AI in JD Edwards: The Assessment Every Company Needs First
March 10th, 2026
29 min read
Many companies say they need AI, but few understand what it actually takes to implement it successfully. In this episode, we break down what really happens during an AI assessment—from discovery and process mapping to identifying pain points and building a phased roadmap. Rather than simply “plugging in AI,” the assessment focuses on understanding business processes, improving data readiness, and prioritizing initiatives based on effort versus value. By the end, listeners will see how a structured AI assessment protects ROI, uncovers quick wins across JD Edwards, automation, and AI, and provides a clear strategy for when and how AI should be implemented.
Table of Contents
- Why Companies Ask for AI Assessments
- Starting an AI Assessment: Alignment Day and Discovery
- Identifying Pain Points Across Departments
- Process Mapping: From “As-Is” to “To-Be”
- Data, Prerequisites, and When AI Is Actually Ready
- Strategic Recommendations and Quick Wins
- Building the Roadmap and Phased Execution Plan
- Why AI Assessments Take Time and Protect ROI
Transcript
Why Companies Ask for AI Assessments
When a company signs up for an AI assessment, are they really just looking for places to plug in AI? And what happens when the assessment reveals I might not even be the first step?
Today we're pulling back the curtain on what an AI assessment actually looks like. From discovery and process mapping to roadmap design and effort versus value prioritization. By the end of this episode, you'll understand why an AI assessment isn't about installing technology. It's about protecting ROI, building the right foundation, and creating a strategic path forward. That may include AI, but only when the business is ready.
Welcome back. Tonight, your guy is JD Edwards. I'm your host, Nate Bushrod. AI is everywhere right now. Leadership teams are talking about it. Boards are asking about it. Companies are putting it on their roadmap. But here's the real question. What actually happens when you schedule? I mean, is it just someone coming in and pointing to areas where you can plug in, or is it something much deeper?
And more importantly, why does it take time? Today we're walking through the entire process. What happens step by step? What companies actually walk away with and why? The effort for value analysis inside the assessment is what ultimately protects your investment. Joining me today is Drew Robb from FP suites, who's been directly involved in conducting these assessments. Drew welcome back.
How are you doing today? Doing great today Nate a little busy today. But you know being busy helps. You know you've been productive. So I'm really excited today to take take a second and talk about really assessments in AI settlements. And where do they fit in your business. Specifically in your JDE ecosystem. So super excited today. Yeah, I know that you're actually a part of a few of them right now.
So let's start here because this might surprise some people. Is it possible that someone comes to you saying, we need AI? And by the end of the assessment, you realize, yeah, isn't actually the first solution. Yeah. I mean, we see that a lot. You know, a lot of times we will have those initial discovery calls, right? And we start seeing, you know, where the customer is currently at with AI, whether AI maturity really is, how educated they are with AI.
You know, really if they have any established use cases in the AI framework or thinking about AI. And what we find to see a lot of times is, yeah, customers can be pretty far along in the process, right? And when the customers are far along in the process, we can jump into figuring out AI in the business. It's still going to have some, you know, figuring out how it processes work and how they operate.
But the timeline may be reduced, right? But sometimes when we start talking about AI, what we realize with customers is, you know, maybe they are doing a lot of manual work in their business, right? Maybe their whole financial system, financial close to is, is messed up and they need to fix that. Or maybe aging statements or aging reports or a lot of whack.
And once you really start to realize is that it's more of a data issue, that I can't solve, you know, like at the beginning because, you know, with our AI solutions, a lot of times we need data to build out good AI solutions to, to train the models, to build out the agents and really understand the underlying process as well, is also very important.
So we start to realize that becomes, you know, sometimes more of a business, assessment rather than an AI assessment.
Starting an AI Assessment: Alignment and Discovery
So let's assume that I might be part of the long term vision. You know, maybe not in the phase one, but maybe in the 2 or 3. Walk us through what actually happens when someone moves forward with an AI assessment, like when they start.
Yeah, absolutely. And I think the important thing to understand is, you know, it's really all around alignment. Even before that, an alignment is understanding where the customer currently is at, right? Setting the groundwork for the assessment and understanding who will be involved from, you know, a specific product champion who will usher and be a part of championing the assessment and making sure everyone on the customer side who is involved in the process improvement is being involved in every session and understanding that their impact means something and they need to speak out, and they need to be open about issues they are having in their business.
