View Recording: Turning AI Ideas Into ROI

Brian Haydin 0:11 And we are live. Welcome to Turning Ideas into ROI. In this session, I’m going to walk you through how to prioritize AI initiatives and translate them into actual measurable business value. Today my focus is not just on ideas, but creating A. structured path for impact. But A. little bit about me. I am Brian Hayden. I’m A. solution architect at Concurrency. I spend A. lot of my time with leaders around the industry helping them map out and implement A. I strategies and hopefully help them ideate and figure out what kind of initiatives are A. good priority for them, like many of you. I’m super passionate about moving past the hype and into results and you know, so A. little bit more about me personally. I’m an avid outdoorsman and I like to talk about being in the outdoors and hunting and fishing. And I’ve got two kids. You can see my, you know, my son and my daughter are there, married. And A. fun little fact about me is that I am A. twin. So reach out to me, follow me on LinkedIn. There’s A. QR code. I I’d like to regularly share stuff. You know, basically insights on AI strategy and innovation and whatever else you know is kind of interesting to me. So and one quick housekeeping note before we dive in, we’re gonna be Turning these concepts into action and that can be kind of challenging. So I’m offering. Currency is offering A. 30 minute session to help you get started. We’re gonna share A. link in the chat later and you can book A. time that works for you. And so more to come on that and love to meet you. Love to hear about the Ideas that you and your organization are thinking about. So let’s get started. Agentec A I is now mainstream. A I agents are no longer experimental. 100% they’re mainstream. 80% of 88% of organizations are now say that they’re measuring value, actual value from A. I adoption. That means the conversation is starting to shift from if to how, how much and how fast. I think it was Accenture that recently just saw A. headline that Accenture has something like 14,000 agents running in their organization, 14,000 agents running in it. So that’s just absolutely incredible. It’s telling me that that most organizations are starting to to adopt this and it’s A. big part of their strategy. So it’s not just hype, this is reality and hopefully you and your organization are starting to get there. And and this is going to change how work gets done. I’ve got A. bunch of numbers here. This is kind of A. I had like 8 slides with all these different numbers on here, so I thought I’d just sort of throw it all together. But here’s where the 88% kind of came from, if you look in the the center of this. In A. world that’s buzzing with hype, business leaders now they they need A. road map and right now there’s broad adoption, limited adoption, full adoption. Add all that up, it’s A. significant part of it. Also kind of calling out that the the growth in companies that are. Currently in planning to use AI that on the right side of the screen, it says 55 to 75. Those are old numbers. Those are from 2023 to 2024. We’re A. third of the way or A. 3/4 of the way through 2024 or 2025. So we’ll see what those numbers look like. I’m sure it’s. It’s going to be huge. I mean, we said it’s 88 on the A I agents. So it’s probably going to be some close to, you know, 90, you know, something like that. But you know, it’s not without challenges, right. And 92% of companies are reporting that they are having challenges with with A I. So it’s not easy, you know, to adopt strategies. It’s not easy to capture actual ROI and real value from that. And so that’s why I thought this would be an important session for us to talk about. But before we get started, when I say agents, I don’t, I don’t mean models. I mean agents. And so I think it’d be helpful for us to discuss what agents are. So agents combine models with tools, with contexts, with memory, and eventually with autonomy. So they can perceive, they can plan, they act and adapt. Much like you and your co-workers would do on A. day-to-day basis. And these are starting to become the building blocks of an AI enabled, the frontier enterprise, what they are. So you know this chart I showed the last time I did A. talk about this topic. Building, you know, use cases and it hasn’t really changed that much because this was really, really super recent study. But what it’s showing in this chart is that the number of job functions that AI agents are able to help individuals with is starting to accelerate. You can see that on the the left side, people that are using agents to do one or more function. That’s A. pretty high number. You would expect that. But look over to the right. You’re starting to see that trend go up as individuals start to use A I agents to do multiple functions, 5 or more functions at A. time. Pull functions, five or more functions at A. time. So we’re seeing, you know, the scale, you know, happen, you know, quite rapidly and we’re going to continue to see that through the rest of this year, which is, you know, the the year of agents at the end of the day. Agents can play different roles depending. I might even come back to this slide A. little bit. It’s it’s got A. lot of content here, but agents can play different roles depending on your needs. So it can do commodity A I co-pilots. You know, Chat GPTS, Geminis, those are kind of the commodity public A I, you know, sort of things and they boost productivity pretty much right away and and most people are using these tools today I would say. But as you start getting into more sophisticated tools like Copilot’s Researcher Agent and Analyst Agent, I see quite A. steep drop off as people just don’t really understand how to use these tools effectively. And then once you get