I read a study that said, if you're trying to, for instance, build a digital twin to simulate a factory, simulate a hospital, whatever that might be, you need 10 different types of people all working together. Think about it. It makes ton of sense. Right? Three d artists. You've got machine learning folks.
Andy Lin [00:05:15]:
You've got developers. You have the subject matter expert maybe in health care if you're trying to digital twin a smart hospital. You have the nurse. You have the physician. These are people in the past that would never have anything to do with each other. Right? Engineers work with engineers. You know, nurses work with nurses. Developers work with developers.
Andy Lin [00:05:31]:
But because of the idea of enabling scalable intelligence to be able to frictionlessly move anywhere through these mechanisms of AI and digital twins, These groups have to work together well. And I tell everyone from the intangibles perspective, regardless of, you know, if you're a teenager just getting into the space, if you're a professional, if you're someone looking to reinvent yourself, you know, you need to be comfortable working with people that have nothing to do with anything that you've ever worked for in the past. And this is a dramatic change from the past, and the organizations that I've seen do this well, you know, through programs like hackathons or getting folks together to solve problems in this way by using these mechanisms are the ones that are ultimately successful. Do you
Jordan Wilson [00:06:12]:
think that maybe one of these ongoing challenges, at least when we talk about, enterprise adoption at scale, is because people maybe view AI, you know, unless your company's been using it for many decades. But when we think of, you know, generative AI and large language models, I think sometimes people just think of it as a a personal, like, personal productivity tool, and they don't necessarily always think about how can this transform our department, how can this change the future of work for our sector. Right. Is that something that you see a lot people maybe just look as gen like, at generative AI, at least, you know, at at at a smaller scale as, hey. This is about personal productivity. Maybe that's why it gets siloed?
Andy Lin [00:06:53]:
I do. I think, ChatGPT has done a lot of good and perhaps not so good things as far as sort of setting the idea of what it is. Right? And I don't mean ChatGPT specifically. I just mean the idea of of chatbots Mhmm. And agents. Right? They are very helpful. Right? Obviously, the bill the ability to type in what you want and then have a coherent human like response to solve your problem or to give you an answer is actually really helpful. But around generative AI and LLMs, the idea is to be able to make sense out of any form of unstructured data in ways that you haven't been able to make before.
Andy Lin [00:07:26]:
So conversational AI is one example, but for instance, you know, we do a ton of work, specifically in the health care life sciences space, where the idea is you can, comb through, you know, proteins Mhmm. And make sense and discover new drugs and find new precision based therapies in ways that you would have never been able to do before using DNA and RNA strands, etcetera. That's just one example of a way that's it's gonna be utterly transformational and affect millions of lives that has absolutely nothing to do with personal productivity. So it is good in the sense that it brought a lot of attention, obviously, in the space, and people understand where it's going. You see what's happening specifically with NVIDIA and the ecosystem around agentic AI, which is really the idea of the next chapter beyond generative. Generative is the idea of basically being able to create things like words or pictures or based on a lot of unstructured data. Right? Agentic is really the idea of having an agent essentially to use those as mechanisms, but to be able to take action like any human would in a automated way depending on how you want it and to be able to scale frictionlessly because, after all, as AI, it's an agent anywhere in the world, anywhere you might need it and it's formed as far as within your business or your enterprise or your industry or your research. So it's pretty pretty exciting, as far as the possibilities, that that may lie ahead for us.
Jordan Wilson [00:08:40]:
Sure. So you you you gave this great example, you know, talking about digital twins and, you know, I think you said that a a study showed you need at least, you know, five to 10 different types of people. Right? So that really explains, you know, maybe how the interpersonal, might change, you know, when you use AI to scale. What about intrapersonal? Right? Like, that's something I think about a lot and, you know, especially as we go into, you know, agentic AI where, you know, we're giving these AI systems agency, right, to make decisions with our data. And a lot of times, you you have, you know, mid career professionals that are like, wait. Those are the decisions I've been making. Right? Like, agency is something I enjoy. So, you know, even, you know, internally, how should business leaders to really get that that good fit between, you know, people, culture, process, how do we need to be, you know, changing how we think even about work?