And that's the most important thing is aligning on on what we're going to do in the assessment as well, you know, what are the deliverables you're going to receive at the end? And all that. And that's really from a alignment and kickoff meeting. Now, when starting the assessment, you know, to answer your question more fully, is, is really to do discovery, right?
Sit down with the functional team members on one specific process. And I think that's the important thing to highlight in the assessment. You don't want to bite off the whole cake or elephant as you call it. So you don't want to start to go after everything in your business, because that just becomes a lot of detail work. Right?
And that comes from the preliminary calls as well. We wanted to find a single process that we want to tackle with these assessments. Right. And having specific discovery calls, about these processes with your functional teams. Right. So really understanding how a business analyst, operates in that specific process, the end to end that they go through, in their day to day lives, you know, and really uncovering pain points there to start off.
But really, it also goes into, you know.
Really understanding. Yeah, the end to end process, you know, identifying pain points and, and having multiple sessions in this space as well to really get a clear picture of what we're trying to solve with the AI solution and, and really start to identify gaps and process improvements. Yeah. So when they walk into this assessment well, virtually or if you're on to do this assessment personnel.
Right. Yeah. Yeah. So when so when that conversation is happening do you see a lot of companies are walking in with their own pain points. Or are you trying to help them uncovers more, or what does that process truly look like. Yeah. Well what we really see here is, you know, after really understanding their end to end process, we like to have discussion sessions as well.
Right. And this is where we encourage customers to really bring in the pain points and bottlenecks of their day to day lives. So with these discussions, we can start to unpack those and really start to realize value, and highlight opportunities from those specific pain points. Again, we're not solution ING yet, but it's really starting to paint a picture of of their day to day jobs where they see, bottlenecks and how we can start to alleviate those in different ways.
Whether that be through as I mentioned before, JD Edwards functionality, automation or even AI, it just really depends right on on the specific pain point. But that's that's where we start. And we we definitely want interaction, right? We want to hear their pain points, but we also want to take these session to uncover on, uncover our own.
And at the end of the of the assessment, present those findings back to the customer as well.
Identifying Pain Points Across Departments
Yeah. And when you're deciding these pain points, when you're working with them, how do you decide which pain point to tackle first? Is it the quick wins or are they the most impactful pain points in the company. Absolutely. Yeah. And that's and that's a great question.
This comes back to having an overall discussion, with the company and really presenting the specific pain points back. Right. And you start to really realize, you know, what are the high value but also high, high effort sort of pain points there. And solutions we want to solution out versus the low, you know, low effort high, high impact solution.
So it really just depends on what they want to start with. And this can really depend on, you know, different departments of the business. Are they ready to start this solution process. Are they ready to start this next project. Or do they have other projects lined up and the the quarter or the year or the next three years?
So it really just depends on where the company is currently at. With the projects and, and where we kind of want to start. But it's really important that we lay out all of the different pain points and all of the different value versus effort and impact. We like to do this a lot of times in an Excel sheet, so the teams can see all of the different pain points we, we figured out, and it could be a cumbersome list, list.
But, you know, when you start to really narrow it down to the high impact, pain points, those are the ones we can really focus on and then focus on department as well to which ones we really want to tackle. At the end of the assessment when we actually start the project.
So you said something interesting there, and you've mentioned this throughout this conversation when you're talking about the different departments that have these pain points, are those departments there are there are people from multiple teams that are there or are you just talking to one person and they're like, oh yeah, the this department said something about this.
I'm not really sure what it means. Yeah. What is that? Yeah. It was eloquent. Yeah. Nate and and and sorry I cut you off there. But this is exactly where we uncover the most pain points is, is during the the really the next phase after there's there's there's initial discovery conversations. Right. And uncovering pain points and darkening them all.
We like to bring, you know, for one specific process, it could be multiple. Multiple teams involved. Right. It could be manufacturing, customer service, finance. Right. And they all had different touch points along the process. And a lot of times what we realize is really there's a lack of communication in the process or visibility to metrics that teams need to be more impactful and streamline the process.
So what you're going to start to see is when we have these cross-functional team meetings, that there is a discrepancy in visibility and communication, and you start to understand and you start to uncover, oh, well, we're missing this data point. You know, in customer service. But manufacturing has clear visibility of it. But, you know, reacting and actually talking to a customer and making sure shipments correctly take longer because there's no visibility to when, you know, a shipment it actually sent out.
