Name: On Oracle
Uploaded: 2026-10-15T19:06:05.832Z
Duration: 44 min 5 s
Description: On Oracle

Transcript for "On Oracle": Good morning. Good afternoon. Welcome to our webinar on, Oracle on premises getting ready for AI. We are going to have a conversation today with, Jason Andrews. He's the senior vice president for solution architects. His focus is to help our customers look at the commercialization, the applications, and modernization. We're also joined by Jason Peterson. He's the vice president for client engagement at Argano, and his area of expertise is on the multi cloud, the database, AI economics, and really speak to what makes most sense from a customer's perspective. So today is gonna be just an informal conversation. Before we get started, just a couple of housekeeping items. Please continue to post your questions on chat, and we'll get to it towards the end of the webinar. So I'm gonna get going right away. So let's say, Jason Andrews. If, a customer is running Oracle on prem products and they are looking at the AI that's going on right now, and I know that everybody is playing with AI, everybody is looking at it, how should they, think about it? Are they already falling behind? Yeah. No. Great question, Srini. Not at all. I I think when you look at the on prem customers, they can still get very targeted value, from their on prem solutions applying AI to it. They just have to be slightly wary, in terms of how they connect to, the systems and just, you know, especially when I think about JD Edwards. You just need to make sure when you're connecting AI to your on on premise application, that that you are factoring in things like security. But the the key thing is you you need to pick a measurable outcome with that solution, to whether you're EBS or JD Edwards, and then kind of, you know, iterate from there. No. That's that's helpful. That sets a good context, and we'll we'll follow-up on a few. So, Jason Peterson, I think question for you. Is there a is there a way a customer can say, where do I start? Like, what are my first steps in this process? Do you have any experience from customers you worked with? Well, we like to say, you know, if they're on prem... Current on prem customers, we like to see what their spend is. What's the what's the actual business driver for looking for something different? So are you looking to move those workloads to cloud because of data center cost, because of infrastructure purchase cycle, you know, that that kind of thing? If you're looking for AI plus hardware and data center refresh, that's a great time to start looking at at moving those those workloads to cloud. And whether they're moving the application to fusion, moving... Looking to, try to consolidate costs and, you know, get rid of some of their technical debt with Oracle database or any other databases or other technology, that's the time to start looking. So so it's not even a it's not even a choice of on prem versus cloud. Right? It's more about taking that first step, making that smart investment, making sure your foundation is right, and and start. Right? I think that's... Yeah. It's just how you're leading to a business outcome. Exactly. Exactly. So let's, let's go into, the next set of questions about timing and urgency about this. Right? So when you see so much going on with AI and when you see investments happening, AI is changing rapidly. What, what is the best way to think about where do I start now? Do I... And maybe, Andrew's... Jason Andrews, given you were recently having conversations with a lot of JD Edwards customers, why don't you share with us some insights on where do people begin? Yeah. I think from from my perspective, the key thing is to find a a a really focused use case to start with. A a big, I would say, yeah, issue that we we see with clients trying to adopt AI is they do AI for AI's sake. And with that, they pick some kind of use case that really doesn't drive value, and then they end up with a a kind of a failed attempt, and that can obviously set you back in time. What you should really be doing is thinking about a focused use case that is gonna drive an outcome that adds value, yeah, to the organization. With that, you actually start building out that kind of adoption capability internally of the governance that you need around AI. So understanding what's the security constraints they need to factor in. How how, you know, how is my data? Is is it really truly ready for AI? Am I getting the outcomes I I I would expect? And at the same time, you put in those governance structures and that management structure in place, ready to then accelerate future as as your as your AI journey moves on. So that's the core thing. Pick the right valuable use case. Drive business value. And then, Jason Peterson, from your perspective, how do you choose what... Where do I start? What's the business value I think about? And that that that it's not just a pilot. It's not a failed experiment. Well, I think Jason and I both talked to clients that just because their competitor is doing something with AI, they think they have to. So do you know what they're doing? Is that... Is it driven? Is it actually provided any value to to your competitor? And so I think they really need to start there. Start looking at your own workloads, you know, your your business justification. You know, really, we need to look at our own security models too. Does... Is every organization set for AI set up for it? And we get customers all the time that just say, we just can't do that yet. It doesn't it doesn't adhere to our security and risk policies or, you know, our corporate governance. It just doesn't work with