Executive Summary:
Join us on the ground at Oracle AI World 2025 in Las
Vegas for an illuminating conversation with Keith
Causey, Senior Vice President of ERP Transformation
and Development at Oracle. In this essential episode,
Keith draws on his unique perspective as both a
former Chief Accounting Officer at Caesars
Entertainment and current Oracle executive to reveal how AI is fundamentally transforming enterprise finance - moving beyond simple process improvement to unlock data as a strategic asset. Discover why the shift to agentic finance represents a complete paradigm change for CFOs, how Oracle's platform approach delivers end-to-end autonomous operations rather than point solutions, and why treatingyour data as your company's most valuable asset will determine who wins in the AI era. Keith shares candid insights on navigating the "noisy space" around AI, the critical importance of clean, continuous data, and how the CFOrole is evolving into a more strategic position that now often encompasses technology decisions. Whether you're a finance leader planning your AI strategy or a technologist supporting business transformation, this conversation offers the pragmatic clarity you need to move forward with confidence. Stream it now, and be sure to LIKE, SHARE, and COMMENT!
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Transcript:​
00:02
And greetings all welcome to the enterprise edge podcast. We are recording here on the ground at the Venetian in Las Vegas at Oracle. AI world 2025 today we are joined by Keith cause, the senior vice president ERP transfer transformation and development at Oracle. Keith, welcome. Thank you, Mark. Appreciate you being here. So we are taking some time out from the busy hallway discussions and the hub. And it's a busy place. Last I heard there were like 50,000 people here. Have you heard a number? It feels like 50,000 I don't know if it's 50,000 but it's a lot. There's a lot of people here.
00:38
Nobody seems to know 20 ish, 20 this. Okay. Anyway, so we're gonna, we're gonna have some fun. We're gonna have some fun. We're gonna learn a little bit about Keith, who has a very interesting
00:46
background, and and we're gonna help add some clarity and precision to a very noisy space around ERP and what AI is actually meaning for the space. And so let's get to it.
01:00
So let's, since we're here, we're at AI world, and today is Day, ultimately, day three of AI world. I think, if my math is right, what I'm curious, what, what? What's the, what's one of the most interesting questions, or maybe push back that you've received from a customer, or anybody, just here at AI world, maybe in the hallway, or any, any kind of interaction that you've had that's noteworthy. That's interesting. I actually run our finance strategy council as well, so I'll bring that into the Yeah, you know, there's just a lot of excitement around AI. I think people are still trying to understand how to start quite, quite a bit.
01:41
So I think there's, and, you know, like you said, you kind of mentioned it some confusion out there. There's a lot of boutique firms trying to do point, point solutions and those types of things. But I think now, at this AI world, it was our opportunity to kind of flex, if you will, the power of a SAS platform, and you know, what we're doing is really, instead of looking at point solutions, we're
02:06
we're solving for end to end, functional, autonomous operations. And so now I think they're seeing the power of what an agent can do, yes and doing that. And I think now they're seeing the simplicity that this platform can bring and solve holistic and bring holistic change, versus, you know, this point change that they've been chasing. And so as a result, I think people are becoming more excited about it. They understand that it's very doable.
02:34
We're going to help them to understand the factors that they need to be successful. And so it's very exciting, because I think we're giving them all the tools they need to really be, you know, become more AI, focused. So, and that's really been the conversation. I haven't heard any. Hasn't been anything negative. It's really, just really positive, and just a lot of questions on how to get there and how to get there quickly.
02:58
And it's funny, now people want more, more than where we're at. So, and the other thing is just, you know, coaching them on, you know, this is like the initial stages. And, you know, the power of a platform is that it constantly changes and improves. And so even the huge step changes that we have now become just increasingly better, you know, over over time. So that's been the positive kind of conversations that I've been having anyway. Yeah, I mean, similar, same as me, I think there's a lot of curiosity. There's also a lot of hunger for tangible pragmatism, like, what do I do tomorrow? Like, what, what do I need to do? And, and I think maybe this is a question, but, you know, you think about there's a lot of providers and software companies, services companies, that truly have advantages, but sometimes it's hard to discern what those advantages are when you're in the buyer seat, right? And one of the obvious ones that I observe is that some companies are kind of checking a box and they're the AI is kind of a feature that they've added, almost like a tool set, right, as opposed to replatforming, re architecting, embedding AI and workflows with more of an AI native development philosophy. And I'm curious, number one, if you agree with that, and number two, what else in the ERP space do you think makes Oracle different and worthy of of note because of it? Yes, you know.
