Ep 238: WWT’s Jim Kavanaugh Gives GenAI Blueprint for Businesses

Driving Innovation through Generative AI: Strategic Insights for Businesses

Generative AI has proven to be a game-changing technology for today's companies. Right from optimizing IT infrastructure to significantly enhancing business efficiency, the potential benefits are limitless. While there may be skepticism about integrating new technologies into businesses, gen AI has been acclaimed to create a massive difference. In recent years, it has become essential for businesses to reflect on the latent power of generative AI in transforming their operations.

Enhancing Business Operations through GenAI Technologies

GenAI technologies promise breakthroughs for companies across sectors. For instance, multi-billion dollar technology facilities are integrating these technologies to enhance their complex system solutions. A particular focus lies in the aggregation and organization of both structured and unstructured data to provide real-time access to teams working on the front lines, creating massive efficiencies and entirely new developments in the business landscape.

The Power of Intelligent Data Gathering

In an increasingly data-driven business environment, intelligent prompts stand out as a novel tool for real-time data extraction. By reducing the time experts and clients spend gathering information, businesses can streamline their decision-making processes, give a much-needed boost to their operations, and provide a competitive advantage.

Preparation for the AI-First Future

As the impact of Generative AI and large language models becomes increasingly evident, businesses must adapt and consider AI technologies integral to their operations. This forward-thinking stance should be mirrored in the highest echelons of business leadership. Prioritizing AI in executive discussions about the company's financial performance, go-to-market strategies, and AI implementation ensures a cohesive integration of these technologies into the organization's fabric.

AI in Practice: Improving Efficiency and Client Relationships

Companies already embracing an AI-first approach are seeing results, especially in software development. For instance, generative AI's influence on business can lead to a 40-50% surge in software development efficiency. Companies are also exploring AI applications in the Request for Proposal (RFP) process for client projects, further underlining its potential impact on commercial relationships.

The Growing Need for a Sound AI Strategy

In an ever-evolving digital environment, business leaders must recognize the potential that generative AI holds for their company's future. From improving internal processes to redefining client relationships, the integration and thoughtful implementation of AI can provide a game-changing edge in the competitive business landscape. Therefore, understanding, communicating, and training around AI should be the priority for every organization looking to pave the way ahead in digital transformation.

Topics Covered in This Episode

1. Impact of Generative AI
2. Role of GenAI in World Wide Technology
3. AI Adoption for Business Leaders
4. Large Language Models and AI Impact
5. Challenges in the Generative AI Space
6. Organization Culture and AI Implementation

Podcast Transcript

Jordan Wilson [00:00:01]:
We're all looking for the blueprint on how to properly implement generative AI into our companies and to grow. But how is it done? You know, you you there's so much, you know, noise on the Internet. Try this. Try that. New models coming out every day. So, luckily, we're gonna be talking today to someone that's been a leader in the industry for decades, and it's gonna help give us the blueprint. Alright. So thank you for tuning in to today's edition of Everyday AI.

Jordan Wilson [00:00:31]:
My name is Jordan Wilson. I'm your host, and Everyday AI is your daily livestream podcast and free daily newsletter helping everyday people learn and leverage generative AI. Yeah. We have an in person setup today. We are actually at the, NVIDIA GTC conference, but we're gonna be debuting this one later. Don't worry. If you have questions, you can still leave them. I'll be in the comments.

Jordan Wilson [00:00:50]:
Try my best to answer them. But with that, I'm very excited to have today's guest, someone, who himself and his company have been leaders in the digital transformation space for more than 30 years. So please help me welcome to the show, Jim Kavanaugh, the CEO and cofounder of Worldwide Technology. Jim, thank you so much for joining the show.

Jim Kavanaugh [00:01:08]:
My pleasure, Jordan. Thanks for having me. It's an exciting time.

Jordan Wilson [00:01:11]:
This is this is a good one. I am I am excited for today's show. But before we dive in a little bit too deep, give us a little bit of a background. You know, tell us when you started, WWT and and what you all do.

