Welcome, everyone, to applying AI in the real world, but specifically from the manufacturer's lens. So I'm really excited to walk you guys through, what we have in store today. This is my favorite topic, so I can go very deep in the technical and get into the weeds of everything. We can save that for questions at the end. Happy to do that. But I'm gonna try my best to, you know, hit it pretty high level. So this way, everybody can kind of stay stay in the loop as as we make our way through this. But a little bit more about me. I started as a technical architect and really as a I like to say a recovering software developer now. I have been programming really since I was sixteen and founded my first AI company over a decade ago now. So even when AI wasn't cool back in those days, I was just the nerd who was doing computer vision with IBM Watson. But now AI is mainstream and all the raves. So fun to be the cool kid in the room now. But really focused heavily on the technical side for a long time, made my way into the Microsoft space by chance, and then turned out to be really focusing on enterprise technology and deploying large scale enterprise resource platform, or, like, very large companies. So one of the implementations that I did was over a hundred and seventy two countries. So in ERP world, that's a hundred and seventy two companies, which is a very large scale global rollout, for those of you that are that are familiar with, ERP enterprise resource planning. And doing this, I realized, doing this for a hundred billion dollar organization, that we were able to do this very fast, do go lives in about a month, and be able to do this for, you know, multibillion dollar divisions within this company. And I thought if we can do this for very large scale organizations, like hundred billion dollar companies, and as fast as this, then why can't we do this for the small and midsize worlds? And that's really when I started focusing in the SMB space and founded Volt Technologies really to service the mission of enabling millions of small and mid sized businesses with cutting edge technology. So while we are a top tier, inner circle partner with Microsoft, we, we really are agnostic from the AI lens. So we implement products like Claude, Chutch PT, Copilot, really all of the best in class models. And I'll kind of talk a little bit more about that as we go. But just know this is more than a job for me. This is my passion and and my hobby, and really everything that I base my person around. So, let's start with, you know, really the main reason why I'm here talking about SMB AI. I firmly believe that AI is not just for the large scale enterprise anymore. It used to be. In order to build out machine learning models that were fine tuned based on your specific need, it took lots and lots of money to get this enabled. You used to have to train these things with large amounts of data, and be able to fine tune it for your specific process. It's not like AI is actually anything new. It's been around for a long time. It just was very, very challenging for you to have a machine learning model that does your specific process. Now with these large tech companies investing lots of lots of money into into the data and into the technology, Now this stuff is available for small and midsized companies to do very fast and very affordably. So this isn't just for these larger enterprises anymore. It's now here for SMB manufacturers. So with that, what I wanna cover today is I wanna get to the real meat of stuff right out the gate. So I wanna talk about the scenarios that are case studies from our customers, and then also just use cases that are out there in the market department by department so you guys can get a flavor for what's possible and what some other small and midsize manufacturers are are doing that's out there. And then I wanna walk you through how we educate customers on the levels to AI and how you can actually start to take advantage of these things, the things that you can do today, and then the things that you can lay out the plan to do tomorrow. I wanna make sure you guys have a framework for getting to your a return on your investment. I wanna talk about the Microsoft framework, how you can do this within the Microsoft stack and then other technologies that that sit out there as well, and then how you guys can leave here and and get started. So a lot to cover. I do want this to be, you know, a a dialogue. If there's questions that you all have as as I work my way through these, I would love for you to raise your hand, ask questions, and I will open it up in the end as well to get some questions from you guys on specific processes that if it's possible to automate or, know, your your pain you're currently experiencing today. So some real world scenarios. These are things that are going on right now that customers have implemented or or on the verge of implementing. And the first one here starts with a tortilla distributor. So this company, they do a lot of shipping out of tortilla to tortillas to mom and pop restaurants and grocery stores. It's a heavy amount of, know, palletization, and then driver goes out, drops off the tortillas. Someone at the back of the restaurant hands him a bunch of cash and then brings it back to the main distribution center. You have three AR clerks who half the day, they spend receiving the cash from the drivers and knowing what customer it's coming from, and they're writing all of this stuff down and putting it into an Excel sheet. So they are basically managing a whole lot of cash for about four hours of the day at the beginning of the day. So they're collecting it. They're processing it into the Excel sheets, and then they're recording it down and actually putting it into a a spreadsheet of of, you know, what