How to Build Real AI Use Cases for JD Edwards Users

Transcript:
[...0.3s]There shouldn't be a fear behind. This is what I'm trying to say, because there's it's, it's AI, it's supposed to be helping you, not taking over for you, [...0.5s] right? Um, cause at the end of the day, you need people to run a business, and that's, that's just something that will never change. [...1.2s]Do you find it challenging to clearly define AI use cases that deliver real impact? Are you unsure how different user roles in your organization should inform your AI strategy?And today's practical how to episode, Renee Lord from ERP Suites guides us through step by step instructions on using user rolls to pinpoint high impact AI opportunities, [...0.5s] learn proven methods, actual strategies, and hear about real life success stories. Stay tuned.By the end, you'll know exactly how to leverage roles for effective AI use case development. [...4.9s] Welcome to not your Grandpa's JD Edwards, the podcast dedicated to practical guidance and actual advice for JD Edwards users.I'm Nate Bushfield and today we're taking a deep dive into how you can [...0.6s] use clearly define roles to identify and implement powerful AI use cases. We are joined by Renee Lord, Director of products at ERP Suites to walk us through this process.Renee, great to have you. How are you doing today? I'm doing good. How are you? Uh, having a great day? It's a little chilly here for me, so I'm kind of confused on this whole summer thing.When is that really gonna start? I, I don't know. It's, it's almost Memorial Day and [...0.7s] I, I'm planning on golfing a lot this weekend, [...0.6s] but I don't know, it's a little chilly for it, so I might be in like full long sleeves and pants.I, [...0.6s] I don't know if you're a golfer or not, but I think we did talk about that actually not too long ago but soon to be, [...0.7s] soon to be. Love to hear that. But, but anyways, back to why we're actually here. Can you give our listeners a brief overview of your expertise?Sure. Um, like they said, I'm Renee Lauren, I'm director of products here to your Piece Suites. Um. We have a couple of products that we've developed. One is our scanning solution called scannability and the other one is [...0.8s] our clarity solution that monitors your JD Edward system.But, um, specifically around AI, we have been learning and developing AI solutions for [...0.6s] the past almost couple of years now. Um.We've [...0.6s] experimented with different [...0.8s] technologies within the AI space. We have, um, done some work using rack, which allows us to, um, [...0.5s] complement the AI solution with your customer specific data, so you get answers that are good for your [...0.5s] current company instead of just generic answers.And then we've also, um, [...0.5s] used machine learning for, uh, doing predictions and anomaly detections and, and things like that. Uh.We have also developed several digital assistants that, um, support different, um, different actions. And so we have, uh, solutions around for manufacturing or finance, um, sales and customer support. [...1.2s]Yeah, and a lot of those solutions are [...0.7s] still growing, which is really great to see. I mean, it's, it's very interesting.I, when I think AI, think of the usual like chat CPTs [...0.6s] or stuff like that, but these are actually specifically built for certain industry that are out there, certain departments, if you may [...0.5s] throughout companies.So it's very cool to see the different applications of what we can utilize AI for and a very specialized way which is this very cool, it's very, very cool.But we can get into it a little bit more, what initial questions should organizations ask themselves to recognize if their [...0.5s] AI use cases lack clarity or effectiveness? Um.Probably start with the, um, the problem that you're trying to solve. So a lot of times people are wanting to improve efficiency, so they identify [...0.7s] what they think might be a good AI use case, but it actually is something that just needs automation, it doesn't really need AI.And so, um, thinking about the problem that you're trying to solve, but also the outcome that you're wanting to achieve when you solve that problem, and then the return on investment as well. Um, another question that you need to ask yourself is what data do I need to solve this problem?And so, um, [...0.7s] you may [...0.7s] look at that data and say, is it clean? You know, it's my data clean, is it gonna really help me solve the problem, or is the data really need be cleaned up and, um, not as effective in solvent problem? And so those are, are kind of the initial questions I think I would start with.Yeah, of course, I mean, you have to identify the problem to come up with the solution, can't really solve anything, nothing's wrong. [...1.4s]I know, I know it seems so simple, but it is one of those things that a lot of people don't really think about is if you don't have a problem, you can't find the solution to it. So really identifying those pain points and where to start is always the first question you should really ask. Um, so what, what problems?Talk about the problems. What problems typically arise when users aren't considered in AI initiatives?Well, um, when you don't, what, what we've seen is when, when you don't think about the role, [...0.7s] a lot of times, um, [...0.8s] customers identify [...0.7s] a use case, and, it's like, it's a really good use case to, to try to solve, but it turns