Ep 232: Creating and Capturing Business Value with GenAI – Insights From HPE

Leveraging Generative AI to Create Business Value: Insights from HPE

Generative AI has been gaining traction across industries as an innovative tool to drive business outcomes. The episode brings to light the critical role of generative AI in shaping the future of enterprise operations, bridging the gap between AI adoption and business impact.

Maximizing Business Value with Generative AI

The podcast discussion sheds light on the role of generative AI in unlocking value across various business functions. From enhancing customer experiences to optimizing internal processes, the technology offers a spectrum of possibilities.

Data as the Key Driver

Data emerges as the linchpin for successful utilization of generative AI. The episode underscores the pivotal role of data in fueling models and facilitating exponential improvements in outcomes. The emphasis on the magnitude of quality data and its direct correlation to AI model advancements highlights the significance of data in the generative AI landscape.

Avoiding Common Pitfalls

The podcast presents a comprehensive view of the common pitfalls businesses encounter when navigating the generative AI landscape. It encompasses crucial considerations such as safeguarding data security and privacy, mindful cost assessments, and the indispensability of a deliberate approach towards tool standardization across the enterprise.

Measuring Return on Investment

A critical aspect illuminated in the podcast is the measurement of the return on investment from generative AI initiatives. The focus on tangible business metrics such as sales acceleration, enhanced customer experiences, and operational efficiencies underscores the practicality and viability of leveraging generative AI to achieve measurable outcomes.

The Future of GenAI

The discussion encapsulates the essential ingredient for sustainable value creation through generative AI, urging businesses to cultivate a strategic approach towards identifying their unique data moats. Furthermore, the emphasis on agility and rapid iteration underscores the dynamic nature of generative AI applications and the imperative for adaptability.

The insights from the Everyday AI podcast underscore the transformative potential of generative AI in reshaping business paradigms. By leveraging generative AI as a catalyst for innovation and value creation, businesses can embark on a journey towards sustainable growth and competitive advantage in the ever-evolving digital landscape.

Topics Covered in This Episode

1.  Creating value in business with generative AI
2. Adoption and Effectiveness of Generative AI
3. Customer and Industry Involvement
4. Pitfalls and Successes with Generative AI
5.  HPE Collaboration with NVIDIA

Podcast Transcript

Jordan Wilson [00:00:03]:
How can you create value in your business with generative AI? Here we are. We've all been using chat GPT and so many large language models, and we're still trying to see, is this working? And how can we keep it working across our entire business? That's what we're gonna be talking about today, and more on everyday AI. Welcome. Thank you for tuning in. You might notice this is a little different setup. That's because we are live at NVIDIA GTC conference, and I'm excited today, to talk about creating value in your business. So make sure before we get started, if you haven't already, go to your everydayai.com. Sign up for the free daily newsletter.

Jordan Wilson [00:00:40]:
We'll be recapping it right after this episode. So please help me welcome, we have Evan Sparks, the GM and VP for AI Solutions at HPE. Evan, thank you for joining the show.

Evan Sparks [00:00:50]:
Thanks so much for having me. Really excited to be chatting with you.

Jordan Wilson [00:00:53]:
Alright. Can you tell us a little bit about you and your role at HPE?

Evan Sparks [00:00:57]:
Absolutely. So at HPE, I oversee our AI solutions business, which is really about kind of combining the best of what we do on the hardware and infrastructure side together with NVIDIA, with some software assets that we've, got that we've been building over the the last few years to help enterprise customers in particular achieve their goals with with respect to AI. Historically, of course, we've been doing a lot of computer vision and kind of classical deep learning and NLP, and lately, a lot of that has become generative AI. And so thinking through what are the what are the places where we can create a lot of value in the enterprise with generative AI, and how do we help customers accelerate the realization of that value?

Jordan Wilson [00:01:36]:
Really like to know from from your perspective, why do you think it is still a struggle for for so many businesses to to realize if generative AI is working? Because it seems like you you see all these studies, you know, you can get back x percentage of your time and, you know, 5 x this, 10 x this. You think that a lot of companies are still struggling to see if they're actually getting value out? Or do you think now that we're, you know, a year and a half into this, you know, large language model world, our business is finally able to say, we're getting value out of this.

