Epicor Kinetic is a modern, AI-powered cloud ERP built for manufacturers who want to maximize profitability, boost efficiency, and stay future-ready in a competitive global market. With real-time intelligence, connected workflows, and end-to-end capabilities, Kinetic helps teams innovate faster, work smarter, and scale without limits.
In this episode, we explore the intricacies of supply chain management, particularly focusing on the role of AI and ERP systems in enhancing agility and resilience. The discussion features insights from Ellen Cox and Dan Houdek, who highlight Epicor’s strategy of transforming ERP systems from mere records to proactive systems of action. They emphasize the integration of AI to forecast, automate, and streamline processes, thereby reducing manual errors and improving decision-making. The conversation also touches on the importance of maintaining a human-in-the-loop approach to AI, ensuring that technology empowers rather than replaces workers. The episode concludes with a discussion on the future trends in AI, particularly the shift towards a connected, collaborative intelligence across the supply chain.
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Show Notes
Takeaways
- AI is evolving from isolated tools to collaborative intelligence.
- The concept of ‘intelligent fabric’ will connect workflows.
- AI will enhance communication across manufacturers and suppliers.
- Real-time responses will become standard in supply chains.
- Organizations will be able to adapt quickly to disruptions.
- AI will facilitate better decision-making in supply chain management.
- Collaboration will be key in leveraging AI’s full potential.
- The future of AI is interconnected and responsive.
- Supply chains will become more resilient with AI integration.
- AI’s role in supply chains will continue to grow.
Chapters
00:00 – Introduction to Epicor and their ERP solution
01:15 – Challenges in Modern Supply Chains
03:45 – Epicor’s Role in Supply Chain Management
06:30 – Integration of AI in Epicor’s Tools
10:30 – Epicor’s Strategy for Agile Manufacturing
13:00 – Transforming ERP Systems into Systems of Insight
16:00 – The Importance of Connected Data Foundations
18:30 – Role of AI in Enhancing ERP Systems
21:00 – Addressing Visibility Gaps in Supply Chains
24:00 – Real-Time Data Utilization for Quick Decision Making
27:00 – AI’s Impact on Shop Floor and Planning Roles
30:00 – Customer Success Stories with Epicor
33:00 – Emerging Trends in AI and Supply Chain Management
36:00 – Epicor’s Unique Approach to AI Integration
39:00 – Final Thoughts and Future Outlook
Transcript
Beau Hamilton (00:00.878)
Hello everyone and welcome to the SourceForge Podcast. I’m your host, Beau Hamilton, senior editor and multimedia producer here at SourceForge, the world’s most visited software comparison site where B2B software buyers compare and find business software solutions. Today we’re taking a closer look at the part of manufacturing that most people never see, but every company feels, and that is the supply chain.
With today’s rapid pace of change, everything from AI automations to trade wars, it’s getting pretty hard for businesses to stay agile when demand swing, when materials get delayed or production bottlenecks really pop up out of nowhere. And Epicor is a company that’s been working right in the middle of those challenges for decades now, building ERP systems that help manufacturers get real visibility into what’s happening across their operations, everything from the manufacturing floor to their suppliers and their distributors.
And one of the biggest sort of unlocks I would say for them recently has been with artificial intelligence. It’s almost impossible. It seems to find a company nowadays not weaving this tech into their products or services nowadays. For Epicore, it’s really being integrated with the tools like Epicore Prism, among others, to help teams forecast more accurately, make decisions quicker, and then just automate a lot of the mundane manual processes that slow down supply chains.
So joining me to dig into all this is Ellen Cox, Senior Product Marketing Manager and then Dan Houdek, Senior Director of Product Marketing. Both of them here have some expertise in the matter. So welcome to the podcast, guys. Appreciate you guys being here.
Ellen Cox (01:36.907)
Thanks for having us.
Dan Houdek (01:37.015)
Thank you.
Beau Hamilton (01:39.352)
So I want to start fairly high level, fairly big picture here before we get into some of the technical components of the problem Epicore has set out to solve. Dan, we can start with you. Can you describe Epicore’s overall strategy for helping manufacturers become more agile and resilient in their supply chains?
