Name: Lunch & Learn: AI You Can Trust - Inside Your ERP
Uploaded: 2025-06-10T12:30:38.800Z
Duration: 1 h 3 min 28 s
Description: Lunch & Learn: AI You Can Trust - Inside Your ERP

Transcript for "Lunch & Learn: AI You Can Trust - Inside Your ERP": And we're live. Hello, everyone, and welcome to this lunch and learn AI you can trust inside your ERP hosted by AGICAP. Very warm welcome to all our attendees. I can see the numbers are building up readily. Please do say where you're calling in from. Keen to see how international this group is, on such an important topic. Couple of household items upfront, then we'll get through we'll get to it. There is a chat, and a q and a on the right hand side of your screen. In the chat, you can say where you're dialing in from. In the q and a, you can post, your questions as they come up. Plan is to set through a lot of content today, a lot of key topics. So please do, put your questions in there if we don't answer them, during the conversation. For the, delivery vouchers, you should all receive, an email at the end of the session with, your voucher that you can validate. Sorry. This is clearly supposed to be done at the beginning. But, normally, we've had a few technical issues, but you will get your vouchers after, the session. And I think, for those of you dialing in from, non native English speaking countries that you can, click on the bottom right tab and translate this via AI into your own language. I, however, am gonna be speaking in English throughout. And without further ado, let's get started. Hello. Quick introduction to myself. My name is Nat. I head up the partnership team here at Agicap, and I'm thrilled to be joined, on the stage today by two ERP experts of over forty years experience combined, and two AI experts as well. William, this is our webinar together. You, introduce yourself to audience and then hand over to Dudley. Yeah. So, welcome. Hello, everyone. So, my name is William McMahon. I'm the CEO and founder of, Gravitai and, the the ProProduce Co. You will know me a lot from, everything we're doing on Odoo, on the implementation, support, community side as well, and promoting out how, ERP technology inside businesses can help revolutionize business, change business, and, again, get a lot more performance from your business. And to Dudley. Thanks. Thanks, William. I'm Dudley Peacock. I've been in the ERP space for around thirty years now, and, and I've seen multiple, changes as as things have happened. And this is one of the more exciting ones, I've got to say. But, yeah, I'm I've got about thirty years of ERP experience, watched the technology change fundamentally from what was initially rudimentary, accounting or or processing transaction processing systems into what what we have today, which is quite quite phenomenal in terms of the change in in that space of time. So, yeah, overall, I'm I'm, my background is really, working with mid market businesses, and we work mostly with vendors such as Sage, Acumatica, as well as Odoo and other, products like Microsoft Business Central, etcetera. So I've got a a lot of, visibility across multiple products and understanding their journeys themselves in the AI space. So it's going to be an interesting conversation, and I'm looking forward to having a chat to to William and Annette. Dudley is also the author of, a book on this exact topic, AI and ERP, and, all those, who stay, throughout the course of this conversation will be eligible for a free signed copy, afterwards as well in case you missed that on the invite. Cool. And just a little bit, about us, the host of this webinar. For those of you that don't know, I see on the webinar this kind of a mix of of customers and and, actually, newbies, we can say. We are a French company originally founded back in 2016. Since then, we have expanded to 12 different countries. We've got offices in six, and we've got over 8,000 clients, and over a 150 commercial and technological partnerships, including, with both Brilliant and Dudley. In terms of what we do, we are the market leader for cash flow management, and this is what our platform does. Agicap is built on two core principles. Principle number one, as a goal is to centralize all of our customers' cash inflows and outflows in one place via direct connections to their banks for the bank feeds and direct connection to ERPs. So we connect to a full range. I think it's over 16 different ERP systems, directly to pull invoices, both customer and supplier directly into Agicap, which we then convert via a series of rules into expected payment dates to produce a live cash flow forecast. We also connect to other systems to pull in all those non invoiced items to give our customers that centralized overview of all their cash inflows and outflows. Once done, we once we have all the data in that place, we want to help our customers increase their cash efficiency. For that, that obviously starts with improved accuracy of the cash flow forecast as well as, our banking connectivity ensuring money is in the right place at the right time, so intercompany movements or, moving excess cash into interest rate accounts. But then we also have these two dedicated modules to make payments directly from within Agicap, and then also the accounts receivable automation module, which helps customers identify late paying customers and then come up with automated workflows to, encourage them to pay more promptly, we might say. If you're interested in learning more about edge gap, it's completely not the focus of today, but you can directly request a demo. I think, books are meeting directly, with me in the top or the middle top right of your screen. In case that was a lot of information to digest, I can tell you here kind of where Agicap fits into the different array of finance tasks. It's basically these six. I've split the finance function here into kind of some core operations of FP and A, accounting operations, treasury and investment, and tax. ATCAP is a treasury tool, so cash management, cash flow forecasting, and treasury reconciliations are really kind of core to that. That AP automation module and the AR automation module play to these kind of credit control and supply payments aspects, and then you can also do a lot of your budgeting, and financial reporting within ATCAP as well. But we're not here to talk about Agicap. We are here to talk about, AI and what it's doing to the ERP system. To kick off our discussion, we pulled together just some of these stats, that give us an indicator of how AI is already impacting the ERP space. You guys are probably on this call because you agreed or maybe you're looking at