out it's pretty complex, and, um, [...0.5s] that can [...0.6s] result in it taken longer than what you're expecting, um, because it is more complex to implement.And so [...0.5s] when you, um, consider roles when you're identifying the problem, it, it helps you [...0.8s] get some quick lands, and, um, [...0.5s] that makes it implement a lot quicker and, and be, you know, give you more returnal investment quicker.And then the other, um, [...0.7s] problem of seeing is that when [...0.7s] customers aren't focused on a roll, [...0.5s] what they tend to do is [...1.1s] pick one. So they'll come up with a lot of use cases, but it's only addressing the problems for one particular role one.So there's other roles in the company that aren't being considered for AI use cases. And, um, I've seen that a bit as well.Yeah, and obviously, [...0.8s] obviously you need to start somewhere or, and I'm not saying that you shouldn't start with one roll and build out some use cases for that. And [...0.6s] then just like you probably shouldn't stop there, you know I mean, [...0.5s] starting at one point is definitely a good way to start.And so like what you said with a quick win, I mean maybe you do start with one roll and have that quick win right there, but the implementation of maybe that little AI solution right there could be utilized in so many different roles.So it's more of a taking a broader look at what an actual use case would be in this situation, right? Right? [...1.3s]Um. So anyways, uh, how can businesses quickly diagnose gaps in their current AI strategy related to user roles? [...1.0s] Um, [...0.6s] well, one way is to, um, think about all the different roles in your company and maybe even just list them out.And then, uh, one thing that I highly recommend is when you're documenting your use cases, [...0.6s] you identify [...0.8s] the one roll or multiple rolls that that use case will [...0.8s] be used by.And when you have your collection of use cases that you're wanting to implement, you can kind of compare them to that list of rolls and see, you know, do we have gaps? Is it, are we missing a role? Did we even consider this role? And, um, it doesn't have to be a user who is sitting in front of a computer.And there, there's, um, benefits to, to all people in the company to use AI [...0.9s] exactly. And it could be something as little as using Chat EPT to, [...0.6s] I don't know, look up something for you or the new Google AI when you're searching up little tiny things.Like, if we're on the coding side, learning a little bit of a quick fix, or if you're on a sales or marketing side, maybe you need, like, a little graphic or something that I know.Chat GPT isn't really great at creating graphics, and I will be the first one to say that, um, some of their, [...0.5s] some of their Photoshop capabilities are, [...0.5s] it's not great.They always make me look like some mannequin or something. It's always creepy, it's always creepy, [...0.6s] but, yeah, gotta start somewhere on some of these things. But, uh, so what are what, what are some of the step by step actions a company should take to define and map out user roles effectively for AI projects? [...1.2s]Um, [...0.6s] the way I like to start with that myself is to think about the characteristics of each role. Um, when you [...0.8s] think about the different roles and, and what are they doing in their job, or what types of information do they need or, um, just what's important to them and their job.When you have that list of characteristics, it can help you think about what are some good AI use cases for these different roles. [...1.6s]Right, right, and [...0.5s] it's, it's honestly very funny to me that [...1.0s] something as simple as, like, looking inward and looking at a specific role and saying, all right, what would make this person's life easier? What would make this role easier in terms of AI?Or maybe they need an outside source or something like that. You know, it's always hilarious that [...0.6s] some people will think, oh, if I say that this would make my life easier, then maybe I get paid less or something like, something along those lines.It's like, no, [...0.6s] you would basically be taking a very simple task, [...0.5s] automating it, or using AI to create that task to be very, very simple. And then you have more time to utilize in bigger projects, or you have more time to increase productivity somewhere else in the company. Right, you're helping the company be more productive.Exactly exactly, [...0.8s] exactly, so there shouldn't be a fear behind. This is what I'm trying to say, because there it's, it's AI, it's supposed to be helping you, not taking over for you. Right, um, cause at the end of the day, you need people to run a business, and that's, that's just something that will never change.But can you clearly explain the roles companies should consider, like, such as, like, an executive [...0.6s] frontline management, and maybe even, like, individual, like, contributors, um, and how to incorporate their insights into AI planning?Sure, um, so [...0.7s] when I think about, um, people at the executive level, that's kind of, like vice presidents or sea level people, um, their role is typically very strategic, and so when you, you think about the characteristics for them, they are making strategic decisions [...1.0s] across the whole company, they're not focused in one area, they're focused across the whole company and also [...0.7s] outside the company as well.And, um, typically