Dan Houdek (01:58.056)
Yeah, our strategy really kind of centers around one idea. It’s helping manufacturers turn their operational data into faster, better decisions, really without asking them to become data science labs or rebuild everything that already running. So our goal in the end is to bring what we call net zero time to value to our customers. And we’re doing that through something we call a cognitive ERP or cognitive ERP vision.
And that’s transforming ERP from a system of record, really into a system of insight and action for our customers. And it comes down to kind of like three things that we really focus in them. So you have industry specific ERP, things like Kinetic or Profit 21 from our distribution side. They’re built around the real workflows that those industries are using so that the data models already match how those factories or the supply chains operate.
And then second, we have a connected data foundation. And so our Grow Data Platform and Grow AI, they unify ERP, MES, legacy systems, even partner data into one fabric, in essence, with, it includes quality checks, has predictive models for demand, inventory supply risks. So the planners move from spreadsheets to basically scenario planning.
And then we have embedded role AI, which is our Epicore Prism AI, and that brings vertical AI agents really directly into the workflows we were talking about. So Prism Business Communications is a great example. It automates RFQs, supplier outreach, quote evaluation right inside of our ERP Kinetic, and that speeds sourcing, reduces manual errors. And then around all of that, we extend resilience through our ecosystem. So partners like Source Day for supplier collaboration or Sovos for compliance.
And through research, we do tons of research. We talk to our customers. All that helps us stay grounded in what’s actually driving improvements for manufacturing. So in short, if you think about agility, it happens when your data is connected, when it’s trusted, when it’s supported by AI. That isn’t for AI’s sake, right? It fits the way people actually work. And that’s the future we’re building here.
Beau Hamilton (04:17.262)
That’s a good breakdown. You got to connect all those different kind of tools and services all into one sort of central place. That’s kind of like my general high level understanding of the ERP that you guys are working on. It’s, well, I mean, from a foundational level, ERP has traditionally been like this central point of contact or place where everything gets stored. And like you were saying, like now we’re starting to see more asked of it. The expectation, I think, is that ERP is, you know, is really going to be helping actually guide the decision making, automate various processes and bring attention to issues that need to be solved, dealt with. How do you see ERP shifting from a passive system of record to more of a proactive system of action?
Ellen Cox (05:07.073)
So I think when we think about kind of ERP for decades, it’s really kind of been in that system of record space. And I’m like, when we think about what do we mean by that, you know, it’s great at storing data, you can track transactions, you might be you’re keeping track of processes or where things are from an order perspective. But traditionally, all of that’s reactive. So you’re looking up what’s happened after the fact.
With some of the AI kind of tools and vision that we’re building towards that Dan was kind of speaking to, we really see our ERP is kind of becoming the system of action and that those records are not just kind of past records, but it’s also starting to help you look in kind of a forward-looking sense. So you’re able to predict, you’re able to make decisions, you’re able to take those faster actions in the moment based on the data that’s available within your ERP, but also, you know, as Dan was mentioning, ability to kind of connect some of these other data points of like bringing additional third-party data that you might have access to, etcetera, bringing that all into one place to kind of help your business ultimately operate smarter. And so a lot of that comes down to that cognitive ERP space using kind of that combination of Epicore Prism.
We also have machine learning AI offerings that we call Grow AI, where it’s really kind of the combination of those two things working together with, you know, Grow AI, machine learning, continuously analyzing signals. And then Prism AI is like the agentic AI side of it of really starting to help further bring those insights, signals, etcetera, to life to kind of be able to work with all of it securely within an ERP.
Beau Hamilton (06:50.51)
Interesting. OK, so you got those two sort of pieces of the puzzle there really working to solve and on tackling this issue. I want to focus on the visibility aspect for a moment. I know visibility in the supply chain is one of the toughest challenges, if not the toughest, because knowing what’s happening across suppliers, the status of inventory, the production lines, viewing just the logistics of everything is really important.
From your vantage point, where do you see the biggest visibility gaps today and how would you say Epicore is helping to close them?
Dan Houdek (07:28.436)
The biggest challenge is a lack of data. The fact that the data is fragmented, it’s delayed, it’s hard to act on. So manufacturers are dealing with islands of ERP, MES, supplier portals, spreadsheets. So there’s no end-to-end view of orders, inventory, even risks. Many teams, they’re still making decisions on yesterday’s batch runs or even information from a month ago, instead of what’s happening right now, even on the floor. So by the time an issue shows up, like a supplier delay or quality variance, it’s already impacted the business that the customers and manufacturers are dealing with, right? It’s impacting production and delays or delivery.
