AI-Native ERP: What Buyers Should Know Before Selecting a System

AI-Enabled ERP vs AI-Native ERP: What’s the Difference?

The term AI-native ERP is gaining traction, but it is easy to confuse it with AI-enabled, and the distinction matters for buyers looking for ERP Software that provides AI capabilities.

AI-enabled ERP systems typically bolt AI functionality on top of an existing platform. In many cases, that means a chatbot layered over your data. AI-native ERP systems are built with AI at their core. The difference, as John Cusick explains in this episode of the Comparesoft ERP Podcast, is that AI-native systems can trigger workflows, flag anomalies such as duplicate vendor invoices on the same day, and actively assist users in adjusting data rather than simply surfacing it.

In practical terms, AI-native is more agentic. AI-enabled is more generative. That distinction shapes everything from how the system is implemented to what it can do once live.

Where AI-Native ERPs Sit in the Market Today

AI-native ERPs are still early. John describes them as an inch deep and a mile wide. They are being built faster than legacy systems ever were, but they do not yet have the depth of functionality that comes from decades of customer-driven enhancement.

Today, these systems best serve small to mid-sized businesses, particularly SaaS and services companies with annual recurring revenue roughly between two million and three hundred million. They do not yet support complex inventory, manufacturing, or wholesale distribution workflows. Companies with those needs remain underserved by AI-native ERP at this stage.

The buyers considering these systems are typically migrating from one of three places:

• QuickBooks or spreadsheets, where the business has outgrown basic tooling
• Legacy ERP systems that were overkill for the company’s actual size and needs
• Failed or heavily customised legacy implementations carrying significant technical debt

For many of these companies, AI-native ERP offers a lower-cost, cleaner starting point with the promise of continuous improvement built in.

Common Misconceptions About AI-Native ERP

The most common misconception John encounters is the belief that AI will automate everything. Companies approach AI-native ERP expecting it to replace headcount or remove the need for human decision-making. That is not how it works today.

AI in finance and accounting still needs direction. It is best understood as an assistant, not a replacement. It helps users build automation specific to their needs, surfaces anomalies faster, and reduces repetitive work. But the human must remain in the loop, particularly in a function as consequential as finance.

The second misconception is around depth. Buyers sometimes expect AI-native systems to match the feature depth of legacy ERPs that have been built and refined over twenty or thirty years. They do not. AI-native ERPs today offer breadth and adaptability, with depth developing over time through rapid release cycles.

What an AI-Native ERP Implementation Looks Like

AI-native ERP implementation follows five stages:

1. Plan
2. Migrate and reconcile
3. Integrate
4. Train
5. Go live

A typical ERP implementation timeline runs three to four months, though this depends heavily on data quality.

Because AI-native ERPs are more configurable than customisable, the implementation focus shifts towards data. Echo Park uses a white-glove approach: pulling data from the legacy system, entering it into the new platform, and having the client reconcile it before moving forward.

The biggest time savings compared to traditional cloud ERP implementations come from three of those five stages:

1. Migrate and reconcile: AI is used not just within the new system, but as part of the implementation process itself, helping scrub and validate data during migration
2. Integrate: AI-native ERPs offer stronger native integrations out of the box, reducing the need for separate iPaaS solutions
3. Train: Built-in AI chat functionality means users can learn by asking questions directly within the system, supported by better documentation generated from how the platform was built

Thinking About ERP as a Finance Stack, Not a Single System

One of the most important shifts in thinking that John recommends is moving away from the idea that ERP must cover every business process. He subscribes to the concept of ERP-plus: the AI-native ERP handles core finance and accounting, while best-of-breed tools handle accounts receivable automation, accounts payable automation, taxation, FP&A, and CRM.

Because AI-native systems are built with stronger native integrations, connecting these tools is faster and more streamlined than it typically is with legacy platforms. Buyers entering the selection process should identify which processes are best handled by the ERP and which are better served by specialist systems that integrate natively.