buying, a new ERP in the near future, but it's it's probably the most important, system that any finance function, has to buy. For many CFOs, it's their most important decision, the one they wanna get right. And what we're seeing, in the market is that 50% of CIOs are predicting they'll change ERP within the next two years. So it's telling us that AI, is leading business senior business leaders to think that they need to change their ERP. We've also seen this wave of AI based, ERPs emerge. So ERPs not built on, just cloud architecture, but built to be AI native. And as part of this discussion, we'll really get into what AI native means. But the real important thing is that these AI specific ERP solutions are growing, and they need double the rate of traditional ERPs. So, the growth there is really, really big, and you expect that this will lead to some kind of self fulfilling thing where more and more people feel a need to jump on that bandwagon. And then the really exciting thing from, a cost and efficiency perspective is that, these AI systems are supposedly having much bigger efficiency gains than, from usual ERPs. So this is what we already see supposedly happening in the ERP space with the arrival of AI, And we're also seeing different reactions from, Teams. So I think as with all, AI, additions to any kind of workflow or any kind of business function, There's some element of fear. You know? If we don't get this right now, will we be left behind? Will we be stuck with workflows that are completely outdated, and will we lose out to our competitors as a result? We'll be able to provide the same service at much lower cost. How do we stay ahead of the curve? What areas do we need to focus on when it comes to AI? Clearly, there are some tasks already that AI is very, very good at, and there are other tasks I mean, I'm saying this. Anyone who uses AI, you realize that it definitely has limitations still. And then for the individuals, the individuals in in the different finance teams, what skills do they need to pick up in order not to become irrelevant? You hear a lot, about the importance of becoming a prompt engineer. What other skills, should you be focusing on and what should you be deprioritizing? Because AI will will be, eliminating that task from humans' workplace. Then we're also seeing a fair amount of skepticism. I think Dudley, made this point that when cloud came along, there's a lot of pressure to move to cloud, when, actually, from a cost perspective, unless you were international, you might have been much better off staying on prem system for a longer period of time. Will AI result in many moves that actually are not cost effective because of the high, costs of keeping AI systems running? And then the other aspect of that is kind of distinguishing between what are use cases, so workflows where AI really is truly effective, and what's just actually hype and very, very difficult to implement, at scale. So these kind of attitudes are resulting in sort of three, and and potentially more different approaches that we're seeing within the market. Kind of here, we've tried to group them into the skeptics, the ones who are gonna resist making any kind of decision, until the last minute, but want to potentially just, you know, dip their toe in the water without risking having to make major investments into new systems. The loyalists, so those who probably wanna stay within the ecosystem that they already have but want to take advantage of AI and are wondering how they might do so. And then the activists, so part of that 26% potentially already testing new AI AI based ERP systems, and who just wanna get testing and really see what AI can do for ERP. So our job on this webinar or rather, Dudley and William's job, my job to ask them the right questions is to help you navigate this uncertainty, that is coming with the arrival of AI into the ERP space. Here's what we'll cover in today's session. As I said, quite a lot of content to get through, starting with what the right questions to ask are upfront when looking at AI based ERP systems versus, traditional ERP systems. Some kind of functionality comparison, how does what you should expect from your ERP change with the arrival of AI. Then what I think is a really interesting question, is how to what extent should you use AI just to get more from your existing software rather than continuously adding new ones. The people element, critical for any successful use of AI. And then what's the right mindset to have in order to get the most from AI given that you can't just start using it tomorrow. You need to test functionality, feel confident and secure, and then, nail one new case and begin to scale. And then how can you do that scaling? And then, obviously, very important when it comes to AI and all the data sharing consequences that it has, how can this be done securely, and in a compliant way? So without further ado, let's get to it. Thirdly, I'm gonna come to you on this one. As someone who's who's been looking at ERP systems, we've seen the change over over thirty years. The typical life cycle of an ERP is often kind of fifteen to twenty years, but we've seen that how AI is bringing changes really, really fast. With AI changing functionality and systems so quickly, how can buyers really update their logic for the purchasing decisions they make when it comes to looking at what ERP they should buy? That it's it's a I mean, it's a very big question that because, often what happens even when even prior to AI being such a big thing and and as we went through multiple iterations of of ERP versions and and and and software products that were being released or some of the older legacy products, One of the key questions to ask is is more inward looking as in what does the company need? What do we need to do as a company if we had a blank slate, we had a clean sheet, and we started today, what is what do we need to do to, to be better? What where are the bottlenecks? And let's start mapping, the processes of of how we how we work, what we do, where are the places that we're finding, and and ask those hard questions initially. So it's sort of the way we are right now, and it's it's the standard thing. It's where we are right now. Where do we want to go? Where what do we want to achieve as in a company? So a lot of companies change progressively over a five, ten, fifteen, twenty year period, and you're absolutely spot on. So the the lifetime of a of an ERP system is generally, at least fifteen years, if not twenty. And we I have clients that have that have gone through one cycle. They've gone into the next cycle now already, and it's it's