We close those gaps by connecting the data. Analyzing in real time and we’re putting the insights directly into that workflow. So things like Kinetic or Advanced MES, even IPNO, those are giving a unified view of production, inventory, demand with ML driven forecasting. Then we have Grow Data Platform and Grow BI. They are unifying ERP, MES, even ecommerce and partner data into a single real-time source of truth.
We have solutions like QuickShip, EDI, integration even with our partners Source Day and supplier management tools. Those eliminate blind spots in procurement and logistics. And then you have Grow AI, which is continually analyzing the data. It flags anomalies, it’s forecasting disruptions, all to surface performance trends before they actually become the bottlenecks for the manufacturers. And then Prism AI, as Ellen was going over to, is, it adds that conversational layer so that planners can actually ask questions in their natural language and they get instant access and cross-system answers. So the result is manufacturers are moving from visibility after the fact to intelligence in the moment. And that is critical for where this market is going.
Beau Hamilton (09:37.422)
Okay. I want to see if you can continue to expand on that a little bit more. Cause I know once you have a clear picture of what’s going on, right? You, the next step is, to act quickly as possible, you know, to address the various issues and then just continue to overall, improve the decision making. How is, how is Epicore using this real time data? A lot of the sources you mentioned from, you know, machine suppliers, logistics partners, how is Epicore using that to help teams react in, let’s say minutes instead of days?
Ellen Cox (10:10.197)
Yeah, totally. So I know we kind of were talking a little bit about that Grow data platform. That’s really where we’re kind of creating this opportunity to bring information from across the supply chain to kind of unify it and allow it to start to be analyzed by Grow AI or that machine learning. And so with everything kind of flowing within there, you know, we can bring those insights from across those different data sources directly into the ERP. Those workers can then use Epicore Prism to be able to ask questions, analyze data, take that action. And all of it, you know, making sure because it is flowing within the ERP, Prism, Grow AI, etcetera, everything is embedded. We’re building embedded AI within our ERP systems like Kinetic, Profit 21. That really enables kind of that end-to-end visibility and really ability to use those tools anywhere on the shop floor.
So, it’s really trying to get towards that smarter, faster, and more confident decision making. And even just to like give you examples, we have capabilities with Prism, for example, that if you were someone working on the shop floor and you’re noticing, hey, you know, we’re manufacturing this part, they’re coming out with defects, you could actually take a photo of the defect, upload it into Prism and say, hey, what might be happening that’s causing this defect? And it can take a look and give analysis and potential sources of the problem to help you troubleshoot faster versus, you know, if you’re working with a procurement team, you might use something like Prism for kind of RFQ process to kind of help in, you know, sourcing materials that you need for manufacturing.
So it’s kind of looking throughout the flow of, you know, different efforts within kind of manufacturing process of where can we start to bring AI in to help speed up the process and make things better for folks and whatever part of their job they are focusing on in the day to day.
Beau Hamilton (12:10.818)
Wow. So being able to actually take a picture of the issue and upload it into the system and have it kind of analyze the context and provide solutions, I could just see the use cases there. That’s really interesting. And it makes me just think of all the different kind of LLM chatbots that are kind of related to that, whether it’s, you know, copying the format of a document and just from uploading a picture of it to trying to figure out, what’s wrong with an image or something.
Really, really powerful tools. All this just has me kind of thinking about thinking big picture about this broader sort of digital transformation journey. We’ve all found ourselves in, especially these last years with all this AI automation, you know, technology. I think I keep thinking about how potentially overwhelming it might all sound for manufacturers who might have maybe a finite budget for what to focus their attention on. Wwhen you have the cloud, you have AI, have all these various, again, automation and analytic tools. I’m curious, where should manufacturers focus first if they want to make the biggest impacted supply chain performance?
Ellen Cox (13:19.71)
So I would say a lot of this starts of really focusing first on outcomes that you’re trying to drive as a business, not necessarily technology goals. And what I mean by that is not using or choosing technology for technology sake, but really making sure that like you have the endpoint in mind of here’s what we’re trying to get to and how are we gonna make that happen?