How AI-Native ERPs Handle Data Governance, Hallucinations, and Post-Go-Live Drift

Data quality is the most common concern finance leaders raise. John’s response is direct: let AI help you fix the problem, rather than waiting until the data is perfect before getting started. Finance professionals are expert auditors. AI-native tools can surface inconsistencies, flag issues, and assist users in cleaning data more efficiently than manual review.

On hallucinations, John explains that AI-native ERP vendors have a critical role to play in building bounded, contextual AI. These systems are not open-ended generative tools. They operate within defined parameters, with specific agents and endpoints designed to keep outputs grounded and relevant. That structure significantly reduces hallucination risk compared to general-purpose AI.

Post-go-live governance is supported by the same architecture. Because AI-native systems are built with contextual bounds and require human interaction for meaningful changes, the risk of shadow logic or uncontrolled automation drift is lower than many expect.

What Software Buyers Should Look for When Selecting AI-Native ERP

John, of Echo Park Consulting, highlights three requirements that should sit at the top of any AI-native ERP shortlist:

1. Can the AI answer your most nuanced finance questions? Test whether a question that would take fifteen to thirty minutes of report-building in a legacy system can be answered in minutes through AI. If it cannot, the system is not moving the needle
2. Does the AI help you close your books faster and more accurately? Book close speed is the north star of accounting software. Any AI-native ERP should demonstrably improve this
3. Does the vendor show an aggressive, AI-driven product roadmap? AI-native systems should be releasing functionality frequently, sometimes daily. If the roadmap does not reflect that pace, the system may not deliver on its core promise of continuous improvement

Buyers should also approach demos differently. Rather than evaluating how a system will be customised and built out over time, as with legacy ERP, the focus should be on how the system will be used. AI-native ERPs are configurable, not customisable, and they evolve through frequent releases. What you buy today will look different in a matter of months.

John’s advice is to bring real data into the demo environment. Test the AI against your actual questions and workflows. The lower cost of entry and faster implementation timelines mean the stakes of trialling are significantly lower than with legacy ERP, so there is no reason not to push the system before committing.

Full Transcript

Ryan (00:02): John, it’s great to have you on the podcast. How are you?

John Cusick (00:04): I’m doing well, thanks for having me.

Ryan (00:07): Excellent. So before we get into your current role, John, your previous ERP experiences lay in subscription billing and leading system implementations, working with a large ERP vendor. How did you get into that specifically, and what sort of problems were you typically solving on a daily basis?

John Cusick (00:26): Yeah, coming from more of a technical background, an information systems degree, it was actually a great path right into consulting. Working for that company early on, I started working with a lot of software companies, which eventually, as time went on and SaaS became a thing, companies started using the software a little bit more for subscription billing versus just doing normal contracts. So I started to utilise that knowledge of what I knew about software companies and apply that to subscription-based companies, which came with a lot more challenges.

John Cusick (01:02): I found it very interesting, mostly because there are a lot of companies out there that are challenged by the fact that they have to work with high-volume billing, which was something that was not as familiar at the time with just software companies. So a lot more complex rules dealing with amendments, the introduction of ASC 606 over my career. So now you have a change in how you recognise revenue in the US, along with IFRS 15 in the rest of the world.

John Cusick (01:30): So really a wild ride, lots of really cool problems to solve, especially as the whole industry was changing.

Ryan (01:43): Yeah, and obviously you were a part of those implementations as well. So when you look at the more traditional ERP implementations you were a part of, what was the most common root cause of pain in that process?

John Cusick (01:57): Usually data migration. I think in most cases, especially in the modern age, data is being used for a lot of different things. But specifically for ERPs, dealing with data that is billing data or revenue data has a big impact on how you operate. Eventually, if you go public, say in the US, you’re going to have to provide very clean books. So a big part of these implementations that caused a lot of pain is making sure your data is in the right spot. A lot of dirty data had to be cleaned up, hard decisions to be made as to what you need to keep versus what you had before.

John Cusick (02:35): And then from there, it’s just change management. Like anything else, you have to move people into a different day to day. And for them, they have to commit to that learning. So that sometimes becomes really, really difficult, especially for traditional ERPs because of the long implementation timeline. You’re not going to be able to just switch it over the next day. You have to commit, do your job that you’re doing now, and then adjust as time goes on.