Andy Lin [00:09:35]:
That's a really good question. I I wish I had a really great answer for it. And that's I think at the end of the day, one of the keys in the space is you need to empower the people who are actually building these things to make them part of the solution. Right? I think a lot of the fear from society about these agents around doing work, right, is you're afraid that somebody's gonna come over the top, right, and force an agent down. Right? And I think, you know, if you just think about as a human, you know, if you have a team, right, how can they be part of the solution to help you create agents to amplify what they're actually doing in the marketplace. Right? Make them part of the solution on actually building agents to actually amplify the pieces of work that they don't like Mhmm. So that they can focus on the pieces of work they do like and that they're great at. Right? It's almost like, you know, I wanna build a twin of myself.
Andy Lin [00:10:31]:
Right? You know, literally a twin. Not a digital twin, but literally a twin. Right? So I have Andy on here, and I have Andy too here. Right? What are the things that Andy too doesn't like? Andy too doesn't like things like doing expenses and doing all these things. Right? Andy one does like working with organizations to help come up with strategies and working with our team to build things. Mhmm. Right? So how can I create an agent to be able to do those things? And I think you're right. You hit the word right on the head.
Andy Lin [00:10:57]:
Agency. Right? You wanna give people agency to help them craft the strategy, to be able to make that happen. And I think organizations that think about that, you know, from a good leadership and a good sort of organizational management standpoint are are gonna be the ones that are gonna be successful, just like with anything else. Right? So it's that's a really good question.
Jordan Wilson [00:11:15]:
You know? And and and getting back to this, you know, the concept of operational muscle, which which I love. Right? Building muscle, you know, it usually involves first, you know, a little pain and being uncomfortable, right, before you can get those that repetition in and actually be stronger. You you know, in your experience so far, you know, working with, you know, different clients and customers, what are some of those, you know, initial things that hurt clients when they're trying to fully implement it and, you know, that they they really have to get through those reps and then finally, they can see the gains on the other side. What is that struggle that may cause pain in the beginning? Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on GenAI. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft, and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use GenAI. So whether you're looking for chat g p t training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do.
Jordan Wilson [00:12:39]:
Go to your everydayai.com/partner to get in contact with our team, or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GenAI.
Andy Lin [00:12:58]:
I think the biggest thing is just an inability to explain maybe your first few experiments upstack. I think one of the things that we help a lot with is helping our organization that we work with set the proper expectations internally, that it's going to be a long road. Right? But if you don't decide to get on now, you're not gonna be able to catch up when your competitors already had the game in a year. Right? That's what I love about the space. It's so all about sweat equity and earned equity. Right? The amount of work you put in is how far ahead you're going to be even if you don't necessarily get to the end of the road right away. Right? If you train a model that's 50% effective, you may say, oh, man. What a waste of time.
Andy Lin [00:13:38]:
Right? But, obviously, over the next couple years, your model to predict pricing, to to do forecasting, or whatever that might be, may get up to 95%, but you have to go through the reps in order to do that. So I think the pain is for organizations perhaps that don't set the right expectations, being able to have to, you know, explain that process. We're actually going through a similar part in the ecosystem right now, in my opinion, specifically around digital twins. Right? Because I think in the long run, what's going to happen is everyone is going to have an AI center of excellence, digital twin center of excellence. They're gonna talk to each other, get to communicate with each other. Because if you think about it, what's the goal? The goal is to build scalable agents, models, experiences, right, that simulate, you know, some expertise in the organization that's ultra scalable, that can go anywhere at the top of the hat. What is scalable expertise intelligence? Right? Right now, it's primarily been driven by LLMs and generative AI, which is the brains and the ears. Can I talk? Mhmm.
Andy Lin [00:14:34]:
Can I understand? Can I listen? The next chapter is all about the eyes. Because if you think about it, people are visual. We all exist in the real world. Yep. You know? But workforces are hybrid now by sheer nature. They're geo dispersed. So how do you create a mechanism to have fruitful conversations about physical spaces when people are spread out? You have to have ways to be able to create a replica of how that actually works in the world. If you look at what NVIDIA's talking about, they're talking about physical AI, they're talking about robotics, they're talking about agentic AI.
Andy Lin [00:15:06]:
These are all the alignment of these items. Now to tie it back to what you originally asked specifically on operational muscle, these things don't just happen because you want it to happen. Right? We all wish we could get to the end of the next five years and then, oh, it's working. But but but that's not how it works. Right? You have to have people to build these pilots, to learn what you don't know. Right? In digital twin side, it's all around creating a three d representation of your store, of your factory, of your school, of the human body. Right? And then being able to iterate that over time to improve the fidelity and the quality of the digital twin and then mix in AI to be able to help you build it faster, to be able to present that digital twin to a what I call a a regular person, right, who can just use it. Right? If you think about maybe my mom or something like that.
