00:15:16 · Irfan Khan
[00:15:16] S1: Well, the the narrative is is actually quite nuanced. Yeah. Trust trust is is is not subjective, but it certainly can be to some extent what does it mean when you when you are outsourcing some level of of the workflow or pipelining based upon an agent. The majority of customers are paranoid, and rightfully so, but they don't want ungoverned agents to do certain things without understanding that there could be reputational risk for the organization. So typically, what happens is that you put much, much more AI governance and guardrails around the process. So trust at the employee level has got to be some checks and balances. It's not uncommon to look at your spreadsheet just to make sure that you knew what last night's actuals were before you make a budgetary decision. Even though the budget and the real time budget may be shared with you directly through the agent because you still have a degree of not mistrust, but you wanna get that validation. So I feel as if the trust element is dependent upon the usage and dependent upon the persona and the intent of the user, certain things will provide you be much more lapsidades. You say, fine. Okay. It's an approximate number. I'm good with that. You know, if you see, think about order entry and you think about inventory on hand, you'd want that to be relatively precise. Right? Relatively very precise for accuracy purposes. If it's not, then you're gonna end up fulfilling or potentially trying to fulfill an order that just wasn't achievable. That has reputational risk. At the same time, if you're now looking at looking at a historical sort of pipeline and you wanna look at conversion of a pipeline in the sales organization and you have some models and we've acquired some companies along the way, which I'm happy to... Love to share with you. We acquired a company called Priolabs, which gives us tabular approaches to being able to look at more of a narrow model in terms of looking at prediction and, of course, simulation and forecasting based upon much more in context learning, which means that you don't have to have a big training model based upon a frontier LLM before you can actually make a decision. So trust will go up if you can bring in more in context learning and more accessible type of forecasting and predictions within a workflow.
00:17:19 · Neil C. Hughes
[00:17:19] S0: So many great points there. And I always try and give people this thing a a valuable takeaway. And if we'd have a leader from an organization that have several pilots on the go at the moment but with very little measurable value under under increasing pressure to prove ROI improve, they're making a difference. What should they be changing during the next six to twelve months?
00:17:39 · Irfan Khan
[00:17:39] S1: The the role, Neil, of of platforms has been undeniable for for quite some time. But having a platform that really is geared towards allowing agents to be built, allowing data to be contextualized, then you can reason with the data, and then having a governance around that becomes an incredibly important end to end set of activities. And not to self promote here, but we have our business AI platform that does precisely that. It gives you the full end to end approach that you need to have. And also, given most customers' experiences of the past, they wanna have super open approaches to bring their own platform, bring their own data lake, bring their own table format, be it Iceberg or other. And this is another key key key, you know, requirement that most customers have. They'd already be locked in within one vendor space, one LLM, one infrastructure provider. And these are the the hallmarks of future success because you only see this, and I think you and I can both testify to this, on a weekly basis, sometimes almost on a... Seems like on a on a on a daily basis. We see advancements being made by one lab compared to another, and optionality needs to be incredibly important. And this is exactly why I think most leaders right now are unrightly so cautious because they don't wanna back they don't wanna back the wrong horse, they certainly don't wanna go down a path which minimizes their scope of of innovation. So I think those are the real issues that most customers are feeling. And our intent through Business AI Platform, leading with openness, supplementing via Drenio, the acquisition that we really made around allowing federation and, of course, strong levels of integration across data lakes and Rails here that we recently acquired also to give the whole notion of being able to do unification of data, what we call unified data products. All these things will make a huge difference. And as I said, that's slightly self promoting, but I feel as if those are really the enterprise grade requirements that most customers are looking for right now.
00:19:26 · Neil C. Hughes
[00:19:26] S0: Yeah. I completely agree with you. And I think in the beginning, many leaders went all in on AI. There were so many questions, and I will cautiously suggest that many were distracted by obsessing over exactly which model they should be using. And, thankfully, that conversation is evolving now, and it was refreshing to see a video of you. I think it was on your LinkedIn feed a few months ago where you said enterprise AI needs more day... More than just data and models. It was great to hear you saying that. But on that side of things, we are talking about all things in the SAP ecosystem now. Can you expand on how the SAP Business Data Cloud powers the contextualized and reasoned layer of SAP Business AI platform to give agents that deeper understanding of how a business actually runs?
00:20:15 · Irfan Khan
[00:20:15] S1: I'm glad you asked that question because I was very eager to maybe address that, and thank you for asking the question so directly. You know, we have, at SAP, been custodians for customers, and and we're very privileged to have this ownership of core business processes and core operational systems for decades. Independent of whatever the innovations and there are many coming out of the Frontier Labs, we recognize that the transactional core is this transactional core of most organizations. So if we take a look at the evolution that's going on, they want to be able to contextualize that data and be able to use that in meaningful AI outcomes. Hence, the BDC introduction we contextualize now. What we're able to do is use BDC as a primary ingest of both SAP and non SAP data, which has now been supplemented with our latest acquired company called Dremio with the Dremio Lakehouse, which now means that you can actually go from zero copy share with any of the data platform or or or hyperscaler environments. That's a great value add if you're a Databricks customer, you're a Snowflake customer. You can continue to interoperate zero copy share within those environments. But now with also with the advent of Dremio, if a customer wants to bring their own data lake, they have an iceberg native, lakehouse that they built, and they wanna be able to allow federation or sequel analytical kind of workloads across those datasets without having to consolidate and move all the data. This is now where BDC has even an additional dynamic now. It allows us to interoperate both on the open side of storage, which is iceberg, and also on the catalog side, which is Polaris, right, which is a strong sort of driver of the standardization as a technical catalog across our industry now. So that's one area. And when now you talk about contextualizing data, if you have that infra kind of foundation where you can ingest data SAP, non SAP, represent that data as primary data products, and then now bring in the Raltio piece, right, which is another key element, and have the means of being able to unify data. Imagine you've got something like a call center. Call center is very, very complex because it spans multiple environments, SAP, non SAP.
00:22:19 · Irfan Khan, Neil C. Hughes
And one of the key deliveries that we have, around about 50 unified data products is what we're targeting for the end of the year, and a good example of that would be cost center where maybe the cost center lives across SAP, Salesforce, and maybe even Snowflake. And to have one single unified data product that has the context of a cost center semantically enriched... Now imagine that your AI and your accuracy through AI, if you can ask a question, a natural language question, in our case, using Dual, and you know that fundamentally there's going to be a high degree of accuracy. This is exactly where Business Data Cloud now contextualizes, allows the data to be reasoned with, and allows it to be served up directly through that Dual engagement layer, whether that's with dual work or dual studio, which we're looking to, of course, announce at TechEd and and giving customers a huge level of productivity gains from from that one end to end sort of foundation now. [00:23:13] S0: Well, we've covered so much in a short amount of time today, and I would love to include links to everything that we've discussed. So people listening can go straight over to techtalksnetwork.com. There'll be a blog post associated with this episode. I'll include all the links that I can find from everything you've mentioned. But, anyway, you'd like me to, point everyone listening?