John Cusick (03:05): And then lastly, I would say decision paralysis. A lot of times these types of companies that are going through this change, it’s one that they’re going to do every 20 to 30 years, depending on the situation. I think that’s probably changed in today’s age, but there are just so many nuances to deal with, exceptions to the rule. And that creates a lot of tech debt as a result. So I think for a lot of these companies, they were trying to decide what to do in the moment. And with all the decisions they needed to make to get this system right, they had a lot of pressure, and that created a lot of customisation, which is what a lot of these companies are dealing with today.

Ryan (03:47): Excellent, and I completely agree on the change management side. It’s so tough to make sure people aren’t falling back to spreadsheets, something they’re really used to.

John Cusick (03:56): It’s tough, it’s very tough.

Ryan (03:58): And so jumping forward, John, you’re now President of Echo Park Consulting, where you assist businesses in implementing AI-native systems. So when we say AI-native ERP, what does that mean in practical terms? And how can a buyer tell if a system is AI-native or AI-enabled?

John Cusick (04:17): Yeah, it’s a great question. It’s going to be harder and harder to make that determination as time goes on, as companies jump into the fold, which they are. And for good reason. I think the AI movement today, there’s a lot of things that we gain as companies from utilising AI. So to answer your question directly, AI-enabled really means that those solutions have AI functionality built on top of them.

John Cusick (04:42): Usually the biggest way for you to tell is that the AI being used in the system is just a chatbot. There are systems out there that just throw a chatbot on top and say, hey, you’ve got all your data, here’s a chatbot you can talk to that is going to help you understand more about your data. And that’s not a bad thing. But the difference with AI-native solutions is that they can do things like trigger workflows or fundamentally change the objects within the ERP so that it works better for your business.

John Cusick (05:15): They also give you a lot of opportunity to do things like notify someone when two bills are the same on the same day from the same vendor, for instance. It creates a lot more opportunity for users to utilise the functionality in a way that’s not just, hey, what’s my data that I need to adjust? It actually helps you do the adjusting, which is really important.

Ryan (05:45): I don’t know if I’m being too naive about it, but could you see it as AI-native is more agentic, whereas AI-enabled is more generative?

John Cusick (05:53): Yes, correct. I think as time goes on, these companies will get to be a little bit more aggressive in how they utilise AI within current legacy systems. And I think they will get smarter. But if you look at it today, the ones that are built on AI natively are pretty far ahead in the sense that they can do a lot of things that would be a bit more of a lift for some of the legacy ERPs today.

Ryan (06:21): And where do these AI-native systems sit in the market today? Who is actually sitting down and considering them?

John Cusick (06:28): It’s interesting. It’s really interesting now because, you think about it, state of the art, brand new shiny toys, and these companies are building ERP. That’s enterprise-level applications. So currently, the challenge they have is that they’re an inch deep and a mile wide because they’re being built from scratch.

John Cusick (06:50): Now they’re being built a lot faster than legacy ERPs were, but they’re still being built right now. It’s very, very early stages. So they’re building a lot of stuff, but they don’t have the depth and knowledge or the things that have been built over many, many years from different customer enhancement requests and things like that. I think what we’ll see in the next couple of years is that it’ll start to work up-market.

John Cusick (07:18): But today, if you look at SMB, the S of SMB, the small companies, are probably the best served to utilise this tool. So a lot of the leading AI-native or AI-enabled ERPs are servicing companies between two million and 300 million ARR, roughly. And they’re more towards services or SaaS-based high-tech companies, because they don’t have things like inventory, manufacturing, or wholesale distribution. That functionality doesn’t exist today.

John Cusick (07:55): So a lot of that functionality ends up being something that has to be farmed out somewhere else. But for companies where that is the biggest part of their business, they’re underserved by AI-native ERPs today.

Ryan (08:14): So does that mean the customers are typically migrating from places like QuickBooks or spreadsheets?

John Cusick (08:21): Yes, still in it. And that’s even interesting too, because the market has changed and you have more options in that stage between two to 300 million. A lot of companies made the decision to jump to a legacy ERP from QuickBooks or from spreadsheets.