Andy Lin [00:15:54]:
Right? Can they use digital twin to figure out how to plan their next trip? Can I can I on the enterprise side, right, can I present it to a facilities planner to be able to plan what my next store looks like? Right? So you have to be able to mix in all of those things, and it just doesn't happen. Right? You know, it it starts a day at a time, you know, creating a or or, you know, if you're gonna if you're gonna switch to a hospital, how do you start? Right? And and these are this is something we're working on, you know, pretty significantly out out in the field today. You start with half a room. Mhmm. Right? You start with a bed. Right? You make the bed great. You you show people what the bed's like. Okay.
Andy Lin [00:16:29]:
The bed's great. Okay. Build out the other half of the room. The other half of the room, pretty great. Okay. Then then you pretty soon, you have a hospital. Right? You don't say, hey. I'm gonna create a hospital and it's gonna be ready in three months.
Andy Lin [00:16:41]:
That will not work. So because because going through that process, people understand what you're trying to do, and they have ideas, and they get bought in tying it back to agency to be part of building what that looks like. And it creates this sort of positive feedback loop that's entirely powered by people. You know? So
Jordan Wilson [00:16:59]:
Yeah. Andy, I like, I I I like how you just broke down, the digital twin concept a little bit because I think sometimes even myself, right, when you think about digital twins, you know, you're like, okay. It's it's a scale. It's massive. Right? It's it's being able to simulate, you know, trillions of of data points instantly. But you said, let's start with one path. Right? So it's it's it's really turning, you know, this concept of of digital twins and scaling with AI on its head a little bit. You you know, I'm interested.
Jordan Wilson [00:17:28]:
Like, why why that approach, you know, starting with just one bed or half of a room when it seems like, you know, the the thing that people are most attracted to is like, oh, yeah. Now I can, you know, like, Earth two last year at at you you know, at the, at at at the keynote, right, people are just thinking huge, huge, huge. So what's the benefit of, you know, a digital twins that's small, small, small?
Andy Lin [00:17:49]:
Absolutely. So it all it ties back to operational muscle and knowing what you don't know. I think, Earth two is amazing, and don't get me wrong. Right? I'm I'm the biggest fan of I was blown away by that last year. Yeah. But that that's like if you think about it, that's like for an organization, that's like the equivalent of being years out. And it gives you a great target. And NVIDIA is the ultimate company of being visionary in the space.
Andy Lin [00:18:11]:
Right? Without them, we none of us would be able to do what we do. But to be able to execute, right, to get down that road, and this is quite frankly is part of our job, is all about baby steps to be able to get to or to. When I say digital twin to an organization, a room full of 10 people, I'll get 10 different answers on
Jordan Wilson [00:18:28]:
what I mean.
Andy Lin [00:18:29]:
Yep. How do you create consensus? Right? The way to create consensus is to build a micro version of what that looks like, a bed. Right? Cloud formation. Right? Part of the body, small organ. Show that to all the people, have them comment on it, and all agree on, yes. That's what I meant by digital twin. And then from then, if you think about it, the rest of it is just 10,000 iterations of that small piece. And I think where it goes wrong is when somebody tries to build the whole thing without consulting the consensus, it only takes a few people in that organization to talking about people process culture to render the entire thing not successful.
Andy Lin [00:19:09]:
So being able to take the pilot and the iterative approach and focus on half technology, half people process culture, it's not only a good way to do it. I think it's the only way to be able to preserve the people consensus part of this, right, in the organization. And I think, you know, I I joke a lot of times. You know, I I love the idea of r of the possible and the what ifs. Right? But if I hear a fifth what if in a meeting, I'm I'm out. Because because it means that they don't really understand what it's going to actually take to grind and iterate to that process. Now if they understand and they they understand the idea of starting small and running a pilot, and and I can tell that they're really built, you know, to be able to sustain the road with us, you know, we're all in with them. And I think the cool thing about right now in this space is that the folks that are making the first steps of which, you know, we have lots of great examples in distribution and manufacturing and health care and in other fields, these are gonna be the leaders in three, four, five years because they decided to make the steps now.
Andy Lin [00:20:09]:
And I think, that was particularly excites me. And in every single one of these organizations, specifically, you have leaders and you have people bought into this process who understand what the road's going to be. And, I'm extremely excited, obviously, you know, with some of these announcements at GTC with NVIDIA around physical AI, AgenTek AI. Right? You can also tell, obviously, NVIDIA is seeing this thing come together just like we have and we believe in the last few years.