into the custom agents. We’re that’s where we’re providing A. lot of business value, A. tremendous amount of ROI, very intentional, you know, type of activities or things that we’re building, but it takes A. lot of engineering and A. lot of thought in order to get there. So this is just sort of showing you that as you go from commodity to more engineering activities, what you’re really doing is you’re increasing the accuracy of the of the generative A I and the usefulness of what it’s going to bring to the table as well. So. As organizations think about getting started with A I, A. bunch of things, you know, come up in terms of the concerns that they have. A I isn’t all upside and people have concerns around privacy, data readiness, talent gaps. Biases and hallucinations and and most importantly ROI. So this today we’re really going to focus on the ROI aspect of it and helping you to compute and figure that out. But these are all signals that it’s important for us to plan carefully. And and think about hallucinations, talent gaps, data readiness and all these other things as we start to build agents. A I is no longer, you know, just about isolated use cases or tech experiments. It’s A. strategic capability for most organizations, and they’re looking at it that way. Nearly half of all tech leaders say that A I is now fully integrated into their core business strategy. As PwCS Chief A I Officer says, you know, top performing companies will move from chasing A I use cases to using A I to fulfill. Business strategies and Sati Nadella reminds us that that this is really the defining technology of our time. And so it’s no longer, you know, optional for growth. But here’s the risk. The movers, the people that are early adopters, they’re pulling away from the latecomers. And the late comers are starting to stall. So early adopters are times the value and that as the late comers, people that are just starting to adopt it, but but it’s not too late. I mean, there’s still time for us to catch up. So one of The Dirty little secrets that not A. lot of companies are going to tell you about is that there are really no experts in this field, except concurrency, of course. But I’d say that in jest. But honestly, if you think about it, ChatGPT, you know, is. Only A. couple of years old and and so we haven’t developed A. lot of muscle memory. There aren’t A. lot of people that have been doing this for, you know, for A. very long time. And so for people to claim that they’re experts in the field is kind of ridiculous. And A. lot of companies, you know, feel that when they hire some of the big consulting companies that they’re hiring people that are actually learning on the job. And and that in A. lot of cases, you know, is is going to be true. I like to be pretty transparent with companies that we work with. When we do that, that we’re we’re going on A. journey together and we have A. hypothesis and this is going to be an experiment. We’re going to treat it as such, but but nonetheless, it’s still time to catch up. Nobody is more than two years ahead of you in terms of level of experience. So keep that in mind as you start to, you know, frame that that journey up. So how do you start? How do you catch up? And just because that gap is widening doesn’t mean that you, you know, should just abandon all hope. So getting started today, I think. I’d start with, you know, four key points, four key things to look at. Scaling up your workforce is number one. Getting people comfortable with A. I literacy and copilot usage. Todd talked about the A I literacy in his in the keynote. And so I can’t emphasize that enough. People have to remove the fear, you know, from working with these tools. And then start with your with your organization’s North Star, aligning AI projects with, you know, the top strategic goals for your department. Department for your organization is going to be important in order to keep the momentum, and we’ll talk about that A. little bit more. Build pilots with urgency, show value that’s going to happen within months, measured in months, not in years. I don’t like to take on A. I projects that don’t have measurable A I or measurable ROI results that are going to pay off in 12, maybe 18 months, depending if it’s A. really big payoff or A. really complex thing. But you need to start demonstrating ROI value very early. And the reason why you want to do that is that the technology is growing so fast and so quick that oftentimes before you’re done building A. sophisticated implementation. It becomes commoditized and there’s other tools that are out there that you can just buy off the shelf. That doesn’t mean it’s not important to do it or not valuable to do it. It just means that your ROI window is shrinking with with the acceleration of this technology. And then scale with governance. So bake your telemetry and KPIs into what you’re going to be building. It’s often difficult to measure because you may not have the KPIs or the baseline data to support growth or improvement, but baking. Into your solution. Some sort of way of recognizing what the value is that you’re providing with the organization is super important and critical. So what about preparing your team? Three key things that you should be looking at. Scaling, change management and Co ES. So AI is really only as powerful as the people that use it. And that’s why we’re emphasizing these three things, these elements, they’re going to make the adoption sustainable and not just experimental, but you are going to experiment, so. Skilling. Every member needs to be equipped with the different tools that they’re going to be asked to be to use. That might be commoditized tools like copilot or ChatGPT, or it might be giving them access to some of the semi-custom tools like copilot Studio. But