been very interesting to see how internally, how they've changed. Additional users, additional company, that they may have bought or companies they have purchased, or there could have been additional products and services, new employees employees leaving. So there's constant change. So it's trying to say, okay. Where am I right now? Where do I need to be? And start mapping out, what where will the real value come in? And and then start saying, okay. What what do I need to implement? What's the best way to go? What is my current system doing for me? What isn't it doing for me? And how does it match my my sort of starting point and my vision, my my future that I'm trying to establish? Okay. Really clear. So, part is really linking it back to the strategy, actually, to the business strategy. Where does the business wanna go? And then, mapping the processes and then identifying the gaps. And just to come to that point there, you know, mapping the processes, I think in ERPs, this is often one of the main, failings is that most businesses don't, map their processes. Effectively, with AI, do you consider that to be even more important now in the sense that, you know, if you're, if you have a process that can be complemented by AI, the the time it can take to do something, I think, of a task like categorization, should be something in Agicap where you can you can do this exercise in three hours that used to take, you know, fifteen hours. Is it is it that much more important to really map your processes effectively, and how can companies ensure that they do do that? If I if I could just give you one one word, and that is automation. So people when we think about AI, we can't just be thinking about AI as in the generative AI if everyone is accustomed now to chat GPT as an example. So you'll type in a prompt, and it will deliver some kind of output. AI actually has got a lot a lot more to do with it than just, just typing in a prompt and getting an an output. It's it starts with an automation. So in at the very base of an automation is a workflow. So if I know what my steps are I need to follow, how do I automate that? That's that's almost phase one of an AI implementation. Phase two of an AI implementation is which one which number of those steps can I combine, automate, and then use additional brainpower? So, essentially, what AI does is is you tap into what they call a large language model. And these days and then I'm not going to go into all the acronyms, but I'm gonna mention one. And it's it's well worth the audience looking this up. It's it's MCP. And, the acronym really is, it's an MCP server. It connects to your large language models, which is a a more powerful brain than your standard chat GPT. So you would have your workflow. You would have the automation. On top of that, you'd have a brain that can do a lot more. So there's things like machine learning where you could take information from other systems, information from your ERP system, and information from customers and suppliers and even external information outside of the company, like competitive behavior, market conditions, market prices, forex values, etcetera. And all of that could come in and could further enhance the automation and add this extra brainpower into that automation. And that's really why it always starts at processing, mapping your processes, automating your processes, and then pulling in the brainpower and that learning ability. So machine learning is really taking multiple transactions and learning repetitive behavior and then being able to predict that forward. So, again, it always comes back down to the for the processes. Yeah. No. Yeah. I would concur on that. It's, like, back to the point of myself is pretty much an activist in AI is, you know, all my life, I've been, programming, learning, IT automations. We've always had AI. We've always had, the ability to code, automated ways, to transition work between different, sections or categories, automate, processes in each, step of that work. And I think it's you know, for businesses, I've always said this and certainly around data, and AI will stand the very same. It's it can only be as intelligent as the information that you really give it. So if you're gonna be if your business business is not ready for AI because you've got, you know, legacy data that is full of pollution and crap, And if you've got undocumented business processes, you know, for your business, you you won't be ready. You won't be ready for AI. You'd probably be expecting AI to try to be intelligent enough to, build this all for yourself. And it'll just model itself off other industry, other business, similar to yourselves. It won't be specific to your business. So getting yourself into that kind of AI AI ready to score, making sure you've got the right governance, making sure you've got, you know, your data, your processes ready, that would be your step to get going. And then, basically, use AI as a toolkit to help you, through that automation. I had a great call with, a client yesterday, and she's, she's a catering company. And I asked her simply what's the biggest annoyance that you have to do every day that, you know, really takes time. And she says temperature checks of her fridge. She has 17 fridges. Said, why don't you just automate that? Why don't you just put in an, a temperature, into that? Automated into AI, whatever you wanna call it, just capture it through API, and then use intelligence services to alert you when, temperature drops. She she was blown away by just the simplest thing. Yeah. Yeah. Great example, I think, of, how you can automate your workflows. Just come back to to Dudley's point there. Yeah, very simple workflow, but just thinking a bit differently, but crucially having your workflows mapped. Nat, if I could come in with one extra point, and this is one of the one of the the, let's say, potholes in in even in any ERP or any, IT implementation. And and that that's really, going trying to do a copy paste of what you're currently doing. When you're changing systems or when you are bringing in new technology into your business, that's a fantastic opportunity. And the good news, really, is it gives you an opportunity to do the spring clean, to clean out, let's say, old cobwebs, old data, just as what William said, any old systems and processes that weren't serving you anymore, that you've literally outgrown as a company. And one of the big potholes that companies often fall into is they try to do a sideways motion. So they might buy more modern software, software with much much more much improved tech, technology functionality, etcetera. But they try to copy