Building Strong AI Use Cases, From Pain Points to Possibilities
September 22nd, 2025
18 min read
This session, led by Drew Robb, AI Advisor at ERP Suites, focuses on the importance of building strong AI use cases as the foundation of any AI journey. Drew explains the need for clear differentiation between AI and non-AI solutions, highlights the role of company-wide buy-in, and stresses alignment with business goals to maximize ROI. Using journey mapping, pain point identification, and brainstorming, he demonstrates how to design practical AI solutions, then narrow and prioritize them using methods like impact versus effort matrices. The presentation concludes by encouraging organizations to adopt a structured approach, ensuring AI solutions are impactful, adoptable, and aligned with long-term strategy.
Table of Contents
- Importance of Building a Strong AI Use Case
- Four Steps of Use Case Development
- Step One: Determine AI vs. Not AI Solutions
- Step Two: Identifying Pain Points
- Step Three: Brainstorming AI Use Cases
- Step Four: Prioritizing AI Use Cases
- Summary and Next Steps
Transcript
Introduction
Hello guys, I'm Drew Rob. I am the AI advisor at ERP Suites. I have been at ERP Suites for about five years now. Uh started in the product space and then moved into more of a data analytics data consulting role and then the past few years have ventured into the great world of AI and my main job is really education of AI to our customer base, our user groups. I present at different user groups, different conferences. Um, so that's my primary role is to really help people get started with their AI journey and they may be, you know, just getting into AI, learning about the technologies. They may be just starting to think about some use cases that may apply to their AI journey. Um, or they may just be fully along and just need some help getting over the finish line. Wherever you may be on your AI journey, um, the biggest thing I do is help you get to the finish line. So, with today um and and sorry if my voice is a little straggly. I was dealing with a cold earlier this week. Um but hopefully you can hear me well. But today we're going to focus on really building strong AI use cases. That's because that's really where you need to get started. A lot of companies that we talk to and a lot of customers we talk to really just jump into AI without formulating a very strong use case. Really not even defining what is the difference between AI and not AI. So hopefully we can shed that some light on that for you today um with this presentation and hopefully you can get a lot out of it. Now one caveat I will say before I start. I am not an artistic person. Um some of the examples I showed today um hopefully I can guide you along and they're not as confusing, but I do have some graphs, some different images um as part of steps for for the AI for the uh building out a use case. So, um, if you ever have any questions, feel free to pop them in the chat today. Um, and I'll help you through it. Um, but yeah, with that, let's get started. Pretty short agenda today.
Importance of Building a Strong AI Use Case
So the first thing is just the importance of building a strong AI use case. You know, why why is this such an important thing? Why is it so important in in our AI journey, right? The first po part is really education. Getting people educated on AI system services. Um, I did an AI 101 session yesterday. So that's really the groundwork. The next thing you do is really really show the importance of building out a strong AI use case.
Um the next thing is really just the four key steps for building a strong AI use case, right? Um really it's just it's just four main steps and we'll go over those today and and hopefully it seems pretty streamlined and it's understandable for you guys. Um there's a lot of different examples and a lot of different ways you can do it. um we call it systems thinking or design thinking but just following these steps will help you in your company provide a better way to build out strong AI use cases.
Um with that we'll start with the importance of building out use cases. Um, if you guys got to see the keynote uh that Julie Holmes presented yesterday morning, um, I felt like it was very impactful and aligns a lot with what at ERP Suites what we envision is the importance for building out a strong use case, which is really company buyin. You saw it with that, right? It's not even just the executives getting buyin from the executives for budget. It's also getting buy in from middle management and even the main business users or end users of a potential AI solution.
Um, you know, it it just really it it's it's all through like AI adoption inside your business. It's all the way up and down because that will really promote usability in your business um and help you really define really good use cases from from top down. And that really goes into kind of the second point here, right? Aligning with business goals. You probably have a year-long goal or three yearlong goals of things you want to accomplish. you shouldn't have to build a whole new road map um for your business, whether it be products, whether it be other parts of your business. You don't want to build a whole new road map. You want AI to kind of intersect with that and these AI solutions to intersect with kind of day-to-day operations.