it. So, you know, instead of starting a project, I think 97% of AI projects fail because Yeah. There's... You know, they end up not having an actual business case when they get down to it. It could have been something that could have been solved with technology that's already in place. So I think we really need to look at that business case and then what that outcome's gonna be. And and then what does a budget look like to do it? And then if changes are to be made, so if workloads are moving from on prem or moving out of a data center or moving from a colo facility or somebody's managed hosting somewhere, what's gonna happen to it when it moves? Where does it need to go? And that's what... That's where we come in because we're we're good at talking to the customer and having them realize the options, you know, and providing a solution, not just one widget or one product or vendor. Yeah. In fact, I know that just looking at Argano history and track record, we've helped a lot of customers reimagine their business processes, reimagine workflows with transformations. I wanna ask a question that either of you can chime in. When looking at, do I take an existing workflow and make it more efficient versus reimagine my workflow? Do you do you have a preference where the customer would start, especially when they're some on prem, they have some data in on prem, they have some data spread across, where... Both of them will have some incremental value for them clearly. Right? But what... Do you have any guidance on... Or a framework on how you would help make that call? Maybe, Jason, Andrew, you're nodding. What if you start? I can I can jump in there, Sri? I think, when we talk to our on prem customers, you know, a classic, situation with on prem customers is they have customizations they built in. They have a number of manual workarounds. They've got spreadsheets in the process, and it's fragmented. It's not, it's not a digital workflow. It's not an automated workflow. It's a many humans in the loop where they shouldn't be workflow, if you will. So I think that's a great place to start. Take a look at where are your custom processes, where are your, you know, fragmented processes, and pick one that's really causing challenges and maybe causing, you know, losing value, taking too much time. And look there and see how you can automate it, how you can streamline it, and add value and not only value but also quality. In a lot of cases, it's not just about the productivity. It's about adding quality into the process because it's more driven by electronic flows versus... But needing some kind of transcription of data and causing errors in the process. So that's really where we look and, again, drives value. Yeah. I mean, because at the end of the day, every single customer is a data customer. So if that data is fragmented, if it's coming from, you know, a 100 different data sources external to the business, if they've got, you know, internal data sources, how are you gonna manage all of that? And where is your... What's your AI pulling data from? Where's your LLM that you're building internally? What's it actually getting data from? So we start looking at, really that data consolidation more more now than ever, you know, with customers have to be able to manage that data in order for AI to be effective, at least internally. You know, if... Or especially if we're looking at historical data, if we're looking at, you know, if we're trying to predict sales for next quarter, if we don't have, you know, data handy from eighteen months ago, how are we gonna be accurate for future predictions? So I think that's that's a really important part of it too. So so what I'm what I'm, I'll paraphrase. What I'm saying is probably start small, but be very clear about identifying an area where you can be successful and you can get the return, you can get the belief, the buy in, and then go on to the next from there. No. It's a great, great segue to my next topic, which is more about the economics of AI and and really to understand. So so this is a classic paradigm for everyone. Given we've heard about tokenomic, we've heard about the cost of AI, we're also hearing about AI is changing so rapidly. Models are coming out left, right, and center. Right? There are also concerns about security and governance and all that. Does it make sense for a customer to wait and and wait for things to settle down before they go in? I'm gonna start with Jason Peterson, and see what... You know, I'll take a... If if you'd asked me this question a year ago, I would have said yes. We saw customers even twelve and eighteen months ago, especially in health care where AI is so important for the work that they do, but the cost was prohibitive. You know, to be able to throw those workloads out for what they're actually trying... The results they're trying to get, You know, doing that with some Frontier model twelve months ago, it just wasn't gonna happen. So we see now that, yeah. So a year ago, the answer would be yes. Now I think is the time because there is competition in the market. We know how to actually maximize the value of which AI you're looking, you know, or what you're trying to do with your project then what vendors you're looking at bringing in for that. And, you you know, I think that that's really important. But, again, think to Jason's point earlier, we're starting with a small subset for... Of workload that we're trying to solve for, and then we can blow that up afterwards. But now is the time. And, yeah, and every vendor has their own way of pricing of of actually pricing AI and each transaction. But at the end, it is it is tokens. It's using