So it's really providing and that's what these solutions are doing too, is providing more visibility across teams and provide and helping with communication as well, because there's an education piece around this as well where we need to, you know, help customers with the assessment, understand that we need more cross-functional meetings. We need to understand how people do their day to day operations.
So we're able to not create bottlenecks from lack of communication between teams, but instead thrive and create one streamlined process. And again, we're talking about a specific process that we want to tackle in the assessment that streams across multiple teams.
I can't mention enough that it's important that during these assessments, we have clear communication and even have if we don't if we as a is an assessment team and ERP suites don't understand a current process fully, that we may even schedule follow up sessions with the customer to better understand that process to make sure we're not missing anything.
Because this is all going to stem back to, you know, whether you create an AI solution or a JD Edwards functionality. We start that up or an automation, it's all going to stem back to having all the right correct information and making sure we understand it. Now, when we start the projects in the end, at a later date, we can get more detailed of of actual processes and how they actually operate.
But it's important to really understand all of that. So follow up meetings are definitely very important as well. And specifically documenting everything, because what might not seem important to us at the very, very beginning, conversations with the customer may turn out to be the biggest pain point, the biggest issue they're having.
So just for communication throughout the process between teams and really documenting everything as well is very important.
Process Mapping: From “As-Is” to “To-Be”
So once you've completed the discovery phase, what is the design phase truly look like?
Sure. Yeah. And definitely, for the design phase, I think is very important as well, is that we want to look at and label the current processes, and we want to really create a process flow in, in this. And this is where as is and what where are the bottlenecks, where the manual processes really indicate that to the customer.
Again, the Excel can get cumbersome. It's important to understand that in a process view, this is what it looks like to the customer. What we're going to start to do with those processes. There's initial as this process flows, is we're also going to attach a potential to the solution to it. Right. And again this is going to provide communication still with the customer to understand, you know, their various decisions, you know, high impact, low value, high value, low impact.
We're still going to have these conversations about value and impact, but we're going to start to indicate, you know, this is a simple, you know, JDA with functionality that needs to be turned on or this is an automation piece that needs to be created to streamline automation. You know, maybe that's ingesting an email more easily to get to a specific, user or maybe even it's, it's visibility.
So it's creating dashboards to to understand what specific customers are doing, specific teams are doing in their business. So it's really signifying the to be analysis in the video, it's functionality, automation and even AI improvements and how they really all connect together as well to creating a small, strong solution. And again, we'll go back and present these back to the customer and, and make sure and deliver to them and make sure that makes sense.
And, and so that these will really help alleviate their problems. And what I want to point out here too, is that, you know, we really look at these solutions as three different things. And I mentioned them. It's three different things. But they all work in tandem. Right? The the JDA with functionality automation and the AI solution. You mentioned before about the phases as well, right?
So it's really going to depend on the phase approach as well of when we start to implement those. There's quick wins, right? They could be AI, they could be automation, it could be JDA which functionality. But that's where the customer is going to really start seeing that is want that to be process flow and how we can start to figure out and alleviate and fix these, these pain points and issues they're having.
Yeah. So you're constantly mapping their as is processes. Yeah. But at what point does that switch to you outlining the to B processes.
Yeah. And I tangle that a little bit there in the conversation. But it's really built on top of the assist process. Right. And that's when we're going to really start to build out the mixture of phases between TDA with functionality and opportunities, the automation opportunities and the AI opportunities.
Right. And, and we really want to understand those to be processes so we can build out the as it is processes and still reference that excel sheet with all the different pain points to build out those to be recommendations. And again, those could be low, medium or high impact and low, medium or high value. And that's when we start referencing that as well to build out those two be processes.
Yeah. And pretend that I'm a customer out there wondering why isn't this moving fast enough? Why why can't you start the mapping and maybe even the outlining of the processes as is or to be in the discovery call? Is it just you haven't uncovered enough, or are you just waiting for the right time?
I, I would go with the former there. It's it's really we we haven't uncovered enough. It's the important point to really understand what specific issues we're trying to solve. Right. And that goes from detailed conversation with the customer. Because again, if we go back to a high, we don't just want to build on an AI solution that will not provide high value and high ROI to the business.