In particular for manufacturers, think a lot of times focusing on that specific problem that you need to solve, whether that’s reducing a lead time, improving forecasting, and then choosing those digital investments that are going to directly support that outcome. I think a lot of times we talk here at Epicor even about like, is AI the answer? Maybe, maybe not. Like, I think it depends on the problem that you’re trying to solve.
So, we really look at that like, there is a lot of noise within the market. We are building kind of as a company with this idea of tangible problems that we can help manufacturers address, solve for, etcetera. So in terms of manufacturers kind of prioritizing digital transformation investments, I think a lot of this comes down to just starting with like that really making sure your data foundation is really strong and then starting to look for, you know, how is your team currently getting work done?
We talk a lot about it, I know Dan was referencing it before of like our AI is embedded within our ERPs. That is by design because we find that if you’re not forcing people to go out into a whole separate tool or adopt a net new element of technology, people are more likely to integrate it. It’s already a part of the workflow where they’re already getting work done. It’s an easier adoption path from there.
I think from there, you know, that adoption piece is kind of a key of making sure that you’re starting to invest in technology that fits how your team is working and really kind of taking that time to pilot things and scale from there. So I think a lot of times we talk about transformations don’t have to be massive from the get-go for something to be meaningful. You can start with a really clear goal of like, hey, we’re gonna start by modernizing our data foundation and we wanna try to use AI to solve this one specific problem that we have. Prove it out, drive the ROI, start to get the teams internally. You’ve got your teams trusting it, they’re understanding how to use it, etcetera, and then start to scale as you build that trust up internally.
Beau Hamilton (15:59.118)
Well, so OK, so you focus on, if you’re a manufacturer, you focus kind of inwardly. Look at how your team works and look at what kind of data you’re working with. And then select, then start focusing on kind of the tools that apply to how you actually work. And then I think the next step would be, for an organization, really just how do you get the most use out of them? How do you maximize the value out of the tools that you’ve selected you want to actually try?
What kind of hurdles do you see companies running into when implementing some of these AI driven supply chain transformation tools? Like, is it, is it data quality issues? Is it actually maybe accessing the data in the first place, or is it sort of like this general sort of resistance to change?
Dan Houdek (16:50.164)
Look, this technology is moving fast. I’ve been in this industry decades. I’ve never seen a time where tech is just, every day something new is happening. And so most of the challenges I’m seeing really are organizational or governance related. It’s not purely technical. It’s data readiness. It’s change management.
So many manufacturers, like they say they want AI, but their data is siloed, it’s inconsistent. That’s where things like our Grow Data Platform is really helping, right? It’s unifying the ERP, the supply chain data, helping with shared definitions. And then there’s just the challenge of adoption, right? So, and trust really in AI, people are still questioning, you know, can I trust the information I’m getting? So that’s why we use what we call a human in the loop or semi-autonomous approach so that people are, they always understand why AI is making the recommendation it actually made. And that transparency really builds trust, it helps speed adoption, and it keeps the human in control.
And then there’s, you know, I probably should have started with this, that probably the biggest thing is the risk around security and compliance, right? So especially you think about it, as you connect more partners, data sources, that risk rises, which is one of the reasons that we invest in and not just the AI, but in secure integration and EDI and tax compliant partners within our cognitive ERP ecosystem. And so by basically grounding AI in the secure data and clear guardrails, we’re giving the manufacturers the ability to deploy AI that they trust and honestly just scale it responsibly.
So, you know, if you think about it, successful AI isn’t just the algorithms and all the technology that’s behind it. It’s the data stewardship. It’s the culture readiness. And honestly, that’s where I would start. If you’re an organization and you’re just looking at this to get going, start there. Make sure your data foundation is set. Make sure your culture is ready. Make sure people understand the changes that you’re putting into place.
Beau Hamilton (19:04.556)
Yeah, that’s a good feedback, a good answer. And I want to come back to the human in the loop approach you mentioned, because there’s a lot to discuss there. But I first want to comment on the adoption part of your answer. I think generally speaking, I feel like when you think about these tools, you have to approach it like these tools are being developed to make it less complicated for you. They’re designed to help solve the real issues, right? And so, yes, there might be some like learning and curves associated with it, but the upside is huge and it really only just seems to be getting better as, technology matures.