Um and really just streamlining those pain points in your business. So really aligning your business goals with these future AI solutions is very important. And then lastly, it's really about maximizing ROI. So the biggest thing we're going to talk about and kind of the four the fourth step of this um presentation is really prioritizing use cases. Really showing the impact versus um level of effort for these use cases and really showcasing that and and defining that so you can understand what sort of ROI you will get from these these uh these AI solutions.
And another thing to quick thing to keep in mind as well is is it also it comes back to adoption and change management as I mentioned before and it's really understanding that we need to promote usability in our business because if our if our employees aren't using these new AI solutions to better their job um then then it's all for not we're just implementing AI just to implement AI into our current business processes. So just really wanted to highlight the importance of building a strong use case. We feel like these three three these three key values are are kind of the top end of that and the importance of that.
Four Steps of Use Case Development
So moving forward here um I've kind I've listed out the these steps um of use case development and this is the four we're going to really focus on today. So really determining AI versus what is not AI, right? Understanding solutions that are in fact AI versus might just be automation solutions might just be other solutions in your business. we that's a key factor when when looking into use case development.
The second one is identifying pain points in your business. And really what that stems from is actually mapping out the current processes and truly understanding them at their different phases, steps, what are the opportunities, what are the pain points inside of those processes. That is fully and probably one of the most important things is understanding the processes so we can implement an AI solution more effectively and really understand what problems we are trying to solve.
Um, the third thing is is really just brainstorming an AI use case. Taking those current processes, those pain points you got from the prior step and starting to brainstorm out potential AI use cases with the AI technology that would be involved in building out that use case.
Um, lastly is just prioritizing use cases. We don't want to try to tackle these all at once. I have listed here that we can prioritize three or five use cases, but really if you want to start with one, that's completely fine. But we really got to get down from these brainstorm current problems and processes that may appear in different parts of your team. Whether it's finance, sales, manufacturing, we really want to narrow down those use cases and make these solutions more applicable and easier to implement um as we narrow those down.
So just keep in mind as I go through these more detailed slides, these four steps is what we're going through. Um and we'll start to illustrate illustrate these here shortly. So just keep these four steps in mind as we go through these steps here.
Step One: Determine AI vs. Not AI Solutions
So with that the step one is determine AI versus not AI solution. So the biggest thing is just knowing that AI does not solve every single problem. Right? You don't want to just implement AI just implement AI into your business. You don't want to just do it to get a competitive advantage against your customers or your current market. Right? You really want to identify that right problem as we talked about that fits that specific AI use case to build out the right solution.
And I apologize that the bottom port portion of this is a little tiny but I will go through the kind of the problems to look for. These are the four things that we found and there's other presentations that we presented at AI week on these four problems. So the first one is manual work steps especially if they're repeatable. So we talked about I had actually had a presentation yesterday on a manufacturing digital assistant that moved work orders to different work centers. So just think of different processes in your business that are repeatable that that may be you know sales order entry is one I think of as well. Just different manual processes is something to really look at um when thinking of an AI solution. You can also build off of that.
Um the second one is analyzing data to identify anomalies and patterns in your data. So that's really just anomaly detection as well as predictive analytics. You know, fraud detection is a big one that comes up. Um and not just basic fraud detection where you're figuring out incorrect data. Um but going into it more deeply. Um another one we can really think about is something along the lines of we actually have a presentation on this tomorrow in our smart smarter analytics session is really warehouse analysis or sales analysis. really get in the fine detail and start predicting the movement of inventory um from from so one warehouse to another warehouse and the cost savings of that. Start thinking of just predictions inside of your data um that you can start to gain insights on with visualizations, dashboards.
Um the third one is reading through documents, text, emails, images. And what I mean by that um and we have a session later on today on this as well is really document understanding um and image understanding right so using these tools out there um to understand and read documents and gain and and get the data that's not so structured. It's really unstructured into your ci certain system so you can one gain insights off of them and one just for data quality master data management. So think of that one as well. That's very important.
