00:08:21 · Irfan Khan
[00:08:21] S1: I think that's a great question because there was a feverish set of activities that have underpinned the whole generative movement, generate more text, generate more content of any description. And that could well be a measure, right? But it's not the measure. And what we tend to find the majority of customers right now is they will have a high degree of expectation around AI and where they look at measures of success. I mean, at the top level, could argue and say, if you're the CFO, you'd wanna be able to break out your company earnings and report e... You know, EBITDA and how that was affected by AI or the initiatives around AI. That's maybe the macro. If you then go down a level, you could be either take any discipline. If it was in my realm of development, maybe you want to talk about developer productivity, which could be everything from vibe coding all the way through to, of course, using maybe more... Not just large language models, but also tabular models to be able to accelerate prediction or forecasting. So the measures actually do depend, and there has to be some kind of an intent based approach that you take here. In the most part, what customers are really looking for is that they don't want to just throw tokens at the problem, and then the tokenomics don't actually add up. I mean, you probably recall hearing Neil over the summer where... I think it was AWS. I could be wrong here, but AWS was eliminating certain sort of roles, only finding that the roles that they eliminated was now being met by AI and token consumption, which was like two x what the individual human being was doing. So that clearly isn't the best vantage point either. So I think measures are gonna be multi... There will be a multitude of measures, but the most important one is really the end to end productivity. And I I personally seen this, at least from a coding and development point of view, we are now fully incubating AI based native development and, of course, AI assisted development. An assisted development was a starting point. Native development, of is the pursuit of of of every single development organization to start with design all the way through to delivery and then operationalizing the way that the service is available. So you'd wanna go really from end to end along those... On those on those paradigms there.
00:10:22 · Neil C. Hughes
[00:10:22] S0: Yeah. You raised a great point there, especially around the tokenomics. And I've read a a few examples of businesses that have done that and thrown everything at AI, but I think by q two or q three, they were already paying more than they were to an individual. And are are you seeing teams beginning to recognize this and you can't use, or burn tokens for absolutely everything, and it's just certain tasks? How how are you seeing them get that balance right? Is it something you... You're seeing a lot or a fear of of those token costs?
00:10:52 · Irfan Khan
[00:10:52] S1: I mean, our own personal experience, and I'm sure I'm probably not in a minority here in the industry, is that you start off with the the desire for everybody to start becoming or working towards the AI native approach. And therefore, it's without guardrails. And then very quickly, when your token consumption gets to a point threshold a or threshold b, consider whichever one is that you you think you're at the moment, listeners are at, threshold a may will be, oh, we better take a look at this because we may be overstretching our boundaries in On top, consumptive type of credits that we probably were not prepared or were not really necessarily gearing up towards in support. And when you get to threshold B, it may well be, Okay, now we need to do something drastic. We cannot operate the same way because there's causality here. And if you look at the value that's been attributed versus the cost, I think it's debatable in some cases whether the cost is actually supplementing the the, you know, the value that that potentially could be there. So I feel as if most organizations are in their their own journey, we for one in SAP, we really let our developers experiment, but like many others that we're also now becoming much more driven by standards and taking a look at what tasks can benefit from consuming tokens versus those that probably will not benefit and becoming much more deliberate in the way that the AI is then put to work. And it can't just be that you use it for every single task because that in itself may not give you the return that you desire.
00:12:15 · Neil C. Hughes
[00:12:15] S0: Yeah. 100%, Lydia. And another big talking point right now is accountability. So when several AI agents contribute to a decision or a complete process, how should responsibility and audibility... How how should these things be assigned, especially if the result that comes from this is unexpected at best?
00:12:36 · Irfan Khan
[00:12:36] S1: You know, we have a quite a... An interesting, I believe, quite innovative way of looking at this, which is company memory. And what is company memory? It's an initiative that we are running within our process improvement process integration layer where if you think about a classic on a sales order process, you know that that process will take certain paths, twists or turns depending upon the time of the month, quarter, whenever it may be, but it operates within certain thresholds. And when you now run the risk of running all these different things with agents all discreetly chipping in, doing things, not not not necessarily necessarily contributing to the end outcome, but unnecessary for context, you have to be super careful that the outcomes themselves are not being heavily compromised. And this is now where company memory comes in its own. And imagine you've got maybe a classical sort of front office desk, right, where they manage procurement for for the organization. They they deal with customer orders, but at the same time, in the background, they do fulfillment based upon certain partners or partnerships. You'd wanna make sure that you manage these partnerships holistically. If you know, for example, that q four typically or Black Friday sales coming up, you have a means of being able to take advantage of the buying power across multiple partners and then passing that through to your consumers, you need to know that. Agents obviously will have no recollection of that. That's the humanistic side of things. So company memory really gives a means of agents to operate knowing what they should do, but equally importantly, what they shouldn't do. And I feel that's one of those key differentiation opportunities because if you own the process and you own the back end and you understand the operational systems, it's well within our grasp, be it through the autonomous enterprise, and allowing that level of fluidity in terms of outcomes and agents participating, let's call them a swarm of agents, who all wanna interoperate, for them to operate within more of a governed and, at the same time, institutionally acceptable manner. This is one of the key areas that we're looking to investigate here.
00:14:32 · Neil C. Hughes
[00:14:32] S0: Your agents aren't producing accurate answers because they don't have a complete semantic understanding of your data, and Denodo is solving this and solving it through semantic consistency. Through semantic consistency, your agents can start making accurate predictions in real time. So see what else Denodo can do by visiting denodo.com to learn more. And I'm curious from all the conversations you're having and the customers you're talking with, what have you learned about employee trust when AI begins completing work rather than just recommending the next step? What what was the narrative like on the ground floor there? What are you seeing and hearing?