EP 238: WWT's Jim Kavanaugh Gives GenAI Blueprint for Businesses

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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, there's so much noise on the Internet. Try this, try that. New models coming out every day. So luckily we're going to be talking today to someone that's been a leader in the industry for decades and it's going to help give us the blueprint. All right, so thank you for tuning in to today's edition of Everyday AI. 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 going to be debuting this one later. Don't worry, if you have questions, you can still leave them. I'll be in the comments. 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 Cavanaugh, the CEO and co founder of Worldwide Technology. Jim, thank you so much for joining the show.

My pleasure, Jordan, thanks for having me. It's an exciting time.

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 what you all do.

Yes. Co founded Worldwide technology back in 1990, which, 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 would say the one thing that I did look at back then which I think is fascinating, 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 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. I think we've got one of the most exciting times in technology. It's literally impacting the world and what everybody does, everybody, you know, how you play, interact, work, you know, and just operate, you know, as, 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 you go back in the day, 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 PCs at that time, which he has morphed and changed quite a bit, but then got into networking and different players around networking. You go to Cisco, to Cabletron, who's no longer around. 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 really focused on, 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 large 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 a 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 five years around Covid, the distributed workforce, people working from home. So it's not only that you have to have a robust and high quality corporate environment, but you've got to be able to connect to your users, your mobile users, your users at home, your employees, your customers, your partners. We've looked at that and really believe that that's still a critical component for organizations to light up and enable AI is to make sure that they have the right 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. Now, with 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 going to do with AI and how are they going to use AI to drive a more efficient organization and to create differentiation. 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 very comprehensive solution provider that drives not only guidance and advice and implementation around complex global enterprises around their IT infrastructure, but also now working with the line of business CEOs boards in regards to their digital transformation and now the consumption and implementation of their AI strategy.

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

Yeah, it's a great question. Fortunately, I would say we had 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 investment that we made years ago in regards to our data science group, 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 try to be 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 chat GPT, it just took the world by storm. So, you know, when I looked at that and I stepped back, it was one that it wasn't. I'm very, I would say I will normally take a very skeptical view of things that are going on and I will call, I would say bs, BS if I see it and I'm like, that's not going to play out. That's more marketing and hype. This one, very quickly I looked at, I'm like, this is game changing. And there are differences of how it's going to impact these large language models in the cloud of being able to go out and write a prompt to them. Because you have, call it general purpose information out there, you don't necessarily have all your corporate crown jewels, you don't have all your corporate data out there and you're not going to want it out there. But it validated the capability of what these large language models can do and it's just game changing. And so I stepped back with our team and really, we poked and prodded and really looked at this and we're like, this is next generation of just that AI. This is something we need to lean into in a big, big way. 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 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 technologist and the user. So, so it's exciting, but I think it's been overwhelming for the world, for everybody.

Yeah, absolutely. I'm sure that a lot of our listeners and those joining us live here can probably relate to that feeling of being overwhelmed. And it seems like there's so many different techniques or tools in the generative AI space that are promising to 10x this or cut this down by 80%. So I'm wondering if you can walk us through and maybe give us a blueprint. So kind of what we started the show talking about, maybe could you give us an example 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 you can give us an actual One, but how is the best way, and maybe walk us through 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?

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So I would, I would first step back and say that we have been working with different customers if you think about it, and I'll take a little bit of liberties here to frame up. Please do take them all what I think is happening. So we've had the good fortune of being working with clients around the CEOs and the line of business, talking to them about digital transformation. So how are they going to use software to create applications, mobile applications to change the patient experience, the fan experience, the customer experience, employee experience, and really thinking about how is that going to drive efficiency for you. So as we're going out and doing that, and we are with our digital strategist and our software developers, there's always a component of that that required aggregating data. Our data science team was always, I would say, part of that capability and that solution behind the scenes working to make sure you aggregate data. You can write a great GUI and a great mobile app, but if the data is not right, it means nothing. So those worlds were coming together and 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 artificial intelligence. So we understood the space quite well and we're doing different things with it. So now you get to what is really just exploded around Gen AI 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 going to really take hold. It's going to take hold. You can deny it if you're a CEO or you're a line of business, but do it in your own peril and risk. So our view is that we've been working in this space and the importance of data and machine learning and natural language processing and all the things around data science and the importance now you have Gen AI in part of the ability to actually take advantage of what is happening today around AI and generate Gen AI is you've got to get to your data. Your data doesn't just magically come together. So you have all kinds of data sources that are happening from ERP Systems, MRP Systems, Salesforce.com, serviceNow. You have structured data, unstructured data. You've gotta get all this stuff and it doesn't have to all be together, but you gotta be able to get it together and organized. So getting back to 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 I've never seen anything like it. I've been to two forums in the last two 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 a room and different people, not going to go into the details of people. 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 your original question is we are highly engaged with a number of customers around that digital transformation around using analytics, machine learning, natural language processing. Now we're taking those pieces and actually incorporating the generative AI side to actually create more robust capabilities. When you think about things that are going on in the fast food restaurants, we work with 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 thru, 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. This is not something that you just, you know, I would say blindly just start throwing, you know, things at, you know, what you're going to do around gen. It's really, it needs to be something that is going to be done pragmatically. It's something that's going to be done very thoughtfully and you need to think about how you're going to implement the back office IT infrastructure to support that, whether it's in a cloud, it's on premises, whatever that structure is, but then also the end users 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. We talked a little bit before and highly focused on one, creating that awareness and that communication to the organization and two, 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 going to 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 do your job better? So again, long answer to your, to your question, but hopefully it helped.