paste what they're currently doing into that. So they literally have a sideways motion. I think the good news in this whole this whole discussion is that that you had now have an opportunity to do a house clean. You could literally clean up where you're at, clean up your data, clean up the old products and services you don't sell anymore, clean up, you know, your chart of accounts because I'm I bet I bet if you're a finance person, your chart of accounts is probably outdated. And this is a great opportunity now to sort of re reinvent. And I think this is why it's so exciting, especially for us guys that have been in the industry for so long, that this gives us a new a new broom to clean and to give my cleaning knowledge and to give an excuse and and really a reason to get to really become good at data and data cleansing and data management, going forward. Yeah. Yeah. Great. So, Dudley, if we just, think also about the how buyers should assess different systems now. If you think about AI native architectures, is there a framework that you you could, give to our buyers on how they should kind of assess whether new systems are sufficiently, AI centric because there's clearly a base that you need to do with your own workflows in terms of understanding how our business works and where we could potentially automate. But there's also definitely some ERP systems, that will be better at integrating AI themselves and also, some that will be better set up to allow for integrations with various different AI tools, that you might want to use for specialized workflows. How can buyers go about comparing, ERP systems, and and feel confident that the systems are looking at both secure and sufficiently AI, centric? Yeah. It's a it's really that's really another good question. The the the difficulty with the with AI being such a new technology is the AI native, as in those new products that are coming out, are probably as are untested. And with the with the with the let's call it the rapid changes in AI some week, if you if you're an avid AI follower, you'll know that every week there's something new and something better and something faster. I think a really good example people would remember when DeepSeek came out, OpenAI and everyone else started getting worried because they developed a model that was a fraction of the price compared to the development of the original chat GPT, for instance. So to to give you to give you a bit of a perspective, maybe one should should be looking at it as not AI native, but to start thinking about, what is what is the what is the best for my organization in in terms of making a decision about AI? Do I do I do it by default? In other words, or do I do it by design? So by default would mean I'm just going to stay with my current legacy system that I'm using right now and wait for the software vendor, like a Sage or an Odoo or Acumatica or Microsoft or one of these, even Oracle. One of their these software vendors to to slowly but surely in include in late in upgrades as we go AI functionality. So AI is pretty much the least I would say that the the the one of the least amount of risk is use using your current AI, ERP system. And if your vendor is any good and it's one of the top vendors and they've proven themselves over an over a period of time, what they're going to do is they're going to slowly but surely add AI functionality into the ERP systems. But the question does come, where at at what time does your legacy system no longer serve the AI that's being added into it. So there will potentially be a cut off point where your software vendors who are doing the current products might get to a point where AI can can no longer be added realistically to their current products. And then they might come up with new products that are then AI native. In other words, they would then have learned the lessons. And, hopefully, by that time, AI would have been mature enough to create a really good AI native or AI, centric, system. I suppose it's it's a bit reminds me of the the change from MRP, which is the old in the eighties, we had materials requirements planning, MRP, which is really initially built for your big manufacturing companies, which was to use MRP, materials requirements planning, software to to manage the inventory, the the stock, the inventory inside large, factories and and and, let's call it inventory heavy, systems. Then we moved into ERP, which was enterprise resource planning. So that was now not no no longer just your materials requirements planning, but now it was it was a lot to do more with bookkeeping and accounting and a whole lot of additional functionality like job costing, time sheets, etcetera. And now we're moving into I don't know what the next one is. And I think that's the meaning we're talking about. It's like, what is the meaning of, ERP? Is it really a useful term going into the future? That's a good question to ask. And I suppose by default, the design would be wait and see. My my software vendor is reliable. I've been with them for a while, and they're going to keep adding. There are going to be a one, which is bring your own AI. And this is this is one that I'm seeing a lot is where people are using their own AI, subscriptions. Let's say they've subscribed to Anthropic Anthropics, Claude or even DeepSeek or one of those. And next to their ERP, they are using, their company's internal data and using an external, AI system to analyze, to generate reports, to to to improve their productivity separate to the actual, company system. That's where, for me, that's where the danger lies is is we have your bring your own AI in environment where you have having employees running off on their own and doing things that are good for their own personal productivity, but not for a company as a whole. And then the area really is where the tech, the, the CIO, the the technical people within the organization are saying, we got too many applications. We got too much software. How do we connect them all realistically? How do we get rid of the ones that are no longer serving us? And how do we consolidate what what we need? And how can we bring in something that's that's, that's maybe more efficient for the entire business? So it now moves past ERP, and it becomes a business management system. And that that's the BMS side of things. So I know I'm using a lot of acronyms, but this is where I see the changes from MRP to ERP now to BMS. And I don't know what it's gonna be all about the meaning of life eventually the way we're going. We were we're all out of a job in the future the way it's going. But, yeah, I I would, again, suggest anyone looking as well. You