without cultural adoption, without change management, things are going to stall. So you need to have have A. change management strategy that is going to ensure trust, ensure buy in and keep the excitement going for the organization so that the momentum keeps going stays. And then A. central center of excellence is is critical as well. Share your learnings with each other, share the wins with each other so that the organization can recognize those things and it gives you A. stable base, you know, with consistency is if everybody understands. How to build things or what the tools that we want to use and what the overall strategies are. So in skilling, AI literacy for all introduced copilot training sessions, AI 101 activities. I’ve seen some of the healthier early adopters. Starting to do like AI 400 level classes and 300 level classes you know for people. So build A. curriculum that is appropriate for the organization where they’re at. Find champions that can help. You know, work with power users or identify power users in different departments and train them up. Get fluency with the executives. I don’t think that this is as much of A. problem today as I’ve some recent studies of have shown that executives tend to think that they’re further along in their. AI journey than the individual workers at the organization. And I thought that was really interesting, but in some cases with the companies that I’m working with, the executives really don’t understand what’s possible or what tools they should be using for what. So executive fluency is and. Important and change management. It really is about the trust and the momentum. So transparent communication, you know, explaining why AI is being introduced, what it’s doing in imploring on the individuals. This isn’t here to take your job, it’s here to make it easier. Make it a rewarding participation for users and frame AI up as a partner, not a replacement. And so all of this is intended to help them embrace the journey because technology is only half the battle. You know, we can throw technology at the problem, but without, you know, cultural change and cultural adoption. And these projects are likely to stall at some point. And then, you know, Center of Excellence really is kind of your base camp. It provides the governance, the reusable assets, cross-functional teams, you know, to help guide scaling activities. You know, governance anchors, reusable assets, cross-functional team, innovation engine. Some of the most successful Co ES have actually built out portals for the organization to share things like the best prompts that I’ve used or the best agents that I’ve been able to create, you know, as part of the organization. And reward people for innovating internally within the organization. So now that I’ve gotten some of the ground things out there, let’s talk about some of the use cases. We need to choose the right trails. We can’t just go deviating into all sorts of different areas. Areas. So let’s identify where AI can align with our strategy and reduce pain points and unlock, you know, actual measurable value. I can share with you some stories of, you know, cases where people have chose the wrong path. And it, you know, here’s one good example that I’ve got is I was working with this company. They have a lot of different business units and a lot of divergent technology is they have grown through acquisition over the years. They had somewhere around 12 to 14 different ERPS. And the data was, you know, terribly messy. So when we’re trying to find where my order was and how to, you know, hook that up to a generative AI and an LLM, it was nearly impossible because there were so many different gaps. As the data journey needed to traverse many different areas, so be careful to you know about the use cases that you choose. Try to make sure that they’re that they’re possible, you know before you can before you bite them off. Um. The next point is, and I brought this up a little bit before. So before you start to brainstorm on any kind of single idea, you really do have to understand what your organization’s North Star is. I hope that you do today, but if you don’t, you might want to pause before you start the brainstorming session and understand what it is that the. Organization is trying to accomplish. Tie these things to the priorities of your organization and make the experience an aspirational experience. How we’re going to make the organization better. Anchor it definitely in measurable outcomes. Identify the KPIs that you’re going to, you know that you want to, that you want to measure, and then use all of this as like a rallying cry, you know, for the organization to go in the right direction. We recommend, you know, focusing on four kind of key areas. These are the most prominent ones that organizations tend to understand where generative AI is going to provide the most value. But you got to start with the why and top performing companies are they’re moving from chasing AI use cases to using AI to fulfill the strategy. So the core areas that we. Focus on our customer experience. Making customers feel warmer about how they’re interacting with your organization is one of the biggest use cases. That could be a customer service type of bot, or it could be any other way of people finding information quick. Quicker, faster, resolving issues, et cetera. Efficiency and cost. So process automation is really popular. People seem to understand that pretty well in terms of coming up with ideas. These are the types of ideas where somebody is using muscle memory to get through the day. I do this. You know, for two hours a day, it’s a button, you know, it’s 50 button clicks. It’s, you know, it’s repetitive and those are often areas where we can provide AI assistance to make those