customer this cache came from. But then on the application side, what they have to do, because they don't know what invoice this cache actually applies to from this customer, they have to search through their emails, that are coming in lots of different formats from salespeople as well as the customer that are coming in different languages as well, both English and Spanish. They're going through call logs of where people dropped off voice mails or sent text messages to be able to look and see what invoice does this cache actually get applied to. And they spend half their day doing this, Basically, archaeology, if you will. They are going through all of their files and trying to find where this this cache gets applied to. And so what we were able to do is build out an agent that goes through all of the context within their organization. So all of the emails in all languages and all file formats, all of their their phone calls that are transcribed and text messages, and determines what invoice that a salesperson said, hey. This customer gave you this cash. This is the invoice that that applies to. And it matches up all of those invoices for them that they have in a file. It gets loaded into their ERP and settled. It turns that four hours for each AR clerk a day into just one hour per AR clerk every single day. And so this is a huge time savings. It's over one year of processing time that we saved, and we built this agent in thirty minutes for them. And then it took us a week to fine tune it and make sure it was right for the scenarios. So that's real ROI. They now are able to spend their time focusing on going and actually seeing where they're they they have age receivables and going and trying to focus on collecting cash instead of doing the manual, you know, recording of of the process. So this was an AI agent, which is different than just inquiry, like type like putting a prompt into ChatGPT or into Copilot and getting a response, but an agent that is actually performing real work on their behalf that has context, it has access into their different different toolings like the m three sixty five suite or Microsoft three sixty five suite, which is Outlook, Teams, Word, PowerPoint, etcetera. So this was not possible before. Like, this is literally an impossible task when you're trying to build this from a deterministic code standpoint, point. To go out and find in all of these different file formats, all of these different languages, all of these different possible sources of data to write a script to go and programmatically pull these invoices, it was impossible. It could have come in so many different ways. But now with LLMs, this type of computer processing is now possible, and it's it's providing real impact. So this is a good example on the finance side. Some other examples on the finance side, that that we've seen, three way match for, you know, the purchasing side. So you've got your, your invoice that's coming in from your vendor. You have what you're physically receiving. You have actually, you know, what you're what you're gonna be paying them. You have your PO that you place with them. You have really almost like a four way match that you can do if you scan in your your warehouse documents. And this match can be done with an agent and alert you when there's thresholds that, hey, maybe there's a tolerance that you accept, a small variance, but when you go outside of that, it alerts you on it. Same thing on the general ledger with having anomalies detected. So if certain accounts aren't supposed to be hosted within certain windows or you're not supposed to have certain transactions flowing from a certain customer, you can describe these rules and these policies and then have an agent that reasons over your GL data every single day to surface these anomalies to you. And these are things that you can build by just talking and having a transcript of saying the things that you look for on a daily basis, and then you run it on a recurring job every single day looking at this, and it exposes it to you. So this is not very difficult stuff to build out. Again, it's about building it and then doing the change management of getting it into your process. It's way easier to build. It's much harder to deploy. Cash flow forecasting now within my own business, this is a good example. I can say project out the cash that I'm gonna have a week from now, looking at all of our expected receivables, looking at previous cash that's coming in from customers when they typically pay. It can analyze all this data, reason over it, and then give you a forecast out in the future so you can have a good picture. You can change it based on the statistical model that you wanted to use and get pretty advanced with us, but this is now now possible. Right? This used to be something that you had to fine tune an ML model, you know, for to be able to do this machine learning model. So now it's available by just transcribing what your pain is and then attaching it to other platform. So that's on the finance side. On the sales side, this is a huge scenario that we have as an off the shelf offering from Microsoft, and this is actually a sales order agent. And what this does, it monitors an inbox It's a share it can be a shared inbox. It could be a personal inbox, like sales at your company dot com, and looks for when people are requesting for items or sending it a PDF saying, here's my, you know, purchase order confirmation with you. It's able to look at this detail and then go into your ERP, look at what the pricing is on that item that they're requesting, looking look and see if there's any volume discounts based on the amount of items that they're, requesting, looking at that specific customer that's requesting it, and if they have a contract with you to see