If we don't understand what we're trying to solve. Right. And again, they could have those use cases in the end. But the important thing is understanding that we need all the details. We we we need to have before processing, before establishing the as this process, the diagram. And then eventually, as we talked about putting the two B analysis on top of that as this problem to where we can alleviate these bottlenecks and they, you know, really with the presentation that customer, they can see how we can start to solve these issues.
And it's really starting to provide visibility. But the biggest thing is making sure we have all the information before we start building out these recommendations for the customer.
Right. So this is the phase that you start to identify where I can fit.
Right? Absolutely. Yeah. And this is exactly what we start to figure out where I can fit, you know, and it might not be involved in the initial process or the initial recommendation as I mentioned before.
But it will be a part of the solution. You know, talking with customers, there's a lot of times where AI is is part of the solution, whether it be a smarter analytics dashboard or a smarter, document understanding tool that can read documents better using historical data and provide corrections and exception reports and handling as well. As well as providing human in the loop manual review.
But it's still important to understand that I might not be the solution for every problem, but we will start to indicate where it fits in the current business processes after we've done the discovery, assessment and discovery part of the assessment.
Data, Prerequisites, and When AI Is Actually Ready
Right. So how like in the discovery phase, maybe even in the design phase, how do you determine what prerequisites must happen before I can be implemented.
Yeah. And I've been I've mentioned this a little bit before. And the biggest thing the biggest thing is data, right. And a lot of times talking to customers, you know, data may be an Excel sheets, it may be in file folders. It may be in the customer's hands. Right. We need readily accessible data. And that's where we start to think about the one source of truth as well.
You know, we do these JD Edwards assessment. We want everything to be in JD. But we know that's not going to be the case. Right. There's other ERP they can use out there. There's other CRMs they can use out there right. Warehouse management systems they can use out there. But it's important that we have accessibility to all of that, as well as getting all that information that's manual or in files or in someone's hand had somewhere in in a source where we can access it, you know, and a lot of times we recommend JD Edwards just putting it in there so we can have access to it with our AI services and solutions.
So it's very important around that is that we have great data. Data is is very accessible when implementing a strong AI solution. And sometimes again, it's just at the beginning fixing those process improvements, making stuff less manual, getting all the data in the correct spot before we can build out the solution. So those are the biggest prerequisites I would say, because without impactful data, data being the ultimate goal, you need to build out solutions.
We're not going to be able to build out a good AI solution for you, but we can also map out and these AI sets make recommendations that could happen at, you know, a phase two of phase three down the line as well. So this isn't just, here's an idea, it's more of, here's your current workflow, here's your future workflow.
Absolutely. Here's what needs happen, needs to happen to get there. Right? Yeah. Okay. So that makes more sense. It's not just, you show up one day and you're like, we need AI. And you sit there and say, all right, you can plug it in right here. See you later. It's more of a process of figuring out how AI fits into their future, right?
Correct. And it could fit in, you know, like like we said before, just highlighting it could fit is a quick win right away. You know, once we establish that process and we're like, oh, this looks really good. You really good data maturity and consolidation. And it's clean okay. We can do an AI equivalent and we harp on clean data on this podcast I think we're going to rebrand to just clean data.
No more JD Edwards. Just clean data. I'm the host. I do that for probably not. You need clean data for everything. JD the JD Edwards makes it easy for clean data too. So how do you formalize everything that you've uncovered through the design phase and even the discovery phase?
Strategic Recommendations and Quick Wins
Yeah. And and really, you know, formalizing that is really into a strategic recommendation document. Right. And we've talked about before a little bit with the with the impact or high impact, this is a low solution. But what we really want to do is we want to highlight the pain point. We want to give our recommendations. Right. And it could be multiple.
It doesn't just have to be one. It could be up to, you know, five and using different modalities as well, such as we mentioned before, JD Edwards orchestrations orchestration being automation or AI, we want to highlight those. We want to we want to level set with the high impact low. We want. So level set with the effort versus impact as well is also in that right, as well as well be involved in the phase approach.
And a lot of times when we show this now, we we want to really categorize the recommendations by the, the various functional department of the business as well. I think that's very important as well as to to silo those out to alleviate the conversations we're having back with the customer, so they understand these are the specific pain points, and then we can come back together and talk, across different departments about the specific pain points and recommendations as well.
So it's bring them all together at the at the last final piece here. When you do the final presentation, but it's really about documenting those details on one central space with those specific that specific information.