Actually, one I think is really cool. This fourth one here, help with development and testing of code. So we actually have a session tomorrow. It's on code assist given by Jason Kraton. Um which is awesome because it will start uh this this soybe tool will start helping developers develop and test their code. Um and one thing we think about in the JD Edward space and one place ERP suites wants wants to take it is it to help us start developing more orchestrations because if you saw my session yesterday orchestrations plus AI is really the power um that is behind a lot of the solutions we build out. So having this sort of automated orchestration building is is just the possibilities are endless for efficiency.
So really think of those four things when we move forward here uh for building out use cases. Those are kind of the four main ones we we saw in the enterprise talking to customers that we want to tackle.
Step Two: Identifying Pain Points
Video assistance for buying customers. Everybody hear me? Okay. Sorry, I think I lost connection there. Is it Is it okay now though? Good. Okay. Just making sure. I I I'm going to jump back a little bit. Sorry, I saw I lost connection. Um, awesome. Thank you guys. Thank you guys for validating that. So, I hope you heard before. Um, I was just going over AI versus not AI. And these are some of the examples. Obviously, there's more. Talk to our team, talk to us, and and we can start to think about and start to realize and really think of those four AI solutions that I mentioned above. Um, and really start to think about those. And that's where you really glean the AI solutions um that we will build out after we define a use case.
Uh so moving forward again, sorry about those that technical difficulty that popped up there. Um we're going to move on to kind of to step two here, right? Identifying pain points. Um because that's really the crust of building out a strong AI use case is having a good strong pain point developed. So I know there's a lot of information here. What I wanted to give you guys here is just example activities for identifying pain points.
Um, so that first one there, and I won't go through all of them, is empathy mapping. So really just understanding how a end user feels when they go through a process, what they say, do, feel, their thoughts. Um, another quick one is is just doing a quick one-on-one interview with the end user to understand that process. And then, you know, there's also other ones, you know, a process walkthrough, especially in the manufacturing space. actually walking through and seeing how inventory has moved um throughout the warehouse is is just another example of of a of a painoint activity we can do.
Um, but what we're going to really focus on today is is is called journey mapping. Um, is is the painoint activity we're going to do. Um, because it really gives a detailed outline of an entire process as well. It focuses on you know the tasks, the challenges, the various opportunities and the goals you'll get out um from a painpoint when you start thinking about it. So I'm going to move forward here and this is where um we're going to walk through the steps, right?
So the example journey mapping steps. So first of all, you just need to develop a journey map, right? Just an outline, which I have an example on the next slide. I'll show you what that looks like. The second step is choosing a persona. So this is really choosing an end user or a person who is involved in this process that you're trying to enhance, resolve, uh figure out what what the issues are, the bottlenecks.
Uh the third one is choose a scenario. So really the scenario is the whole endto-end process. What does that process look like for a user? And then the fourth is define the various phases that are involved in this process. And then the fifth is define interaction. So how are they interacting? What other processes inside of that are they doing?
Um sixth is really just the user's internal emotions. And then lastly, really defining the opportunities, the ownership, the goals, and the things you want to get out of this um this journey map. So with that, here comes my artistic ability. I'm so excited to show this. I hope you guys can see this.
Um, so I gave the manufacturing digital assistant example yesterday. So I just kind of wanted to walk you through this um, example of a potential journey map using a material planner in the top left. So we're using a material planner. The scenario is really just them scheduling work orders to work centers. And the and the end goal here is the ability to schedule work orders to work centers and move work orders to alternate work centers if they need to be created to get orders out more quickly, if capacity has been met.
Um, so for example here, what I want you guys to focus on is how this is broken down. So you'll see at the top there, the the top left is that phases, right? So you got the schedule work orders to work center, then you have a work center for the second one, and then you have move work orders to alternate work center. And below that are various steps. So when talking to this customer, they had to write down where certain work center or work orders were going in in Excel sheets. They had to enter data manually in JD Edwards. They had to get that information from JD Edwards work center and then enter work center information to an alternative routing and then they had to manually change the work order.