decisions that are kind of routine for our brain into more of an agentic approach. Revenue growth is another big area. So why would we do this? We want to grow revenue and sometimes that might just be like, you know, having better insights so we can make better decisions, but you know, being able to. Automate the sales process or make upsell opportunities available to users is another good use case and then risk reduction. So I’ve talked with companies, we don’t really have a problem. I’ve heard with some companies with compliance, we’ve only been sued two or three times. You know, in the last five years, well, maybe we can make that zero. And what are those lawsuits cost you? Was there a settlement? Was there legal fees? You know, risk is a thing and happens to a lot of organizations. So sometimes it’s the things that we’re that aren’t going to happen to us. That are important for us to try to capture and measure as well. So aligning with these four, you know, big four kind of outcomes in these brainstorming strategies sessions tends to come up with a lot, generate a lot of ideas. And in manufacturing, we see AI agents cutting, you know, performing all these things, you know, readily. So I gave you an example here on this screen, 5 different examples of ways that concurrency has built agents to help improve decision making. With data insights, drive revenue for the businesses, reduce costs and error, you know, for operations and increasing your competitive advantage. I’ll dive a little bit more deeply into these, but sales and quoting, auto quoting is something we’ve done many times. And we can respond, you know, we can cut response times from maybe somebody sending an e-mail for an order or for a quote from, you know, a couple hours maybe down to like minutes or you know, seconds even a sub minute, you know, and and a driver for that especially is in high turnover. Sales activities, supply chain optimization comes up quite frequently as a demand forecasting agent can help reduce inventory. We’ve done some really, really significant demand forecasting. Projects that have resulted in up to $40 million a year in savings. It was huge procurement, you know, AP matching, you know, anytime that somebody’s working within an Excel spreadsheet doing V lookups and trying to match data from a bunch of different systems, that’s, you know, those are opportunities. Opportunities for us to help out with AI and then on the vision side quality. So the QA inspectors in the manufacturing systems happens quite a bit and it’s getting the AI detection classification, all that stuff is. Accelerating and being much more productive. And finally, the last one that we’ve done quite a few of are the virtual support agents, and that seems to be a place where a lot of people start for a number of reasons. One, there’s a lot of documentation that supports it. And spinning up these agents are pretty easy to do. So give you a little bit more of a visual of what I’m talking about when with some of these ideas. So automated quoting is basically taking a e-mail that might be in the language of your business. The this e-mail has a bunch of, you know, metal parameters for, you know, somebody that’s selling like sheet metal and world metal and stuff like that. And you and I probably wouldn’t understand what you know all the different notation is, but AI can be trained on that very easily. And help them figure that out. A lot of times with with these quoting, you know, quoting problems, it’s trying to figure out what the special item is for that customer, what the special pricing is for that customer and we can make that up to like 80% faster or even more. So it saves hours and hours and hours relying on agents. I got a question here I’ll take a look at. So relying on agents, customer support, support, reduce customer confidence. People do like to speak to real people. That is absolutely true in in a lot of cases. But I would actually challenge the premise that people like to speak to real people on a frequent basis. That’s closer. That’s a true statement, I think, for my generation. But my children and my children’s children generation don’t. You know, they’re used to speaking through chat bots, and I think you’re going to see. A higher rate of adoption in that area, but I also would say that people like to get their answers really quickly, and if it means waiting for two or three minutes as a frustrated user to get what could be a simple answer, the user experience is likely to be better with a customer support agent. And that is escalated to a real person very quickly when you can do sentiment analysis. But great question. I love that. Back to the other real world example, purchase order matching. I talked about this whole idea of. You know, streamlining that, get the data, put it in Excel, get the other data, put it into Excel, do the V lookups. We can get like really, really quick at this stuff with the multimodal processing of things like PDF documents. So I can, I don’t have to like copy and paste a PO or go get a PO data from a different system. I can just pull it straight out of something that somebody sent me in an e-mail attachment and then compare those things field by field to make sure that everything matches. Anything that is abnormal or has abnormalities, we can flag that for a human review and that’s what we typically do is we start with a human in the loop kind of aspect and allow the users to sort of. Tell us and coach the AI agents that we’re building. If it’s, you know, doing the right things, once we get comfortable with it, we can automate it and end. So to surface these opportunities, you know, bringing diverse teams together I think is one of the more important things. So now we’re ready for the brainstorming session. Let’s start getting