Jim Kavanaugh [00:01:23]:
Yes. Co founded Worldwide Technology, back in 1990, which is quite amazing. Didn't really know what I was doing back then, or we didn't know what we were doing. I actually played a little bit of professional soccer before that, so you could see I was really, you know, took my my studies and, all my background was aligned to tech, but no, that's not the case. You know, I I would say the one thing that I did look at back then, which I think is is fascinating, one of the one of the right decisions that I've made, was I wanted to get into tech. And I wanted to because I just felt this was such it was going to be such a growth area. Well, that is one decision I made that I think was spot on. Because you see the evolution of of technology and the things that have happened and right where we are today, you move all the way from 33 years ago to where we are today.

Jim Kavanaugh [00:02:12]:
I think we've got one of the most exciting, times in technology that are it's literally impacting the world and what everybody does. Everybody, you know, how you play, interact, work, you know, and just operate as an individual, you know, the things you do. So Worldwide Technology started out as a call it a quickly, I'll just work through this, a value added reseller of technology product. And it you know, you go back in the day, you know, it's, computers of some sort, could have been IBM back there. Dell Technologies, Michael Dell, being here and doing a lot of things. So he was just coming out with his his, you know, his PCs at that time, which he has morphed and changed quite a bit. But then got into networking and and different players around networking. You go to Cisco, to Cabletron, who's no longer around.

Jim Kavanaugh [00:03:02]:
You had organizations like the Sun Microsystems, Digital Equipment Corporation, Compact Computer, all of these that we play with, but more of a value added reseller. From there, we we really focused on we we need to not only go out and help organizations acquire product and help them get it as efficiently as possible, but build services around that. And so we built a professional services organization, started out really around networking, Cisco. We're the largest partner with Cisco in the world. And as we've grown and we've built great partnerships with almost all the the large, you know, OEMs, in the technology space, and we've expanded our services portfolio to cover everything from compute to storage to networking to cyber to now software development. So we have a large software development group that really focuses on digital transformation. And so that evolution of helping organizations, evaluate, test, build, and deploy complex back office IT infrastructure, everything from complex data centers to networks, to all of the things, voice, video, collaboration. You think about what's happened in the last 5 years around COVID, the distributed workforce, people working from home.

Jim Kavanaugh [00:04:18]:
So it's not, you know, only that you have to have a robust and high quality corporate environment, but you've gotta be able to connect to your users, your mobile users, your your users at home, your employees, your customers, your partners. So we've looked at that and and really believe that that's still a very critical component for organizations to to light up and enable AI, is to make sure that they have the right, you know, back office, AI and IT infrastructure to do that. But at the same time, we're highly focused on, in the last 10 years, not only building out our digital strategy and digital transformation software development team, we've, been focused on data science and our data consulting and data science team that has really worked very closely with that digital transformation team. So now with, you know, AI really going mainstream in the last 12 months, these worlds have come together, and we're highly focused on still helping organizations, chief technology officers of large Fortune 500 organizations, public sector hyperscalers, design and implement what does their back office corporate IT infrastructure need to look like. But at the same time, working with the CEOs in the line of business in regards to what are they gonna do with AI, and how are they going to use AI to drive a more efficient organization and to create differentiation. So I would say I'll pause for a minute. Maybe just we've come a long way in regards from, I would say, reselling product to being, I would say, a a very comprehensive, solution provider that that drives not only guidance and advice and implementation around complex global enterprises, around their IT infrastructure, but also now working with the the line of business, CEOs, boards, in regards to their digital transformation and now the consumption and implementation of their AI strategy.

Jordan Wilson [00:06:23]:
Yeah. And that was actually a very succinct, like, summary of of 30 plus years on business. And, you know, if if you're joining us live or if you're on the podcast, you know, WWE is is one of the largest, global leaders in in technology and in digital strategy. So, you know, I'm I'm very curious, you know, Jim, from from your point of view. So you've, you know, seen and kind of been through, you know, the.com and and and the cloud and the web 2.0. You know, how did generative AI kind of, come onto, the the scene whether for you or your team? And what was your first, kind of response or reaction to it from a, you know, a business perspective? Did you immediately see that this is going to be, the future of work? Or was there a certain level, which I think a lot of, you know, executives can probably relate to, was there a certain level of, I'm not really sure if if this technology is is maybe ready for the big time yet?