credits off the gift card. You know? And how fast you burn it is gonna be real important to your results in the end. Jason Andrews, I think, just can you add to that, but also talk about how large should the investment be to start with? How do they think about the size of the investment? Yeah. It it doesn't have to be big. And the interesting thing, we've actually done some, interesting research in terms of comparing how different models deal with different, workloads and different, if you like, tasks. And I think one of the things you can do, we can help with this, of course, is it's... It doesn't have to be big, and it also... We need to look at the right model for the right function, because a a great example, we actually... With JD Edwards recently did a test where, and this is a really good example use case, actually. We had, AI look at, some open batches that were that were in JD Edwards as an example. And we wanted to understand why these batches didn't post. And we asked that same question to multiple LLMs, and came back with different value points depending on, you know, the LLM that was being used. Some got the answer completely wrong. Some got it totally right. But they also had different economics against it. Some of them were... Did it in the... With a couple of sense, literally a couple of sense. And if you think how long the human would take to do these... To do this research, it was it was in in hours and days in certain circumstances. So that means a very small investment, a very small targeted workflow, if you will, can can drive high value. But the thing here is, you know, my point is we can help you decide the right option and keep the costs small. Awesome. Awesome. Awesome. So I think the summary is don't wait. Just start now. Start small. Find your high impact business cases, and start to gain the benefits as you move forward because you just wanna keep moving your business forward. Let's, let's flip over to just, like... I call this the pathway to get there, right, in terms of I want to... I'm not ready to replace any of my customized on prem products. Many many of our customers are there. Right? It's a rip and replace maybe for budget reasons, for other reasons. But how should they think about maybe the surround use cases or identifying where they begin in their environment, especially thinking about it from a JD Edwards or Hyperion, EBS, any of those on prem customers? Like, where do I start? What are my steps? How does how does our Arduinos experience, that we have guided other customers to? Do we have process framework? Very open question. But, Andrews, why don't you give us a view on this, Luis? Yeah. Sure. So, I mean, in terms of, you know, making a decision on how you road map the future, if you will, one of the things I think you have to look at is, you know, where are you and where are the gaps? We like to say, well, I wanna meet the customer where they are today. And a lot of cases, that means I'm not moving my entire application to, you know, a modernized application suite like Fusion, for example. But what I might be doing is looking at customer processes that need replacing. I might be looking at gaps in the process, that either I could build an agent to cloak, as we just talked about before, or potentially add on, like, you know, a, an application from Fusion Suite to plug those gaps and kind of creep a little bit into that modernization area. So so for me, the thing is it's looking that way you've got the gaps in the process, and then, being able to plug those gaps with new tools, but at the same time, relying on that core solid infrastructure that you know and love, JD Edwards, EBS, Hyperion, etcetera. And I think, Jason Peterson, again, I'm gonna come back to the economic aspect of these. How do we make sure we are continuing to invest and improve and not throw away what we just did Yeah. As we have those balance. And what's that step by step kind of approach there? I mean, we have infrastructure, discussions with customers weekly. So when we look at, okay. Hey. Cost of memory has gone up. Cost of everything has gone up. If I'm doing refreshes in a data center now, this is gonna cost me x dollars, which, by the way, we do that complete economic model for our clients. But when we also look at, you know, when we look at moving workloads to cloud, we look at that. There's a there's a consolidation effort that usually comes along with that. How many licenses can I go ahead and save by moving this x workload Oracle Hyperion in your case that you mentioned? JD, not... Maybe, well, maybe on the database. So for moving the database to cloud, I might be able to to get some consolidation out of this and actually renegotiate my contract with a vendor and save some money on my support costs. You know? So we do these assessments for for our clients all the time, free of charge. And we can run... We can show them the value of moving from there... From from on prem, from a colo, from another cloud provider to another cloud provider and show them actual real economics, Give a detailed report is exactly what a CFO and a CIO need to see in order to make a decision. So so what I'm hearing, Jason, Andrew, I'm gonna come to you. But I wanna I wanna think about the big picture here. Right? Which is my my assumption is there is multiple entry points for a customer. Right? They can enter at any point in time given their stage, their maturity levels, their budgets, their propensity for evolving change, adoption, so many factors going to it. So how important is it to look at the big picture and a road map and then take those steps? And I think that'll be good to get your perspective on. Yeah. I