Now we’ve talked a lot about AI and you’ve mentioned some great use cases already, but I want to give you another opportunity to just share some more examples, you know, where is AI already starting to prove its value and what major use cases are you seeing today in supply chains?
Ellen Cox (20:01.224)
So as we were mentioning before, like Epicore Prism, for example, has been built as kind of this agentic, role aware assistant or agentic AI that’s really kind of aimed at that speeding up everyday processes. So, you know, we’re building out kind of this series of agents that are taking on various tasks, capabilities, etc.
One of them that we’ve recently launched was actually, it’s called Prism Business Communications, but it basically is aimed at speeding up that RFQ process. And when we talk about RFQs within manufacturing, a lot of times this is a process that is highly manual. Usually it’s being handled over email. So you’ve got fragmented communication going out. You’re having to take and combine and consolidate these different responses. Yyou might have things like a supplier who says, hey, here’s an attachment and then they forget to actually put the attachment in there.
So Prism Business Communications can actually help you manage those RFQs within Kinetic. It does things where you could deploy the RFQ with the click of a button to a number of preferred suppliers. As those responses start coming back through, it’s actually parsing the information out, it’s laying the responses in terms of like, pricing within a table so you can start to compare the prices on a side-by-side basis. It can flag if there’s missing information of like, hey, this person said there was an attachment, but there’s no attachment detected in this email, or maybe they accidentally left a piece of the pricing out. So it can flag those types of things. It can even help you to go ahead and proactively draft a response that you can then look at it again, human in the loop of edit it and send it back.
But all of that’s happening now within the ERP. So we’ve essentially taken something that was previously highly fragmented, highly manual. We’ve moved it into the ERP where it’s secure, it’s auditable, it’s connected to kind of these PO creation. So that’s one example.
I think another one that comes up a lot is our knowledge agent. So, you know, as a manufacturing business, you you may be hiring employees or we know, you know, parts of the workforce start to kind of retire, things like that. It really allows for this kind of ability to surface procedural answers a lot easier. It can allow you to onboard team members faster or even just improve worker productivity. Let’s say you’re someone working on the line and you realize like, hey, I need to execute this process. And it’s either been a while or you’re filling in for somebody one day or something comes up or you’re onboarding. Instead of having to necessarily stop someone else from what they’re doing, hold things up, etcetera, you can go first to knowledge agent, ask that question, and it can pull materials from the system that you can then reference. So there’s a lot of examples like that, just in terms of the different agents that we’re building, and we’re even continuing to build.
So we’re in active communication with customers on a regular basis, continuing to evaluate where are these opportunities to just continue building out, to continue to kind of make those workflows better. I think beyond that, like longer term in terms of vision of where we’re going with all of this, this kind of ability to start to connect some of these capabilities just from like an end-to-end standpoint. So this would be essentially allow like multiple AI agents to start to work together. So maybe you go from like that Prism Business Communications that’s helping you with your RFQ to PO process and then, you know, there could be another agent that it hands off to of, once this has been secured, know, keeping track of inventory, et cetera. So it’s really, you know, working through kind of these different opportunities, but really making sure we’re ultimately working kind of towards the space of this connected, intelligent space across the supply chain where those insights are flowing kind of seamlessly, whether it’s from planning to execution. And again, all of it keeping human in the loop and kind of humans in control every step of the way.
Beau Hamilton (24:17.996)
Yeah, well, those are some great examples. In my mind, was just going to start to think about how I feel like I’m constantly making little mistakes with regards to the email example you mentioned about not including an attachment, maybe including the wrong link. Basically, these little things you forget do ultimately add more time. It just doesn’t allow you to tackle other issues. And you’re constantly reaching out to other members of your team trying to ask for clarification. And then it just kind of slows down the process. So I just can think of kind of the ripple effect of if you have this sort of agent kind of overlooking things and just offering suggestions of, hey, are you sure you want to send this email because you forgot your attachment? Like little things like this do have a big sort of ripple effect and ultimately improve efficiency.
And I think, you know, again, like, it comes down to, it’s a really good, um, to have in today’s environment too, where we’re so everything’s so fast paced and our attention spans are all over the place. And we do kind of make these little mistakes, with, with emails and whatnot specifically, but. Okay. So a lot to, a lot to talk about there. I want to come back to that question, Dan, or that comment you made about keeping humans in the loop and Ellen you mentioned it too.