Jim Kavanaugh [00:07:18]:
Yep. It's a great question. Fortunately, I would say we had the the advantage of we've been working with NVIDIA. I've had the good fortune of actually spending some time with Jensen a number of different times and working with his team. So because of the the investment that we made, you know, years ago in regards to our data science group, we've we've we've built a partnership with NVIDIA. So, we've been working in this space for a while, and we've been highly focused on the software development digital transformation side that I mentioned. So it's been our goal to to to try to be, you know, anticipating what's going on, skating to where the puck is going, and not just focusing on what's going on today. All that being said, with generative AI coming out and OpenAI and ChatGPT, it just took the world by storm.

Jim Kavanaugh [00:08:17]:
So, you know, when I looked at that and and I stepped back, it was one that it wasn't I'm I'm I'm very I would say, I I will normally take a very skeptical view of things that are going on. And I will call, I would say, b s b s, if I see it. And I'm like, that's not gonna play out. That's more marketing and hype. This one, very quickly, I looked at it. I'm like, this is game changing. And, like, you know, there are differences of how it's gonna impact, you know, these large language models in the cloud of being able to go out and write a prompt to them. Because you you have, call it, general purpose information out there.

Jim Kavanaugh [00:09:04]:
You don't necessarily have all your corporate crown jewels. You don't have all your corporate data out there, and you're not gonna want it out there. But it validated that the the capability of what these large language models can do. Mhmm. And it it's just game changing. And so, you know, I step back with our team, and and really, we we poked and prodded and and really looked at system. We're like, this is this is, like, next generation of just that AI. This this is something we need to lean into in a big, big way.

Jim Kavanaugh [00:09:41]:
And fortunately, we had the data science teams and the digital transformation teams that we were working with that could really help validate along with the partnership with, Jensen and and NVIDIA. So I would say we we were not, you know, you know, we we we did our homework. We did our due diligence, but we were really fast to to move on this because we are already engaged. But it's different. You know, there's a lot of learnings that are going on in regards to generative AI and the things that people, you know, and everybody, the technologists and the users. So, so it's exciting, but I think it's been overwhelming for the world, you know, for everybody.

Jordan Wilson [00:10:22]:
Yeah. Yeah. Absolutely. And and I'm sure that a lot of our, you know, listeners and those joining us, live here can probably relate to that feeling of of being overwhelmed. And, you know, it seems like there's, you know, so many different, you know, techniques or or tools, in the generative AI space that are promising to, you know, 10 x this or cut this down by 80%. So I'm wondering if if you can walk us through it and maybe give us a a blueprint. So kind of what we started the show talking about. Maybe could could you give us an example of a a of a client or a customer that you've worked with and just kind of walk us through, and it can be theoretical if you want or or you can give us an actual one, but, you know, how, you know, is is the best way and maybe walk us through of of how a company or client came to you, said, here's the problem, and then how did you apply generative AI to help them solve that problem?

Jim Kavanaugh [00:11:10]:
Yeah. So I would I would first step back and say that we we have been working with different customers. If you if you think about it, and they're, I'll take a little bit of liberties here to to frame up Please do.

Jordan Wilson [00:11:22]:
Take them all.

Jim Kavanaugh [00:11:23]:
What I what I think is happening. So we've had a good fortune of being working with clients around, you know, the CEOs and the the line of business, talking to them about digital transformation. So how are they gonna use software to create applications, mobile applications to change the patient experience, the fan experience, the customer experience, employee experience? And and really think about how is that gonna drive efficiency for you. So as we're going out and doing that and and we are with our digital strategist and our software developers, there's always a component of that that required aggregating data. And so our data science team was always, you know, I would say, part of that capability and that solution behind the scenes working to make sure you aggregate the data. You can write a great GUI and a great mobile app, but if the data is not right, it means nothing. So so those worlds were coming together, and we've we've been working with a number of clients, over the years, in specific areas around machine learning, natural language processing. So things that were not necessarily generative AI, but were around data science and and artificial intelligence.