I I mean, I think it's very important. You know, and and as as Jason was kind of alluding to, the steps may be... You may go through more steps to get to the final goal, but I think you have to have the final goal in mind. And in our cases, clients don't know what that is, and they need help to understand what that road map looks like and why and what the value is in it. Step number one might be getting value by, you know, as Jason said, moving your current applications to a cloud suite that gives you AI enablement. Some... In a lot of cases, when you're on prem, your tools are much harder to connect in a secure way to an AI solution, and you have to think more about it as you move. For example, if we were to move a JDM, what's customer, to Oracle OCI and get the 26 AI database as part of that process, it opens up many opportunities for you. If they're staying on prem, not that they can't get them, but it becomes more complicated A little bit more, you know, there's a little bit more friction involved in it. So I think you have to look at the steps you want to go through, and step number one might be that modernization of the infrastructure. Then you might look at, as I mentioned before, step number two might be, you know, how I plug some gaps, knowing that the end goal is to be fully transformed on an AI enabled, solution like Fusion, for example. So I think, yes, the end goal, the road map is very, very key. And then building that journey is how you really make sure you stick to the end goal as you go. Right. Right. And connecting back to that business case and the business value and the problem that you're trying to solve, so you're continuously moving your business forward in that. Perfect. Perfect. This... All of this discussion may sound a little theoretical, so I wanna speak to some examples of customers that have been through a journey along these lines. And I picked a few examples from our website. And I wanna first start with, a customer by the name CTB. Maybe, Jason Andrews, can you tell us a little bit about the challenges they were facing and how did they approach the modernization? Yeah. I mean, just for those that don't know, CTB is a, a manufacturer of, different types of farm feeding equipment, so grain silos, feeding equipment for, you know, for swine and also for fowl, if you will. And it's great great customer advice. They were actually on older version of JD Edwards, and they had a number of issues, you know, being on this older version of JD Edwards. Number one, they had, you know, a... There's the skills drain in that market. Just go and try and find RPG for developers. Go and find JD Edwards World, you know, skilled users and, and, BAs out there. They they just don't exist or they've retired, quite frankly. Secondly, some of the other challenges is they had so many boundary systems, around their solution. They made it really, you know, really complicated and very prone to system outages, which, again, obviously, in in in a business like theirs was was a huge problem. So one of the things that they did is they decided to to take a phased approach based on the road map process I previously mentioned. We road map where are the value points for this particular customer. They had a huge issue in hiring and managing hiring, so HCM was key for them. So they decided, well, let's let's go with HCM first. They also had some three... Two to 300 separate entities that were consolidated on JDM as well, believe it or not. Some of them are already on well. So for that, it was an easy win with EPM. So we started with EPM and HCM, and then we rolled in the ERP and supply chain functions later, and they were kind of, you know, somewhat connected between two systems. So that gave them a risk free phase approach, and then they modernized their system over a period of, you know, about about a couple of years. And now are 100% on fusion, actually. They are AI enabled, in a number of different areas, and they have something in the region of a, actually, I think it's something in the region of quarter of the boundary systems they previously had. So much, much less structural concerns and complexities. Interesting. So... And they almost were doing all of this without any significant disruption to their everyday business. Right? That was one of the key points I heard. Yeah. Absolutely. Yeah. It was a very a very easy go live for them. Part of that was ensuring that, you know, we heavily tested the solution before they're live, of course. But it was a very, very easy go live for them, and they are still, a very happy customer of ours today. And, you know, we'll... You know, I'm very glad they made the move. That's awesome. Good... Great story to hear. Jason Peterson, I wanna ask you about Oriental Trading. In their case, they have so many integrations. How did their modernization go? So, you know, they ended up moving their entire EGRP suite and supply chain to fusion. And when we're moving from legacy JDF, they came from JDE World as well, you know, like Jason's example. And when they moved there, they had all these external data sources. They have these workloads that are that are adjacent to ERP that that rely on that, that they use every day. So once it moved there, they're able to use Oracle Integration Cloud to have that integration that integration, functionality with all those external workloads into fusion. And that helped them, you know, minimal downtime, you know, smooth transition, easy go live. And those are the kind of things that that that customers are looking to do. And there's a lot of integration platforms out there, and a lot of them are... You know, they're terrific at what they