if they have specific pricing. It pulls that information, builds it out into a quote, we'll send it back to them, and then they're able to then say, yes, let's move forward with it. It'll go back into the ERP, turn that quote into an order, and then you're able to move forward with your processing, like your warehouse, the actual production of the goods, and really turn this around. So this whole inbound order creation process that used to take you know, a human monitoring an inbox, looking at these order confirmations coming in, and then manually typing out the items, checking the quantities, checking their prices, building out the quotes, sending it back to them, that whole interaction now just gets surfaced as an approval where, hey, I can move forward with this quote. And this is what that looks like end to end here. This again, this is a off the shelf offering. This is something that's already built by Microsoft and supported, that you have a request that comes in from a customer. It goes into the the agent, which is that middle section there. It adds a a task for you to review. You then, as the human on the very right side, these are just gates that you can set up. If you wanna have human in the loop, you don't have to. You can let it run if you'd like, but it'll go create the quote. It'll send back for review. Do you wanna send the quote to the customer? Yes. I do. It sends the quote. The customer will review it. It'll come back through. It'll say, yes. Do you wanna create the order? It creates the order, and it sends the order to the customer. So really this end to end flow, you know, is where we can now have an agent introduced that handles these scenarios. And I know the big thing is, well, I have so many different procedures of when I have this customer request this, they like this file format and things like that. This is the off the shelf offering. We're able to tailor this as a partner that's specific to your needs as a business. So let's say that you don't receive all orders in this way. We can fine tune this to make sure it's right for you. Or if you never do quotes, somebody says they want something, you're placing an order for them, you know, we can fine tune this stuff specific for you. And so some other use cases, you know, that we've seen, looking at customers when they should be surging demand, like when they should be reordering typically within the year. You can analyze your sales history and pull out where customers should be reordering, or if you're, you know, doing a renewal basis, this is a great area for analysis. You can do follow ups on quotes that you've sent, so you can have automated emails that come after your quote gets sent where you are pushing them to to give you the feedback or if they want any iteration or revision to the quote. And then lead qualification agent. This is one, you know, that that we do internally when something comes in, when it it hits into our that Jackson manages all this for us, our marketing engine. But it will go and look at various sources that are out there about the recent news from this customer and or from this prospect. It'll look at their their surging intent of if their people have been searching online for Microsoft solutions or for, platforms that that you guys sell. These these things are able to then qualify and enrich that data automatically and then share it back with the salesperson. So this way, somebody is able to drop their name into a lead form on your website. It searches through all their recent information, gives them a profile on that customer, and then they can a salesperson can make a call to that customer with all of this rich data that says, oh, yeah. We interacted a couple years ago at a trade show even though that person wasn't maybe employed at that trade show or at the time that they went to the trade show, pulls that data from the CRM. So you have this ability to really enrich leads as they come in and build a better qualification engine. On the purchasing side, this is again another Microsoft off the shelf agent that is able to capture AP invoices, so accounts payable invoices as they come in. It's able to use OCR, which is Optimal Character Recognition, which looks at all of the the image or the the PDF and scans the text from it, so then it can create the invoice within your system. This has been around for a long time. I worked at a company back in twenty fourteen that did I was a concur group competitor and did OCR. We had tons of people scanning in receipts for accountants. But what's different now is when that data comes in, it actually analyzes your chart of accounts and your transaction history and understands where should I map this to my my general ledger. Like, where what account should this AP invoice line item now map to? And that always used to take a human looking at what that transaction was and, oh, this is a shipping charge. I have to put this to freight, and they always had to do that mapping. That is now done within this process, and then you're able to bring in that invoice and then post it. So this is what that flow looks like. You either get an email from us, a outside vendor or your employee. They forward it in. That email comes in. It's a notification. Again, you can set that up if you want to. You don't have to have human in the loop here. It extracts the invoice information, identifies the vendor. If there isn't a vendor, it can create the vendor for you. It'll then draft that invoice in there, and then it will post and and finalize that. And then now, the latest capability is the three way match, that's being released off the shelf for Microsoft. Another one around inventory is aged inventory. So normally, you have a report that's looking at all of your inventory that's on hand and saying, hey. This has