the decisions that they make impact, like profitability and revenue. So [...0.9s] they, when you think about them, [...0.5s] they need data from a lot of sources to make a lot of their decisions.Um, [...0.8s] but then when you focus on like frontline managers, like you said, [...0.8s] they, um, are more focused on managing like day to day operations, and, um, they directly manage individual contributors. Um, [...0.7s] when I think about their goals, they're probably focused on improving efficiencies or, um like [...0.9s] eliminating errors or risks [...0.6s] or, um, maybe, um, [...0.7s] reducing costs things like that.And, um, then when, when I go down to the next level where I think about individual contributors, [...0.9s] what comes to mind there is there the very front line employees.And so you actually have [...0.5s] higher turnover rate there. And the turnover is not necessarily [...0.6s] people [...0.5s] leaving the company. It could be that they've [...1.3s] developed in their job and they're moving to another job, you know, they're expanding, they're climbing the ladder, that kind of thing.But, um, because there is more movement there that individual contributors little more fluid [...0.7s] they typically, when I think about them, they, um, often need more training, um, because they're like being on boarded more or whatever.Um, they need help developing skills, whether it's the skills for their current job or skills for a job they wanna move into. Um, they often need more guidance or help.And so that's just some examples of the way that what I'm thinking about roles and how they might lever J I, [...0.6s] I try to consider [...0.6s] what all are they doing in their job? You know, what else do they need for doing their job, those kind of things.Yeah, and [...0.7s] so maybe a different way to, like, to look at it is [...0.6s] like, how would you say in terms of, like, a JD specific role, like maybe C&C, [...0.7s] how would you incorporate, like their insights into AI planning? Um, for them?They are often [...0.7s] troubleshooting things [...0.9s] and very much so, yeah, looking at problems and things like that. And so [...0.6s] when, when I consider, like, a C and c, I consider them more of an individual contributor, but they are, um, [...1.0s] very experienced with what they do typically.And so they're, they're not, like, um, [...1.6s] usually, they're not brand new to the job because you have to have JD Edwards experience, B, C, and C [...0.8s] so they, they probably are, um, more interested in current information and current technology and things like that.So I can see they're having questions around, you know how does this software interact with JD Edwards? Or if I upgrade this, what are the things that [...0.7s] might be the issues for me?Things like that [...0.7s] exactly. And it's, it's interesting you brought up the individual contributors before we were talking about CNCS and how you're, right, if we're moving up in companies, they, it has one of the highest turnover rates, is an entry level job, [...0.5s] but with AI, [...0.5s] the training that really would go into it, [...0.8s] it cuts it down a little bit, right, because you do have that helpful tool that can elevate your business and can make an entry level position [...0.5s] not easier but easier to learn, right, if you have that AI tool that will not only film the knowledge gaps that maybe you might have, but it also support that person in completing their task and elevating their productivity and their level of experience to a height that they wouldn't have unless they were actually in that role for a very long time. Right? AI is definitely gonna help with that.So it is very interesting to see how AI will actually work with lower level employees, but also higher level employees to [...0.6s] really increase the productivity, increase the effectiveness of them on boarding or them moving up in the role or anything like that.Um, but it, it's almost endless on what AI could really contribute in this situation.I know endless, it's such a such an AI term these days, I swear. But, yeah, other than that, yeah, [...0.8s] so anyways, what practical tools or frameworks do you recommend for organizing, like, role based AI use cases? [...1.5s] Um, [...0.5s] I think [...0.6s] for, for me, what I found [...0.5s] is that it's very beneficial to have some guidelines when you're documenting your AI use cases.And so [...0.7s] I said earlier that I think it's important to include, you know the role or roles that the use case is being written for, um, to consider that, but then [...0.6s] also, um, having a detailed description of [...0.7s] the, the goal you're trying to achieve or the flow that you want to have happened with these cases important, and then, um, what data you're expecting to use for this use case.That's really important because that helps you determine if it's clean or not. Um, security and permissions is something that you need to consider as well when you're defining use cases. Um, and then [...0.8s] maybe 3rd party integrations, you know or are we gonna have to, uh, pull data from multiple sources for, for this use case?But then as part of the guidelines for use case, one of the things that to me is also important is, um, [...1.0s] to document the return on investment.And so, [...0.6s] you know how is this use case going to, um, [...0.6s] make us more efficient or, you know how is it going to improve [...0.7s] our processes or [...0.6s] how, how is