need a you need a solid foundation base right now. And if you're assessing a market, for an ERP system or for an AI agent or AI salute solutions, you have to look at, again, on that foundation base of what you're getting today, but also, you know, where is it going to last? Where is it going to be in the next ten years? Okay. So you have to assess that vendor's, road map. What have they done to date? Where are they investing, into their, systems, that's where you'd be using it in future. A lot of people are running off right now and going, AI is great, and it it certainly gets through business efficiency. You can improve a lot of, inefficient processes using AI. It is a tool. It's another tool. It's another acronym effectively, as MRP, ERP are as well. So you have to see it as that, where you want to be in five years, in ten years. You do expect that AI isn't going anywhere. It's going to be more ingrained in every business. And if you're not starting to at least assess it now for where it would be in future, you're then just you're risking falling so far behind that your your foundation will be, insufficient in future, and then you're having to look at, again, a whole ERP and AI kind of re remigration. I think that's right. You need to be very, wary of, falling behind. If you don't invest, you don't work out where you can best use AI for your business. I would I would actually actually add on to that, just onto the openness that, and bring your own AI, kind of approach is, again, finding that ERP system, or he was a great example of it, is as an open platform and how integratable it is. So if a if a platform vendor is just trying to build their own AI model, I'd rather have it the ability to integrate with other AI models that I choose, that I have the ability in. I think it's waiting for businesses right now to spot the opportunity of where they can use AI within business. That is where it's gonna take off, and that's where it's gonna revolutionize. And having that openness just gives us more flexibility to play, try different things. One model might might work better than another, or there might be another AI tool that solves it, in a more specialized way than what any other independent ERP vendor could actually build inside their system. I think open over closed, leads us nicely into our into our next real topic. I did want to look at kind of, different or best use cases already of AI. But I think if we just move straight into kind of, that open versus closed system question, around how companies can get more from their existing system versus where they should look to add on, I think for functionality, the areas where you you already see, AI doing phenomenal things are kind of the areas you'd expect around sort of supplier invoice management within Agicap product, for example, categorization, the very repetitive tasks, where the data points look very, very similar, and AI can can manage the data at much quicker speed. But one question I think is really, really interesting, and I'm hoping, Dudley, you can you can dig into because it's something that applies for all major systems, whether it be an ERP system or a CRM system. There's always so much unused functionality within the tool. How can AI for you, help teams finance teams really, maximize, the functionality that they have within their existing systems? And then on the flip side, how can they recognize where they do need to invest in an additional tool, and really where assess how how much better those, add on tools would be versus the existing functionality within that tool. Yeah. So it's another great question. So NetApp, for me, the the experience that, that I've seen, that a lot of CFOs experience is really the biggest complaint is that, systems that are being purchased are not fully used usefully utilized. And that's there's a they they use a word called adoption. So, I mean, it depends on on how you look at it. But adoption really is is always at at a at a low point depending on how good the leadership is, how good the software is, and also how good the people are that you've got in your team. So the use of an ERP system has got a lot to do with external factors as well as the internal particular, challenge, to to have a to to understand sort of the the use of, of ERP. But if I if I look at to answer your question, the the adoption is relatively low. So we also have a problem, which is the difference between structured data. In other words, if, you know, most CFOs and finance people would know what a spreadsheet is, so you know the rows and columns. And that in in in any ERP database, in in the current database systems, we have what we call relational databases. Those are very much flat databases. They're two dimensional. So you have rows and columns. And and in ERP system, you have lots of spreadsheets essentially, and they're all linked by a common ID. And then when you're typing in and you're doing your invoicing and you're doing your bank reconciliations, it'll call back and put and post to these different spreadsheets in the background of your ERP. The beauty about AI is that AI can work with unstructured data. So if you think about your CRM system, your emails, you know, if I just look at my inbox, I'd I'd I'd dread even looking at my inbox every day, the amount of hundreds, if not thousands, of emails that come in and enter an organization. That's all unstructured data, but actually very, very useful data because that's going backwards and forwards between customers, suppliers, the company, and different, you know, different conversations. If it's a sales conversations all the way from prospect all the way through to close, to post close, to customer service, to customer onboarding, etcetera. So there's a ton of unstructured data in an organization that's also not being touched. So we have we have these two types of things. The the the use case there for me is the improved adoption of your accounting system or your ERP system will be, better if you can link that with your unstructured data. Unstructured data in your CRM and and other systems if you've got an ecommerce environment or a warehouse environment. If you could bring all of those datasets together and make more sense of them, you're automatically going to be using more data. Your users might not use the data directly, but they could be requesting useful information that is being processed via a very powerful brain to give them better, information to make better decisions in the organization. So what AI allows us to do in machine learning and the connectivity between multiple systems using AI as a backdrop, the adoption rates will go up, the