How Oracle’s AI Agents Are Shaping the Future of Organizations

AI is a tool—but it’s also much more. AI is now transforming the core of how enterprises operate.

At The Six Five Summit, host Melody Brue is joined by Chris Leone, Executive Vice President of Applications Development at Oracle. As one of our Enterprise Apps speakers, Chris shares insightful perspectives on how Oracle's AI agents and automation are reshaping job roles, workflows, and the future of enterprise agility.

Key takeaways include:

🔹Automating Operations with Oracle AI Agents: Explore the significant impact of Oracle's AI agents in streamlining daily operations, with tangible examples from HR and supply chain management showcasing measurable outcomes.

🔹Customization & The Future of Enterprise Automation: Learn how Oracle's AI Agent Studio empowers customers to tailor AI agents to their unique needs, shaping the future of enterprise automation and adaptability.

🔹Evolving Roles & Building Human-AI Trust: Understand the necessary shifts in organizational structures and job roles driven by AI, emphasizing the crucial importance of building trust between humans and AI agents for successful collaboration and avoiding common pitfalls.

🔹Cross-Functional AI Integration for Agility: Delve into Oracle's strategy for achieving organizational agility and resilience through seamless cross-functional integration, specifically leveraging HR and supply chain data.

Learn more at Oracle and Oracle AI.

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Melody Brue: Hi everyone and welcome to The Six Five Summit: AI Unleashed. For this Enterprise App Spotlight, I'm joined by Chris Leone, Executive Vice president for Oracle's Application Development on how AI agents are shaping the future of organizations. Thanks for joining us, Chris.

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Chris Leone: Pleasure to be here, Mel. Good to see you.

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Melody Brue: Good to see you. So Oracle has introduced a range of AI agents across its applications. Can you share a specific example of how Oracle AI agents have improved a key process for employees or managers, perhaps in HR or supply chain, and what measurable outcomes have resulted?

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Chris Leone: Sure. You know, good news is we've kind of started with a what I would call crawl, walk, run kind of approach. We've been delivering generative AI services for the last 16, 15, 16 months, where we started what I would call kind of the crawl phase where we were helping just kind of automate basic processes for some of our customers. And I'll give you a few examples of those processes like pre building a job requisition based on the user's role and a little bit of background on what assignment they're trying to fill, what role they're trying to fill, and being able to take maybe a job requisition and create it in a matter of minutes versus maybe a half an hour to 40 minutes for a manager to create a really compelling job requisition. So if you think about just that example, if you know you're doing 5, 6, 7, 10,000 job requisitions a year and you're saving 30 minutes per job requisition, that starts to add up to real dollars. And I'll give you another one. As we moved kind of beyond what I would call generative AI services work that we've been doing into kind of more than real AI agents. And as we define AI agents, it's really kind of using the large language model as the brain, the decision maker as to which tool they need to call in order for them to make the right decision. We started building a lot of AI agents across supply chain management, HR, CX, ERP. So I'll give you a great one. We built a maintenance repair advisor agent. And so what this agent is able to do and how it helps is a maintenance work order may get generated from connected equipment. A machine goes down and it generates a maintenance work order. And what usually takes a maintenance technician time to evaluate, understand what the issue is, maybe look up an error code, maybe look up the service history of this particular machine and then identify who is the right service technician to be able to fix that particular issue. Instead of doing that in a kind of sequential step by step manual process. We created a repair advisor, a technician, repair advisor that can do all of that virtually. It has access to the work order, the maintenance work order comes in. It can look at the machine, it can look at the service history, it can understand what the error cones mean from the manual that we've stuck into our vector store. It can identify potential fixes and then it can dispatch a service technician to go fix that particular machine. And so what this can do is lead to better, more uptime for these particular machines. And as you know, the more uptime you have, the less cost you have because your machines are running longer, you're able to save the number of steps. You don't have to have a person engaged every step of the way. So you can save dollars, you can keep the machines up longer and it can lead to real dollar savings.

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Melody Brue: I appreciate you defining what an AI agent means to you because I think throughout the industry that sort of varies. And so it's really helpful to kind of say like, well this for us, at least for now. Right, because I think this could possibly change. That's what that means. So you also, Oracle also allows in AI Agent Studio, you allow for you to tailor these AI agents for their unique needs. So how do you see customers using this capability and how do you see that shaping the next wave of enterprise automation?