Jordan Wilson [00:20:34]:
So, you know, one kind of common thread that I'm picking up on here is this, concept of explainability. Right? You you know, and and really building that operational muscle and and getting those, you know, as an example, the the 10 different personas, involved is maybe starting small and starting with something, that's explainable. Is that the case? Right? Because, yeah, a lot of times, you know, companies that maybe are sitting on mountains of data and, you you know, they've had data for many decades but haven't gone all in on AI yet. Maybe they just wanna do the whole thing at once, you know, overnight. They wanna see transformation as quickly as possible. Is it maybe just as important to make it as small as possible and to really, you know, be able to, kind of, uncover the veil of, explainability, so to speak?
Andy Lin [00:21:23]:
Absolutely. You know? And I think, like, I get quite frankly afraid when somebody tries to go big too big too soon, just like you were you were mentioning. But, explainability is a really important part of the equation, and it's an area that's quite frankly unsolved. You know, there are a lot of companies that do nothing but focus on the explainability of models. And, also, to a certain extent, I think, you know, you're gonna see sort of the explainability of digital twins simulations. Also be a field that's gonna be emerged as that field grows going forward. It's to be able to explain to people, especially when there's an error. Right? You know, you have some a model that's 98% accurate, and you may have a two percent error.
Andy Lin [00:21:56]:
Like, why did that happen? Even though we all know maybe humans have an you have a 10% error rate. Right? Yes. But But you can attribute, okay, it's that person. Right? It's John who made that mistake. Right? I hate to put it that way. But if you think about it
Jordan Wilson [00:22:08]:
It's always John.
Andy Lin [00:22:08]:
It's always Yeah. That that John. But, you know, if you think about it, like, I think people are trying to come to some sort of consensus or sense about what happens when that happens in an AI world or what happens out in an in a simulated world effect, around digital twins. So, yeah, I'm honestly, I'm kinda fascinated to see, you know, where that goes. Obviously, that ties into, you know, things like, you know, governance and regulation, I feel like maybe none of us really have the answer to yet. But, yeah, it's definitely something to to take a look at. And I think for that reason also, it's even more important to build operational muscle, to start small, to build a pilot, to get everyone on the same page, to go to iteration two, to make sure everyone's on the same page. Because anytime you could have a consistent community, it makes the idea of explainability that
Jordan Wilson [00:22:56]:
much easier. Right? You know? Yeah. That's that's a great point. You know, another thing, Andy, I'm I'm curious about is, you know, building up this this operational muscle and and the people, the culture, the process. As we you know, obviously, the buzzword in 2025 has been, agentic AI. And, know, when you couple that with, you know, digital twins, right, and and multi agent environments, how do you have to or how can you protect, almost that that that people culture process side when sometimes, you know, the the the more and more that we get into this AI, right, specifically, you know, multi agentic systems, you know, even digital twins, it almost seems like so separated, from from some of those, you know, people, culture, process. So how do you protect that, and and keep that as a integral part of of growing that operational muscle?
Andy Lin [00:23:51]:
That's that's a great great point. I think it really just starts with basics and making sure that you have the right team that's empowered in place. You you be able to build the right mechanisms for education when you train your first model or you build your first digital twin. Right? Because if you think about it you're around a Genetec AI, around some of this concept, what it really just means is you have lots of models or lots of simulations, and they're all mixed together to simulate some form of intelligence that matters for a business or an enterprise or research institution. Right? And if you think about it, it's sort of like having 50 different models or 50 different models and and simulations or whatever that might be. If you have a good team and a good structure around each one of those, you'll be able to create a modular system that will allow you to scale from a people process culture standpoint. Right? I think, these models and these agents still learning. You know, so you said these terms.
Andy Lin [00:24:47]:
Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah.
Andy Lin [00:24:48]:
Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah.
Andy Lin [00:24:48]:
Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah.
Andy Lin [00:24:48]:
Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah.
Andy Lin [00:24:48]:
Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah.
Andy Lin [00:24:48]:
Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah.
Andy Lin [00:24:50]:
Yeah You know, you you have it's a living and breathing thing. And, living and breathing things require care and feeding by people. And behind every great agent, there's typically a great person or a great team. You know, these agents don't build themselves. Right? And that's the agent should be an amplification or a personification of the best people and the best attributes that your team has. You know? And I think, that's the ability to perhaps embody the best of a company, the best of a team, the best of leadership. Right? That's the promise that this space has, and where we are in the cycle. Right? And I think it's not just a matter of doing it once.