been out in the warehouse for this long. You know, like, why is this out there for this long? Let's go look at this stuff. You'd normally have some sort of report that's doing this. So all of that's still able to happen where you're reviewing aged inventory. It's giving you suggestions around should you mark this down in price, should you actually write this off if this has been out there for too long, and maybe it's even expired. But the key difference with now being able to do it with an agent is that bottom right section down there. It can actually go out into the market and look at your ERP data or your your business data and understand why is this product actually sitting out here. Did this, you know, does this customer that typically buys this, did it did it go bankrupt? Did they go bankrupt? Is there no more demand out there in the market for this? So it's able to apply, you know, the human level reasoning on top of what you would typically get with a deterministic process of reviewing aged inventory. So this is a a very powerful tool. On the manufacturing side, a bunch of them here, obviously, for manufacturing time. AI assisted build material creation. So this is a really great one. So I have a new product that I'm wanting to release, and I want to actually instead make it red, and I wanna add a little more loving care when we go through a process of maybe the inspection or when we add in a additional piece to the product. Right? And so you can describe this in a way where you can say, I wanna add a product that's similar to this other item and ins but I wanna change these route operations on it. I wanna have longer times here for more quality inspection. I wanna add in different colors, and I wanna add in a little piece to this. It's able to analyze your other bill of material data and your route data, so the the operations that it takes to complete the finished good, and give you a bill of material and a suggested cost for this based on previously created bill of materials and previously performed route operations. So this helps dramatically with product inception, product life cycle management, allows you to speed through that testing and iteration of, should I even move forward with this this product? Let me what if we created an item like the one that sold really well last year, but we added this extra widget to it? And then it comes back and says, that's gonna be way too expensive. Right? It helps you iterate because you can get real changes in real data based on your business data. So some other ones, subcontracted production management. So when you're working with third party manufacturers that maybe they perform a specific operation against your your good, maybe they finish it, they they gloss it, or maybe they embroider it or something like that, then you're able to actually have the agent manage the interactions with that subcontracted vendor. In this way, whenever they're emailing you updates to it saying, hey. We completed this these set subset of items. We're gonna be sending these back to you on this date. You can have an agent monitor that inbox and update the production order line items for you in real time. So this way, you're not having humans manage that process, you know, going back and forth and being that in between there. So it can coordinate that that whole end to end. One of the ones that I love is the voice enabled shop agent. So this is just a use case. We don't have anyone that's doing this right now, but I implemented a lot of voice to pick solutions in the in about a decade ago. And in kind of the old days, it's not a it's not a new technology, but something that, you you have a headset where you say, I'm picking item one two three from bin three two one, putting on conveyor a b c, and it puts that into the system and actually marks that pick as complete. Now you can do this with a language model, and it can reason over the things that you're saying. You don't have to say it in that specific order for it to capture it. You can instead say, hey, I'm done cutting item one, three. It's gonna be, you know, square feet x, and it can report that as finished for you. Doing it hands free with your with your teams is, you know, a great idea. So a really great use case for natural language use within the shop floor. And then quality compliance. So you can use things within your ERP, like production order data and route operations, analyze where there could be nonconformances, where there has been previous nonconformances, and proactively surface up quality metrics around, hey, maybe this specific person is working with this product. They're new. Maybe we need to do more sample checks or more quality checkpoints with those items that are coming off that line. So you're able to look at the data and be able to analyze proactively where you could have some some quality nonconformances. So I just hit you guys with a ton of scenarios. There are a whole lot more. These are literally just ones that, yesterday, was just transcribing the different ones that are possible and what we're doing with customers. Gonna open up in the end as well and ask you guys questions on, like, what areas would you like to automate, and I'm gonna help kinda coach you through that. But this is how you should approach AI. You shouldn't be thinking about, alright, how do I immediately get to all of those scenarios? Because I promise you, if you're running on paper today and scanning out, you know, manual paper based job cards and, you know, you've got basically paper aged AR reports that you're running everything on, you're not gonna be able to jump to the autonomous agents yet. You're gonna be limited by what you're able to do based on where you are at within within these levels. And so the levels are one, two, three. You start