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Chris Leone: Yeah, no, really, really good question. So first, as I said, the kind of crawl walk, run scenario, we started building generative services AI agents, rag based AI agents. We've been using our document store in our vector database for the last 16 months as I said. Then we came to the conclusion that we were using an internal set of tools that my development organization, the entire fusion applications development organization, was using to build these agents and associate them to business objects and APIs in our fusion application suite. We decided to turn that over to our customers and partners in the form of what I'll call an AI IDE or development environment and that's called AI Studio. So AI Studio is really that ability for not only us to build brand new AI agents, seed them and let our customers configure them and use them, but really allows our partners and our customers to extend what we have or to build agents from scratch. And so to answer your question specifically is I think our customers will start with hey, taking that maintenance tech advisor agent, loading up their documentation and maybe at the end sending, you know, adding a step to send a summary at the end of the day to their manager about all the, you know, Maintenance work orders that they may have fixed. So they might take what we have because they're just kind of learning and extend it. But partners will be able to create very, very verticalized industry specific agents because they have a lot of domain knowledge, they have a lot of understanding of what our customers are looking to do and they can just use their imagination and the power of what agents can really do for business. It can really automate these processes in ways that we weren't able to do before because they can think on their own, right? They can be more probabilistic versus just kind of the way we've always coded everything, which is step by step and we know how it's always going to end. Now we can let the agent make decisions about what is the right tool for it to use to solve a particular problem or a particular use case. And so I think our partners are really going to be able to really just imagine new types of solutions and really fill what I would call some cracks in our processes, maybe even extend to systems outside of our application and bringing external data sources in order to make the solution that they develop even more robust.

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Melody Brue: And as agents sort of start to take on some of this more routine work, how are you seeing kind of the more urgent skills gaps that are emerging for enterprise teams? And how do you think that organizations should start preparing their people for that today?

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Chris Leone: Another. Yeah, it's a really good question. You know, I think and what I've seen, Mike, and we just came off of a very, very large internal hackathon where we had, you know, a large part of our development organization really experimenting and building some pretty cool agents, some super unique, I would never have thought of those. And but the skill that I think they all first had to understand is what is the unique power of building an AI agent? What can they bring that we couldn't do with some of the technology of the past? What new additional sources of information, new ways to automate, new decision making that these agents have problems that they can solve that we couldn't solve in the past. And so that was the first kind of hurdle that had to get by. And once you start to understand, look, I can bring in best practices information, compliance information, marry that with data in our system, I can marry that with Internet specific data. I can look at the weather, I can look at Internet searches and bring back different types of information and start to solve problems and not just recommend, but actually take action in our system. So I think the first problem that people need to understand is what are the types of solutions that you can now solve that we couldn't in the past. And then I think the second thing is how to really get your mind around developing these system prompts in order to really ask or tell the agent what it needs to do. So it's a really common language, but there's ways to write these prompts, prompts that are very specific to what you want it to do. And there's different ways to go about it. You can start with a very, very complex prompt, I don't recommend that. Or you can start with a kind of trial and error, start with a basic prompt, see if it does solve the solution that you're asking it to do. Call the right tools. If it doesn't, add another step in that prompt. So I think they really need to get their mind around how this common language, English or whatever language you speak can drive the behavior of these agents. And I think those two skills are things that people need to develop.

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Melody Brue: Yeah, it's interesting. This like the digital workforce, people are going to have to start to learn to work with kind of like a new teammate. As Oracle is enabling more autonomous AI driven workflows, also having to deal with sort of building this trust between people and AI agents. And that could potentially prove harder than deploying the technology itself. So where are you seeing kind of that start to stop mistakes in organizations, how can they avoid that? What advice do you have on that?

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Chris Leone: Yeah, you know, so what I tell my team is, you know, people are worried, you know, are agents going to replace people? How is that going to work? You know, what I tell my team is we need to take advantage of this technology. Everybody should be at least a 5x employee, maybe even a 10x employee. Leveraging the partnership that we can have with AI, whether it's agents or just generative AI services, we all need to be more efficient and more productive in our day to day work. So that has to be kind of first and foremost. And then what I would tell them as far as where they can, you know, where kind of agents will fit in and how you can make sure that you're getting the right answer and the accuracy rate is high enough, is first define what that needs to be. Is the error rate 95%, is that going to be okay for this particular problem? And if it's not, can I stick a human in the loop? And we have those capabilities where hey, look, before I charge your credit card, before I place this order, before I send out the service technician, do I send it to the supervisor and have him say, yes, this is the right thing to do. So I start to automate these processes one step at a time and until I get more comfortable and then maybe I can take the human out of the loop in that step. Take the human out of the loop in that step and then it becomes a fully autonomous process. But what people have to think about is this is not about job elimination, this is about productivity, the ability to increase productivity for organizations. So you will start to see small organizations, medium sized organizations, being able to be as productive as very, very large organizations because they're going to be able to take advantage of these autonomous processes. And you can do it step by step. As I said, pulling a human out of the loop to make sure that you're more comfortable. I'll give you one more example. It's kind of short. We rolled out a benefits advisor solution to 60, 70,000 people across our Europe organization and it answered questions on benefits. There are different benefit plans, compensation plans and the SVP of HR said his comment back was that the regulations in Europe for some of these benefit plans is very complex and even their best benefits advisor has a 85 to 90% accuracy rate. So if we're at 90, 95% accuracy with an engine with an agent, that's better than where we would be without an agent. You know, so, so you have to take into account what is the accuracy rate and where you want to stick humans in the loop in order for you to feel confident in the solutions and the answers that you're getting.