John Cusick (08:42): And now they’re stuck with a high cost in general, a subscription-based model for some of the legacy ERPs, and maybe they have failed implementations that carry heavy tech debt. So they’re like, well, that makes a pretty good case for me going to an AI-native ERP. It’s going to be cheaper. I’m going to be able to clean up again, and I get to use some of the AI functionality.

John Cusick (09:08): And I have a system that’s maybe not as much overkill as going to a legacy ERP, which is built for more of a north-of-300-million company, which they are not today. So yeah, it’s really interesting because there’s a mix between that and the companies that are at the point where they’re outgrowing QuickBooks.

Ryan (09:25): Would you say that’s the biggest pain point for that migration? The system they’ve got is quite overkill?

John Cusick (09:32): Yeah, I mean, that’s one. And then it’s the fact that they are probably looking at a system that may be messier than they expected when they came into the implementation, because of the problems we talked about earlier. Change management took a lot of time. People didn’t really get into it. Maybe they’re utilising it in a very customised way, so it’s not really servicing what they need.

John Cusick (09:58): So for them, they throw their hands up and say, let’s try something that’s going to be actually out of the box what I need, and that is going to be evolving as time goes on because it’s AI-native. And I know that it’s going to grow and change as I grow and change my business.

Ryan (10:15): And what are the most common misconceptions you see when you get inquiries about AI-native systems?

John Cusick (10:22): Yeah, I think a lot of times companies will come and say, hey, it’s going to automate everything for me. AI can replace so many people. I think there’s a lot of companies out there that say that’s the advantage of going with AI. AI is powerful, don’t get me wrong. And it’s as dumb today as it will ever be in history. It’s going to get smarter as time goes on.

John Cusick (10:48): But it still needs a lot of direction. And I think these companies, especially in finance and accounting, there’s a lot of structure. They need that human interaction to make those important decisions. And by having more of an AI-assisted approach, they can actually help with creating automation that is very specific to what they need, and not just automation for automation’s sake.

John Cusick (11:12): The other misconception is that it’s starting from the same depth of functionality that legacy ERPs have. No, that’s not the case. Like we just mentioned, inch deep, mile wide. Legacy ERPs have had all that experience for a long, long time. They are in a position to provide you a lot more depth, more customisability. With AI-native ERPs as it stands today, you’re going to allow for that adaptivity over time, but for now it is going to be the basics.

Ryan (11:50): So it’s important for these people to remember that there still needs to be a human in the loop. That’s essential still.

John Cusick (11:55): Yeah, it’s still important. And especially in accounting and finance specifically, this isn’t just a small business function. It’s one that could impact whether your business stays alive or not. So it’s a big deal and you don’t want to hand over those keys. You can utilise the tools. You can take it for a drive, but there should still be a human behind the wheel.

Ryan (12:23): One thing that really stood out to me about what you’re doing currently at Echo Park Consulting is your goal to make implementations smarter, faster, and more enjoyable. Now I know that’s going to be music to people’s ears hearing that. What does an AI-native ERP implementation process look like? How long does it take on average? How many people are still involved?

John Cusick (12:47): Yeah, I mean, in classic consulting fashion, it depends in a lot of ways. But the basic structure of how the implementation looks at Echo Park, we really have five stages that we go through. There’s the planning stage, migrate and reconcile, integrate, train, and go live. Now, since AI-native ERPs are more configurable and less customisable, the highlight is really on that data.

John Cusick (13:15): So we, for instance, use a kind of white-glove approach where we’re pulling some of that data out of your legacy system, entering it into the AI-native ERP, and then reconciling it, having you reconcile it, and we’re onto the next. So it’s a lot of pulling data, making sure that it’s in the right spot. That’s that migrate and reconcile phase.

John Cusick (13:40): Integrate is specifically around being able to integrate with a lot of these best-of-breed solutions, which I’d like to talk about a little bit later if we can. But the native integrations that are being built into these AI-native ERPs today are a lot better and smarter than they ever have been for any of the legacy ERPs. So for us, integrating is really helping you make sure that things are connected in the right way, in the right fashion.