ability to, to process tons of data in a meaningful way. And bringing all of that together will automatically increase the the the adoption and and not not the adoption, the usability of your your system as well as the unstructured data. And I'm gonna add one more point. And the and and where it really is very, very useful, that's a powerful use case, is we're using in in our AI models that we build, we use vector based databases. Now there's a difference between a relational database. In other words, lots of spreadsheets with connected by an ID. We have a vector database has got multiple dimensions. And in the current AI models, we've got, the the one of the models we've been building out this week has got a 596 dimensions. In other words, if you think of it's two dimensional as a spreadsheet, imagine having 1,500 odd dimensions or ways of looking at your information. And so so what we're doing is we're now finding that by connecting all these different systems using AI as as the brain, we now have a oops. I think I I got a I missed. I missed I I lost you there. Okay. So we now have an incredibly powerful system that allows us to connect structured and unstructured data. I think we've lost Dudley again, have we? I think I'm I'm I'm still here. I think I'm back. I think I'm back. But I hope I hope you caught that last bit. So it's it's literally to try and understand the the using different types of databases across multiple systems is now with AI being able to go through, loads of data more than a human being can do it. I think it's going to open up the opportunity of ERP and all your other systems that are in silos these days across your your company. Yeah. No. Yeah. I'd, I would say the same is just exactly to that same point at the beginning is that, are we getting more from our existing stack, versus that add on? And, Dudley, your description of the Ferrari in the garage, I've used the very same thing many times. Is does your business need a Ferrari to go to Tesco's and do the shopping, or is a Volkswagen Golf more suitable? So it's trying to find that suitability, and try to utilize, as much of what you have at the moment. A lot of businesses are still, Excel based and post it note, processes. How can you just simplify that, into a more automated way? How can you, reduce down the effort on that person's day to day, by simplifying the process or automating that process, utilizing more of your your stack? AI is evolving, so we're only really I would say, at the very beginning, of it, but you're seeing it evolve week by week, release by release of each model. It's got a lot of use cases. I'm heavily using it, ourselves within Gravitai for, like, I've done a, an employee three sixty, leadership three sixty, feedback, for myself, and I've used all of that feedback to help, with the use of AI is to consolidate all of this, feedback that I've gotten from all of my team and start building out action plans, building out, synopsis as to, where the business is at. It's really that kind of, utilization of the platform, has benefited my use case. Other cases that I've seen, it's a benefit, in in, businesses. Again, if it's done properly and you have clean data is, assisting clients when they ring, when they call up to the, to your customer service, help center is that the AI cannot have all that information about their last order, what they ordered, is there any issues, and the AI can handle it instead of a human. So there's a lot of queries, common queries that we can automate and alleviate from many people's day to day. I think, clear then that it all, starts with processes. An interesting, take from you, Dudley, on how you can actually use your ERP completely differently by taking advantage of the unstructured data in a way that previously was just impossible to imagine. Spencer, thank you for taking it upon yourself, to kick start the q and a. I think I shared at the beginning, that more and more of us should be posting in the q and a, please. If you do have similar questions, put those in the q and a, and we'll come back to them at the end. I'm sure, William and Dudley have a good answer for that question, Spencer. But we'll just keep moving forward onto, the next question as I think, this is one of the most important aspects for any, change, but particularly with AI where often so many people can be scared of the impact it has on their job. I mean, it's been a big theme, from our discussion and from the discussion everywhere on how people feel about AI, that kind of mix of fear and skepticism. How can CFOs overcome skepticism, fear, and inertia to drive impact in terms of the processes that we've talked about, but also, ensure that their team have bought in. William, maybe maybe, you can answer this 1. Yeah. Yeah. I I again, you have to manage people through the change. Again, identify your AI champions and identify the people who, have that appetite ambition to want to learn and to want to to, take on new tools or technologies or hungry for solving problems and give them, the backing, the support to be able to, learn, utilize, and pilot things. It's really identifying those low hanging fruits. Like I said earlier, the the temperature check of all of those fridges, is finding somebody who has that appetite that, yes, you can implement kinda digital solutions to solve a very manual mundane task. And, just put each one into simple pilot, identify the, business value that you're getting, in this, temperature check. It was saving half an hour a day by doing that. It relieves a lot of pressure and even a compliance risk. So there's a lot of business value, a lot of justification of why you could do it because then the investment is actually, a lot lower. It's a it's a worthwhile investment doing it that frees somebody else up to then start investigating the next thing that they can automate or solve as well. But really try and find those champions. You could also describe it as we do it quite a bit of hackathons. You know, just bring people together, that have like minded, ambitions to wanting to solve, improve, optimize, business processes. Bring them together in a even a committee meeting every two weeks, every month, whatever suits your business, and, just go tackling problems. Drive it through the change, from the top down. That they have to see it that you're behind it as well, that you're not resistant, to such change. Interesting. So I think, again, you're really hearing the the importance of having mapped your processes effectively, and then it would enable you to empower that AI champion, that person who wants to solve the problems and take that kind of, responsibility for for