04:40
So first of all, you know, the conversation I've been trying to have with people is, yes, this is a, you know, so I focus on finance. So yes, the ERP finance platform, you know, the historical conversation has been, I want to make my accounting operations smoother. I want to close faster, you know, you know, I want to fix a process issue or whatever. Now the conversation is changing. You know what we're doing is removing all of the manual work, letting ai do the heavy lifting, and then what are we doing? We're changing data into an asset. And so now we're able, with AI to activate that asset, to provide intelligence, and with natural language processing, now we're able to interact with that conversation, drive actions right to drive better insights, decisions, actions, and now the conversation is around strategic and operational goals, and how do we now use the platform to really drive that? So the conversation with the CFO is changing dramatically. So now we talk to CFO. It's what would you like to accomplish with your data that going that can help you? You know, because now you have continuous real time insights, so there's no longer chasing down information. Now you have these insights that are driven by AI that will help identify issues and opportunities. And so now it's just, you know, we've been chasing this for 40 years by turning it finance into something more valuable, more insightful, more operationally oriented. And so now it's just a much more positive energy to driving results. And I mean, it makes the hair on the back of my neck stand up, you know, talking about because I'm so passionate about it. But now we're here, and I, you know, I think it's just completely game changer. There's been so many different inflection points, you know, from a transformation perspective, over the last four years I've been involved with this one's so dramatically different, because it's really changing the conversation, the focus and the experience of work it is. Well, speaking of that, I think one of your previous roles is easier with Caesars Palace, or Caesar's forum or Caesar's entertainment, entertainment. I was the chief accounting accounting officer, which is and then I ran out it for my last year there as well. Gotcha so you have an interesting voice of customer perspective. And I'm wondering, put that hat back on, if every day, if you and it's a very valuable hat to have. I have. I have one. I was at NCR for eight years. Very different role, but the end user, the practitioner, the one who's trying to navigate and make bets on technology, yeah, right. You have incredible bias now, because you work for Oracle, but if you, if you have that hat on again for Caesars, and you're trying to figure out what's real, what's not, what do I do? How do I make my bets today, tomorrow and the day after tomorrow? What would you be thinking? What would be the most important criteria you'd be looking for from partners like Oracle? What would you be thinking about and evaluating? Well, since, I mean, I picked, you know, I was a lifelong customer of a competitor, yes, mentioned their name, yeah. But, you know, when I went to Caesars, it was a transformation play, and we also had to take the company through bankruptcy, so it was very disruptive. But during that time, we also completely changed out our systems, right? And so at the time, cloud was very new, but I saw the possibility right cloud platform, you speak one language, you saw it's a business tool, not an IT tool. No offense to it, but it can do more valuable things than run, you know, finance projects.
08:19
So that partnership was they were with me every step of the way, solving for my business issues. It was still a process efficiency, play back then. You know, that was the conversation.
08:31
But the amount of strategic capability that that unlocked, because automation, my applications, my infrastructure, was all built into one. I didn't have to worry so much about how to make things work. It was really, how do I make things more valuable from a data perspective? So even data back then was important.
08:53
We also knew at the time, you know, it wasn't going to solve all of our problems, but over time, it might. And so it was also jumping in, you know, from a partner that I knew was going to continuously update, transform and be cutting edge, always be current, so I didn't have to worry about that. So really, that changed the conversation, from a business perspective, right now, it is focusing every day on getting the intelligence around data. It's more proactive thinking around solving issues and problems. I didn't have to worry about IT projects and, you know, those types of things. It was just really my team focused on continuous innovation and continuous process improvement so and now from, you know, 2016 to where we are today. I mean, it's completely night and day. We added machine learning and other capabilities along the way, so it's embedded automation, but now with the step change, change towards agentic AI agents and agentic finance, yes, I mean, that's next level, interactivity with your data and intelligence. So, I mean, that's what you want. So you want continuous innovation. You never want to be out of date. I mean, that's what we always were chasing as well. I mean, that's why we get off of on prem. They're outdated. They're customized and and quite frankly, I mean, I've been at so many different companies, customizations overrated.
09:53
10:16
You know, standardization is where it's at, and it's all about strategically.