with foundation, which is getting your data right, your people right, your process right, and the technology right. And then you can level up to level two, which is inquiry AI and skilling of your people. So this is, you know, where you're you get the chat GPT and Copilot, great answers, and great reasoning from your data. And then you can move to full autonomous, which is a lot of the scenarios that I was just going through, right, where agents are actually performing actions on your team's behalf. So I'm gonna break each each one of these down because they're really important here. And you should you should try to progress in this way linearly and, you know, take take each step as it comes. So the first level is level one foundation. So getting your data right. If you put an agent on top of your data that is let's say it's your your your production data and you have incorrect lead times for all of your items, or maybe you don't have lead times for all of your items on how long it takes to produce, and you say, how long is this gonna take me produce? It can hallucinate, and it could make something up and say, it's gonna take you two years to do that. Obviously, that's not true, but it's because you don't have data that it's relying on. Data is the gold of this new intelligence era. So getting your data right, having a single source of truth around where this data is coming from makes it much easier for the AI to adjust and and reason over. And having things that are, like, clean and accurate, just as it was just as important over the last thirty years of writing deterministic code for processes within businesses, it is just as important now, but but even more so because now you're letting agents run autonomously on top of that data. Humans have the ability to say, yeah, that's it's not gonna take two years for us to produce that. It should only take us a week. But AI doesn't. Right? It's basing it off of your data, so you have to have that clean. On the process. So just as it was important before to document what you do and have standard operating procedures on exactly how your business, you know, runs, it is even more important now. Because if you don't have these things documented, if you're running on, let's say, just whatever Nancy whatever's in Nancy's head, right, around how you guys should plan, or whatever is in Bob's, like, tribal knowledge around how he sells to customers, that makes you a major risk for that person retiring, you know, like what was brought up initially. And, you know, this having this this process documented, you can give this to an agent, and it learns immediately how to perform that process. So not only is this important just to operate your business and to, you know, be not as vulnerable in the future for having bottlenecks with people, but also it's important now to have this as the highway that these agents are able to run on, by having this very clearly documented. I'll get into how we can do that. How am I doing on time here? Alright. Got thirty minutes. Good. Halfway. And and then on the people side, you have to have your organization understood. You have to have your your departments understood. What is the purpose of this department? What is the objectives they're trying to achieve? You have to have all of this documented, and this way that your agents are able to understand how you're compartmentalizing your business and how to operate within that environment because every business is unique. And then the technology side, so ensuring that you have systems that are accessible by AI. If you're running on, like, a green screen, a s four hundred, like an old school IBM I series or something, and, you know, you have to use function keys, there's no concept of a mouse, AI is not gonna be able to operate on that. You have to have a modern solution that is accessible by AI, having modern APIs and even MCP servers that are available for these AI agents to be able to access and perform actions. So it is very important to have this core foundation of technology in there that's accessible by AI. And this is what you should be focusing on first. Before you start really diving into a huge agentic project of deploying it for all of your businesses, this is what you should be focusing on first and getting right. Because this is really the foundation that everything else relies on. The level two is getting into upskilling your team and building out inquiry AI. Using AI now, your team should be ten times faster at retrieving information. You should be able to ask, what's the available inventory at of this item at this specific location? And have that answer instantly. If it takes you a day or going out on the warehouse to actually figure that out, you need to go back to level one. Right? You need to you need to start working on that foundation, having that data right, having everything right. But with these tools, like Copilot, ChatGPT, Quad, a lot of these modern LLMs, they all plug into these core foundational technologies, modern ERPs and CRMs, to be able to answer and reason over that data that you have available there. So you're able to now ask questions like, what's the top ten slow moving items that I have? And it will go through reason over the data that you have, build out a report for you. And not even just that, you can say, I wanna create a document from this that shows all my executive team our top slow moving items. So not just the text that comes back, you can have actual artifacts that are created using these technologies now. You know, the the basic questions around what customers, have outstanding invoices. Who is our largest past due customer right now? Building out all of your aged AR reports. You know, things that used to come back as just big long text. Now you can nicely format these