Revolutionize your enterprise: Master SAP modernization with AI

Welcome to our webinar today. We're so glad you could join us. Today's session is revolutionize your enterprise, master SAP modernization with AI. My name is Sarah Enders. I'm here on the marketing team with EPI-USE Labs, and we're so happy you could join us. We are recording today's session, and so we'll share that with all of you tomorrow or at a later date. And just a few housekeeping things, throughout the webinar, can post your questions into the question panel on the GoToWebinar screen, and we'll take those at the end. And then we also have some materials for you to download in the materials section on the GoToPanel screen as well. So now I'll introduce today's speakers. We have who's the team lead for next gen SAP development here at EPI-USE Labs, as well as Spencer Brown, who is our global BTP go to market leader at EPI-USE. So I will turn it over to you now, Spencer. Great. Thank you. Yoni, next slide. All right. Yeah. So just, yeah. No. That's great. Thank you. Okay. So for those of you who are unfamiliar with Group Elephant or EPI-USE, I'll give you just a quick overview. So Group Elephant, we operate a little bit like a holding company. We have over twenty five brands. I think we're closer to thirty five at the moment. And we operate across fifty two countries, and one of those companies is EPI-USE Labs. I think the name EPI-USE is known in the industry, and it's probably known for two areas. So one is our data software, which we excel in, and EPI-USE Labs runs globally in multiple countries. And then the other area that we're very well known for in the SAP arena is around our HRIS capabilities around SuccessFactors and HCM. However, one of the that's a bit of a misnomer because that really makes up a very small percentage of our global revenue between these two areas even though they're the most recognized areas for us. So we do quite a bit of work in a lot of different areas, and we'll cover a little bit of that today. And one other thing I do wanna highlight before we move on is we have an NGO called ERP. So not the ERP you're thinking of. This is actually elephants, rhinos, and people. We take one percent of our top line revenue. All the administrative costs are covered by us, and all of that goes actually to the preservation of endangered species through the alleviation of poverty. And so I encourage everyone, if you have a moment and remember this, to go out and look it up. It's a very good cause that we participate in. Okay. Next slide. Alright. So just to highlight a little bit of our BTP capabilities, we operate again in fifty two countries with BTP Innovations, We do a lot of work around clean core. So we work with several multinationals, helping them establish their clean core guidelines and make sure that they're able to operate and maximize the SAP investment. And then We do participate in SAP's global BTP partner advisory board. We are a tier one migration factory partner for integration suite migrations. We're an SAP AI Lighthouse Council member. And as a result, we have early access to and are able to contribute and give feedback on SAP's AI platform, including Joule Studio, which we'll hear a little bit about in a moment. And we actually are one of the larger BTP organizations globally, and we have over seven hundred consultants, including several BTP certified architects, which is a very difficult certification to get. And the rest of this, we'll talk a little bit more about a little later in the presentation. Next slide. Alright. So I'm going to turn this back over to Sarah. We're gonna do a quick poll for everyone and just get an idea of what AI means to you. Okay. You should be able to see this on your screen. Take a second here to respond. How would you describe your organization's use of AI today? Experimental, we're testing ideas, tactical, using AI in select business areas, strategic AI as part of our transformation plan, or not sure where to start? I see the responses coming in, but we'll just give another twenty seconds or so for you to pop those votes in. Okay. Couple people still responding. Okay. I think we can go ahead and we have most people, so let me go ahead and share the results. Can you see the results, Spencer? I think you're on mute. Sorry. I can see the results as they were coming in. Okay. Great. Alright. Alright. Alright. Thank you. No. That's I appreciate that the feedback. So I think we'll be able to see that's pretty consistent with what's happening in the market. So if you go to the next slide, please. Okay. Alright. So one question that a lot of our clients are asking us is, you know, is AI hype, or is it here to stay? And so you know, if we look at it, some of the technical leaders on the market, you know, Jeff Bezos has been very vocal about he believes that we're in a bit of a AI bubble similar to the tech you know, the dot com bubble with the Internet, you know, back in the early two thousands. One of the good things about the bubble, though, even though you have diversified investment, typically, the output of a bubble once it bursts is you then get consolidated investment into the stronger players. So next slide or I'm sorry. Click, please. And then Mark, CEO of Salesforce has, you know, has a little bit of a different take on this. His take is that this is a pivotal area. So AI is going to be transformational, and it's going to lead to multitrillion dollar opportunities for organizations. And click, please. And then Jamie Diamond, who's the CEO of JPMorgan, very, very well known person. You know, not a technology person, but certainly a indicator, you know, a student and indicator of where to invest and where markets are going, he believes that this is real. Right? There is hype around AI. But There is hype, but unlike the Internet bubble, this is actually real. And so despite all these different perspectives