do. But, but Oracle Integration Cloud, in this case, is really the only one when it comes to fusion and and kind of binding those Oracle workloads. Awesome. Awesome. So this is Oracle Integration Cloud that lets you Yeah. Get in... Integrations, like, at scale and make it, smooth. Very interesting. You mentioned phases earlier. So they did this at a phased approach, you know, one module at a time, getting in there, making sure it all works instead of, you know, boiling the ocean as everyone says. You know, that that phased approach is definitely more palatable for both customers. Yeah. I want to talk about one more example, Chase. I think they took a hybrid approach. Right? And, the the sequencing was key here. So can you tell us more, Jason Andrews, on this? Yeah. 100%. So so so Chase, you know, again, ironically, another JDM as well customer. They they were concerned when they moved to to, you know, to modern systems infusion that with all of the functionality available to them in the first go, they're actually a process manufacturer. So what... You know, they they have products, their inks, and so on and so on and so forth that require some process manufacturing. For them, the manufacturing side of their business was also seen as a very key, you know, unique selling point, if you will, that was their core business. So they really didn't want to drive any risk into manufacturing. So with that, they decided to take kind of a a phased hybrid approach where they would start with their centralized operations and, also, at the same time, centralize certain operations that needed to be centralized but weren't. So their first phase, they actually took oil management, procurement, some inventory, and financials, and had all that integrated back into JD Edwards World for the phase one. So manufacturing stayed on JD Edwards World in that first phase. What that gave them is the ability to basically bring on a number of plants at a time versus doing kind of a big bang across all of their plants with different manufacturing styles, different manufacturing types. And like I said, there was some concern in certain areas that would would it... You know, with the fusion applications at the time truly support their process manufacturing approaches. Well, over that second... During that second phase, we proved out easily that the manufacturing process could be supported, and then they were easily transitioned, you know, plant at a time onto onto fusion, without any disruption and, you know, at a very, very low risk level. And, again, another very, very, happy customer who is actually actively using AI. One of the things that we actually, found and implemented during the project was a couple of AI agents in the early stages of AI agents that were deployed with Infusion. As an example, they have an agent that, on shipping, there are certain packaging standards that certain customers require. What they do is they will look up the customer, they look up the item, and it literally shows them a picture and a document of what the packaging standard is for that. So the the the human in the loop, if you will, the actual person doing that packaging can look at it visually and say, yes. That maps with what we need. Things like that, they actually implemented from an AI perspective and... As well as, you know, those core features and functions that were within Fusion, which helped them move forward. So it's been a great story. And, again, using that phased approach to derisk the project, but at the same time, you get a high value when they when they went through your life. No. It's so good to hear those success stories. Jason Peterson, any kind of summary takeaways from the customer stories that we just heard that you want our listeners to know? Jason's hybrid approach, to that client seems to be one we're seeing more and more now, you know, where customers say, hey. I need I need this transformation. I can't do it all at once, but I need to do it in in phases. And I also wanna exit the data center, so in some of our customers' cases. So I'm going to move certain modules out of JD Edwards, put it in fusion. I'm gonna take the rest of it, move it out of the data center, put it into OCI in this case, you know, to really see some value. I have some some cost savings by moving it into cloud. Now I turn my CapEx into OPEX. I can go ahead and and do all that. All the good things that we've been doing for a long time, moving to cloud. But now it gives us a a fresh start with that modular approach, and we see this quite a bit. And, you know, at the at the end, when all those modules end up moving, it's it's more of a, seamless of a transition as possible. Not that it's always daisies, but, you know, that's that's what we're hoping for. And that includes the database, all of the data. So if we're doing that, we're gonna centralize that data. We're gonna do all of that as part of that project. So what I'm gonna do is I'm gonna move into, like, a rapid fire q and a. I think these are not... I mean, there's a lot of customer hesitation. Right? There are many places where people say, oh, maybe it's not the right time or maybe not right away. Maybe... So one of the... Like, so I'll just see if I can throw a few questions at you and see what you're, what you're hearing from your side and how you can guide us. One of the examples is customer saying, well, I want to start using AI, but not sure if our environment is ready. Our data may not be ready. Where do we start? This and Andrew's. Yeah. I can take that one, Sri. Yeah. I mean, kind of like we mentioned before, the key thing here is to not boil the ocean. I like Jason's, you know, comment around