10:22
Getting control of that data in order to drive the insights and value so, and the companies that really understand that are the ones that are going to win, you know, as we move forward so, and we're here to help them. That's great, yeah, that's great. No, I love it. I love it, you know, I talk about agentic finance, I don't know how much of Larry's remarks you listened to yesterday, but I think he was on there for about a couple hours. And I love what he said about private intelligence. I don't know if those are the words he used, but this notion that there's sort of the ceiling the use case today about specifically generative AI, but also agentic. It's basically just using publicly available data that's on the internet, right? Internet, right, and so for the most part. And so there, there is this advantage that Oracle has, and that is their database position, and the fact that you have access to all of this sort of call it private data, that's company specific, industry specific, regulatory, specific, and that sort of level of private intelligence can really drive precision and accuracy and value from a lot of these emerging autonomous workflows. And I thought that was a very it was kind of an aha moment for me in the light of the previous question, which is, what's different? What is it? What should people note when they listen right to what's different? And it leads to a question I have in the agent world into finance. The question is around sort of governance and orchestration. When you have multiple agents performing different tasks, some of them might be partially autonomous. Maybe someday some will be fully autonomous. How are you thinking about governing all of that and orchestrating all of that, and are the agents interacting in a way that sort of human workers interact and learn and collaborate? And how are you thinking about that sort of multi agent world unfolding in the finance space that might be a little too deep for me? Okay, Keith, that's okay finance perspective. But first of all, I want to, you know, just go back to data, and that's why I'm calling I'm specifically calling out data as an asset. Yes, you know, and we say, if we have the data, we can do anything, right? And companies need to start thinking that way, and they need to protect their data.
12:24
12:41
They need to make sure they have clean continuous technology. They need to have clean continuous data, and it needs to be uninterrupted and not fragmented. And what I mean by that, and that's why we have a platform strategy, it's clean continuous, it's uninterrupted, and it provides continuous results and insights. That's a clean straight line. You know, people like these shiny tools, and they like to bolt on third party applications, because somebody in the company likes that. If you treat your data as an asset and you treat your platform as an asset, I think you're going to make different decisions, right? So now it becomes a strategic question, is it strategically more important for me to have a bolt on point solution, or is it more strategically important for me to have more continuous insights, right? So we're teaching people that that, you know, people don't understand. They're trying to solve for their accounting. They're trying to solve for their financial planning. We're trying to solve for unlocking the value of your data, while we're also providing you the best solutions for your accounting and your financial planning, right? So, and that, when I think about what Larry said, and I just I hadn't heard it, but if I think about what Larry said and how that kind of relates to us, I think that's exactly how it relates. So, yeah, so now, when I think about autonomous and these agents, yeah, I don't think we can get too far ahead of ourselves right now, because I do think it's early days. I mean, we're in the days of creating agents to replace functions. What do I mean? And again, I want to make that distinction between, you know, a point solution. So a point solution is solving for an individual process, issue within a function. What we're doing is replacing an entire function, end to end, to make it autonomous, right? And so that's why. So if I think about our payables agent that replaces the entire payables function with an autonomous process, will it do 100% of everything? No, but here's the conversation I was having with a prospective customer this morning, what we tended to do over time in our process designs is we solve for the 10% and we over complicate the 90% right? What an agent does is it completely simplifies the 90% so that you could focus on the 10% and, oh, by the way, we give you the tools to also build agents so that you can solve the 10% based upon your needs, wants, desires, right? But the name of the game, ultimately, is to continue to standardize, innovate, automate, you know, and bring that into your agentic flow with our AI agent studio, that will still allow you to do that, but you still want to try to standardize the 10% and that'll be the ongoing name of the game, right? But what you're not doing is over complicating the 90% so that's one. We're replacing entire functions and making them autonomous, and again, that unlocks the data. So now we're not trying to pay faster, but what we're trying to do is get the data faster, to process it faster. For that. Now we have the data that's available so that we can look at business decisions. So now I can use that data for something different. It's no longer procure to pay. It's like, okay, now I have my my spin data. I can do spin management analysis on a real time basis. I can look at working capital opportunities for payment options.
16:00
AI will drive insights and give me recommendations on what to do in order to meet my cash flow objectives. So we change it to now a business conversation and more operationally oriented actions instead of process discussions, right? So, so that's one and yes, now we have other agents that we bring into place. So we have a payments agent, yeah. I mean, that is talking, to some degree to your payables agent, and yes, they're sharing information. However, humans are still in the loop, right? I mean, at this point in time, yes, the AI agent will make a recommendation for a payment option, but the human has to be involved in the final decision. And, you know, tell the agent whether or not it accepts its recommendation. And you know, the AI agent will continue to help in completing a transaction. Will that become fully autonomous over time? I think only time will tell. I mean, we still have resistance to adoption, you know, from a legal and it and auditor perspective. So I think those are the things that need to over be overcome as well. But I you know, and then maybe over time, that trust factor will continue to build. And then, yes, I think over time, it will become more autonomous. But I think it's just too early to to say we're going to be there yet, but over the next five years, yeah, I think things will become more autonomous, and the agents will work together and more intelligence. I mean, if you think of just about predictions and insights, you know, from that perspective, yeah, that agents going to work with your operational data to draw that out, you know, and to be able to provide recommendations issues and opportunities from that perspective. So it's to some degree they are now, but I still think there's a huge amount of human intervention,
17:49
particularly when you think about an insight. I still think humans need to review that there's a certain level of human intelligence that needs to go into final decisions and the actions that arise from them.