things and have these these available as reports for your team. And so getting level one right, having the data right, the foundation right, your processes documented, your team understood, your people understood, you can now very easily plug in these, LLMs to reason over the data that's available, and it understands your process, understands your people, your data, and it's gonna give you better responses because of that. And how you do this, like, how you get here is you first off, you enable it after getting to level one, and then you build champions within your team. You wanna get people that are excited about AI. You wanna get power users, people that are gonna be influential within the organization, and you want them to really be the ones that take hold of this technology, start using it, building out use cases, working with a partner like us to build out those use cases and and kind of train you and show you how to take advantage of these tools. But this this level two, the hard part is the is the change management. Right? It's getting somebody to change the way that instead of going into their their email every single day, they go into the tool and ask, hey. What are the items I need to move on? What are the production orders that need to get pushed through? Which orders need to be released today? And that process there is the hardest piece of this. The technology is pretty easy. Like, you can snap your fingers. I can deploy AI for all of you today. Like, go into Quad, ChatchiPT, or Copilot and enable the m three sixty five connector. It's gonna connect to all of your applications and be able to go in and reason over all that stuff. But your team is not gonna auto default to going in there and using it. And they're when you open up these chat box, it just is a blinking line that looks at you like, alright. What do you wanna ask me? You need to educate and train your team on what to ask and how to use these tools, what they should be asking and and what they should not be asking. So leveling up the skilling and then building out that inquiry AI is the level two. And then you get to the fun stuff. This is the full autonomous agents. So this is where you're actually taking that inquiry AI and you have it reason over your data, and now it goes and does something for you. So this is the sending a sales quote to a customer, sending a, you know, a a purchase order confirmation to your to your vendor. This is actually going and recording and posting transactions within your system. If you don't have level one right, you could be posting the wrong stuff. You can be posting incorrect things in the GL. It's gonna be hallucinating, giving you wrong data because you have the wrong context. All of that has to be done right so you can trust these autonomous agents to move quickly on your behalf. So, really, you move from having the manual process of checking email, manually doing the data entry, to then having this in a core system that someone is there's high integrity around the data and someone's going in and able to ask and reason over things, like show me my open purchase orders, show me my open sales orders, show me what's getting released to the warehouse, show me my current on hand inventory. That inquiry AI, you want that to be really dialed in. And then when your person, like your team, is doing something after they inquire, they go, okay. Show me my open purchase orders. Alright. Now I I reason over it, and I'm gonna go in and release some of these purchase orders. So that action, you bottle that action up and you give it to the autonomous agent, and now it's able to perform that on your behalf. That's where the real ROI of AI comes up. So this is getting all the way to that level three, and there's not that many people that are doing this. Because it's so hard and has been so hard to get to level one right and then get to, you know, level two, it seems easy, but then getting to level three, it kind of all falls apart because you're on a a rocky foundation. You can't trust your data. You know, Janet never updates CRM or something. You know? Like but you have to have those things dialed in so that way you can actually take advantage of what's possible here. And just like the tortilla distributor that I talked about in the beginning, when you have good data, you can implement these things very quickly. We built that agent in thirty minutes, deployed it in a week, saves them a year. So these things are are very powerful. And like we talked now through the levels, level one, level two, level three, what's the framework that you can use? This is just the the framework that we look to first. There's there are other technology stacks that can be used as well. But for the foundation, you wanna have a productivity suite that you're leveraging. So Microsoft three sixty five is the, you know, the the productivity suite that ninety five percent of, you know, Fortune five hundred companies use. It's the kind of the most well known and most well used. There's also, like, g Suite or, you know, I think that's really the other main one, honestly. And having your use of this tool allows the LLM to leverage that future. So having that as foundation is key. Having a modern ERP. We recommend Dynamics three sixty five being Microsoft provider that encompasses all of your financial sales, purchasing, inventory, manufacturing, service, projects, really everything end to end in a single solution. So this way it's easy for your AI to ingest and reason over your data, perform actions within a single source of truth. But having a modern ERP is absolutely critical. Having a modern CRM, you know, Salesforce, Dynamics three sixty five CRM, you know, anything that is modern, has accessible APIs and MCP servers, reporting analytics, and really just building