John Cusick (14:10): And then training is very different in the modern age than it has been in the past, because you have AI sitting there as a chat functionality, for instance, as a minimum, to answer those functional questions. So you can teach someone to fish, basically giving them the tools to say, hey, these are the questions you can ask, these are the prompts you can use to help learn and train. But we’re there and we also help train you on the basics of how you utilise the system.

John Cusick (14:40): And then go live is very much as you would expect from any other ERP, in the sense that we’re connecting everything for the first time. We’re putting things into place that allow you to start utilising the tool. But you’re also trained on how to use the AI, which is really the push there.

Ryan (14:52): On average, what sort of timeline are you looking at?

John Cusick (14:55): Yeah, about three to four months. But again, it depends. It can be shorter depending on your timeline. But it comes back to what we talked about with legacy ERPs. There’s still a commitment from the client or the customer in the sense that you need to be able to commit the time to support the nuances that come out of the data. Because data cleanliness drives a lot of this. So if you have dirtier data, it’s going to take a little bit more time to go through it, because you need to make those decisions with our help, of course.

Ryan (15:31): And if we look at those five stages you mentioned, where are the biggest time savers in that process versus a more traditional cloud ERP implementation?

John Cusick (15:40): Yeah, it’s interesting. About three of the five. I would say integrate, because there are more native integrations built into these solutions. So you kind of take away some of the need for iPaaS solutions, for instance. There’s less time and effort being spent trying to connect things.

John Cusick (16:02): For training, like what I just mentioned, you’re utilising more tools, there’s better help documentation as well, because it’s AI-native from the get-go. So all that data, how it was built, is right there already for you.

John Cusick (16:18): But then migrate and reconcile is really where I think a lot of the time saving is, because we can utilise not just AI within the system that you’re implementing, but we can use it in our implementation process, which we’re starting to do. So utilising it to scrub data, to help with the process of actually taking the data out of the system, putting it into your system, and doing that changeover so that you can reconcile it.

Ryan (16:45): So we’re using it to cleanse data as well before it’s put into the system. And obviously you’ve mentioned already that these systems often start finance-first. So how should buyers think about the add-ons: inventory, HR, CRM, different modules like that?

John Cusick (17:05): Yeah, great question. I think I mentioned the term earlier, but I really subscribe to the idea of what they call ERP-plus. And what I mean by that is, companies are looking at a solution today that’s going to cover all of their business processes. And because we have that inch-deep, mile-wide experience with the ERP itself, you have to utilise these other best-of-breed solutions around it.

John Cusick (17:29): So I think having that as something you expect going into the buying process will help you determine what systems are actually important to you, what business processes are most important to you. AR automation, AP automation, taxation, FP&A tooling, CRMs — all of these solutions. Going with the best of breed and connecting them might be the right answer. So I would say to a buyer: don’t think of your ERP as everything, because you don’t have to. And that’s really the answer.

Ryan (18:13): Yeah. And so there are a lot of conversations among ERP experts about the transition to AI-native systems. Some are completely for it, they see it as the future, while others are more fearful when it comes to data governance and AI workplace policies. So if we just take a hypothetical, what actually happens if AI is applied to finance processes where the underlying data is inconsistent or loosely governed?

John Cusick (18:43): Yeah, I mean, it becomes more of a scrubber for you in a way. I think about it this way too: if you look at the people that are actually utilising ERPs today, accountants and finance professionals are expert auditors. Let them utilise the AI tooling to help find those inconsistencies and fix them. Because your data is not something that you can just magic-wand and fix.

John Cusick (19:10): If you have dirty data and you’re in that position where it’s inconsistent and you don’t have great governance, when you bring it into the system, let the AI be the assistant to the people that actually know what makes it that way. And help them be the people that make the fix, do the change, so that you can feel confident that you’re gaining a better set of data governance moving forward.

Ryan (19:35): And so you might have had scenarios where a CFO has come to you and said, look, our data isn’t ready for AI. And I’m guessing that’s your response: use it, help get their data ready.