themselves within the business. Okay. There's another another practical side of that as well, and I'm sure William would probably have experienced this too, if if I may. And that that is, the the the, often the shortage of number of people in the department. In in a finance department, often because it's more of a cost center than a than a profit center in a company, they're often running very lean. And what happens is when we start having conversations around, here's a here is a new function, here is a new way of doing it, stop using your spreadsheets, do all these additional fancy things, They literally say, I don't have time. I don't have the, the ability. We just don't have the flexibility in our day to sit and think through what's how do I how do I improve. And one of the one of the ways and even coming probably coming towards Spencer's question too is why are a lot of lot more companies not adopting this, new technology? Why aren't they at least looking at it? And even in the bigger companies, it's actually even a bigger problem because people are are are sliced so thin. And that is to have the standard project mentality. It's got to be dealt with like a project. So you need to resource it. You need to apply people to an additional person, additional people who can spare up time to look at how to do particular AI, or automation functions or workflow functions. But it comes down to a business analyst role. So somebody literally sitting there mapping the processes. And and I I've seen this this unravel many times where companies say, Yeah, we want a new ERP system. We bring in an external Business Analyst and they spend one and a half months trying to map processes and then they still don't have a clue of how the actual business works because a lot of it is done on autopilot. So, a lot of it's inside the heads of people and that they just do it every day on autopilot. So, managing people through change, we've got to understand a lot of the intellectual properties often sits inside people's heads and not inside the systems we are trying to change. So it it is definitely the human condition, but it's also it's a legacy thing from the past where a lot of systems weren't able to cater for the functionality required by a company. So it's it's the it's the leadership function now to say, okay, how important is a change in our organization and how much resource and people and and and brainpower are we willing to invest in this process and then being able to take them to to at least the next step up. One of the the beauties about and I'm just gonna I'll finish there is that once you've got at least one or two AI tools, installed, Instead of, instead of, typing, you know, long doc documents, you could simply write a chat prompt. And I think, Nat, you mentioned at the beginning of the of the, webinar is that we have a chat prompt. So imagine you're able to use your accounting system as a as a chat GPT internally. And you can type in a message, to prompt your ERP or your entire system to deliver an output. To show little mini use cases like that, help people understand the change, and how how much easier it is to prompt something. Just ask it a question as opposed to doing endless amounts of research and trying to manipulate data on a spreadsheet. That's that that's a bit of extra, input, that that I can see about managing people through change. Super clear, both of you guys. I'm very conscious of time now. We have slightly overrun. Maybe, we were a bit too ambitious on how much, we should get through. So I'm gonna, move to one final question, and then, I'd like to get into the q and a. So, guys, please do, post questions in the q and a if they've been coming up. I think the question I'd really like to get into is the the scaling of our use case. We've talked a lot about kind of the importance of mapping your processes there, how you can kind of speed up the mapping of your processes. Already, we see in finance lots of use cases like supply and voice management, reconciliations, stuff that AI is already helping out with a lot. I think it's easy to identify those different AI use cases. It's easy to potentially, use Gen AI in your day to day to increase your own personal productivity. But how do you build an enterprise wide system to do this, and how do you scale AI in a cost efficient way? I think, William, maybe do you wanna start with this one? Yeah. I think, Godly mentioned it on to this as well. You you do have to kind of have, capacity within your own business. Like, if you're trying to take it on yourself, in your business internally, you have to have, the capacity to be able to, run it as a project. And a continuous optimization project is you know, this this is gonna be a multiyear kinda optimization piece. So you then likewise have to have the right team, in there, say, heads, who are specialized in each department area, who know, what they want and, you know, they're all bought into this, same kind of journey, of what they're aiming to get from, ERP or AI, cases. So that is all well and good in me saying it in theory, of course, because it's ambitious. Nobody has that amount of time in their day to days to just allocate two or three days a week just to spend on a an AI project and do the business analysis and build it. So working with special partners, experienced partners who can come in and, work with the business to understand, to map out the flows, to just understand the as is and where they are going to be in the to be and see it as a partnership rather than a project, because, again, this is evolution over time. You're going to, improve, on what you're doing today, but next week, you're gonna be improving on what you've done last week. So it's continuous evolving piece, and it's ensuring that you have the, you could say, the right, focus, the right team, the right commitment into doing that. And, like I use work with, expertise in the in this area of, field because, again, a lot of businesses aren't software engineer developers or, tech firms that have understand this. They're manufacturers. So use your your, your own professional knowledge in the area of your expertise, but work with partners in, sharing your expertise in how we can then build it into a technology solution. Great answer. Thank you, William. And, Dudley, give me one more minutes to finish off. Yeah. One one quick comment. I won't I won't take too much time on this one. Is is really what we've recently done the way the way to build this out across, a company wide, type of project would be to use AI to your advantage. So what what we've built is we know the questions and and we've accumulated this over