That’s one of the biggest kind of concerns I hear about AI is just, is how it’s going to replace workers, especially manufacturing, but at the same time, many leaders I’ve spoken to say it’s actually gonna result in more jobs and empower people in new ways and being able to tackle their other ideas that they haven’t had time to tackle before. And there’s two sides of the argument there, of course, but. I’m curious to hear what examples are you seeing where AI is helping people actually do their jobs better, both on the shop floor and in planning roles, for example?
Dan Houdek (26:15.284)
I’m a big believer that the real story of AI and manufacturing is going to be about augmentation, not replacement, especially when you look at like mid-sized companies or mid-sized manufacturers. So it’s really exciting when you think about how AI is helping people do their best work. It’s giving them, you you mentioned it’s giving them back time in their day, right? To think about these things that are just kind of these, you know, monotonous type of things that a machine could do.
So it’s becoming a digital teammate, rather than a replacement. So on the shop floor, AI is highlighting patterns and downtime scrap or throughput. So operators, they can solve the issues. Like I’d mentioned before, they hit quality or delivery. And planning AI is shifting the teams from, you know, I think about they’re still using spreadsheets, firefighting, right? It’s shifting that to a scenario design, right? Maybe with Grow AI or IPNO. We’ve talked about Prism AI a few times here. That’s handling things like the RFQ, answering questions or procedural questions. It’s even prepping supplier outreach.
So the exciting thing here and the way that I think AI has been used, it’s keeping people in control. AI is removing that friction that I think just a lot of times just eats up times of the day that you could be actually focusing on more strategic things.
Beau Hamilton (27:46.284)
Yeah, I mean, we get we do kind of get ahead of ourselves with all these sci-fi novels and movies too. It’s it’s maybe it’s moving in some of these futuristic dystopian directions. But I think generally speaking, it’s maybe more far out than than it seems and not quite in touch with reality. But it is nice to hear about those real world impacts. And I think reducing the friction and just helping everything just become a lot more smooth and efficient is what it’s all about.
Dan Houdek (28:15.892)
Well, it needs to be a people-centric AI strategy. I mean, again, like I mentioned, the culture, the importance of that, that’s why you build trust when you do that.
Beau Hamilton (28:27.04)
Absolutely. Now, out of curiosity, is there a customer success story that maybe comes to mind where Epicore really helped improve their efficiency? Maybe doing all sorts of things, shortening lead times, reducing inventory, just responding more quickly to whatever supply chain issues are thrown at them? Do you have anything that comes to mind?
Ellen Cox (28:45.93)
So we’ve been working with a number of customers directly, but on an ongoing basis. One of our customers who kind of raised their hand pretty quickly when we first started announcing like, this is coming, was Madsen’s Custom Cabinets. So they’re a multi-generational manufacturer based in Alberta. They’ve actually recently moved to kind of Epicore Kinetic in the cloud.
They started using up a core prism and a lot of kind of even the early results success that they’ve seen has kind of been this ability to eliminate hours of, you know, manual updates, communication bottlenecks. I think in particular, one example that they were talking about was just in weekly production planning meetings of like, they previously were spending hours per person, which when you think about it in kind of, you have 10 people going into a meeting and each person is spending, you know, three to four hours having to prep for that meeting. Being able to kind of cut down on the amount of time that folks are having to spend to prep for those things allows you to really kind of get down to focusing those efforts elsewhere on more value.
So we’ve seen a lot of success kind of on that front. They were able to kind of embed Prism into some of those just weekly project reviews, team being able to like check status or pull insights kind of directly within Kinetic. So ultimately like they’re saving hours per week just in terms of like support queries, estimators, saving time ahead of kind of those weekly check-ins.
We also worked with a company called New Wave Design. They had some of their employees use Prism Knowledge Agent just to kind of find products and procedural related answers to questions. I think they reported back to us saving around like 50 minutes per question, which like again, 50 minutes is substantial. When you start to add that up of multiple questions per day, etcetera, that’s allowing people to be able to answer questions faster. That’s ultimately better support to both for your internal employees as well as your customers. It’s a huge difference.