So what I'm basically dictating there is just a lot of different steps located in those phases. So you just want to map out every specific step. From there you want to move down into the goals. So what's the goal? What are we trying to accomplish when when mapping out this current process? You know the the first goal is to automate the work the work orders to specific work centers, right? The second one would be automate creating a new work center for new work orders and the last one is the bulk transfer of work orders to the new work center if a work center is at capacity.
So you start with phases, steps, goals, and then you move into pain points. What pain points are we trying to solve? And as you can see, too many steps, data inconsistencies when inputting that data into JD, which for the first one. The second one is too many steps again, and then it takes way too long. There's confusion as well, um, when trying to move certain work work orders to work centers. And then lastly, it just really compounds on itself, right? Too many steps takes too long.
So you can kind of see how we map these out. we can start to see what our current pain points are as well as the entire process and how we can implement this. This will be important as well when we start to build out an AI solution to really understand the discovery of these pain points in full because only then can we implement a strong AI solution into your business.
And then lastly, what are really the opportunities? So I have the automate of the automation of movement of work orders at the bottom left there, but really what are the opportunities that can go beyond this as well? You got to think about those being able to potentially predict where work orders move from a certain work center eventually get to autonomous ERP. We can start to list out all of those potential opportunities here for what we would look at like uh you know what one AI solution would look like for this specific current painoint and lastly who would be the ownership of this. So obviously the customers business analyst understands this process through through and through. they would be the owner of this um process as well as I put the ERP suites team as advisors. We would help you through this process as well when implementing an AI solution.
Step Three: Brainstorming AI Use Cases
So if we move on here to brainstorming AI use cases, this would be the third step. So we understand our entire process through and through. We understand the phases, the steps, the goals, right? We understand various opportunities. We want to move forward here to brainstorming of these.
So there's a few examples here. We have like a how we might activity and you know that focuses on clear processes discussed in the pain point phrase. We can do reverse brainstorming. It's just a different way of thinking where you think of the solution and get the you know you get the current process um needs approach benefits competition. These are all good um potential brainstorming activities.
The example I'm going to show you today is what's called AI possibility mapping. um and it really understands the entire current process in full from that journey map and it takes it further to a sort to a brainstorming phase where you can start to attach AI solutions to various current processes you've mapped out in full. So basically what I'm saying is all the leg work was done before and now we get to the point where we understand the entire current process. Let's move on to actually connecting these current processes to AI technologies that can fit your business needs and actually solve the problem.
So moving forward into the steps of AI possibility mapping. So the first as as I mentioned was the gathering of insights from the journey map. What you're going to get out of those, what do those look like? Um the second step is discuss and group potential AI use cases. That's very important as well. So if if ones are similar, we think of similar solutions out there, we can start to group those together into into one uh one singular use case.
Um and then the fourth one is document AI use cases and really start to understand the technology involved with those and start to document them um in this in this brainstorming um session. So so what I'm going to show you here and again very color coordinated um are the current processes on the left. So the ones that you see on the top left in that more oranges color are the planner moving work orders and the manual entering of a work center. So those two are involved in that that journey map process that I showed you before, but they can be solved with a work order routing digital assistant, right?
Another one we can look at is it's kind of in the middle there, the training of new employees at your at your business can being an onboarding digital assistant as well. And then another one we can look at as well is like for the bottom left there, issuing forecasting demand. So issuing for demand and forecasting demand we can use an embedded AI demand forecasting. So we can kind of see here it's really a discussion right starting to understand what current problems we're going to solve and what sort of uh AI solution we are going to we are going to use.
Um one last one I'll point out just to be different from the digital assistance as well as the embedded AI is the defective rates and re rework costs. So basically that's sending images into JD Edwards and and and seeing it's if that image is defective or complete. We can start to use that as as a potential different use case. So try not to get too technical here but just showing the breath of different solutions that will come from current processes we've mapped out before in that journey mapping. And when we brainstorm these, right, we start to brainstorm these solutions, there's there's we start to see the connect um between the these problems and and the current brainstorm solutions.