Andy Lin [00:25:36]:
How do you maintain? How do you iterate around that? And that really ties it back to the muscle. Right? I think it's really interesting. A lot of organizations may think, hey. You know, I just bought a big tech platform. I just bought a bunch of GPUs. Right? Oh, I have an AI strategy. Right? It's funny. I there's a lot or on the commerce side, right, you may have a lot of organizations, right, who's like, you know, I won't have the large amount of funding for a year.
Andy Lin [00:25:59]:
Right? Should I start then? And the answer is absolutely not. You need to start now because you can build an operational muscle without technology to make the strategy work. But in my opinion, you really can't build an AI strategy without the muscle if you start with technology on on flip side. So like I said, it it it comes down to people. It comes down to alignment. It comes down to balance between builders and operators. If you have that, you have alignment, you're probably gonna be successful.
Jordan Wilson [00:26:24]:
Alright. So, Andy, I think you've done a great job of, you know, laying out the case, so to speak, about why operational muscle can be a key missing piece of, company's AI strategy. But, you know, as we wrap up today's conversation, because I think it's been a great one, what do you think is the one most important takeaway, for organizations, you know, to kind of glean from today's conversation. Right? Because there's a lot of, you know, a lot of new movement. Right? We're here at GTC. There's so many new announcements. What is the one most important thing, to to build that operational muscle?
Andy Lin [00:26:59]:
I think just on a very practical note is just to learn by doing. You know, I think that we wanna sit back and, you know, watch all these announcements and plan and hyper analyze and worry about when we should get in, when we should do this. You're really kinda not gonna know the right answer. It's very similar to to running start. You just have to start building stuff because you don't know what you don't know yet. And, again, that's part of the operational muscle mantra. Right? It's the idea of starting by a micro example of what you're trying to do. Right? So just think about, you know, what the vision is for five years.
Andy Lin [00:27:34]:
Right? Am I trying to do is build a smart hospital? I'm trying to build a smart manufacturing plant. Right? So I could simulate anything. So I could simulate any scenario as far as, like, around throughput. Just start very small and and have a have a really, diverse cross functional team, you know, going back to the idea of having ten, five to 10 different types of personas working together. And part of the process and part of the journey is not just the technology making it work, but what you learn from each other. You know, I think it's cliche, and it may seem a little bit sappy, right, about teamwork. Yeah. Because you're like, yeah.
Andy Lin [00:28:06]:
Of course. Yeah. Of course. It's teamwork. Right? But I'm shocked how often that's completely overlooked. And it's going through the hard work every day on building. Right? Figuring out what's broken, figuring out what actually works. Right? And then iterating over a longer period of time.
Andy Lin [00:28:20]:
The organization I see most successful in the space, their overnight success took years. Yeah. You know? And and it's a it's a very close knit team of people who have all different types of skill sets who have all worked together over a long period of time and made it happen. So find your small team. You don't need to be in a large company. Right? If you're at a university, find other colleagues in other majors, other disciplines, you know, people that you would feel very uncomfortable with working with maybe in ten years ago, but who need to be part of your micro team. Because you yourself could also learn about how to build your own operational muscle from a personal journey standpoint so that when you get to that point in your organization, you know exactly what to do. And like I said, a lot of times, it's very much the same.
Andy Lin [00:29:03]:
So I said, you know, number one, learn by doing, Get comfortable with being uncomfortable with working with people completely not like you, and then just sort of have faith in the process. You know, I think from a personal standpoint, and then also from an organization standpoint, if you put in the hard work, if you're aligned and you have a balance between me versus us, you will be successful. Such such great insights, on today's show. Andy, thank you so much for taking time out
Jordan Wilson [00:29:29]:
of your day, to share with our audience. I really appreciate it.
Andy Lin [00:29:32]:
Thank you. Appreciate having me on.
Jordan Wilson [00:29:34]:
Alright, y'all. That was a lot. My gosh. If you were out there, you know, on the treadmill walking your dog, you probably missed 90% of that. Don't worry. I'm gonna be recapping it in today's newsletter. So if you haven't already, please go to youreverydayai.com. Sign Sign up for that free daily newsletter.
Jordan Wilson [00:29:48]:
We're gonna have a lot more from today's conversation, a lot more from GTC, and everything else you need to get ahead in leveraging AI. Thank you for tuning in. Hope to see you back tomorrow and everyday for more everyday AI. Thanks, y'all.