it gonna reduce our cost? And, and how much cost is that gonna reduce?And so when you have all that documented, and it [...0.5s] comes to implementing it, you, you know what you're going after, you know what you're trying to achieve, and [...0.6s] you know which roles are gonna use it, you know what data you're gonna use, and then you can kind of look at the return on investment and [...0.9s] determine, is it worth it [...0.5s] to implement this use case? Is this really gonna help us?Like, we think it is, um, and those, that's probably, probably the guidelines is, is the [...0.6s] biggest framework that I think is helpful and in defining these cases, and that could vary, you know company, the company as well, what's important to them [...1.0s] exactly.And it's all about what a company actually finds valuable, right, what they find to be important. An outside source can't walk into a business on day one and say, you know, what, this is the most important thing you should think about this. I don't know why you haven't thought about this.No, of course not. You need the people that have been there. You need the people that actually understand what the business is and where they are problems, [...0.5s] right? Cause yes, maybe you could hire a consultant and within 3 months they'll give you some fancy presentation that'll tell you all your problems.And yes, that might be a great way to actually have this happen. Maybe you're maybe there is a company out there that is too close to it and maybe they're like, oh, we're fine, we don't have any problems. And maybe a consultant can really point that out.But at the end of the day, they, the consultant will lean on your business. They will lean on the people that are actually there, that are in the weeds day in day out, as they will understand to a degree that no one else really came. Yeah, so, yeah, you're completely right. Like you have to have framework in terms of [...0.6s] what could the problem be?What steps are you taking that you find repetitive, that you find that you can definitely automate?You can definitely use AI [...0.6s] to implement a different solution there that will elevate your business in a different way, [...0.7s] right, yeah, so to go with what you've seen from the data and everything [...0.7s] exactly, and, yeah, and in terms of security and that side of things, we actually had a we had a podcast with Brian Connor not too long ago.We were talking a little bit more about security and talking about data and all that stuff. Um, but if you if the listeners out there wanna learn a little bit more about the security side of things, go check out that podcast.It is incredible, we do a deep dive on what you should really look for in terms of your security, but enough plug in my own podcast.I guess we'll I guess we'll move on a little bit into, um, more like, success examples, so could you describe practical examples of how company is successfully applied to find user roles to create impactful AI solutions? Um, [...0.8s] yes, I can.Um, it's, it's kind of interesting if you, you think about the roles that, that we talked about earlier, and [...0.5s] you think about the executive level, um, [...0.5s] and that they are looking at things across the whole company and stuff like that. [...1.2s]For [...1.2s] Bam, you're, you're not gonna get a quick win, and most companies are looking for quick wins when they start their AI journey.They, they wanna see some results really quick, and so if you started that top level, that's gonna be pretty complex because it's gonna be pulling a lot of data together.And that data may not be clean, and, and it may not work together. Well, there may be a lot of work there, so you probably don't want to start at the highest level in the company as your first use case. Um, if you start at the lowest level in the company, you can get some really quick lens there.And, um, it's kind of like starting there gives you the crawl walk run, [...2.1s] starting out with something small, but it's good, you know and then you learn from that.And it's kind of like what you said, [...0.7s] having that quick win and people seeing how AI can be used and the value of it, um, it builds trust in it.Um, some [...0.8s] companies have expressed that their employees don't necessarily trust it, like what you said before, they think it's out replacement job or, you know that kind of thing.But when they see how [...0.8s] it [...0.6s] makes them actually more efficient or they're, they're more independent and with [...0.5s] being able to make their own decisions, um, that really helps their productivity.And so, um, that's probably, [...1.2s] you know how roles can help with getting started with AI, [...1.3s] exactly. I mean we've talked about the crawl walk run on this podcast a lot.Oh, have your, oh, of course, I mean when it comes AI, when it comes to any solution out there, just start small, gotta start somewhere [...0.5s] is right.When you start, you'll instantly build trust [...0.6s] to a higher and higher level, especially with something like AI. Cause when I think AI and I think I've said this other podcast before, I always think of I Robot.I don't know if you ever seen that movie. It's with Will Smith, great movie. But I always think about it. This is like, all right, dude, how much should I really trust what they're selling me? You know, I robots in general, they have their own thing. If you lock two robots in a room, two AI [...0.6s] pieces in the room, they'll create their