Yeah. And when you're looking at this document, I assume that, recommendations are categorized. How do you categorize that? Is it AI automation, JD Edwards are those two categories or is there more to it?
Yeah, a lot of times, as I mentioned before, it really goes around, you know, the specific I'm going to say it depends. Right. So a lot of times it's around the function of the business. Right. And like I said the different departments is is very important as well to do it that way. But we also like to look at, you know, impact versus effort is another way we like to, to organize these.
It's also very important to look at the different recommendations as well, which, you know, in the end, in the later phase when we actually go to do our, recommendations and building out that PowerPoint, this will be a little bit cleaner, but it's really about putting it all out there for the customer to see, everything there that could be done with the specific recommendations and seeing if that is of any impact to them as well, and seeing if it's worthwhile put on the story.
And I'm talking really about the day to day business. Analyst does this make sense? Right. And again, we can we can start to identify quick wins as well. And here, and that's very important to make sure that we have you know good AI quick wins also involved. So the customer can get well I in general. So I in general get the customer really excited about AI because it's on the road.
Yeah. So when we're talking about these AI quick wins. It's something that we've mentioned on this podcast before. How do you truly identify that or is it just something that everyone knows?
Yeah, I mean, it really comes down to the fact that, you know, it depends on conversations with the customer. Right? I think that's the most important thing is understanding that, you know, we need those prerequisites in there to actually start to implement AI, right?
You know, we talk about the ability to have good strong use cases around AI as well, ones that we can start to implement right away, or ones that are strong use cases with good, good data maturity.
Good data consolidation. Right. And we can start to categorize those, by team. Right. And understand where they would fit. So there may be a dashboard for, you know, a customer service team, you know, maybe a dashboard for a manufacturing team, you know, maybe one for R AP. It just really depends on really the function of the business and how they're going to utilize the different AI solutions as well.
Whether that be through various automation technology that's AI related, that can do more reasoning, or dashboard related, that can provide recommendations. That's really just going to depend by department.
Yeah. So when you're finding these AI quick wins, it's usually just a mix of AI and automation. Or this is it just AI. Is it just automation.
This is this is great Nate. Yeah. So usually it's really a mix of AI plus automation. Right. Well, we like to mention a lot and ERP suites as we like to leverage, a lot of words, tools and technology. And we really leverage orchestrations when building out a lot of our AI solutions because of the ability to connect back seamlessly to JD's words and grab and reference data.
Right. Different accounting rules, different policies, different regulations. And so we can reference that to make sure when we implement an AI solution. And you've talked about agents in the past implementing an AI agent into your system. We're understanding how a process ultimately runs, based off that data inside of your JD Edwards system.
So that's that's something we really look at as well, with, with these quick wins and, and it's really around but also around.
Yeah. Like you said, AI and automation. Right. And it's it's really just not around like process improvement for AI. We want to just leverage solutions that have both orchestration automation capabilities as well as better intelligence to help customers make smarter and better decisions in the future.
Right. And maybe to backtrack a little bit, are there cases out there where maybe just a standard JD Edwards configuration, does that solve the issue, or are there cases out there?
Oh, 100%. There's cases out there like that. A lot of times what customers will say, what the bottlenecks, the pain points in the business is that they don't see something correctly on a screen or they didn't know. We can do MIP or demand planning or forecasting or various other JD Edwards functionalities or, you know, maybe utilizing a widget for analysis.
Just quick analysis inside of JD Edwards. So it's ultimately comes down to the fact that we need to figure out the specific pain point to site solution it. And sometimes the easiest thing to do is to just provide visibility via AdWords or a different way of doing things inside of JD hours.
You know, maybe it's status control and whatnot. With the current process. So it definitely it comes into play a lot, actually. And a lot of times the JD with core functionality can help with the data consolidation and data quality as well as well as visibility that will lead to an eventual, AI solution as well.
Building the Roadmap and Phased Execution Plan
So it's really it comes down to this phase being transparency. And there's like understanding from us what we're seeing versus what their pain points are. And having that strong, I guess, relationship in this way of, being open and having those real conversations that, yeah, that like some people will have a hard time maybe seen. But at the end of the day, the more open you can be when it comes to your pain points, when it comes to your actual process, you'll find things in there that could be fixed with the simple JD Edwards configuration.