Evan Sparks [00:02:06]:
You know, I think back to, you know, November of a year and a half ago when we had our ChatGPT moment hit. And I really think that that was the moment where all of our imagination got captured by what this technology could do, but the practical reality of That was really worked out over the course of 2023. So we've That was really worked out over the course of 2023, so we saw a lot of, big companies doing pilots in 2023. It was sort of the year of the prototype, And I think 2024 is the year where we're gonna see a lot of these experiences move into, much more kind of production, environments and and start to see a lot more scale come. I think that, you know, we talk to customers about how they're seeing, this value get created and it, a lot of them will want to have a customer facing experience among their customer set. So talked to a large retailer yesterday who embedded generative AI right in search on the front page of their mobile app, and it was a totally different customer experience, but it was something where their customers could enjoy a much more valuable shopping experience. They're also doing things to help the workers in their warehouses or in their stores be more efficient with their time by helping them find products faster, these sorts of things. And then they're also thinking about how do I make business processes much more efficient.

Evan Sparks [00:03:31]:
Generally, I can help in all of this, but prioritizing those things and then getting it right from a safety security kind of perspective, that is hard stuff, and, we're trying our best to accelerate them in those, efforts.

Jordan Wilson [00:03:43]:
Yeah. Evan, I definitely wanna jump in and talk about those, a little bit more. But, you know, our audience is is from all over the spectrum, from small business owners to people who work in in big tech. So maybe first, before we dive in a little bit, can you tell us the type of companies? I'm sure it runs the whole gamut, but what type of companies are you generally working with that agency?

Evan Sparks [00:04:03]:
Yeah. So, I mean, big multinational company, and we sell to, from small to large. We definitely have a big footprint in the Global 2 1,000 and in on prem data centers for sure, but we're also working with a number of I look out over the show floor, and I see a number of customers in Cloud service provider land, in in large Global 2 1000, but also in in the the mid tier. So, we're working across the board, and I think that, our view really is that AI is a, is fundamentally a hybrid workload, and we're going to see, people choose to run some AI workloads in the traditional cloud, some in their data center, some right at the edge. We wanna be, our corporate strategy is to be an IT provider that, that helps people run technology wherever it makes most sense for them.

Jordan Wilson [00:04:51]:
So so, you know, you kind of talked about this this transition, you know, from 2023 into 2024. By, you know, you having worked closely with with so many companies, what are some of the biggest, maybe mistakes that you saw companies make when they are trying to create value with generative AI? What are some of those common pitfalls?

Evan Sparks [00:05:11]:
I I think the first thing was just blindly trusting the tools and thinking, oh, not thinking about the data security and privacy implications. I think, especially last spring, we saw some high profile cases of accidental data leakage by going out to some of these, these, web service providers that we're offering the models. I think, companies are getting, more, attuned to that being something they have to watch out for, particularly if it's, you know, if it is some marketer trying to, write better marketing copy or something like that, that's not corporate secrets that you're worried about getting out there. But when it comes to, your HR data or your really sensitive, trade secrets, that's where you really wanna have some protection around around that. So that was one mistake we saw people made. The other was, was sometimes the cost of these things is just really, really high. And so if you don't do the math ahead of time around, okay, how much usage do I think this particular feature is gonna get, and and, how many queries do I have to fulfill, and so on, it might break the bank really quickly. So we saw that happen, and then, people start thinking about, okay, how do I maybe use a smaller model, tighter model? Gonna give me the same level of accuracy and these sorts of things.

Evan Sparks [00:06:22]:
But they tend to be not first design principles. They tend to be afterthoughts as as kicked off a successful prototype of the application, and sometimes that's okay. You know, people have to see what's possible before they decide they really want to invest in, in making a craft.

Jordan Wilson [00:06:37]:
And I I I think one of the things of making something practical is is finding the right provider or or partner. And and speaking of that, we are here at NVIDIA, and I think they're probably one of the best in the world. I I I mean, can you talk a little bit about how you and your clients work, with NVIDIA, and and what that enables you all to do that maybe, you know, many years ago wouldn't have been possible.

Evan Sparks [00:06:58]:
Yeah. So I think, you know, we've been a deep partner with NVIDIA at HPE for many years. I mean, decades at this point. And probably the 1st high density, GPU server, that got created was was, was an HPE box. But, over the last few years, we've really seen NVIDIA become the leader specifically in silicon, but also in the lower layers of software for, for all kinds of AI, specifically generative AI. And so, we've we've really leaned into partnering at the ecosystem level with NVIDIA and saying, okay. You want the best way to run this open source language model? There's probably an MGC container for that. Let's embed that directly in our user software experience.