Jim Kavanaugh [00:12:41]:
So we understood the space quite well and we're doing different things with it. So now you get to what has really just, you know, exploded around Gen AI and and all the capabilities, and they're very real. This is not something that it's like, oh, no. We're not sure if this is gonna really, you know, take, you know, take hold. It's it's gonna take hold.

Jordan Wilson [00:13:01]:

Jim Kavanaugh [00:13:02]:
You can you can deny it if you are a CEO or you are a line of business, but do it in your own peril, you know, and and risk. So so our view is that we've been working in this space and the importance of data and and machine learning and natural language processing and all the things around data science and the importance. Now you have GenAI. In part of the ability to actually take advantage of what is happening today around, AI and generate Gen AI is is you've gotta get to your data. You know, you you your data doesn't just magically come together. So you have all kinds of data sources that are happening, you know, from ERP systems, MRP systems, salesforce.com, ServiceNow. You have structured data, unstructured data. You've gotta get all this stuff.

Jim Kavanaugh [00:13:52]:
And and it doesn't have to all be together, but you gotta be able to get it together and organize. So getting back to your your point, there are so many organizations that we're working with today. Every one of them that I talked to, by the way, is incredibly interested, scared, you know, see it as an opportunity of what's going on. What should I be doing around AI? So, you know, they're every and and I've never seen anything like it. I've I've been to 2 forums in the last 2 weeks. One with large Fortune 500 CEOs, and really talking about policy, around AI and how the US should be treating it. And there was a senator in the room and different people. Not gonna go into the details of people.

Jim Kavanaugh [00:14:41]:
And then there was another forum I was at just last week with probably 15 CEOs of more of middle market customers. In every case, every CEO is highly focused, and I would say highly confused about how should we go about the implementation of AI, knowing that they need to do it, they just don't know how to so back to the your original question is, we we are highly engaged with a number of customers around that digital transformation around using, analytics, machine learning, natural language processing. And now we're taking those pieces and actually incorporating the generative AI side to actually create more robust capabilities. And so when you think about things that are going on in the fast food restaurants, we work with, you know, 15 or 20 of those, you know, in different times and spaces really building out their digital strategy. They're all looking at how can we use these different capabilities. And it may be, you know, the the incorporation of, multimodal languages, you know, of how you incorporate that through a drive through, how can you use some of these capabilities. So there's so many different opportunities, but I will say they have to be thought through. Mhmm.

Jim Kavanaugh [00:15:59]:
This is not something that you just, you know, I would say blindly just start throwing, you know, things at, you you know, what you're gonna do around Gen AI. It's really it needs to be something that is gonna be done pragmatically. It's something that's gonna be done very thoughtfully, and you need to think about how you're gonna implement the back office IT infrastructure to support that, whether it's in a cloud, it's on prem, whatever that structure is. But then also the end users Mhmm. And the line of business to figure out what are those use cases. And we're working not only with our customers on these, actually driving some of those use cases, but also internally. We're doing the same thing. And it's really I have our entire organization, our entire company knows that my mantra has been we are now going to be an AI first organization.

Jim Kavanaugh [00:16:52]:
We talked a little bit before, and highly focused on, 1, creating that awareness and that communication to the organization, and 2, training the organization around what does that mean to them. Because I think it's also very important culturally, that you communicate to your company that this is not, you know, this is not about, you know, replacing jobs. This is about creating a more competitive company, a more efficient organization, a differentiated organization. And if you think individually or from a company standpoint that the way you're going to get better is by ignoring this, and that's gonna be job preservation. No. You need to lean into this and you need to figure out how do you leverage this technology? How do you, as a user, write prompts to get better information to to to do your job better? So, again, long answer to your to your question, but, hopefully, it helped.