No, you know, Jim like, and I want to remind everyone watching and listening as well, Jim is not the cto, he is the CEO. And you know, that's, that's something I picked up there 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, etc. 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 CEOs one of their main priorities or something at least that the CEO needs to personally be vetting?

I absolutely believe so. You know, I'm not sure, you know, there's a lot of C is, you know, very busy, a lot of things on your, your plate at this point. But to look at that, 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, smaller organization, but we would always have a 7:00 clock Monday morning meeting which everybody loves. 7:00am you know, and so talking about discipline, rigor, I was a big proponent of discipline and rigor around the organization. It needs to start with the leaders. So we have a, to this day, every Monday morning we have a two 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 two hours. Where I'm going with this. We talk about, you know, financial performance, you know, month to date, year to day, 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 two hours which will be 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 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 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 four that we have. So you got to prioritize those while we build out the platform that we're using that is our large language model and our Genai platform that is going to be driving these outcomes. Back to your point, I absolutely think Genai and the Digital transformation process and strategy should be an absolutely focus of every CEO.

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 do want to ask Jim is outcomes. Right. Because that's ultimately, you know, what that executive team 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 AI like you have, and maybe they're not working, you know, as an example with Nvidia, we 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.

Yeah. So I'll walk through a couple of the focus areas that we have and I would also, you know, 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 going to continue to iterate on. So some of the things that we looked at was one we need to create, and I would advise and recommend for all clients, is to create our own, call it ChatGPT. So you have to create your own generative AI platform internally. Now again, that requires you aggregating your data. You're not necessarily going to want to put your data out in the general purpose large language model. Whatever one you're using, you got to figure out what is the architecture and how you're going to do that. But you're going to start putting that into your own, call it chat, you know, large language model, Chatbot, whatever you want to name that where employees are going to 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 going to make it just much more efficient. So think about that experience that your employees are going to have just around general purpose information they're going to get faster, quicker and it's going to be at their fingertips in a more personalized way and you're not going to 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 going to work. And again, it's not as easy as saying, okay, yeah, just go put it in there. You got to be thoughtful about what data you're going to put in there. What's the governance? There's data around personal records, there's a thought process that needs to go into that. But the outcomes and the efficiencies can be very, very significant. There's all kinds of use cases. As you build that out, then you think about what we've done. I'll just randomly run through a couple. 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 more significant areas is around QA process, around software development. We've seen 40 to 50% improvement in efficiencies 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 more efficiency. So that's one I think that we'll continue to iterate and we're always looking at ways 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 common use cases that we, we have looked at are that we're actively, we're not just looking at, we're 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. So when I get back tomorrow, 7am as well, there is a sales meeting at 7 right after that while we have. Okay, so, so this one, there's another meeting. Yeah, they would not like me if I did that again. But we're currently working on another effort is the RFP process. So if you think about clients that we're working on, you have some, that these RFPs could be 70 pages of information that you have. And so being able to incorporate that information and sort through that using Genesis to actually produce what we would consider is a base model of things, of questions that we could then distribute out to different Subject matter experts. It needs information 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 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, again that data structure is also part of that General call it ChatGPT platform that you need to create 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 almost a billion dollars 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 are cyber range. So there's a number of very complex products and architectures and solutions and 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 I.e. 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, 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 positives and the negatives of that and give us the best outcomes of the architectural solutions that they would have. 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, the client, what kind of solutions have you provided or what was the analysis and 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 do capture the flag type scenarios but as you capture that data you can provide what are the outcomes that we're finding and what should CISOs be thinking about? So these are things that we're looking at just inside a worldwide 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 got to make the commitment to these platforms and you've got to think through the data, the aggregation of the data, the type of AI platforms you're going to put in place. But the outcomes and the differentiation that is going to create for your organization I think is going to be massive. So we're just even as far advanced as I think we are in regards to this AI platform because of what we've invested in internally and the alignment and the solutions that we have, we're just scratching the surface.

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 using it in a very gener 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 Gen AI success blueprint, what is that one piece of advice that you'd like to leave them with?

I would say from a leadership perspective, my one piece of advice is they need to, I just believe that every CEO and leader needs to take this incredibly seriously and understand that this is, it is a journey, it's going to require effort, but efficiencies and differentiation it's going to create is going to 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. What they're also doing is not only just creating the back end GPUs and say that the power to drive these environments but also applications and I would say operating platforms that are going to be very different. So my point going back to it, this is not going away. 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 be out in the front and making those investments internally and as a go to market. Because I think it's going to be game changing for companies and if you're not focused, I really think you're going to find yourself at a disadvantage.

That's words of wisdom from someone that's been there, done that and more. Industry leader for decades. Jim Cavanaugh, thank you so much for joining the Everyday AI Show. We really appreciate your time, Jordan.

Thank you. I look forward to staying in touch and you're doing a great job and yeah, I'm going to learn a lot.

From you on this.

So thank you.

All right. And hey, I actually cannot wait after this is done to go back and listen to this myself. I'm going to 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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