the use cases on the table, bringing a very diverse set of users. Don’t look at leaders. I mean, you should look at leaders, but like, don’t always just think we’re going to bring the head of sales, the head of customer service, head of operations together, and they’re going to brainstorm. Bring the people that are really doing the work and bring lots of them together. They’re going to feed off of each other. And bring insights to the organization that a lot of time managers and leaders don’t really have an understanding of. I see a lot of friction happen when the leaders of an organization are the ones that are determining what the priorities are and the people that are. In the trenches on a day-to-day basis, they’re like, that’s not going to help me. I don’t know why you guys are focusing on that. So bringing them to the table early, you’re gonna get buy in. Nothing is going to railroad your AI initiatives faster than nobody wanting to use the thing that you built. So. And then facilitate A strategic way of opportunity mapping and do it in a way that that brings everybody together. So we have a we have a way of doing a couple of different workshops together that start with you know a COE kick off and bringing this. Strategic team together. What we’re really trying to do is set the stage and make sure that we get things like that North Star defined before we dive into each of the functional domains. And so at the second workshop, what we’re trying to do is capture ideas, the bigger ideas and building an idea. Registry. Once we have a set of ideas, then we can do deep dive workshops and understand like, all right, if we were going to pursue this opportunity, what would that look like? And you know who you know, what data are we going to need? What’s the tech that would you know that we would bring into it? And how will we actually measure out the the outcomes to this? So a good example, you know our buddies over at the Fox World Travel, they had an organizational wide brainstorming session and it led to the the creation of Colby which. If you check out their blogs and stuff like that, they’ve got a multifunctional customer facing agent and it doesn’t just give you questions and answers or do the question and answer aspect of it. It generates charts, it’s very knowledgeable, it’s insightful and. Has provided a lot of great feedback, you know, to the organization from people that are using it. So here’s what we would typically do. So coming out of your workshops, it’s common for us to have tangible output output from these like an idea registry. So we’re really just kind of like listing out each one of the concepts that came out of it, assigning it. Category, maybe aligning that to a technology stack that is closely mapped to it, but we’re really just trying to capture the different ideas and build on a backlog of things that we can that we can look at and pursue over time. And even if we categorize something as really high effort today, I mentioned before that these tools are shifting into commodity, you know, pretty rapidly. We can assess that six months down the road and say, oh, that used to be really hard. Now that’s off the shelf. Maybe we should start looking at something like that. And then not every idea is, you know, doable or good. And so using a simplified framework to help you prioritize, you know, the, you know, the different ideas, putting them into a quadrant, you know, I’ve used a couple of different methodologies. But a BXT lens would be a good one to start with. Microsoft recommends this. It examines. It first examines the business value. What are you going to get out of this and the degree of strategic business impact? And then and then the technology feasibility, can we actually do this? So and that could be based on skills that could be based on data readiness. But you plot them out in this this matrix and you’re going to see that they’re going to fall into, you know, one of the four quadrants, so research. You know, high value, low degree of of executional fit, meaning it’s going to be kind of hard. So maybe you you invest in that, but high value, high degree of executional fit, that’s an accelerator. You definitely want to pursue those opportunities, you know out of the gate and then the other thing to. Look for quick win opportunities. One of the customers that I’m working with, we found a very quick win, took us 6 to 8 weeks to get a pilot built out for them and it demonstrated almost instantaneous. Value to the organization. Remember how I mentioned that AI isn’t intended to replace people in this particular case, and I’ve worked with several different organizations that have retirement cliffs coming up. They have an aging workforce. And they’re subject matter experts in this particular area. And so if we can generate some sort of agent that takes over 758090% of the workload of that individual or simplifies the workload of that. What that individual was doing, maybe when that individual retires, we don’t, we don’t replace them. And so this company that I was working for, they had that exact scenario. We took a quick win. He was able to take that to the executive team and the board and demonstrate that AI can. Turn around and make very impactful business sense right out of the gate in 6 to 8 weeks and that kind of opens up the flood gates for the executive team to want to pursue more opportunities that have those kinds of wins. So demonstrating value fast is key to ensure that the. Leaders of the organization can see the value and want to invest more into it. So let’s let’s get into. I think everybody was like, how do we how do we do this? So let’s let’s start talking about some ideas. Let’s imagine that you have three AI ideas, that sales forecasting agent that we wanted to predict demand. And