Jordan Wilson [00:17:54]:
No. You know, Jim, like and I wanna remind everyone watching and listening as as well. Jim is not the CTO. He is the CEO. And, you know, that's that's something I picked up there in in in your answer is you're now talking to rooms full of CEOs where I think, you know, traditionally maybe I'm wrong here, but, traditionally, you know, innovation, whether we're talking, you know, cloud, edge, etcetera, it's maybe not always CEOs. Maybe it's, you know, those in the chief technology, roles. Do you think that, you know, regardless of where a company's at, should this AI being an AI first organization, like you said, should that be a CEO's one of their main priorities or something at least that the CEO needs to personally be vetting?

Jim Kavanaugh [00:18:41]:
I absolutely believe so. You know, I'm not sure, you know, there's a lot of CEOs, you know, very busy, a lot of things on your your plate at this point. But to look at that, I I just think it is it would be a very wise decision to make that one of your top priorities. I'll give you an example. Today, if I go back, yes, you know, the founding of Worldwide, I go back to 30 years ago or so, Started with our executive team, which is much smaller at the point, a smaller organization, but we would always have a 7 o'clock Monday morning meeting, which everybody loves, 7 AM. Yeah. And they love it. So talking about discipline and rigor, I was a big proponent of discipline and rigor around the organization.

Jim Kavanaugh [00:19:29]:
It needs to start with the leaders. So we have a to this day, every Monday morning, we have a 2 hour meeting from 7 to 9, central time. And every one of the executives that report up into me, there's about 15 or so, that are on that call for 2 hours. Where I'm going with this, we talk about, you know, financial performance, you know, month to date, year to date, quarter to date, operational issues, positive, negative, employees that we're bringing on strategy, some things, but it's a continuous cycle. Part of that 2 hours, which we extended for a half hour, now an hour of it is around our go to market, around AI, and our implementation of AI internally. So we actually have our data science consultants and advisors along with our data scientists that are on there and our our our data analytics folks along with some of the line of business. And the goal is that this is a very iterative focused topic that we're having of driving use cases internally. And my perspective, I want all of the executives to understand how these AI platforms work.

Jim Kavanaugh [00:20:40]:
And I want them to be thinking about, and they are challenged on specific use cases in each one of those areas in their respective areas of the business, whether it's HR, it's finance, it's material operations, logistics, you go down sales. So they all have come up with these use cases. So we came up with 75 different use cases. We're focused on 4 Okay. That we have. So you gotta prioritize those, while we build out the platform that we're using that is our, you know, large language model on our our Gen AI platform that is gonna be driving these outcomes. So, back to your point, I absolutely think Gen AI and the the the digital transformation, process and strategy should be an absolutely focus of every CEO.

Jordan Wilson [00:21:33]:
It's fascinating, by the way, that, you know, half of that time, you know, with with your executive, team is is spent on AI. I think that a lot of people in in organizations, enterprise, small, medium business, can learn from that because it it does seem no matter which industry you're in, that's something that's going to be affecting, us all. I I I do wanna ask, Jim, is is outcomes. Right? Because that's ultimately, you know, what that executive team and and other business leaders always care about is outcomes. So maybe for those companies, you know, medium sized large companies that haven't fully invested, into generative a, AI like you have, and maybe they're not working, you know, as an example, with NVIDIA. What can you say about how generative AI, has changed outcomes whether it's for your own company or for your clients, because that's ultimately what people care about. Yep.

Jim Kavanaugh [00:22:30]:
Yeah. And it's, so I'll walk through a couple of the the focus areas that we have. And I and I would also, you know, let's say, we are sharing, these focus areas with our clients on things that we're doing internally. And it's and it's incredibly validating, and they appreciate it because we're very transparent about, you know, the investment of time and effort that you need to put in to actually make this work. And this is this is not a one and done. This is this is a journey that you need to be thinking about that you're gonna continue to iterate on. So some of the things that we looked at were was 1, we need to create, and I would advise and recommend for, all clients is to create our own, call it Chat GPT. So you you have to create your own generative AI platform internally.

Jim Kavanaugh [00:23:20]:
Now, again, that that requires you aggregating your data.