John Cusick (19:47): Yeah, get it ready. Plug it in, see how it can be done. Rip off the bandaid. I think people are scared of their data. They feel like they can’t make those decisions, because the data hasn’t had the opportunity to actually be looked at.

John Cusick (20:05): And yes, there might be times where it’s like, hey, this is not a good situation, this is not data I want to use. But to make those determinations, let AI help you in that process, because it’s laborious. And you want people to see it in context, not in a situation where they’re having to do it themselves and try to look through all the little pieces, or say, let’s not use this data at all. The chances are there is something to be salvaged, and you use the functionality that’s out there to help salvage it.

Ryan (20:37): And in your experience, do CFOs take a lot of convincing on that side?

John Cusick (20:43): Yeah, I mean, I would also say there’s a different set of individuals that are looking at AI-native solutions at all. CFOs specifically, and really in the C-suite, it’s a challenge. Are you going to take that leap into AI? Is it something that you feel is going to help you and push you forward in your business or not? And I think asking questions like that helps you determine where your risk level is. Because like I said, AI is as dumb today as it’ll ever be.

John Cusick (21:15): And so as time goes on, I think there’s a lot of opportunity for people that maybe are not ready to jump in to look at this again and say, yep, I feel more confident. So I think it’s an okay question. It’s something that I get a lot, but the answer isn’t the same every time, because time will evolve and change.

Ryan (21:38): So we spoke at the beginning about the difficulties of change management once a system has been implemented. I’m curious, how do you set up governance so that AI-driven automation stays safe after go-live and doesn’t drift into shadow logic or workarounds?

John Cusick (21:53): Yeah, I mean, that’s the cool thing about these AI-native solutions today. They’re built with context in mind. They’re in more of a vacuum with bounds. And that’s a big part of their role in the market, to be able to build those bounds for you. So being able to create the governance is a lot easier than you would think.

John Cusick (22:15): Because you’re not pushing in a bunch of extra stuff that doesn’t actually apply, and you’re not creating automation that is going to get you caught in a bad situation. Like I said before, you still need human interaction with AI automation to make it really work best. And these solutions are built with an infrastructure to help control that so it doesn’t get you in trouble.

Ryan (22:40): Another of the worries I’ve seen online in particular are hallucinations, which obviously in finance is a non-starter. So how do these AI-native systems avoid that in calculations and reporting?

John Cusick (22:55): Yeah, kind of a similar way to what I just mentioned. I think that’s one of the largest roles these AI-native ERP software companies have to play in the marketplace. They have to be there to help create those bounds. And that’s another reason why I don’t subscribe to the idea that AI will make SaaS obsolete or make software obsolete, because it still needs to be controlled and put into context.

John Cusick (23:25): And these companies are building AI agents and building specific endpoints so that the actual user can feel more confident in the data, and not see it as something that might not make sense because it’s not in the right context. So it really trims down that possibility of hallucinations, which I know is a big concern in this space.

Ryan (23:53): Yeah, awesome. And how would you recommend finance teams or CFOs test their data trust before they go live?

John Cusick (24:02): Play with it. I think there are a lot of companies that just don’t see the benefit in actually trying it. Like going through, pushing through. And it’s becoming more palatable today than it ever was. You’re not making a purchase of a legacy ERP that’s going to be in the millions of dollars to then not have it work for you. In this case, the stakes are a lot lower.

John Cusick (24:28): Get in there, try it, play with it, do your day to day in that environment, feel it out, ask those questions that you maybe have wanted to ask your ERP through AI. And that will make you feel more like it understands what you’re trying to do. Because I think finance and accounting folks, at least the ones I’ve talked to, yes, they have a structure, but they are challenged by a lot of the things that they don’t know.

John Cusick (24:55): And being able to make that easy for them by utilising AI in these tools can help open their eyes, so that they can trust that what they’re actually putting in place is something that is going to work, help them answer the questions they wanted to answer, and make their job a lot easier.

Ryan (25:22): Yeah, awesome. So it’s very similar to when you’re in the trial or demo stage of a selection process. You want to take your real-world data into that. You want to see it.