boiling the ocean. Start with a single valuable use case, that you can really develop your processes internally. It'll allow you to develop the governance model. It'll allow you to develop your data readiness, and that's key because understanding how ready your data is for an AI, initiative, and then putting that operational experience that, you know, that governance model around it. So that's the key, to find a single... Doesn't have to be big, but valuable use case that you can focus on. Then from there, you have that, you know, pun intended, harness that you can use moving forward. So... And then I think that another, common thing we hear, and this is Jason Peterson for you, is we're just staying on JD or EBS. We don't wanna touch our existing... How do they take advantage of what's new out there and Oracle offers or Oracle OCI or others? I mean, if if we're looking at that, the application doesn't have to have any changes made to it. I mean, we can do, you know, a lift and shift. It's just a platform change. We're moving it from some operating system, some server hardware, and putting it into Oracle OCI. And the biggest value there for customers that are just looking to move what they have, move it to cloud, is really the data optimization modernization. So if we're getting data put into... Jason mentioned 26 AI, you know, that's the game changer right there with its Vector Search AI built into the database. Now customers can start taking advantage of that, especially when it comes to any sort of... With their... With data warehouse or data lakes, and they're trying to get that, that observability into their data. We can modernize that as part of it. It doesn't have to touch the application. And, really, that's that's a... That should be a number one... Really, a... But but, you know, an upfront task for any plan, getting that data there. No. It's also good to hear that we can offer that end to end sort of pathway and options for our customers, whether it's database, OCI, application, any of those app modernizations, and almost in any sort of steps to get there, then that's good to hear. But let me ask you another one, Jason Peterson. What if they are using a different, hyperscale? Cool. Don't care. It's great. If you're an Azure shop and you do everything in Microsoft, you have Oracle Database and you have, you know, you still have an Oracle Database estate, Oracle has their multi cloud program. So it's database at hyperscaler. So if I have Oracle Database, it's it's the same... You get the same cost savings, same performance, same efficiencies, same advantages of cloud if you were moving it to OCI, to Oracle Cloud Infrastructure, and putting it into Exadata Exadata Cloud services. Well, I can do the same thing at the same pricing, same performance, same advantages of moving them into database at Azure. So we arrange all of that. We help the customer along that journey, moving that data there. If they're an AWS shop, that's great too. JD Edwards can run-in... In in this case, JD Edwards can run-in AWS. We can move that Oracle Database there as well and put it in database at AWS. So the multi cloud piece is really a game changer for clients because they're not saddled to one provider if they have to move their data. They can also take advantage of those license savings by moving it to those... To that multi cloud, platform. It it... I mean, it's it's it's where... If... Again, to James' point earlier, it's meeting the customer where they are. If the customer is at GCP, okay, we can we can put your data there. We can move your application there. So... And to us, it's really making, you know, doing the right solution with the with the right outcome for the customer regardless of where it sits. Starting with the business outcome. So Yep. Jason Andrews may may be one more in this area. Right? We know that budgets are shifting. Most companies, they're moving... Like, your transformation requires budget, but AI is taking away some of their budgets. So I'm sure this is not new, but many customers might come and say, hey. We can't afford a full transformation right now. How do you how do you guide them, and how do you plan with them? And, how can Argano help? Yeah. But that that happens a lot, actually, Srini. You know, the the... In a lot of cases, you know, big transformations, a full transformation to any new ERP, is is a big chunk of change. And in a lot of cases, you know, coming back to what we said about meet... Meeting the customer where they're at, they're not at that point at this point in time. Maybe those, budgets have been moved to buying a piece of equipment on the shop floor if they're a manufacturer. That quite often happens. So what we need to do in this case is, like I said, well, okay. That doesn't mean to say you don't do don't do anything. Comes back to, you know, take a small valuable, portion of, of the business or of a business process and define an ROI for it, define a business case for it because that's key, and then start with that. Because that... What that does is twofold. First of all, you're not then suddenly, taking a huge chunk of budget, over what is usually quite a long period of time before you actually start seeing the return. You start with a smaller amount of money, and then you usually have a faster time to benefit demonstrating the value of that, of that solution. And it allows you to, you know, to to then potentially open up more funding because you demonstrate the value. There could be a number of different arms, you know, examples as well. So we've already talked about potentially moving to a different hyperscaler and then opening up the database. A lot of these can