Jordan Wilson [00:23:24]:

Jim Kavanaugh [00:23:24]:
You're not necessarily gonna wanna put your data out in the general purpose, you know, large language model, whatever one you're using. And you gotta figure out what is the architecture and how you're gonna do that. But you're gonna start putting that into your own, call it, chat, you know, large language model, chatbot, whatever you wanna name that, where employees are gonna be able to go in and search for information. And it may be HR information. It could be general purpose information that employees have that today they have to go to different individuals to get. And as you continue to iterate on that and train your models, it's gonna it it's gonna make, it just much more efficient. So think about that experience that your employee is gonna have just around general purpose information. They're gonna get faster, quicker, and it's gonna be at their fingertips in a more personalized way.

Jim Kavanaugh [00:24:14]:
And you're not gonna be burdening a lot of your operational people to have to go aggregate that. And as you update it and you digitize that information, it stays current. So think about, you know, how that's gonna work. And again, it's not as easy as saying, okay, yeah, just go put it in there. You gotta be thoughtful about what data you're gonna put in there. What's the governance? You know, there's, you know, data around personal records. And so there's a thought process that needs to go into that, but it can be the outcomes and the efficiencies can be very, very significant. And there's all kinds of use cases as you build that out.

Jim Kavanaugh [00:24:48]:
Then you think about what we have done, is I'll just kinda randomly run through a couple. So we've also focused on GenAI, and how does it impact our software developers internally and our teams that are working on client projects? So one of the the the more significant areas is around, QA process around software development. We've seen 40 to 50% improvement Wow. Inefficiencies around how we're doing that, how we were doing it, and how we can do it with GenAI to drive a higher quality product with with more efficiency. So, that's one I think that we'll continue to iterate. And, you know, we're always looking at ways, you know, how can we use it on the front end? We still haven't found as much efficiencies around the design side and the actual development side, but we believe that is something that we're iterating through. The other, I would say, a couple of common use cases that we have looked at are that we're actively we're not just looking at work actively working on, which our team knows every and this this meeting is every we have meeting every Monday, as I said. I also have a meeting on Friday.

Jim Kavanaugh [00:25:52]:
So when I get back tomorrow 7 AM is fine. But, there is a sales meeting at 7 right after that one. We have this. So so this one, there's another meeting. Yeah. They they would not like me if I did that again. But we're we're we're currently working on another effort is, the RFP process. So if you think about, you know, clients that we're working on, you you have some that, you know, these RFPs could be 70 pages of information that you have.

Jim Kavanaugh [00:26:19]:
And so being able to incorporate that information and sort through that using GenAI to actually produce what we would consider as a base model of things, of questions that we could then distribute out to different subject matter experts. It needs information on from our cyber experts, from our networking experts, from our software development team. So it's able to actually digest that. So if you think about it, and then actually kick out, you know, a potential response that may be 80% complete. So we're still working through this effort here, but seeing, really great outcomes of what we think is potential to drive the efficiencies. To one, to provide a better experience for our customers in regards to response time and quality, but also the efficiencies that we have. And you think about it, just about every organization has some type of RFP, you know, process that they go through in regards to this. So, proposals, RFPs.

Jim Kavanaugh [00:27:18]:
And, again, that's part that that that data structure is also part of that general, call it ChatGPT platform that you need to create. So data is really, really important how you organize, aggregate, manage, govern the data to actually drive these outcomes. Another one I would say that I'll pause, is, we're building a front end that requires the aggregation of we've created, over the last 15 years, what we call our Advanced Technology Center, which is, at this point, it has, you know, almost a $1,000,000,000 of hardware and software in it that we have integrated over the years that allow for organizations to come in and test and evaluate very complex integrated architectures, whether it's around cloud platforms or data science platforms and our purpose built AI platforms, could be around voice and video solutions, cyber platforms, big data and cyber platforms, our cyber range. So there's a number of very complex products and architectures and solutions on proof of concepts that we have in these labs. And it comes in all kinds of different forms. There's white papers, there's there's complex documentation, there's actually products that we have. So you have all of this data, some that is structured dates, some data is unstructured videos that we have. So we're aggregating all of that and giving access to all of our engineers and our salespeople and our business development, where before, when they would you know, where they will be able to go in and write a prompt and say, you know, you know, give me, you know, the the last five proposals or opportunities that we had with large Fortune 500 banks in regards to cloud solutions and why they work for, you know, and what were the the positives and the negatives of that, and and give us the best outcomes of the architectural solutions that they would have.