own language and start talking without you. God, do I trust that?I don't know [...0.8s] AI in this AI in this way it's better [...0.7s] in the terms of, it probably won't take over the world. So we got that going for us, but starting small and building and building up, yes, yes, like [...0.8s] the maybe a lower, [...0.5s] lower, um lower role in a company.There's a lot there's a lot more opportunity in terms of using AI for quick wins.You're completely correct about that. But then with that, [...0.6s] with that utilization maybe that opens your eyes up to, oh, [...0.7s] this level, the next level up the manager level, the director level, whatever you want to say, maybe they're having a similar issue [...0.9s] where [...0.8s] they can utilize almost the same type of AI product, AI application for that.And it just opens up your eyes to, all right, if this is one problem and we have this solution, what's other similar problems to that, that we could utilize that solution for in the future? Right?So, yeah, so yeah, I mean starting with one little quick win could open up the possibility for a very large win in a very real way, right? Um.So anyway, [...0.7s] could you walk us through ERP Suite's process of taking identified user roles from initial discovery to fully implemented AI solutions, and could you explain a little bit of what each phase really entails? [...1.1s] Sure. Um, like I said, most of the people that we've talked to [...0.8s] want a quick win.They, they wanna see the return on investment. They, they're interested in [...1.1s] a longer term strategy, and a lot of times they have ideas for where they wanna go with it, but they don't want to, to make a whole in an investment right out of gate.And so [...0.6s] what we usually focus on is identifying a use case for an individual contributor or it could be frontline manager, but we try to identify one that's going to [...1.0s] give us a quick win, something that can be turned around quickly, that they can start using quickly, and that they can start seeing the value of it quickly, so that they get the buy in across the board and can expand, um, what they're doing with AI.But, um, one thing just like this is kind of a made up example, but, um, [...0.7s] you might have [...0.9s] an individual contributor who, who works on the shop floor and they are, um, [...1.1s] moving certain materials that require [...0.6s] safety procedures to be followed, for example.And so, um, what you can do with that is you can create a digital assistant that's trained with your safety manual or your safety documentation.And, um, [...0.7s] bad individual if they had questions about what are the safety rules or whatever, they could go to a kiosk or they could pull it up on their phone, you, the digital assistant on their phone and just ask the question and get an answer without having to [...0.7s] run, track somebody down and, you know, be delayed and what they're doing with their job and stuff like that.And so [...0.6s] there's ways when you, when you focus on [...0.9s] either the individual contributor or use case that is [...0.9s] high value but low cost, that, um, it's what people are looking for and their quick lands.And then [...0.6s] it when they see it and when they realize the value is kind of funny, how that just [...1.0s] opens up new ideas, they want more, they want to use it in this other area. And so it kind of [...1.0s] evolves, [...1.4s] right? And so [...0.8s] it's kind of recap a little bit like your each face that we're talking about here.So from an initial discovery, it's find the problem [...0.7s] and then from there it's make sure what you're building is complying with your safety regulations, with your security, with everything that maybe is in your industry in terms of coming from the government or if it's an internal thing of you have to follow these guidelines.And when you are building that actual AI [...0.5s] solution, [...0.7s] we're making sure that we are following all those guidelines to make sure that this solution is not going to somehow screw someone over, [...0.7s] yeah, somehow bite someone in the ass.And it is kind of like, um, [...0.5s] when you think about what we had talked about earlier and you're trying to define your AI use case, you have to consider the data that you're gonna use.And so [...0.7s] a lot of times companies data is not always clean, it needs some work or some certain parts of it around a date or not maintained well. Um, whereas [...0.6s] if you think about safety manual that's usually pretty up to date. And that's like really important.And so [...0.6s] when we're looking at use cases, we're trying to, especially for a quick win, you know, just getting started, [...0.6s] we consider all those things that we talked about earlier.And then, you know, what is the outcome and what is the return on investment and all of that, we, we consider that up front and then are able to select a use case that, you know, [...0.6s] gives them what they want quickly and, um, has a good return on investment [...0.5s] exactly, and does it in an effective way in terms of we're going to be working with actual people that are on that job, we're going to be making sure that this is actually something that brings benefit to them.And at the end of the day, that's all that AI truly is, is bringing benefits to your workers, bringing benefits to your company.So throughout this entire phase approach, [...1.2s] at the end of the day, the [...0.5s] implemented AI solution should