One thing seems to hold true across a lot of the experts in the industry right now, and that's AI is not just incremental tech. It is foundational. And companies that choose not to or delay their involvement in it run the risk of being left behind. KNX Here we go. Alright. So there was a fairly recent Famous study that was published recently from MIT. And, you know, very, very interesting statistic that ninety five percent of AI projects fail. And only about five percent of those move forward with any type of measurable Success. Next one, please. And it really happens for three reasons. So one is the technology versus the execution. It's typically not the models are at fault. It's actually making sure that everything is in place around it. So all of the integration, the governance, the adoption strategies. Two is the misalignment of the use cases. So a lot of the early use cases are focused on sales and marketing, and they and over fifty percent of the budgets were going towards that. And what A lot of customers are realizing is that they would be better served focusing on their back office. So looking at automation, looking at different areas there. And then the last area is around a learning gap that exists. That exists. And this is basically creating the feedback loop. So AI can go out. It can do the analysis. You can do some automation. But if you're not creating the feedback loop and creating a knowledge or a learning as part of this, you run the risk of not evolving. K. Next. But I would argue that there's an actual bigger group here in this, you know, that was not addressed as part of the study, and that is the group that never gets started. Right? And there's a term in Merheme Webster dictionary called ebullia. And ebullia is simply the abnormal lack of ability to make a decision, and it's usually because of an overabundance of information. So we're seeing a lot of information from SAP. We're seeing a lot of information from our hyperscalers. We're seeing a lot of information from pure play platform AI companies, and it's a little bit overwhelming. And so there has to be a bit of a discipline that goes into place as part of this. And so the key is just knowing where to start or how to start. K? And at that, I will turn it over to my colleague, Yani, to go through a couple of our There we go. Thank you. Great. Thanks, Spencer. Great. And, I'm gonna pick up exactly there where Spencer, stops. So I'm calling this far to insert AI here, and I think that's what we wanna do. We wanna insert AI everywhere where it makes sense. You know? It shouldn't just because you have a hammer doesn't mean everything is a nail, but that's pretty much what we're aiming for. Now in the SAP world, there has been a very big focus on business technology platform for the last couple of years, and we've done some webinars on this, you know, where to get started with BTP because that's kind of the, you know, that entry point. How do you get that first project? Going and, you know, there's a lot of lessons learned when you do that first project. And what we wanna focus on today is how do we get started in each of these different pillars? So if you look at app development and automation, the way that you start a project there is quite different from an integration project. Right? So each of these pillars are in their own unique in how you start. But in the given day and age that we live in, you cannot just start a project without AI, really, or you can't start anything without the assistance of AI or the booster that AI provides you. So our goal here today is to look at each of these pillars, how you can start, and some tips on how you can integrate AI into that process so that it's an accelerator for your projects that you deliver in the end. The project that you deliver in the end can include AI or not. Either way, this will give you that booster that you can deliver it faster. Right. So we're gonna start off with our first pillar, and that is app development and automation. So if you think of BTP, that is a build apps part, low code, pro code, then also the process automations that you can build in there. And a place where we ourselves find, you know, it very useful is using AI to analyze your requirements for a project that you want to build. So if you have an automation or a bold app that you want to do you want to create, getting that specs to an AI that knows your the SAP processes and the business logic in there. And you give this to that AI and tell it to refine it for you and look for gaps. It can be very valuable because what you can get from there is, let's say, you create a bold app that has a four per purchase record. Requisitions. And for that, your approval, you did not account for It's temporary soft. You know, what will happen there? And then AI can highlight that for you so that you build that into your spec from the get go. And at the end, you'll deliver something more complete, and you don't have to build in workarounds of things that you did not consider. Generating code and improving prompts, that is something that is being used a lot by all the billboard companies almost these days. You can generate code. So SAP provides a bunch of options where you can create generate code inside of the SAP build code to create CAP apps. And then also just to improve prompts. We are we had the privilege to play around with the dual studio and then to create AI agents. And our initial agent that we created was doing what it needed to, but then we asked AI to improve our agent prompt for us, and we got much better results be because it was much more complete. And then lastly, to create automated processes or do simple task through natural language. So inside of the build process automation, And the AI agent in there or designer in there. You can give it a prompt to tell it what process to automate, and it will go and build that out for you automatically. All