Evan Sparks [00:07:40]:
We've been, we announced a technology preview of a product again, inferencing software that will, leverage under the hood, a bunch of the NIM microservices that, Antonio talked about on his keynote at the show yesterday, or the day before. And so, there's there's a number of, of ways that we can partner with NVIDIA down to the level of that hardware integration all the way up through the software stack. So, we're really participating as part of this ecosystem.

Jordan Wilson [00:08:08]:
Yeah. And and Evan, I wanna go back to some of these actual use cases because I think that's where, you know, people that haven't found or companies that haven't found value can learn from others and try to implement it themselves. So you mentioned as an example, you know, a client that that, you know, just kind of launched a new initiative, on their website, but they also had things on the back end. Maybe could you talk us through where are, you know, maybe for those smaller enterprises out there who still haven't yet, you know, fully adopted to generative AI, where should they be looking both on the consumer side and on know, the back end for for their own teams?

Evan Sparks [00:08:40]:
I I think probably the the clearest example I I can come up with right now that is, pervasive across industries is enterprise search. So, you know, it used to be a model where you'd, you'd have your enterprise document database behind something that looked kind of like a Google search, and that wasn't exactly it. You search for the keyword and you get the document back. Okay. It was never as good an experience as Google, and it was, but you at least had an index on on those things. Now, because of the tools that people are using, they're actually starting to expect, I want something that is gonna synthesize a response for me out of my enterprise document store and, and answer questions that, span HR, sales marketing, and so on, and know about my products and my terms and so on. We've piloted a number of these use cases internally ourselves as well, and we've seen lots of customers adopt these. And so we're starting to see these rack based applications become a conventional architecture that, that customers are asking us to deploy.

Evan Sparks [00:09:41]:
Unfortunately, those architectures have like 15 components just in software land to to stand up, But one of the things NVIDIA is doing really well with its, retriever, microservices is, giving customers a blueprint for that. And we do, our best to fill in the blanks on on some of the technology choices there and and offer something that's gonna be robust and scalable. But that's that's an example of an application where AI powered search shouldn't just be on, on documents that are on the open web. It should also be on documents that are relevant to your business, but you want to be able to protect those in in all the right ways. So that's a a been a really good use case.

Jordan Wilson [00:10:18]:
Yeah. So you you talked there about RAG and how important that is, to bring, company data into the picture. You know, even as we talk about, you know, going from 2023 to 2024, I mean, is is Rag going to be, you know, what a lot of these, enterprises are focusing more attention on in 2024 as we look forward in the future, or where should enterprises be focusing on right now? Because it seems like, if you're not looking ahead, the pace of the technology is so fast, it feels like you can get left behind.

Evan Sparks [00:10:46]:
Yeah. I think so. There's table stakes stuff and I think that actually, for the table stakes stuff, you're gonna end up with a lot of cases where the large cloud providers and maybe the big software vendors are doing commodities. So if I auto complete my email, probably that's gonna be handled by my email provider. Similar support tickets and so on. But every enterprise is gonna have applications that are really unique to its business, that is, built on their data, their unique data assets, their 30 years of, or however long, of doing business in insurance or in oil and gas exploration or in defense. And those applications, I think, are the areas where enterprises are gonna be able to build a defensible moat. So I don't think there's a one size fits all answer, unfortunately.

Evan Sparks [00:11:33]:
I think the real answer is treasure your data, really, introspect. Where can I create an an intelligent application that only I can create and use this as a as a way to provide additional value to my customers? It's hard work. It requires a lot of thought, but I think that's where we're gonna see the most value get generated over the next 5 years.

Jordan Wilson [00:11:52]:
Yeah. And and speaking of value, it seems like that's where everyone's focusing But how can companies or how should companies be looking at measuring the return? Is is is there a good, you know, formula to say, hey, this big investment is is paying off, and and what should, companies be be looking at? Well,

Evan Sparks [00:12:17]:
top line and bottom line. Those are my 2 two big numbers that I think most, companies should care about. A lot of these efforts should directly translate into sales. More sales of your product, faster, better customer experiences, higher NPS scores, etcetera. That can, that can be one important measure. The other is is cost savings. If there are things that you are using an army of people to do that can now be done by a smaller set of of analysts or associates, invest there. That doesn't mean those jobs go away forever.