You can find things that can be fixed with just an automation, or you can find out that you're actually really good at a lot of the baseline stuff that we've been talking about. And you're ready for a, so there's all three of those things that really come down to how transparent are you with us, but with yourself as well.
But all right, so once everything is documented and once that phase is really kind of wrapped up, what does the roadmap phase look like?
Yeah. And this is really the exciting phase right now that everything's documented. You've had your conversations, you've had those discovery calls. You've had the process discussions were understanding everything. They've seen some recommendations. They've seen some high value versus low impact customers has made have made ideas or suggestions on that for where they want to go.
Now we really start to consolidate those things. What are the things that are the highest value? How can we solve them and how can we lay this out to a customer? So that turns into really our roadmap phase, right. You know, giving those multiple recommendations in, in a presentation and having to understand, you know, what they need to do.
You know, that might first be, you know, clean up, right? It might be getting some documents, you know, some information from Excel in the JDA words or information that's in the heads of the customer into a specified data source. So we can actually leverage that.
Right? It may even involve, you know, potential automation, right, to minimize touch points between teams or provide more visibility between teams.
And then, you know, an AI solution in the end, really providing recommendations, to make the customers more proactive in their business. But it's all starts with that manual cleanup, right? The manual process, clean up the data, clean up, and then automating how the different touchpoints go to minimize the pain points between different departments.
And then lastly, it's around, you know, building out AI solutions that can enhance, you know, whether it be automation or various ways customers can be proactive with better visibility on how their they operate or even helping them do certain things in their business.
That will allow them, again to be more proactive, instead of reactive.
Right. But how do you determine what kids tackled first? I know you listed a few things there, but say they have multiple of those issues.
Yeah. How can you determine the specific one that they should go after?
Absolutely. Yeah. And again this goes back to having those conversations with the customer.
Right. It's conversations to figure out what are their high value high impact. And it could be low effort. It could be medium. It could be high. What where do they want to tackle first. What's on the roadmap as far as for the business to ask you what are the various projects and initiatives? Where can we even start first and what department can we start first?
Right. All those things really come into play when building out those recommendations, but it's really a full conversation. And really with the assessment we're providing, the high value recommendations. But we want the business. We want to have a conversation with the customer to understand where to actually start first.
Right. So I think that's important to understand that that really comes after the assessment, but just know that it will be a partnership throughout the whole process with ERP suites and the customer.
Yeah. So this is where you would actually create the phase execution plan, right. Like an absolutely. Yeah. Yeah. Yeah.
So absolutely right. So it's important to understand that we want to have a phase. That's the execution process when building out the assessments, because we want to give the customer a clear picture of where to start.
And we've mentioned it before with JDA with functionality, the automation, the AI, none of that. All of that really does matter. But at the same time, it really we want to give a timeline and we want to give a good roadmap for how the customers can actually accomplish this in the end.
And that's where the phase approach that Nate mentioned comes into play, where again, phase one might be cleaning up your data. Phase two might be automating to get rid of the manual processes in phase three maybe I it might not go in that order every time, but it's about presenting and building out that roadmap to show them where they can go and where they can be, and what's all possible when, when we do the assessment and we uncover all of the very cool initiatives and things we can alleviate in the business in in future state, it looks very promising for the customer.
But a lot of times, again, you can't tackle everything at once. And that's why we like to break it out in different phases.
Yeah, you can show all this stuff to a leadership team, but how can you really get them to align to this roadmap?
Yeah. And that's and that's a big question as well Nate. Right. The biggest thing to get them to actually align, around this map is really around complete transparency around why we did what we did and why we recommended what we recommended.
Because we've spent this time right in the discovery phase is really understand how your business operates. We created trust that we understand your issues. And we've heard you and we we know where these are causing problems.
And the biggest thing is that we created that trust. Now we can move forward with this is what we think you can do.
Well, let's talk about let's think of what you had in mind in the next quarter or the next year. Let's think about where you feel like that, where you feel like we can provide the most impact with our solutions, but it's really going to be about, you know, establishing that trust, the understanding that we have spent the time we've understood the business, and we're ready to move forward with these projects and initiatives.
In the end, and again, this is all presented in a PowerPoint presentation. It's just so we can indicate then easily explain why we're doing what we're doing. And we want to have again, in that, that final conversation conversation as well, and get on the same page with where we want to go and what initiatives we want to tackle.