of these things will help you to deliver These apps that you're making or these automations just a little bit faster and maybe a little bit more compete from the get go. Right? So let's quickly look at a couple of examples just to show you how we've used this in the past. So the first one is, software migration. So if you look at the Neo environments, you need to migrate some of that code over to the new Cloud Foundry environment or running in BTP because the new environments will be deprecated. What you can use is in our case, we have an AI agent system called System Fortitude, And we use this because it has a general AI agent, and we have specialized agents as well. For instance, the software, analyst agent and then a software engineering agent. So what it can do is it can take all of your code and documentation, issue trackers, access to all those things, analyze this existing old application, and it will write your document spec to recreate that application. It will create the Bitreba for you, the issue tracking, all of those things, and then start coding this solution or recoding the solution for you in your new technology stack. We're at basically the traditional way that we are migrating software at the moment. And for that, you would gather very project information, and then you would start your normal development process, which go through different versions. But as it is at the moment, that's quite slow, growing to errors, and, you know, it's very manual. Where you have the technology to available to you so that you can use a more AI approach to that. So as we mentioned with this system forty two structure migration agent that we have. You just give it all of the information you have on that project. It will analyze it, write the spec for you, and then go and reimplement it from that spec that The other agent specialized agent actually wrote. So we've seen great success with this. So this is definitely something that's, you know, you can look into. There is also another thing. We had this very fun very interesting project with the London Stock Exchange Group where we do payroll variance analysis for them, but we're using an AI agent to look at the variances and explain that so that you, you know, the A our analyst doesn't need to go and inspect all of these different details. And when we got access to the to dual AI agents, we decided, let's try and recreate that project, but for SAP an SAP environment. So this would run-in SuccessFactors. You can prompt it to say, okay. Give me a breakdown per employee for all the payroll variances, and it will go and analyze that for you using data that's coming from ECP. Now this was really interesting to see because we could tell it, okay. Well, now do a deep analysis for employees who are in a warning or in error state. And it will look deeper into those employees, and it will look at different kinds of data. So your absent data, time data, basic pay, all of those things to explain why, for instance, a regular working hour is paid less this month than a previous one. Was it personal leave? Did they was there unapproved time, situations like that so that you can resolve it? But the thing about this where AI helped as well, not just to create a cool solution, is we had this AI agent prompt description, and it was just a little bit light. And we asked AI to improve it for us. We got better results. So in our design and implementation process, we used AI, and that's where we got better results from that. Moving on to our second pillar. This is our integration pillar. What we've seen here as well, another migration, thing that pops up a lot is the IPO to the integration suite. And then also just from solutions that we've seen in the past, we have custom codes, so no clean core custom code from an ECC system that did manage the integration from an external or third party application to SAP. And those If you go king core, you need to get rid of that. Code sitting there and rather port that over to the integration suite. And having a tool, having AI help you do the mappings, we provide it with the third party data and tell it to map it to the correct SAP APIs that you'll find in the business hub, will save you a lot of time. And I'll show you little example of that in a Interface design is also where AI can be very valuable. I mean, you can give AI some sort of model the facts of what you need or your situation. So it might be something that is a delta load once a week that is scheduled, or it might be real time data that you need to integrate it. And in that case, your AI might recommend the event mesh at a high frequency rather than a scheduled job, for instance. So in terms of making decisions in your designs initially, AI can assist you there as well. And then lastly, the new AI integration suite monitoring that SAP has released is really great. I mean, it learns from patterns from your business, so it's very contextual. It doesn't you know, all difference all businesses are different and red flag something in your data that is not really weird or strange for it to pop up. So it will really, take into consider and learn the patterns in your business and then flag things from there. So there's a lot of value that can be added in the integration suite as well. Quickly looking at the example, where we had We have software that was developed by us That takes creates an integration from ProSolutions. So it's a planning and scheduling tool assistant, and that data needs to be synced to, your SAP system. So all traditional way, OData services gets the data, writes it to the right place. But you wanna go clean core, we want to recreate those in the integration suite. And the way that we did this is we used our System forty two agent once again. And just to give you an idea of the, you know, detailed results that this can give you, because this will go and look at the APIs from SAP. You go