be something that you find value in, something that you can [...1.0s] point to and say, yes, the return on investment is this, [...0.6s] and it'll just open up so many possibilities, endless possibility, some say, [...0.8s] and I gotta quit saying that I really do, but [...1.2s] I already said it.So, [...0.8s] um, what ongoing activities should businesses incorporate to maintain effectiveness after the AI use cases have been deployed? [...1.3s] Um, [...0.8s] well, [...1.2s] one just using that example that we just talked about, you know, for the, [...0.8s] the safety manual for moving materials around.If, for example, um, the safety manual gets updated on annual basis or whatever, you would want to make sure [...0.7s] your AI solution is retrained with the latest information, so that it's always giving the latest and greatest information out, out to your users.Um, so just keeping it, [...0.6s] the keeping it up to date with the latest data is important, but then [...0.6s] also I think, um, [...1.1s] companies [...0.5s] should always be [...0.6s] evaluating their processes and how they're doing their work for continuous improvement.And so [...0.7s] you don't want to just always implement his way out where you stay out and then just stop there. You wanna [...0.5s] keep looking at it and looking at your processes, where is something inefficient? How can we make this better?Where, um, [...0.8s] you can actually use AI to determine, [...0.6s] you know, [...0.6s] which products are getting returned the most and why, and what do we need to do, what do we need to change to make an improvement there?And so just looking for those [...0.9s] constant improvements is another thing that I think companies should always be doing [...0.8s] exactly.Always pushing the envelope, always seeing if there is something else in your company that [...0.7s] is being under utilized. Or maybe [...0.7s] they say, I use case, oh, this is great, but what if they could do this? What if they could change this other little process that maybe the same person is doing?And it's like, all right, wait a minute. If it can do the if it can do X why can't it do y, Z? You know, why can't it do these other little tiny things that [...0.6s] maybe somebody would really have thought of through the first discovery. Maybe it's something else like that.And in terms of effectiveness, we have to make sure that you're always trying to look for new gaps, [...0.6s] always look for new opportunities. Because again, at the end of the day, an AI solution is going to be built for the company. It's gonna be built for specific roles.So if you can [...0.8s] start with one [...0.9s] and then move further and further along in the process, it will take [...0.6s] one simple AI solution that was your quick win, and it will expand it into something that you've never really seen before in terms of utilizing AI in [...0.9s] one of these places like [...0.5s] that.It really is it's it's very exciting to think about at the end of the day, cause, like, I know for me, we, uh, we started implementing certain AI things for what I do and for what my past job was, [...0.7s] and the little tiny things that we started doing with it.It is now snowball effect into almost everyone on the sales team is utilizing AI.Oh, that's something, yeah, that's something I never thought I would say out loud, especially in the beginning of the whole, oh, we're rolling out with AI, we're testing out AI. Um, I wasn't really here where that was gonna go.And the amount of effect, like the amount of time that we spent just training with AI as paid off in the long run because we're no longer doing those remedial tasks, we're no longer wasting time on things that we could do in our sleep, we are now going out more in terms of [...0.8s] talking to our people.We are expanding ourselves, we are learning more and more things, um, which would not have been possible without utilizing AI in the way that we have.So, yeah, it's very cool. It can also um it can analyze data in ways that we could never do it. I mean, it can find things in the data that it would be [...0.5s] very time consuming and difficult for, for somebody pouring through the data to, to pull that little nugget out. Um, so it can really do some amazing things.Very, very true. I know in my past rule, I was digging through a lot of our different contracts and everything like that. And with our new, [...0.6s] was it the document understanding that we're, I don't know if that's actually what it's called, but document understanding that we have put it in place.It has made my life so much easier. And well, whoever will actually take on my old role, it has made their life a lot easier too, which is fantastic to see. Um, they'll never know my struggles. Haha, that's a good thing probably, yeah, that is a good thing, that's a very good thing.But if you're ready to apply what you've Learned today, ERP Suites is here to help you start defining roles and building effective AI use cases. Connect with Renee Lord and her team at ERP Suites. Com to begin your practical journey towards transformative AI solutions.But that is all for today's practical educational episode of not your Grandpa's JD Edwards.Huge shout out to you, Renee, for providing us with clear, actual insights. If you found this useful, subscribe, leave us a review, and share this episode with your team until next time. Define rules clearly, implement strategically, and succeed with AI. Thank you for listening. [...5.4s]