18:01
So, you know, but I think the quality of the decisions and the speed of actions will, you know, improve quite dramatically. And that's exactly what AI is driving. From my perspective, it changes the experience, it changes the outcome, it changes the focus, and it changes the value, you know, overall the results. I don't know if that makes that's a great answer. That was windy, but no, those are awesome. I mean, I got like, a million more questions, but we only have three minutes, so okay, but maybe, maybe I'll I was on a, I was on a discussion panel in July about the role of the CFO and this, and the chief finance and accounting officer in the AI era, yeah, and how it's likely to evolve and change. I'm curious, how do you see the function the profession, the career of finance and accounting professionals, evolving as AI matures and perhaps takes off the table a lot of the things that have been consuming time and maybe reroutes to be more of a strategic role. I mean, what do you what do you think about that? Well, I do think the role of the CFO is becoming more strategic. Okay, I think that's absolutely certain. I mean, we've seen it as well. I mean, technology is starting to roll under the CFO, you know, and a lot of companies that I've seen, and I think the reason is because of what we've been talking about these decisions on what tools you use are very strategic to how you run your company, right, and the amount of capital, human and money wise, capital that goes into technology. I mean, for every piece of technology do you have, I mean, organizations build around it, right? And then it proliferates itself throughout a corporation. So that's why the simplicity and this platform approach to me, you know, back in 2016 I got it, you know, I wanted to focus more on running the operations, versus making things work together and spending, you know, capital there, yeah. And so I think people are really understanding that now. And so that's why the CFO is getting more involved in the technology decisions,
20:04
because, as a result, it does, you know, affect the way that you are running your operations.
20:12
It affects whether or not you're providing the tools necessary to your your business people to do their jobs better and more timely, and it affects their access to data. I think data is another area that the CFOs, that's the next levels where CFOs are going to have to get more involved. I think they're going to have to be the arbiters of data, because that's the next thing you know, who owns the data. You know, it's not a function that owns the data, it's the company that owns the data. So, you know, if you're in the CX world, and a piece of your data might enhance the predictive capability of your finance planning. You should share your data, right? It's not your data, so and vice versa. I mean, if there's finance data, that's, you know, it would help with a prediction and interaction from a customer perspective. Okay, well, you know, we need to share data, but what I would tell you is the platforms do need to make sure that they are controlling that data in that seamless manner that I was laid out earlier, clean, continuous, you know, uninterrupted, in order to have that continuity and drive continuous business results. So so, you know, that's that I think people are just going to have to step back a little bit and and look at it on a macro and micro level and figure that out for their operations. But it's a conversation and a strategic, very strategic thing that needs to be addressed, and it needs to be addressed very quickly, because that's the key to the success of AI.
21:47
I love it, and we're gonna, we're gonna wrap up one more question. I love your data. Data is an asset, is a key takeaway. And in that spirit, I'm curious, there's been a lot of discussion about data residency and sovereignty and regulations, geopolitical, you know, where does the data reside? What's the chain of custody? All of that is that, do you do you see that as like a legitimate, significant requirement that's coming from more and more clients that are global and you're having to address, is that something that you have visibility into, is that in order to enable AI globally, I would imagine that you have to cross that bridge, right? Yeah, I mean, the rules regulations, you know, across the globe. I mean, obviously we we design, you know, our tools to be compliant with those rules and regulations.
22:39
But also, you know, customers also have to play a responsibility. I mean, we give them the tools, right, right? So they have to make sure that they're also, you know, governing and being compliant. So, you know, it's a two way street. I mean, we have to build the tools that are compliant, but, you know, they have to be used in a methodical and and governed way and and that's a critical piece of the puzzle as well, governance becomes one of those very critical areas that companies have to organize around data and AI have to be governed, and companies need to understand exactly what they're doing, and they need to make sure that they're regulatory compliant. Yep, Keith, I wish we had more time, but we don't. Yeah, you have another engagement, as do I. But everybody who's listening wish you could be here, but we probably don't have any room for you. Thank you for listening. Do us a favor. Give us a like, a comment a share. It helps us out in the community. Keith, thank you so much. Appreciate, appreciate your time, and we'll catch everybody else on the next episode of the enterprise edge. Take care. Great.