scrape the web page for all the information. It will take information that we provided of the field mappings, and then we will do the mappings for you automatically. It will tell you how confident it is with the mappings that it's done. It tells you the endpoints that you need to use in your OData services, gives you a nice little summary, and then it will list all of the fields, you know, the to and the from, and how confident it is in all of that information, including the API entities. It doesn't stop there. It gives you a lot of additional information. And all of this just helps you. You're how do you start this implementation, like, with authentication that you need to consider? And it will give you examples of how you will go and implement this engine in the integration suite. So all of these things, it will give you as a starter pack. It tells you which methodology to use to figure out what it needs to do. All of those things are already in here. This will be a lot of work if you have to go and do that daily. So this is really a fantastic way to save a lot of time with these projects. Right. Then moving on to data and analytics. So when you start with a pro a project and you need to get your data in the right state, AI can help with that a lot. If you have unstructured data that you need to put in a structured format, fantastic. If you want to enrich the data with sentiment analysis, things like that, categories. AI would be fantastic. Generative AI would be fantastic for that. It will save you a lot of time of manually going to do that, and it's really accurate with these type of categorizations and data migrations. They're role based dashboard designs. If you need to create, you have a project, and, you know, you want an HR dash dashboard. If you are a HR the head of HR versus a team lead There's different information that you want to see, and it can help you design that initial you know? Dashboards, and you can say that to those individuals to get feedback. At least you have a starting point. And lastly, to create test data. A lot of times, the creation of these dashboards or apps or, you know, just insights are paused because you need to get your data ready and your data in the right place. But in the current state that we are, it's so easy to create very realistic data using AI, and we'll look at the little example of that in a bit as well. Quickly looking at these, the generated dashboard that I mentioned. This was literally Gemini creating, you know, two dashboards, one for a team lead and one for head of HR with different stats on it, and you can see from the text that it is actually AI generated. But that's just an idea. This, can now take to a team in Southern and ask them, you know, is this important? Are these metrics important to you? And it's a starting point because if you have nothing to talk to, it's it's quite a bit difficult, more difficult than that. And then the second one is something that isn't exactly creating data for a dashboard, but what we did is we created a build app. So this was a BTP low code build app that we created. And this was for our own use internally because it took us very long to create org units, realistic org units for our own testing inside of SuccessFactors. So what we're doing here is we we give Our the Molacra bot or our AI agent. Specifically what we want, the data that we want in SuccessFactors, and it knows that it needs to go and create positions and, you know, pay grades and all of those things that they need employees. But we also tell it's a company, and we tell it the HR structure that we want. And this goes and generates everything in there for us so that we end up with an org structure without doing anything. So if we navigate, in this case, to Charisma, you see all of the information there. And if you look at the people in these org units, it's very contextual using South African names as well. So this is just to show that with an agent, you can tell it, you know, you know success factors, you know what is needed to create an org unit. Go and create CS day stuff for me for this. So same would count for an HR dashboard. These are the metrics we want to share on our dashboard. Please go and generate datasets for us that is realistic in this context, and you will get fantastic data that you can use as mock data while busy building out these dashboards and waiting for the right data to be available. Also, if it's sensitive data, know, you might not want that actual information there, You want a subset or just mock data initially. And that is quite a lot of gains. I mean, this was going from two days to two minutes for us to get it all structured. Fantastic. And then I am at our last bonus tip for this for the session. We looked at nine tips on how AI can help you. I mean, the bonus step is documentation. Documentation takes a lot of time. But for you to get consistent documentation throughout any project that you're doing, if it is for user manuals, test scripts, any technical thing, just capturing information that you need to share with other people, use AI. You can give it the code and tell it to write a text, you know, a technical spec. You can take the requirements document, and if we can write test scripts for you to that's so that you can make sure that the project delivered to you does everything that it needs to do correctly. This will help you to create really good complete documentation in a consistent way. And that is basically our team tips. So for now, we're going to questions, and then we have a last little bit that we want to cover just after this. Sarah, will you let us know if there's any questions? I will quickly look at the chat as well. Yes. Yep. Okay. So we do have a couple questions. The first one, what are you seeing in the market for adoption of AI, and what kind of use cases are you seeing? I'll take that one, Annie. Yeah. So I think yeah, so I think