out this data this data foundation here. And then on level two, Microsoft Copilot is a really great tool not because of the models that it uses. So Microsoft Copilot is not a model. I'm kind of breaking the the mold. Everyone thinks of, shall I use Copilot or shall I use ChatGPT? They're not the same thing. Copilot is a framework and a platform to use models. Microsoft has over thirteen thousand models available on its Foundry portal that you can access and embed into Copilot. You can fine tune your own organization's AI model and embed it into Copilot for everyone to use. So Copilot does not equal OpenAI. It does not equal Anthropic. It is a platform to use those tools because Microsoft, in this world of really, really fast jets and airplanes that are going from point a to point b very quickly, that's those are the agents. Microsoft wants to be the air traffic control and the the airport to make sure that all of those jets are running on time, that are getting to where they need to go and making sure that, you know, everything is done securely. So it's a great tool for it. You can just embed directly ChatGPT or Claude into your level one stack, so into all your productivity tools for that inquiry, and then you have this ability now to reason over all the data. And then autonomous agents. So this is, again, where the fun stuff gets. There is a ton of this is where the most amount of innovation is happening right now, especially over the last three months, honestly. You have tools like AI Foundry, with Microsoft that you can, again, like I said, fine tune all of those models and then embed them into your into your Copilot, or you can build custom agents that perform very specific processes. You have Anthropic, which is basically the leader right now when it comes to model performance with Opus, Sonnet, and even if some are familiar with Meetos. Maybe get too technical here. So stay stay higher level. You can build Copilot Studio agents, that embed directly into all of your your level one technologies, and that's a more drag and drop off the shelf type experience, with Microsoft. But this is really how you see those levels and, how you can adopt it with, with this technology. So these, I'm not gonna cover too much of this piece. I kind of already explained a lot. These these ERPs, like Dynamics three sixty five Business Central, are are all in one. Having a productivity suite, enabling your team to have all of their processes be performed within a cloud solution that is accessible for AI. On the level two, having things like Copilot, Quad, ChatGPT, those are really the main ones you could use. You know, Perplexity, which is like another wrapper around all of these other these other tools, and then the autonomous agents. You know, these areas are are really focused on the custom build. You will need a partner like us to to start delving into these things. I would highly recommend, yeah, doing so and creating a program around it with us. This is what it looks like altogether. So you have the foundation of modern ERP, modern productivity stack like an m three sixty five, and really a a data estate that is secure on, a cloud computing like Azure. Then you have your inquiry AI, which is your Copilot, JACQUITY, Claude, and then your autonomous agents, which are your custom agents you can build, Claude managed agents, your Azure Foundry agents, Copilot studio agents, etcetera. And I know I try not to bring up too many names here. I don't wanna confuse everyone. If you have questions around this exact tech stack, I'm I'm happy to talk it through with you guys. But, really, what you're what you're seeing in this is this evolution of the technology stack. It's no longer just ERP and CRM, and that's what you need to do. You now have these layers are evolving around the core systems of record that are equally as important and require processes around, and you have to be thinking in this way now of not just managing the system, but being a context engineer and a prompt engineer and having that understanding and that skill set within your teams to be able to take advantage of these tools. And then having agent engineers and people that are thinking about how can you take manual processes and turn them into agentic processes. But it's important to look at it in this way that everything builds off of the system of record. If you have bad data, your agent is gonna be making mistakes. If you have poorly designed processes, your agent is gonna be making mistakes. So you have to get the core right, the heart of the business right first. Then you can move to the fingers and limbs, which is the context, and then you can move to the brain, which is the agentic layer. And so you have to be thinking about it in this way. And it's this these top two layers did not really exist in a systematic way previously. You always had systems of record. You had context with that was available out there, like phone calls, Teams messages, emails, you know, all of these relationships that you have with with customers or vendors, all of that stuff existed, but it didn't exist in a system previously. Like, it wasn't accessible. Now you have tools like m three sixty five, like Notion, like these really good tools that are available to manage that context for you. So you can be the context gardener of your data estate, and then you can embed the whole new layer of the agentic scenarios on top of that, which is an entirely new, paradigm that's being innovated every single day, honestly. So the the technology stack is is honestly being rebuilt, and it's doing it in a way that still relies on those core systems and then expands outwards into now what's possible. So how do you get started? This is a lot of information, lot of levels, lot of use cases, and potential ways that