John Cusick (25:29): Absolutely. And it’s a huge advantage too. You get a chance to really put your hands on something that is going to potentially completely change how your business is going to run for years to come. You would want to do as much as you can to understand how it could interact with your specific data.

Ryan (25:53): So John, if a buyer is searching for an AI-native ERP today, what should they look for that’s different from a normal ERP selection? How should they run their demos differently?

John Cusick (26:04): Yeah, it’s very different. AI-native ERPs are really changing how companies work. So you have to flip it on its head. You need to look at them as something that is going to evolve as you go.

John Cusick (26:18): They’re not releasing functionality once or twice a year. They’re doing it once or twice a day in some cases. So there’s a lot of functionality and evolution that’s going to happen over time. And you have to expect that what you’re buying today is not what you’re going to be seeing in the next couple of months. And you have to think about it as ERP-plus. The ERP that you’re getting is probably not going to give you everything you need for your full business process.

John Cusick (26:48): So I think looking at it from the lens of, what are the processes that make more sense for me to farm out to other systems that are best of breed and that can natively integrate much easier, more streamlined than it would be with a legacy ERP.

John Cusick (27:05): Then you also have to think about that ERP from the lens of how it will be used, not how it’s going to be built out. That’s probably the biggest one. A lot of legacy ERPs, which is not a bad thing, are evaluated from the standpoint that you are going to build it the way you want it. It’s more customisable versus configurable.

John Cusick (27:28): And I think a lot of companies will come into it with the mentality that they’re buying something that is going to be used for many years to come. And that’s the truth. But if you’re looking at a legacy ERP, you have to look at it from how you’re going to customise it, most likely. Because you don’t have that rapid release, you don’t have that change happening over time. Whereas with AI-native ERPs, you’re going to expect that things are going to change, and change quickly. You’re going to evolve, and you’re going to get the best of what they have, but in a much quicker fashion.

Ryan (27:58): Excellent. And so if a buyer team is sitting down and building out a shortlist, what requirements should be at the very top of their list that are specific to AI-native ERP?

John Cusick (28:10): Yeah, it really has to be future-looking. And if you’re looking at an AI-native ERP today, you’re probably more forward-looking in the space. And so I think there are really three things.

John Cusick (28:25): One, does that AI functionality answer your most nuanced finance or business questions? Challenge that when you’re looking at them. What are the questions that maybe would take 15 to 30 minutes of creating a report, poking around in a legacy ERP? Could that be answered by AI in a couple of minutes? That saves tons of time as time goes on. So think about that as you’re looking at these.

John Cusick (28:52): And then look at how that AI functionality allows you to analyse your data more efficiently to close your books. Because that’s really what you’re getting. Accounting software, at the end of the day, comes down to how quickly you can close your books and how accurate it can be. So closing your books is the north star. And moving to a newer ERP, regardless of whether it’s AI or not, you need to have that functionality. AI-native systems should be giving you that. You should be closing books much faster. So if they’re not, it’s not moving the needle enough for you.

John Cusick (29:28): And then thirdly, does that AI-native ERP show an aggressive product roadmap utilising AI? Because like I mentioned before, that’s a big part of the advantage — it’s evolving. If you’re buying a system that is AI-native and they’re not growing at a pace that actually makes sense for the fact that they’re using AI-assisted functionality, then it’s probably not the right system for you. They need to make that promise that it’s going to evolve as time goes on, that it’s going to grow, and that it’s going to be able to support your challenges of tomorrow, not just the challenges of today.

Ryan (30:11): Like you said, it’s at the dumbest point it will possibly ever be. You don’t want that system stagnating there.

John Cusick (30:16): Yeah. For sure, for sure.

Ryan (30:18): John, you’ve been brilliant. I’ve really enjoyed this chat. Thanks so much. You’ve brought such a grounded perspective to what can sometimes become a very hype-driven conversation about AI. I’m sure a lot of finance and ERP teams are going to think more clearly when it comes to implementing AI-native ERP systems. John, thank you so much again for joining us.

John Cusick (30:38): Absolutely. Yeah, it was great. Thank you for the time, Ryan, and I appreciate it.

Ryan (30:45): And thank you all for listening, and we’ll see you on the next episode.