Step Four: Prioritizing AI Use Cases
So moving forward to prioritizing an AI use case. Um that's the fourth step here and that and that's the most important. We have so many uh use cases out there and as I've said in the past we we can't deal with them all at once. We don't we want you know probably heard from Oracle earlier today a crawl walk run approach right knock out one or two or three right just start to prioritize those and figure out which ones we want to tackle moving forward.
Um so here we have the prioritizing of activities and examples. Um what we could use here is an impact versus effort matrix. So this really helps us under determining determine um which use case takes the most effort versus the level of impact. This is where we come back to the point that I mentioned before of of return on investment, right? What's our best return on investment on these AI use cases that we've actually gotten from our our current uh pain points, right? So we can start to map that out via matrix um is is one example.
Another one is also called dot voting which I'll walk through here. Um where that one is really just you know you start to prioritize in use cases. The biggest thing with this is step one you sort ideas based on complexity of implementation. So you got the top most complex to the bottom most complex. The second one here is is really just determining ROI. And this is what I'm talking about. That's more of a discussion. Determine ROI based on t-shirt sizes. So, small, medium, large, which I'll talk about next in in the image I'll show.
Uh the third one is is Dodford on best ideas. Now, this isn't an end all be all be all just based on you know who you know the impact versus level of effort sort of thing. It could be on resources time like what what sort like if if you're in a finance or if you're in manufacturing if you're in sales what team can we allocate to actually implement this solution what what level of effort is it going to take from your side as well um is also very important um when starting to implement these solutions as well a lot of customers we talk to are are really intrigued on it goes back to the executive buyin really getting that quick win into your business showing executives showing upper management that this AI solution is is very profitable and and and efficient and improves ROI.
That's another important thing we really got to think about here too um when prioritizing these use cases as well. So these sort of things will go into this um you know this will just sort of help um how to show level of complexity really. So I'm going to go forward to this next one. So this is really the prioritizing phase, right? So we look at the brainstorm ideas here. So, as I showed before, um we'll start with those top left. The work order routing DA um is one thing we looked at there. Um supply chain management is another one. So, you saw those all from the initial one or the the previous step.
What you're going to see is those brainstorm ideas listed on the left and then you have the complexity pyramid in the middle. So, on the bottom there, we'll go through a few here. So, you got the rag training, employee training for an agent. So, that's the bottommost. That's just training customers on on onboarding them being able to understand manual sent in um you know work order routing DA since we fleshed that out earlier in the in the current process. Um that will be also another one there.
Um and then the DA from uh inventory management and and as you see as you see we move up there we we just validate that the supply chain uh digital assistant is is the most uh complex one to deal with. And then after that gets done after we do the complexity pyramid we move into dot voting and then from dot voting this is where the you know people um that are working with this you know people who are involved in the business process the managers you know the CEOs they'll decide based on all the information we've gathered in earlier steps what are the best solutions to actually prioritize and that's what you're going to see on that final frame there is you're going to see that we prioritize top three these top three use cases we really look into so the work order routing DA, the training employee agent, and the embedded AI for demand forecasting were really the three we decided to focus on based on the prior four steps above.
Summary and Next Steps
So, so with that, um, you know that those are the really the four steps there. So it's all about understanding AI versus not AI really going into understanding to a full extent your current pain points and processes and then moving into brainstorming use cases right you can brainstorm as many use cases you want could be uploaded to 20 because just knowing that that fourth step you will then prioritize them into three or five that we can take forth into what we call you know in an implementation roadmap which is also another session that I'm giving um tomorrow afternoon.
So feel free to join that and we'll show you how to take this take this idea further um into actually you know what does the development roadmap look like who would be involved as far as resources where's the budgeting needed you know go into more um use case um what is needed as far as technologies um that's all going to be shown tomorrow um in that presentation so would love for you guys to join and we can move this getting started forward and uh yeah that's that's pretty much it for any uh any questions.
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