I think a lot of it is like, these are just a few examples. think we’re continuing obviously to connect with those customers accounting for their feedback and really continuing to just focus on how do we kind of turn insight into action, drive more efficiencies, reducing delays, help further address customer pain points going forward. So we kind of feel like we’re just at the start of this journey and there’s still a lot of potential ahead and a lot of things that we’re really looking forward to getting into our customers’ hands.
Beau Hamilton (31:30.146)
Well, thanks for those examples. Yeah, I think that that’s really what helps paint a tangible sort of picture of what it is you guys are able to do to help. And yeah, I think those listening right now are probably getting, you know, really able to visualize it a lot better now. And I want to kind of zoom out to that bigger picture, just looking at where things are going. What emerging trend do you think will have the biggest impact on your industry in the next handful of years? I know things are moving really quickly, so it’s hard to predict exactly where things are going. You mentioned sort of some of this agentic AI, but, where, what emerging trend is, is going to be really something you’re going focus on?
Dan Houdek (32:24.267)
Yeah, I mean, that’s hard right? It’s like, is it the trend in the next month or right there in the next three years, it’s going to be changing so much. I would honestly say, you know, if you look at AI, it’s going to shift from isolated AI tools to really a connected collaborative intelligence. It’s something that, you know, we kind of call it intelligent fabric, right?
So today AI is living inside the workflow. And I think within the next few years, AI is going to connect those workflows, not only within your organization, but across manufacturers, distributors, suppliers, across the entire supply chain. So agents are going to share insights without sharing the proprietary data. And that’s going to enable this entire supply chain to respond to change in real time, as opposed to waiting to find out what your distributor that they’re down and you need to change locations or something.
And we’re already building that into our technology. So we have role aware agents, machine learning across our ERP, and eventually across our partner ecosystems. Predictive intelligence that we had mentioned. We have the unified data foundation, right? And so when you think about the results is going to be the supply chain that just doesn’t react to disruptions, because we’re seeing that all the time, they’re going to anticipate it and it’s going to be able to adapt together. So it’s kind of this, you know, your entire supply chain is connected as this fabric of intelligence that is shared equally.
Beau Hamilton (33:53.622)
Yeah, I totally agree. I think once you, we’re starting to see some of these, these protocols sort of being adopted from a lot of these established like AI companies and, you know, tool makers for lack of a better term for them. But it’s, that’s helping really sort of increase this sort of adoption of, or communication between these different areas of, of where data is stored. And then, just being able to allow it to talk to each other, I think is going to unlock all sorts of different possibilities.
I think from a consumer standpoint, one way to look at it is, you know, the LLM chatbot you use to help, you know, write a document you might be working on. That same chatbot is going to be integrated with, you know, Uber or even some of your social media apps. You know, it’s all going to be from one central location, one central spot. You’re going have a hub that you’re going to be able to interact without necessarily leaving the platform you’re on.
Dan Houdek (34:53.556)
Well, sure. When your airline’s delayed and that data is shared to the Uber person that, you’re delayed already and you don’t come and get me for another hour. And then maybe it’s you have a reservation at night, right? And that data is shared with that piece. So yes, the idea of sharing the data, but also keep in mind, there’s this whole security and risk piece that we had mentioned earlier that you need to be aware of when they’re doing that, and the importance of what we’re trying to do behind that.
Beau Hamilton (35:24.918)
I think just taking that sort of general philosophy of how that works and applying it to the supply chain side of things and the business side of things. Yeah, I think the value just speaks for itself there. But going back to this whole idea of an experience of change and how things are changing so quickly, I think there’s naturally some room for some misconceptions.
Ellen, maybe you can tackle this question, but what’s something your competitors are getting wrong that Epicore is determined to do differently?
Ellen Cox (35:57.889)
Yeah, so I think in particular, this probably ties back to some of what Dan was talking about previously with like keeping humans in the loop. I think we’ve seen a lot of vendors kind of building AI with this lens on replacing workers of like fully autonomous systems. They take people out of the loop, set it, forget it. And I think that’s just not the reality of where we see the future going.
We’re really focused on this idea of building semi-autonomous AI that’s empowering people. We want to make sure that we’re embedding those solutions directly in the ERP to really make sure it’s this grounded, trusted data that’s rooted in business context. It can help summarize or automate things, reducing risk, but at the end of the day, humans always make the final call.