Jim Kavanaugh [00:29:13]:
So think about how much time it takes to get to your experts to actually put that data together. So the amount of time that we'll be able to, by writing, intelligent prompts that we've talked about earlier, to be able to extract that data real time when a rep could be sitting down with a chief technology officer or the client. What what kind of solutions have you provided? Or what what was the analysis and the the main, points that you've gotten out of your last cyber ranges that you've run, where we will run these cyber ranges and they'll they'll do capture the flag type scenarios.

Jim Kavanaugh [00:29:48]:
But as you capture that data, you can provide what are the outcomes that we're finding and what should CSOs be thinking about. So these are things that we're looking at in, just inside of worldwide Mhmm. That we're using, that will really differentiate the way we go to market, and it'll also differentiate the customer experience and create massive efficiencies. All that being said, you've gotta make the commitment to, you know, these platforms. And it's and you've gotta think through the data, the aggregation of the data, the the the the type of AI platforms you're gonna put in place. But the outcomes and the differentiation that it's gonna create for your organization, I think is gonna be massive. Mhmm. So we're just even as far advanced as I think we are in regards to this, AI platform because of, you know, what we've invested in internally and the the alignment and the solutions that we have, we're just scratching the surface.

Jordan Wilson [00:30:47]:
Yeah. And I think you mentioned something there that I think all, business leaders watching and listening should take note of. You know? That that, you know, WWDT has has, taken your your your knowledge, your expertise, your data, your IP, and in many cases, and has created it, as a tool for for yourself, for your employees, and, you know, you're really taking ownership over that and and using it, in a very generative way. But but we we we've talked about a lot on on today's show, Jim, you know, from, data and the importance of, bringing executives around the table and how you can really leverage your your knowledge and your expertise, in in your own domain. So, you know, what's what's maybe that one takeaway, you know, as for a business leader who's out there and they're they're still working on their GenAI success blueprint. What is that one piece of advice that you'd like to leave them with?

Jim Kavanaugh [00:31:48]:
I would say from a leadership perspective, my my one piece of advice is they need to I I just believe that every CEO and leader needs to take this incredibly seriously. And and understand that this is it it is a journey. It's gonna require effort. But the the the efficiencies and differentiation it's going to create is gonna be massive. And the point, I guess, I would leave you with is that coming to, you know, the NVIDIA GTC here, and to see what Jensen, you know, his keynote, and to think about how fast things are changing and evolving and what they're generating. I don't think the applications today, the outcomes are even close to keeping up with the back end compute and the GPUs and the infrastructure that they are creating. And what they're also doing is not only just creating the the back end GPUs and say that the power to drive, this this these environments, but also applications and, you know, I would say operating platforms that are gonna be very different. So my my point going back to it, this is not going away.

Jim Kavanaugh [00:33:08]:
Mhmm. This is a differentiator for everybody. And NVIDIA is leading the charge, and Jensen's leading the charge. And what they just demonstrated just fires me up even more about what we need to do to to to be out in the front and and making those investments, internally and as a go to market because I I I think it's gonna be game changing for companies. And if you're not focused, I I really think you're gonna find yourself at a disadvantage.

Jordan Wilson [00:33:38]:
Mhmm. That's words of wisdom from someone that's been there, done that, and more, industry leader for decades. Jim Kavanaugh, thank you so much for joining the Everyday AI Show. We really appreciate your time.

Jim Kavanaugh [00:33:50]:
Jordan, thank you. I look forward to staying in touch, and, you're doing a great job. And, yeah, I'm gonna learn a lot from you on this, so thank you.

Jordan Wilson [00:33:58]:
Alright. And, hey, I actually cannot wait after this is done to go back and listen to this myself. I'm gonna write, hopefully, one of my best newsletters ever because I think there's so much value in today's episode. So make sure if you haven't already, go to your everydayai.com. Sign up for that free daily newsletter, and we'll see you back tomorrow and every day for more everyday AI. Thanks.

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