you guys can get started, but you're probably thinking like, man, how do I do this within my sales department? Or how do I how do I get my manufacturing team on board? Like, how do I actually start tomorrow on this thing? And we have a framework for this, And I like doing things in threes. So there's three levels. There's three steps. So the three steps, first, you wanna look at your foundation. You wanna fix the cracks that are in the foundation. If you have bad data, clean it up. If you have bad people, clean it up. If have bad processes, clean it up. If you, you know, have areas, like, of your systems that aren't accessible to AI, you wanna get them into a modern platform. Then you wanna look at piloting. So you wanna look at the areas of inquiry that you would trust your data on, and then you wanna be able to pick a department, pick a lane, and then move forward with that. And then that allows you to actually measure the ROI that you're gonna get from your first agent. And so here's how I break it down. So the first thing you wanna do, understand your business. You wanna map out from a functional standpoint all of your end to end business flows. So here on the right, you can see this is a business model as we call it. We build these out. If guys want us to work with you on this, just reach out. On the left in green, you can see are the vendors. On the right in red are the customers. In the middle in blue are the legal entities or companies, and then in yellow are the inventory locations or the physical locations. The arrows between them reflect the transactions that flow. You wanna understand your business in this way so then you can map out, okay, our our this location that we have within this company, it has very well defined processes. It has very understood people. Like, we know the we have control of, like, what those people are using and the processes that they're doing. That's gonna be right from a functional standpoint to implement AI. Right? So you need to look at everything from a functional lens first, understand kind of general business flow, and then look at how well documented are my processes here, how well documented are my people and the organization here, and get an understanding of that. And then you wanna look at it from the technical standpoint. So going underneath the hood and looking at your systems architecture. So this is a systems model here on the right that lays out what is the enterprise resource planning system that we're using today. What's the customer relationship management system? The product life cycle management system? The payroll? The benefits? The ecommerce? The the manufacturing execution system? Literally every single one. There's, like, hundreds of systems that are possible. You guys need to understand what you're using and then look at it of which one is accessible by AI. So which ones are on prem today? Which ones are literally just paper systems that we have a name for that that we're using today. And you look at it from this lens, understand where the integrations are, which is the lines that go between the systems, understand that's manual flow of information of a human reading from FedEx, a tracking number, and typing it into your ERP against the shipping against the shipment, or if that's automatic where ecommerce orders are just flowing in to your ERP and they're getting pushed to the warehouse automatically. You need to understand that level of depth, and then you can identify those areas where it's right for transformation from a technical standpoint or right for AI adoption from a technical standpoint. What you can do is layer these things together, your business model and your systems model, and areas that are right for transformation are gonna become very clear in that. So you're able to see, like, where are we actually ready to do this. And normally, just on average, what we see when we come in to do this, organizations have about ten to fifteen percent of their business that actually is ready for AI. Most of the time, a lot of that level one is not figured out. You know, everything that's happening within manufacturing is completely manual, and, you know, it's all in tribal knowledge from from the the the supervisors that are out there. But our finance team, you know, they run on a, you know, a cloud based ERP, and they have really well documented. You know, they're super organized. Like, that area is right for transformation. So you wanna understand, like, where are you ready and where are you not? Because that then gives you the framework or your road map of how do you actually get to really level one, getting everything to to that point at which you are actually right for AI implementation across your entire business. So you'll look at that through the lens of also where is the pain. Right? If things are, you know, working incredibly well in one area, it's not like they they they can obviously get better as well, but, you know, most of the time, you're looking for pain. You wanna solve the pain. And then a good way to get versed in this is to pick a lane and then start moving. So let's identify finance as a good area for agentic solutions. Let's, like, figure out where those agents are that we can implement that we trust the data. We trust the process defined. We trust the, you know, the output the AI is gonna give us from that, and then start moving on a pilot. Again, you can do these things very quickly. We can help you and work with you on that. And what's gonna happen, gonna get very excited because you're gonna see what's possible with introducing that agent, and then you're gonna look at the other eighty percent of the organization and say, alright. Time to get everybody to level one so we could do what we're doing in finance because it is pretty incredible.