Melody Brue: Yes, you mentioned people should be 5x10x but then there's, you can take it kind of a step further with the value of integrating HR supply chain data with these AI agents. So how does Oracle's approach to that cross functional integration help organizations become even more agile and resilient?

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Chris Leone: Yeah, you know, I think what it kind of breaks down the boundaries and not that we couldn't have done this before, it just makes it easier to grab different pieces of information to help drive decisioning in one part of the system. And, I'll give you the skills example in the technician repair example. So skills are something we traditionally have in HR. It's a very, very well organized HR process, not something we have traditionally done in our maintenance application. Right. It was very rudimentary, we had a few skills. Now I can easily grab that talent information, that skill information and go look at what this technician has and be more accurate. Oh, they've worked on this type of CNC machine and they've done it for this Many years and they can be to the top of the barrel versus in the past it was CNC expert and you just stuck them in and maybe they didn't know this particular machine or they didn't know how to fix the particular problem. So now we can solve these kind of broader problems because we've kind of made it easier to open up the information that we can grab from different parts of our system and even external parts of our system so we can go outside, like I said, into the Internet, into services outside, and bring in external information to help make better decisions inside our organization.

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Melody Brue: Yeah, so in a way that's not just, you know, the sort of easing the fears of AI taking your job. AI is actually helping to help people advance in their careers. It's putting them in the right place at the right time and kind of figuring out where people belong.

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Chris Leone: Think, I think you know, where what it's going to come down to in the next year, six months, nine months, is organizations are going to have this kind of productivity dial that they're able to turn up and, and it's going to give them more advantages to say, hey look, you know what, I can deploy maybe, maybe I don't need to deploy as many people in this one area. I can turn on a few more agents over here and take some of the people and have them work on this more cognitive oriented problem that I really need a different type of leader to be able to solve. And so they'll be able to, you know, it's kind of elasticity in our cloud. They can turn up elasticity to productivity over here and you know, move resources over here and give a lot more flexibility to the product problems that they can solve, allowing them to solve more problems and not, you know, always having to be the largest organization that could compete in, you know, different industries.

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Melody Brue: So if you had to make one bold prediction about how AI will reshape enterprise organizations over the next, say five years, what would it be and what's the most important action that you think leaders should take now?

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Chris Leone: To be honest, I think in the next five weeks, maybe five months. You know, so it's really hard to think five years out because it really is. Things are changing. Things are truly changing that fast. You know what, what I tell my leaders, I literally just sent out a note to all of them. They need to look at a few of their key deliverables that they have to their customer, internal customers that they support and they need to automate those, a couple of those by the end of this fiscal year or this end of this calendar year. And I said, you need to start thinking about, hey, look, these are deliverables that we have. These are problems that AI can solve for us in a much more automated way. Let's go automate those and get to 80, 90% automation and then go to the next step. I'll give you the one example I gave to my strategy team that helps enable our field. I said, there's different ways to learn now, it's not a PowerPoint presentation and a webcast that you go have these AI can create podcasts, and they can create these dynamic podcasts where two people are talking and interacting, just like we're interacting with from a document or from a PowerPoint. And it's different ways to learn. So we have to be smarter about how we can deploy these new technologies so people can learn easier. This is how kids are learning in college, right? They go to these websites, they take notes, and they get these flashcards and they're still learning the content. But we've made it much more efficient for them to do it. And that's how we need to think about different parts of our organization that we can make more productive. So I've challenged each part of my organization to say, hey, look, you need to think of processes that can be more automated and take advantage of the pattern recognition and what these large language models do really well, and let's automate those processes.

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Melody Brue: That's awesome. I love that. I tell my kids all the time, use all the AI tools that you can because that will give you the superpowers that you need when you join the workforce.

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Chris Leone: Absolutely.

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Melody Brue: Well, Chris, thank you so much for joining us. And for everybody who has tuned in, thank you for joining us for this enterprise app spotlight at The Six Five Summit. Stay connected with us on social and explore more conversations at SixFiveMedia.com/summit. On behalf of Six Five Media, thanks again.

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