we're, you know the poll in the beginning is a pretty good indicator. I think what we're seeing is a lot of people are testing the waters on it. But we're seeing some of the more strategic companies actually getting engaged in pry piling programs. And it tends to be more IT driven, so we're seeing more comfort on the IT in the IT department about getting things started. And as, you know, the one use case that Yani covered with the migration of code. So, obviously, there's a lot of clean core activity around migrating code into a clean core SAP environment. But just thinking beyond that, the example that she gave where we're taking early versions of other platforms like Java and Power Builder and things like that and migrating that code. And these are typically in organizations that are rather large. They've been doing development for many, many years. They get tens of thousands of customizations, and it's not financially feasible to actually migrate the that code unless you're using an AI tool. And so that's where we're seeing IT really dig into using it. And then on the business side, it again, it's, as I said earlier, kind of in line with that MIT report. There are areas that are trying it out. We're seeing it in call centers. We're seeing chatbots and things like that. But, really, the biggest impact to the organizations are coming when you can shorten your month end close or your year end close by using AI agents or payroll with the example that Yani gave where we're act actually able to reduce it by three days per payroll, Ryan, per region. Right? And so those numbers can get rather large, over a period of time. Okay. Thanks, Spencer. We have, another one. Let's see. Someone said regarding AI models, these AI agents and functionalities are accessing OpenAI Anthropic Models or SAP exclusive AI models? So, I think the question there is, like The how does SAP do it? Maybe? Yes. They should addition I'm sorry. Sorry. Can you just repeat that, Sarah? If you look in the question, so it says regarding the AI models, the AI agents and functionalities that are accessing OpenAI Anthropic models, or are these SAP exclusive AI models? Not sure if that makes sense. Okay. Yes. It is. Got it. Got it. Yeah. So if we look at Jewel so for us in System forty two, we use different kind of models depending on the kind of AI agent the agent and what it works best with. But then also if you look at the dual agents, those are those ones, if you go through SAP R core, you select the model that you want to use, and then the same for the dual agents. You when you create your dual agent, you select which mod AI model you wanted to use. So they have all the big ones available there, and the latest ones, you know, as soon as it's released and just a little bit later, it's available in there as well. So it still uses those AI models. I do think SAP probably hosts their AI models privately. So it's not like the open models, but it's still All the main ones that you would find in there. Even Mistral is available in there. Okay. Thanks, Jani. We have another one here. How do I ensure that the information gathered by the AI is actually correct? Yeah. That's it's still very tricky. If you look at the results that you get, you know, you if you know what it's supposed to generate, you would very easily, you know, identify where it goes wrong or, you know, lose sights completely. But That is why it's quite important to keep that human in the loop when you're generating this. You know? In any of these cases, when you ask it to analyze the requirements and it tells you, hey. You need to add this requirement. Make sure that you or whoever is expert in that domain verifies that. Because it's very tricky. You can put guardrails in place as much as you like. It can still go and do its own thing. I've seen even with the SAP dual agent, even with very clear instructions, it could generate things that, you know, isn't exactly what we were looking for because it still uses those AI models underneath. Even if you turn down the temperature, meaning it can't be too preheated for things, it's it still gets things wrong sometimes. I mean, we're getting much more accurate, but they still make errors just as humans do. So, just that you would have somebody check the junior's work, that's pretty much what should happen here. In all of these cases, when you generate something, you need to know that there's an expert, just keeping your eye on this junior that you just hired into your company. I think that's the that's all of the best way to do it at the moment. Okay. Thanks, Annie. So I think that's all the questions we have for today. So I think we can go ahead and close it out. Thank you again, everyone, for joining us. Thank you to our speakers. We do have a couple more slides. Oh, yeah. Sorry. That's alright. That's alright. Sorry, Sarah. We're just good to go there for you. There we go. Alright. Yeah. Next slide. However, we can see it is lagging quite a bit on my side, I've realized. That's fine. Alright. So, again, you know, one of the things that we work with our clients on to sort of help them get started on AI is it's really a discipline. Right? So we help them establish what are the goals that they're looking for. So as an organization, where do they wanna invest? How are they gonna measure success? We help clients with their data. Obviously, you know, the old adage, you know, garbage data in, garbage data out, you need to have your data structured in a way that you can leverage and optimize AI, and then, you know, set very clearly defined budgets. K. Next. And so there's a couple you can just build this out, please. Alright. So the next thing that we'll do is we'll actually work with our clients and help them identify what the opportunities are. So, you know, looking at where what are the best use cases? And I call this A lot of clients sort of get into this use case chasm where