Evan Sparks [00:12:48]:
Means those people maybe can be retrained and and start, adding value to your business in other places. And that's really where we hope to see this this all go when it comes to creating efficiencies. It's about, it's about leveling up, ourselves as, as a society. I think about, farming, for example. Do we wanna stop getting people plummels because we're worried about there's not enough people in the field, or do we wanna start harvesting a lot more food for everybody so that there's a market? I think we're we're hoping for a future that's important. So that's the value that, that I'm hoping.

Jordan Wilson [00:13:19]:
What could that look like? Because I think that, you know, some companies, even ones that we've talked to on the show, you know, are sometimes hesitant, especially smaller businesses because they don't know what it looks like if AI works. So what is the equivalent in business of, you know, kind of your analogy of the farmer in the plow? Is it just, you know, maybe people who are doing a lot more to build AI systems or to collect better data? Or what does it look like in the enterprise if AI works?

Evan Sparks [00:13:47]:
I think, it is it it can be hard to put your finger on it, but it's one of those things you know when you see it. When you there was a recent study out of, out of, BCG and and APS that talked about, analysts in an an an entry level, consulting class. And, half of them were were given Genai tools and half of them weren't. And what they found pretty quickly, the the half that were be given those tools to do their job were about 30% more efficient, started performing at the level of the 2nd year, substitute very, very rapidly. And that kind of that's a natural AB test to say, okay, I have more of these people. If I'm a client of one of those businesses, do I want a bunch of 2nd year associates or do I want a bunch of 1st years? I would much rather take the 2nd year, and I'll probably even pay a little more money for that. And so I think that's the kind of, of value that can be generated here and that's really measurable for for these kinds of businesses.

Jordan Wilson [00:14:41]:
You know, earlier we we talked about common, mistakes that you've seen, you know, having worked with so many businesses implementing generative AI. What are some common wins that you've seen, that that maybe companies out there might not be thinking of?

Evan Sparks [00:14:56]:
Yeah. I think the the, the common we talked about the common mistakes. I think the common wins come from 1, standardizing on tools across the enterprise that will allow you to iterate rapidly. We all know this is a really fast moving kind of, kind of part of the industry right now and the tooling is changing on a daily basis. And the nice thing about is that if you give them more data over time, they get better. And so, leveraging what's going on in the open source and the new models that are coming out, the leaderboards and being willing to drop in the latest and greatest, throughout your development cycle. But then also being able to inform those with, with, the company's own there can really drive optimized return. And so that nimbleness and that, building for iteration ends up being a really positive pattern, among companies that we see that are successful with this.

Jordan Wilson [00:15:51]:
Common common theme I keep hearing is is data. Right? Not just in this conversation, but in so many conversations. Have you seen, in in your experience over the last couple of years, especially, you know, after this chat gpt boom, is there a bigger focus on on data than there was before? Because we've been told for, you know, a decade data is the new goal, but now that people are seeing what data can create with generative AI, is is is there even more attention being paid, or should more attention be paid to data?

Evan Sparks [00:16:18]:
I think for sure. I think, data's the fuel for these models in a lot of ways. We have seen pretty scientifically documented, at least the evidence suggests that order of magnitude more data leads to significantly better models, and so people are going to be looking for more and better data that they can feed these models as they over time. It's not gonna eliminate the need for better modeling and better thinking and and so on or even choosing the right application to go after in the first place, but, to a first approximation data rules.

Jordan Wilson [00:16:51]:
Yeah. And and, Evan, just just as we wrap up, you know, today's show because we've talked about a lot from from rag and and guardrails to, you know, how you can actually measure business value. What is your one most important takeaway, you know, for for those listening out there, in order to create and and capture, value that generative AI can bring?

Evan Sparks [00:17:14]:
Think about where you have a moat in your data, and if I'm allowed to, iterate rapidly. Those are my those are my 2 key takeaways.

Jordan Wilson [00:17:22]:
I love it. I love it. Well, hey, there's gonna be a lot more in today's newsletter, so make sure you go to your everydayai.com. Evan Sparks, thank you so much for joining the Everyday AI Show.

Evan Sparks [00:17:33]:
Absolutely. Thanks so much. Alright.

Jordan Wilson [00:17:34]:
Thank you. And hey, we'll be here back all week for more at GTC. So thanks for tuning in, and we'll see you back tomorrow and every day for more Everyday AI. Thanks.

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