00:00:04 · Neil C. Hughes
[00:00:04] S0: What does an AI agent need to genuinely understand before you trust it to complete work right across your business? An assistant might be able to help with an individual task, but coordinating procurement, finance and supply chain activity will possibly bring a different set of questions and a certain amount of caution. And the reason for that is many will be thinking, does it understand the state of an order? Can it access the right information? But who controls what it can do? And how do you measure whether the result was worth the cost? Well, today, I'm delighted to be welcoming back Erfan Khan back onto the Tech Talks Daily podcast. He is the chief product officer for SAP Data and Analytics, and our conversation this time will be looking at the business context behind reliable enterprise AI. Because we're gonna discuss why problems often appear when a pilot reaches operational scale. We'll discuss all this along with the role of SAP Business Data Cloud and the choices leaders are facing right now when connecting agents to the systems that their organizations already heavily depend on. But enough for me. Join me in welcoming back Erfan to the show now. So a massive warm welcome back to the show. For anyone that missed our previous conversation, can you tell them a little about who you are and what you do?
00:01:36 · Irfan Khan, Neil C. Hughes
[00:01:36] S1: Neil, great to be here again. Erfan Khan here from SAP. I'm the chief product officer for SAP's data and analytics organization. So I've been here around about eighteen years, came via an acquisition from Sybase, and I've been pretty much in data and analytics my entire working career, which is too many years for me to to agree or even to acknowledge at this stage. [00:01:57] S0: Love it. And as you know, every day on this podcast, I try and demystify a different area that people keep hearing about. There's a lot of buzzwords out there at the moment. And when SAP talks about the autonomous enterprise, it's something we hear a lot about right now. What does that mean in the daily operation of a, I don't know, a finance, supply chain, or even HR team? What does that mean on the ground?
00:02:21 · Irfan Khan
[00:02:21] S1: That's a great question. I mean, autonomous enterprise is playing on on words to some extent. I mean, the word autonomous means running with potentially a human in the loop, maybe no human in the loop. And across the enterprise, naturally, there's a big distinction when anything runs within an enterprise landscape. You have to honor and manage all of the enterprise grade requirements, whether that's disaster disaster recovery, availability, all the way through governance, security and all the other aspects that you need to cover off. And when we announced, albeit from an architectural point of view, the Autonomous Enterprise earlier in the year, our intent was very, very clear. Take, for example, the domain of finance. In most customer environments, you tend to have multiple stakeholders that all contribute towards finance. I mean, it could be something from SAP, from a general ledger. It could be maybe data coming out of a completely different system or a different environment. And in order for the one end to end foundation to be delivered to an end customer, and they wanna have that one single experience where they're not just looking at task automation. They wanna be able to string all of these discrete activities together. It needs to operate as one autonomous enterprise, not disparate or discrete little aspects that have to then be coordinated. This brings everything together under one single foundation, and that's essentially what we've been working towards ever since the announcements back in May.
00:03:43 · Neil C. Hughes
[00:03:43] S0: And I've been to a lot of tech conferences this year, and one of the things that I've learned and seen firsthand is how many companies are already using AI assistance for individual tasks. More recently, I was at a conference last week in Orlando, and they were talking about digital workers, and we're talking about teams of agents now. But what do you think has to change before these coordinated agents can complete work across several different business systems?
00:04:10 · Irfan Khan
[00:04:10] S1: The the fundamental gap that still needs to be addressed, and we are feverishly trying to address this, is the context gap. Yeah. If you have an agent and it operates within the domain of, say, procurement, and you have another agent that operates within maybe supply chain, it's pretty pointless with those two agents just sending each other potential instructions or even coordination attempts if they don't know what the state of a purchase order is, for example, or the order on hand maybe or the inventory level. So this is why the foundation of agent to agent or even for that matter, broad coordination of agentic workflows right now is un... Unfortunately, right now, it's not with all of the necessary requirements of context that are needed to be really able to drive the enterprise grade. So that's one fundamental problem that needs to be addressed is the context gap. The second one is around the trust and governance. I mean, you could imagine when you have a very well defined deterministic IT system, everything is curated based upon user access and control. Now if you have an agent that runs completely independently, you want to make sure that you manage the same level of governance and security, fine grain and coarse grain. So that's another element. So context gap and of course authorizations and making sure that the governance piece is there. And then the final piece is really is the time to market and time to value. Most customers are eager to get from experimentation to a full blown deployment, and I feel as if that's where the biggest challenge lies because you have to deal with the previous two points before you can get to the the actual outcome now.
00:05:39 · Neil C. Hughes, Irfan Khan
[00:05:39] S0: Yeah. And you mentioned a few challenges there, and I do repeatedly hear around problems that are typically around data quality, process design, and permissions. But I'm curious. When do these things usually become visible when an organization moves from outside of that AI pilot and into operational use? What what typically happens here? What have you seen? [00:06:02] S1: Most IT departments are pretty well governed, and hence, they're they're wholly curated environment that you tend to have, data lakes with data that is nicely curated and lands in a in a storage format of your choosing. And now answering the question, when do these problems materialize is really when you go from that POC to actually then let's run this at scale, be it in a in a departmental level experiment or even beyond or or, you know, maybe even beyond that. And I feel as if most of the organizations right now are very mindful about these topics, but it's not straightforward because it's almost as if you've gotta go back to the fundamentals. You can't really have a reliable AI without reliable data, and reliable data means that you have all of those things that you just mentioned covered off. Quality, making sure that you have the right governance, making sure that you have the right security, all those things need to be given as a strong foundation before AI could be successful.