be smaller... You know, really much much much smaller engagements that open up value. We've already mentioned, you know, the 26 AI database, but related to that comes things like AI data platform. Being able to connect data from multiple sources, smaller project, opens up a lot of value, without necessarily converting everything you've got to a new platform. So I think you gotta look at where are the issues. You've gotta look... Keep it small, look at the value, and then start there, and it helps you justify, you know, the... This... The much smaller spend. And also to that, Jason, it's also... To help them realize how much it's gonna cost and keep to it. You know, those... That scope and cost creep come in real quick. Absolutely. Absolutely. No. And so this is great to hear. So just like like bring this back home. Right? We talked... We touched on many different areas. We looked at... There are options for customers. You can start small. They can start in different places. And I know that Argano itself has the ability to look at, you know, connecting business processes, connecting different data platforms, looking at infrastructure, cloud. I mean, it looks like we have a good place no matter where the customer wants to plug in. So as they are looking at what they need to do, to me, it sounds to me like how we used to call it, you know, this is not the time to do nothing. This is not the time to say, I'm gonna wait. It's not the time to... Like, we used to call it the risk of standing still. You're going to definitely be left behind. The world is moving fast. So in conclusion, what are your final thoughts, Jason Andrews and then Jason Piazza? Yeah. I mean, I think the key, we've said this a few times now, is you don't have to wait. You know? You don't have to wait to modernize. If if you don't have defined budgets for a large transformation, you don't feel that the the business is ready for it, that doesn't mean to say stop. And in fact, far from it. You know? If you stop now, you'll be left behind, and that's the last thing that any company wants. So I think, you know, you can still do portions of modernized ice and using AI. We've talked about the options for things like Oracle Cloud Infrastructure, 26 AI database, database app, and Jason, before we go into more of that. I've talked about options to, you know, to to to modernize a small section, looking at supply chain planning, procurement, EPM, those types of things. You don't have to make it big. You can actually just look at the value and start from there. But to me, from an AI perspective, you know, pick a small defined value point and and and go now. You don't have to wait. Jason Peterson? It's too late in the game for business as usual. You know, if we're still doing it like we did five years ago, you're gonna be left in the dust by your competitors. And so whatever you think that you need to be doing, your competitor's already doing it. And so we... It it is not the time to wait around. So there is... There are ways to save money. There are ways to gain business improvements of... You know, for processes and workflow and data management and all of those things we talked about. But waiting two more years to do it is just not gonna work, especially when we see the evolution of AI. I mean, it's not changing every six months. It's changing every two weeks. And, you know, this is... And somebody else, some other customer, some other competitor is adopting those new changes and implementing that in their environment. And so, you know, we tell customers, let's let's go now. And, again, to Jason's point, start small, grow out of it. But, you're gonna need that in order to, you know, in order to thrive. Okay. I'm looking at a few questions that are coming in. There's couple of themes, and I want to paraphrase one final question and one final thought, and I wanna hear your perspectives. I think there are a lot of these new new age kind of AI, companies claiming that we can build a whole ERP with AI. We don't need the classic AI. Personally, if I was an enterprise, I wouldn't wanna risk it. But what are your thoughts on that, and how do you want the customers to think about it? Jason, do you want... I can jump into that. Shrini, for sure. And then this is a question I've been asked a number of times, actually. And and the way that I think of this is if you look at the controls, the business processes, the best practices, the logic that's been put into the, you know, to ERP systems such as Fusion, JD Edwards, EBS over tens of years, decades and decades and decades. Trying to replicate that in a new kind of vibe coded system in a secure, managed way doesn't make any sense. It just doesn't make any sense. Now does that mean that you can't use AI tools to enhance? That's a different scenario. You know, to plug the gap, different scenario. But, you know, to to build a whole system from scratch that is managed, is compliant, and is safe and secure with AI, I... You know, I would say good luck. Good luck. I couldn't agree more. Said it more perfect myself. You're a three or five man shop. Cool. Knock yourself out. Build it from, you know, build your ERP from scratch. But if you're gonna be... If you're really trying to compete, I don't think it... We're there yet. Okay. No. I think I think we will, leave this at, at this point. I just wanna say, to everyone out there, thank you so much for listening. Thank you so much for your time today. And, you can find us on Argano.com. You can also find us on LinkedIn and reach out to us. Very happy to engage further. Once again, thank you for your time, and, wish you the very best for the rest of the day. Thanks. Thank you.