The Exchanges

Q Is that the biggest lift for companies trying to work in the AI space right now, getting their data in, in order?

A Um, I think it's the biggest hurdle. Yeah. It's the biggest hurdle. Not even a lift. It's the biggest hurdle to get past or be able to demonstrate there's enough of an area to prove it. Cause a lot of the areas need to be proven still. So boards, uh, a lot of the C-suite it's, this is, there's a tremendous visibility, not just on tech for tech anymore, but can, you know, can the technology enable a business opportunity or an outcome? So that visibility is the first, you know, really large speed bump. Right. The, the, the next lift is scale. Scale is the real lift, by the way. Like, how do I, if I proved it, how do I scale it?

AI assessment note: “I think it's the biggest hurdle. Yeah. It's the biggest hurdle. Not even a lift.”

Q Um, you guys are partners with NVIDIA. Um, talk a little bit about the nature of the partnership and then also what's it like working with a company like NVIDIA? They are, they are fascinating the way that they operate.

A Uh, yeah, it's, um, I think we learned something new every, every turn. I, I, uh, so one, you, you can't hide, you know, they are driving a material growth in the market. Um, and when we look at, you know, kind of the partnership opportunity, you kind of, first people think initially, oh, it's all about revenue. It's all about kind of growth, turnover, and sales. But one, you know, to our delight working with them, it was really around how do we accelerate? Opportunity. How do we identify and co-create adoption work? And, and I, I see your smile sort of like, cause even I was kind of like, wait, this sounds too good to be true. I mean, I'm, I'm, this is, there's usually a hook that's going to get you somewhere around the corner. Um, but you know, from a, I'll, I'll, I'll probably give you from perspective. It is probably one of the best engineering partnerships that, that demonstrates you can co-learn, you can, you know, develop and target opportunities and you can, you really can think about how you co-create. So a lot of the things that we've started to do and the partnership was based on was can we start to natively extend our capabilities to use their, for example, AI and their agent frameworks? Can we develop it faster? Can we use like their interfaces, like the NIM interface kind of scenarios where we can speed to market, right? Opportunity. And can we shield our customer…

AI assessment note: “the partnership was based on was can we start to natively extend our capabilities”

Q So now that we've established that, who is generative AI working for? I mean, what are the 10% that this actually makes sense for?

A Um, you know, we're seeing, you know, and I thought I was going to laugh and say not, but there's some really interesting things that, that are maturing. So, um, and depending on, you know, happy to go into more detail on some of this, depending on your questions. But if I look at things like some of the telecom clients that we have, um, a lot of the work in how they're approaching AI, approaching information, gathering, approaching the analysis, um, is working very well. And how we start to apply again, gen AI looking at procedurals, product, you know, capabilities, um, you know, selling opportunities. So how do you go and upsell? So I think there's a huge kind of gain that we're seeing in certain industries. So they're seeing benefits in that. So now they've evolved into cart generation. So how do I understand what may be better targeted for you? Personalization. So they've, they've gone from understanding what they built within the workflow. They built the, the, the efficiencies within the workflow, the product life cycle. And now they're using, you know, Gen AI to assist within that sale process. And now agents involved in personalizing baskets and shopping carts for you. So reducing the time in which to actually get you to sign a contract for a new service. Automotive a little bit differently. Interesting though. Um, big push on understanding personalization. So harvesting…

AI assessment note: “if I look at things like some of the telecom clients that we have”

Q You said it's helping in the sales process for telecom companies. Can you talk a little bit about what that looks like in practice?

A Yeah. It's a little bit of a double click and, and, you know, kind of through the process in itself. Um, think about even, I mean, we're, we're, we're all customers, right? Telcos in some, some, uh, realm or another. One, as you start to call in, you know, getting almost the immediate, uh, analysis or information on our profiles, what services do we have? How long have we had the service? So things like a person would normally talk to when they're trying to upsell you on a new contract or a contract renewal, but it starts to build that knowledge base of information. The system can very quickly, we start to identify opportunities. Hey, we can reduce your bill, you know, by X cause you're not using these services. So can I improve your experience? So all of a sudden suggestions start to come in and you don't need the same human interaction associated with it. And as you start to get into that, you can now give scenarios to show you this can reduce your price. So instead of you going through this human process and I'm not sure where everybody is, but if like I'm a customer of one large telco in the United States, They'll call me every year with different scenarios. How do I reduce that? I wound up having to do the analysis myself. They've closed the gap, and many of them are starting to close the gap on how they approach and demonstrate the information of your usage, how they can …

AI assessment note: “getting almost the immediate, uh, analysis or information on our profiles, what services do we have?”

Q Some really bad ones. Um, so talk a little bit about who makes the decisions about, uh, whether to go forward with AI. Is it the, is it a traditional tech decision or are there new folks in involved in it?

A Great question. Oh yeah. It's a phenomenal question. I, um, I can tell you without much hesitation, there is a material shift. We're seeing a lot more business leaders involved in the decision process, and I think that comes in a couple of different, a couple of different reasons, or I think environmental changes, and I kind of made that evolution comment before, but the consumption model is evolving too. There's a lot less appetite for build. There's a lot less appetite, you know, for creating solutions into the business. It's, it's a need-it-now scenario. So we're seeing more and more. So the analysis, at least we feel it being, we feel it in the sense of we're seeing large, the majority of investment being directed through business leaders and how we're driving into it. Um, it also shows a lot less patience and drift and a lot less patience in the thesis being proven wrong, but it is, it is a shift, um, within that, within that, uh, within that operating environment.

AI assessment note: “We're seeing a lot more business leaders involved in the decision process”

Q all, and no, sorry, only 10 or 20% have made it out the door, and 80 or 90% haven't really been proofs at all, they've just been sort of prototypes, and you know, they look nice, you go to a management meeting, everyone cheers, and then they never see the light of day. On the implementation side, why is it so hard to get AI projects out of the door?

A Um, wow, this is, I gotta be careful on how I answer this one, right? I'm not sure I'm gonna help the numbers or statistics in the conversation. The, um, I'll probably reflect first a little indirectly and then directly. So indirectly, it's, it's no different than the rate and pace of solutions hunting for a problem. I mean, the market is amazing that way, and the investment is phenomenal. So we have so many things pent up in the opportunity side. Um, the best part we're starting to see is the willingness to adopt, the willingness to try. The POCs have actually gone up exponentially. They haven't gone down. They haven't died down at all in that scenario. But what we often get into is the expectation. The expectation that AI is going to naturally solve the problem where we haven't really defined it yet. So there's a lot of, uh, I'll say missed expectations. And although, yeah, 80% failure rate is not uncommon or call it, you know, getting thrown on the shelf, right? First of a kind is last of a kind type of scenario. Um, the, the, the approach we often get into is, yeah, we, we, we worked on the POC, but it's actually trying to find out what's missing. And we continue to work on. So a lot of the effort we bring is in the approach. So how do we understand what you're looking for? What's the business problem? And we often find many of the POCs turn into, they don't require complex…

AI assessment note: “The expectation that AI is going to naturally solve the problem where we haven't really defined it yet”