After the AI system.
Why AI Assessments Take Time and Protect ROI
So say that there's a little bit of, pushback, I guess, from leadership that they think that their system is fine and they just want AI.
What would you say to that? To try to align them a little bit better?
Yeah, I think that's important to really establish. Earlier, honestly. Right. It's coming from really those calls before we've, we've even started a specific, process assessment or AI assessment. It's it's really understanding. Hey, and telling them this is where we think you are at, right?
You don't really have the AI education to really just jump into building a solution because you're not educated on the specific value it can provide you or really how long it's going to take, to potentially build out that AI solution if we don't have good data, right, good security, good infrastructure infrastructure and good access to the data.
So it's really important to understand that we need we need customers to understand that, you know, AI is not a solution for everything. And we need to build towards AI. And again, some customers may be ready to do that quick one. But it's really going to depend on the preliminary conversations we have in the assessment.
So which route we go down. And a lot of times these discovery of these initial conversations with the customer will lead to customer into wanting to actually realize, oh, shoot, there is a lot of issues in my process. Assessments, trying to not trying to plug it in, I hear I guess I well, ERP suites is a tool and hence providers, so we can help you in any way with you.
Geo. It's functionality in the end. But it's really just that, that basic understanding at the very beginning, and just knowing that I will be in your own right, it might not be the first phase, it might be the third thing, but just knowing it's there is also important for the customer.
Yeah. Knowing that you are building towards AI, nothing that ERP suites or anyone would suggest before that step. They're all very important. They're all going to address certain pain points. They're not just going to come up with some random idea that doesn't impact your business in a positive way.
So it's about understanding that, yes, you want to get to AI and it might be out of reach right now, but it won't be. It won't be for long.
As long as you understand that getting to that next step, getting to AI, making your lives easier, making your lives easier will start in the first phase. But getting to I might be a little bit later.
So the real question here is with this roadmap, will you get a price or how will that kind of be decided?
in the end when we present our recommendations will really have, you know, specific pricing around that too. Right. Once it's all going to take what's the again level of that. The timeline and the price of what this initiative will actually take is very important as well. And these could just really occur after the assessment as well.
Because we really want to focus on those specific recommendations that can help your business. Right. And, and price will definitely be a factor. But with the assessment you got to understand what you're getting out of. It isn't just a specific price, but it's a list of where you can go with the business.
But we can also attach price on to various initiatives as well.
That's. Yes. Awesome. So instead of walking away with just, tool recommendations or walking away with visibility prioritization and actually your execution strategy.
Right?
Yeah, absolutely. Yep. Absolutely.
So let's let's address the elephant in the room. We've mentioned it throughout this podcast. But most of these take multiple weeks. Why is that the case or is it always the case.
Yeah. And again it's not always the case. Right. We mentioned it before that some of these assessments can be shorter. It's really going to be dependent on how we feel about a certain process. How streamlined is that process that we want to focus on.
Right. Because when that process is more streamlined, data is in a good spot. It's easily accessible. We can start to do these really forward looking, impactful AI initiatives from the very start, right? It's still going to take some conversations with the customer to understand through their processes at a high level.
But it's important to know that these will take less time if we don't need to go back and actually rework, certain, specific processes that require a lot of manual effort and work and all that, and have a lot of different pain points, a lot of different touchpoints that we talked about that can create bottlenecks in the business.
So it's not always the case where this is going to take multiple weeks. Right. But I also wouldn't say it would take one week to do this right. We still need some great understanding as well as configuration around, you know, security infrastructure and all that to actually implement a solution as well.
And specifically an AI solution.
So yeah, and I'm sure if your leadership team and all these teams have a very free schedule that they can talk every day, you could get this done in a week for sure, but you're talking about a lot of time allotted for these important people that have very busy schedules of their own.
So it might be a little bit difficult, but what would you miss if like this was rushed, if you did have like, oh, we need this done in a week. And you only had two meetings, right?
Right. And I think the important thing to look at here is you'll really miss out on very valuable high reward AI or automation solutions.
Right. And that's the biggest thing. You use a bolt on AI solution to fix a certain business process that isn't actually going to fix anything, but provide more headaches in the future because you're just bolting it in there without any real testing, without any real education around, you know, usability with the specific solution, whether that be automation or AI.