a number of years, and we also built up intellectual property within our organization. So in the let's call it the older days, we used to go in and do what they call a business requirements review and and understand all the business requirements. What does the business want? Where are they going? Where are they now? What are their sticking points? Where are they finding problems? And then the other one is a functional requirements review. In other words, thinking about what functionality would make your life easier. If only you press that button, what what should it do? And so the business requirements review and the functional requirements review used to give us all the answers to then, suggest the right, the best fit software, the best fit implementation plan, whether it's one phase, two phase, three phase, and so on. And using AI now, we can use the meetings that we have, the business requirements review meetings or the functional requirements review meetings. We collect all those transcriptions, and we've trained our AI models now to to generate a lot quicker the best fit solutions, the best match between software functionality and the business requirements. And we're able to do it at three or four times the speed at which we used to be able to do the BRR, the business requirements review, and the functional requirements review and come to a really, I I would say, even a superior answer now that we couldn't do before. And a lot of companies could do that internally as well. They could use transcriptions from meetings where they're getting stuck, maybe have just specific meetings, and then let the AI develop, an outcome for them. So that's that that's really where AI is coming in very handy for us as an implementation partner for our clients. Great example of AI. And we just had pop up in the q and a, one of the questions I hoped would be would be asked, which is that AI takes a lot of investment. Why wouldn't a CFO just trust a NetSuite or a Microsoft who have the resources, to fund this AI development? Dudley, maybe do you wanna take this one again? Yeah. So so they are. And, and NetSuite, and you can look at across the board. Microsoft, they're all bringing in Microsoft's bringing in their Copilot, NetSuite's bringing in their own version. But what they're doing doing is they're using legacy system as the base and they're building it for 80% fit across their entire customer base. So what we've what we found over the years is that a lot of companies are unique in their your their requirements. And that's why when we when we go through a bit, an implementation with regardless of the software, we look for the best fit software, and we generally get an 80% fit regardless whether it's NetSuite or or or Sage or Acumatica or Microsoft or any one of the other products, SAP. We generally never get a 100% fit with any software for any organization. So we'll get a sixty, seventy, or 80% fit, and then it's all about budget, how much money you're willing to spend. So all of these companies are incorporating AI into their ERP stacks. However, they are building it for the bulk of their of their customer base. They're not building specific use cases. And if any of you, you know, this is my my my favorite one is the is the Pareto principle, the eighty twenty. It's 20% of the things make 80% of the difference in your business. So you're gonna find that a lot of the AI that ERP companies are implementing are a great fit, and and they're good for most people, but they might not be great in terms of taking your company even much more forward in terms of its competitiveness and the competitive advantage, which is what you're really looking for in in your in your system. So, yes, the easy way and the and and the way the safest way is let your software vendor bring AI into your system. Great. You're gonna have a 80% fit, the balance, the 20% you're always gonna have to build out yourself. And that's really where the external AI tools are helping us a lot to take it from 80 to a 100%. And I don't know if that that answers your question, but that gives that hopefully, that gives you a bit of an idea in terms of matching customer requirements to update. Thank you, Dudley. I think, we did lose you, but we got the, the bulk of the answer, and it was very it was very clear. Thank you. We are now overrunning by, two seconds, but I just want to quickly answer the previous question that came up, which just now appears to have disappeared. And, Spencer, is it true that many large organizations are hiring dozens of AI developers and that they have nothing to do? William, would you know anything about this? This is not something I have heard, but potentially is it? There is a lot of recruitment going on around, AI, developer hiring. Likewise, you know, there's I'm talking on here at Salesforce. There's switching of, developer roles from, you know, traditional development into AI development. Developers, themselves, again, AI is almost becoming a developer, for developers. So, the need of, kind of senior developer is simplified. So there's a lot of roles that are, like we mentioned earlier, is like a prompt engineers, test engineers, people who, are developers perhaps, but, they are able to utilize AI, to, prompt and develop and do the testing, and they know, how to, you know, they're kind of more functional kind of, functional developer engineers that can expand the system through, using AI prompt and then be able to test what, outcomes, outputs the AI has given them. So, I that's what I'm seeing. I don't I've if people are hiring people to do nothing, it's I might apply for a job there myself, but, I think, yeah, there there there's a lot of investment now into AI. Certainly, in The UK, government's, backgrounds and, government back initiatives are bringing, The UK into, being an AI powerhouse. I think it's going to be a a race, to the to the top as to who's gonna be the, new Silicon Valley of AI as such. Yeah. Okay. Cool. Guys, we will now sadly have to bring this, this session to a close. It's been a great discussion. Thank you everyone for joining, and, you will receive a recording. And if you want to learn more about Agicap, as I said, you can click request a demo to write. If you wanna get in touch with either William or Dudley, they're both very active on LinkedIn. I highly recommend, you add them. Each of them, has their own content, and you will, as I said, receive a copy of, Dudley's book on this exact topic where he will go into much more detail, on all the questions that we've covered briefly today. Thanks very much, guys. Excellent. Thanks thanks a million. Talk to you soon. Bye bye. Thank you.