And so I think it’s kind of finding this balance between kind of that trust adoption measurable results that you can get from that type of approach in terms of just weaving it into the workflows that manufacturers already know where it can start to deliver value immediately. I think that’s really where we see kind of competitors aren’t playing within that space, they’re really focused on replacing people and we’re really more focused on amplifying them. Like how do we make folks who already are experts within this space, how do we make them even better at what they do than what they are already capable of? So I think that’s really where we’re trying to go is just make every worker more capable, confident, effective.
Beau Hamilton (37:34.636)
I love it. Now I want to wrap this conversation up with one sort of message that you’d want to leave future customers with or just listeners in general. If you had the chance to deliver one message to every, let’s say every manufacturer, sort of thinking about these these AI tools, think about the next steps to focus on and invest in. What would that one big thing be?
Ellen Cox (38:03.968)
Yeah, I mean, I think some of this is AI doesn’t have to necessarily be complicated to be powerful, like we’re focusing on kind of building AI that’s already fluent within the industry, understands your workflows, understands how your workers are getting their work done. So it’s not necessarily like you don’t have to rebuild your entire system. You can take what’s already there and start to activate to get more out of your ERP going forward. So I think to me that’s the big piece of this is like driving an AI transformation doesn’t have to be complicated. It can actually be really intuitive for both your business and your workers.
Beau Hamilton (38:46.402)
Gotcha. And, and it doesn’t have to be complicated. And also, I also feel like by one of the risks is not so much adopting this technology. It’s not adopting. It’s just kind of being complacent. I mean, right?
Ellen Cox (38:57.91)
Yeah, it’s a very big risk. I think as we get further and further in, like there’s an increasing risk. We talk with customers about this sometimes of like, it’s, yes, we’re in this space where like it is moving fast, but it’s also not too late to get started.
Dan Houdek (39:19.769)
I would just reiterate security, you know, culture, data governance, all those things that people don’t think about, get that in place first before you try and adopt anything from an AI perspective. Small steps can make a big impact, right? And so, so don’t do AI for AI sake. Make sure that it has an outcome that’s valuable that can show ROI even just small projects. And that builds that trust inside your organization. And, you know, I just keep going back. I, people inside my company probably get tired of secure, secure, secure, right? I can’t emphasize enough to make sure that you’re, whether, you know, you’re using Epicore or any other vendor or any, you know, consumer tools, understand the rules and the security they put behind it. How is your data shared? Is it not shared? Where is it stored? That is just critical, but it’s going to make a big difference going forward.
Beau Hamilton (40:23.03)
Yeah, no, it’s a good thing to stress for sure. Cause you, I mean, it takes just one big security blunder to, really kind of decimate a company. And, we’ve seen all these headlines. I mean, I, there’s not a day that goes by where there’s not a company being affected by some sort of security breach or, or lapse.
Dan Houdek (40:39.143)
In August, it shut Jaguar down for many months that affected the entire supply chain. So that’s just, you know, a big company, great, you know, that they’ve got money to back them. But then you’ve got mid-sized companies that can’t get their, you know, their supplier is done, right? Or they can’t get their inventory over to Jaguar and such. So yeah, it just, it can’t be overemphasized, I think, at this point.
Beau Hamilton (41:05.698)
Yeah. Well, there’s, there’s a lot of great insights. I appreciate you all for sharing everything that you shared for those listening, who maybe they’re interested in learning more about Epicore, Prism, the AI, some of the tools you guys are offering. Where should they go?
Ellen Cox (41:20.576)
Yeah, you can actually go to epicor.com/AI. That’ll take you to all of the information about our artificial intelligence solutions, both information about Grow AI, Epicore Prism, and I mean, from their ability to kind of continue learning more even within the space of like data that we were chatting about or like security features of our AI.
Beau Hamilton (41:52.184)
All right. Well, Ellen, Dan, it’s been a pleasure. Thank you so much for everything. And hopefully we can have you back on these days.
Ellen Cox (42:05.014)
Yeah, that would be great. Thanks for having us.
Dan Houdek (42:04.853)
Yeah, fantastic Beau. Any time.
Beau Hamilton (42:07.916)
All right. Well, thank you all for listening to the SourceForge podcast. I’m your host, Beau Hamilton. Make sure to subscribe to stay up to date with all for upcoming B2B software related podcasts. I will talk to you in the next one.