they can't decide, you know, what use case to use, and we can help guide our clients on figuring out really where's what's the biggest impact for you. Also, making sure that you have the talent ready in house to design, deploy these AI initiatives and support them Within once the data's in place, we start to look at the different technologies that are available, and then we help our clients actually produce an AI scalable road map. Right. And we look at governance, and we look at security. K. One more click. Bottom line is that this is a discipline, and it has to be you know, it's not an it's not a one time project. It's an ongoing discipline, and it requires alignment on governance. Vision, execution, and security to make sure that you get measurable results. Alright. Next. Sorry. There's alright. And then, again, you know, just knowing where to start is difficult sometimes. And I know people are based on the poll, people are starting to kick tires and looking at tactical things to do. One of the easiest things to do is actually, you know, not reinvent the wheel. Go with something that's available to you, have that as a starting point, and then use that to build and customize and build into your own scenarios. And so we actually have an innovation catalog with over a hundred BTP applications, and included in that are some of our AI agents, some of which we covered today as well. But this typically, a lot of our clients are finding this is a good place for them to start. They can start to leverage that, and it's easier for them to position internally with the business because it doesn't require imagination. You can see something that's up and running and see measurable. Results and then use that as a starting point to for your own company. Alright. Next. Alright. So just in closing, we will bundle this up and make this available, and we'll be sending this out. But we've put a couple of other links in here for some other downloadable BTP oriented and AI oriented information for you just to help you in your I think decision process. If the if you guys just look in the, in under the materials tab, you'll see two of these the downloadables already available to you there that you can just grab, and then you can, you'll find the we'll release these top ten tips, really soon. We'll send that out to everybody. Perfect. Okay. Okay. Alright. If you're cut, just We've got just a couple more minutes. We'll offer one more time if there's any questions. There was one more that came through. And Since we have a couple minutes, what can you share about how consultant skill transformation should aim? Because AI still requires critical thinking guidance. Yes. It does. But yeah. Sorry. Do you especially you go for it, I'll add something at Go ahead, and then I'll add my mine on to it. Okay. Well, if you even look at what SAP is providing at the moment, they even have, you know, specific AI tools available for consultants, and it's definitely necessary. You know, you do need that critical thinking. But thinking alongside AI is the same as a developer using AI alongside it to generate Oh, you know? Coding is thinking, and it's a specific way you need to think about how you're gonna solve this problem. And I think In a consultant way, it's the same thing, but you can use this as a buddy almost to, you know, even improve and enhance whatever you deliver. And then consultants, if it's documentation or all those things, it can also assist you in that. And you can create custom tools for that or use out of a box things per job. I think, Spencer, if you wanna add some into that as well. Yeah. No. I think you touched on part of it. To me, the biggest impact for consultants and for just projects in general. Is the creation of your functional design documents, your technical design documents, your test scripts, the test data, all of these things that historically have taken months on a project to actually produce and create these extended project timelines, a lot of that stuff, you know, is going to change over the next six, twelve months, eighteen months for consultants because that will be automated. So we're gonna see condensed timelines on these big projects like RISE, big transformational projects. And so it's already starting to happen. It's going to be more adopted more in the next twelve to eighteen months. And I think just AI in general, I think everything changed in January of twenty three with chat GPT becoming more mainstream. And now you know, we're seeing kids in school using AI to take notes. It's becoming It's, you know, becoming embedded in our day to day life. I mean, I would venture to say a lot of people don't even do a Google or an Internet search anymore. They just go straight to ChatGPT or Grok or one of the other tools to ask information because you get richer answers back. So that's the biggest impact that I see is and I think this will transfer into other parts of the business too, is I think your just day to day life is going to get easier as you're starting to automate some of these sort of tedious manual task over time. That's the key. Okay. Alright. Any other questions? Anything else for me too before we close out? I don't wanna shut it out if we have more slides to go through. No. No. We're good. Okay. Yani, any other comments? No. No. No hidden slides. No hidden comments. I'm all. Okay. Alright. Well, I just thank everybody. Thank you, everybody, for attending. Great questions. I appreciate the interest in the topic. Our contact information's in here. If anyone has any follow-up questions, feel free to reach out to us directly. And, again, thank you very much. Thanks everyone for joining. We'll be sharing the recording we'll be sharing the recording soon, and feel free to reach out to any of these, to any of us if you have any questions, and we'll we hope to see you at the next one. Thanks, Yani and Spencer. Have a good day, everyone.