It's important as well to really think about that before doing any of these, you know, implementations, that we have this assessment where we're understanding the, the clear value behind it, right, and how it's going to really differentiate and help you with achieving your overall business goals.
Right. Because that's what we're really striving for here. How can we make everything more streamlined and better, more streamlined in your business, but also help to achieve the goals you're striving to achieve in that next year?
With process improvement when you AI initiatives.
So it's really going to depend on that. And if you ask it again, you're not getting any value. You're creating more headaches for everyone. And you're, you know, it's pricey to build out in solution A any of these solutions.
So we want to make sure we have the right solutions. So we're not having to do rework and failures and wasting money.
And that's the biggest thing.
So yeah like if they truly did skip like the prioritization step, there'd be a lot of broken models. There'd be a lot more money having to pour into this because you're basically working backwards.
And when you're doing that, it's just it takes more time because you have to you make the AI solution and then you realize, oh, wait, this is broken. This isn't right. We have to go back and fix this.
And then once that's fix, you try it again and then something else is broken. Something else needs to be fixed.
So at that point it'll probably take more time, more effort, more money. And you won't see AI for even longer than what you're trying to do here, which is rush it, which is an interesting way to think about this.
But to take your time and to really understand your business processes might be the fastest way that you can implement AI.
Even if you do want to rush it, if your leadership team is really pushing you to do it or board or whatever is out there, it's it's interesting to hear that point of view because, again, a lot of people want to rush into this thing called AI, and it's a very hot topic in the market right now.
But if you don't have your processes aligned to get to that step, then AI is a fever dream for some of these companies.
Yes, there are those that are out there that have done their homework that know exactly what they need to do, that have everything they maybe did, upgrade recently that made them get all their ducks in a row.
And so they're ready for it. They're ready to take that next step.
But if you're sitting there and you've been on the same JD Edwards for 15 years, which we have seen, you might not be ready to take that 100%.
And that. Yeah, you brought up a great point, Nate. The upgrades are huge right. The and we talked about in past con podcasts the ability to authenticate directly to some of these AI services.
And we use ACI but and the ability to leverage orchestrations that are heavily used in AI solutions especially Aria's AI solution.
So and upgrades an absolute fantastic point to to mention as well that that's something we definitely before we even start an AI assessment, we know where the customer is with their upgrade and what's possible there.
Yeah. So if you rush AI, you automate chaos.
But if you take the time to evaluate effort versus value, you protect your ROI and ensure that when AI is implemented, it actually drives measurable impact.
Yeah.
So if someone's listening to this and their leadership team keeps saying we need AI, what should they do before scheduling an assessment?
Yeah, I think the most important thing before is scheduling assessment is, is one educating them about if the assessment is the right approach.
Right. Because a lot of times leadership will be, you know, from the highest power screaming, we need a AI, we need a leader now.
We need a solution now. And we've already talked about it. We're right. We can't rush this. We we need to have education around AI right?
You know, I present it various user groups and there's different things out there on a website about AI and the evolution of AI and what's out there, but they're all really starts around education, right?
And from from the top down and bottom up. It's education around AI.
And that's the important thing in understanding, you know, how it can really be impactful in your business.
Really starting to think about use cases as well.
I think that's also important, having a list of strong use cases to bring to leadership of where you think I will fit as well.
Into your business is also very important.
And also really thinking about emphasizing, you know, hey, I've noticed now after my education that I really doesn't solve everything.
But we might need to take a step back and look at the overall process as a whole and see the issues we're having inside of various processes to understand that we need to fix these things prior before really jumping into a full A on AI solution that won't provide any real valuable ROI to it.
So it's really emphasizing to leadership that there is a road to AI without jumping straight into it.
It's really about understanding your business and where I truly fits based off, like we said, you know, assessments to help educate yourself on the business process as well as who's out there.
They'll educate you on AI itself and how we can start to combine those together to build a really strong AI solution.
Exactly. An AI assessment isn't about buying technology. It's about building clarity.
And clarity is what drives smarter investments inside JD Edwards.
But if you want to learn more, go to ERP suites.com/ai.
We have a page there that talks about the different AI agents that we're doing and what I truly means for your business and for your future, and might be a part of your future.
But the real value of an AI assessment is understanding your business deeply enough to know when and how to implement it.
If you found this helpful, share with your leadership team and and subscribe to our podcast here.
see you next time.
Video Strategist at ERP Suites
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