so we looked at like how much value that’s going to provide to the organization and we calculated that it’s going to generate 20%. Uplift in sales opportunities and and then we looked at what are the things that need to happen in order to make that make that work. And while there wasn’t something that was out-of-the-box that we could purchase, there were a few tools that we could stitch together that would automate this. So we felt that there was a fairly. Decent, um, you know, uh, certainty that we could do it. So that that jumped up into the accelerate quadrant. Um. And. I was going to try to use, there we go, use my pen and then all right, employee help desk copilot. Those are becoming easier and easier. I would actually sort of force this one over here. I mean, it’s really, really easy to put together a help desk copilot. Especially if you have documentation, but maybe this organization didn’t quite have a lot of the like standardized documents or wasn’t all in the same place. And then the third one was a procurement optimizer. So auto negotiate with suppliers was kind of the idea that came up. And that one was, I mean a lot of value if we can auto negotiate with our suppliers for sure. But it’s really difficult to do optimization projects and machine learning. Typically to do machine learning projects, you’re going to need a robust data set that goes back. 23. Years at the kind of intervals that you need to make the decisions and most organizations don’t capture it at that level. And so here you can see that the degree of executional fit was, you know, pretty low. We might want to experiment with that, you know, to understand that you know the concepts a little bit better, but it’s certainly. Not something that we’re going to pursue very quickly, so keep it in mind, but don’t touch it for now if you don’t have to. So next after we sort of prioritize these, the next step is ensuring that your selected use cases. Case has some sort of an executive story to it. So how does this link back to the strategic objectives of the organization and who’s gonna benefit? So the executive story, we can actually generate a prompt that will help us create the story. How does this align? Aligned to our Northstar, make sure that we hook that into the messaging to the leaders that are going to decide whether to invest in this the strategic context. What is the pain this is intended to solve? How does the outcome actually solve this problem and what is the what are the KPIs? That are going to that we’re going to measure that will will support this as an initiative, give it kind of credibility. We’ve done our due diligence. We know that data is here. The technology is capable of doing XY and Z. And and then in an area where we know that there’s some risk or some ambiguity or some decisions that we’re not going to be able to make until we get, you know, into the into the weeds, call that out so that if things do get derailed at some point, you can say we knew that this was going to happen, we had. Plan and right now we’re going to abandon for now and then make it clear what you’re asking from the organization. I need licenses for this. I need to get a pilot for this. I need to, you know, make sure that it’s very clear what you’re asking for the organization. I do see that I’ve got another question. Without getting too specific, how do you folks charge for helping us incorporate Agentec AI into our workflows? Mark, that’s a great question. I don’t want to get completely specific, but for us, a lot of it starts with these ideas. Sessions and helping to make sure that we align the right agentic AI’s into your workflows. And sometimes it’s just us coaching the organization on what they should be doing or how they can improve it. And that might be for a small fee or sometimes even just, you know, I tell you in a 20 minute. Conversation you should do try this and sometimes we build custom solutions as well and the cost could be anywhere from $1.00 sign to many dollar signs. So but great question when we get to that point. Why don’t you set some time up and we can talk a little bit more about your use case. All right. So even a great idea needs compelling business case to get funded and supported. So got to transition this into a, you know, ROI cost benefit, what are we getting out of it? So define the objectives and the KPIs metrics like cost savings, your revenue increase, get specific, get as specific as you can in these and and and lay those those calculations out. Make sure you can establish a baseline. I mentioned this before. You’re not always going to be able to do that, but if you know that your conversion rate, your sales conversion rate today is 70%, that’s your baseline and everything that you measure after a I is going to start with we were here, we got here now. And so, even if you aren’t able to capture it right away, start collecting the data so that you can establish a baseline before your project rolls out, if at all possible, and set some realistic time frames. We expect this to be ready for production in X. We recommend going into a pilot pilot phase where we might do two or three users in a group of 30 before you roll it out to all 30. So make sure you call that out. We’re going to pilot this. We’re going to see what the value is. It’s going to be six weeks after the pilot before we get into production. And then we’ll see what the value is. So we’re not going to capture any real ROI in 2025. We’re looking at Q1 of 2026 before we’re going to make it a a real assessment. So frame it up that way and then. Craft a data-driven story. So I like to structure the business case, you know, with the problem, the opportunity 1st, and then explain to them what the solution, what the AI use case is going to do for the organization. How that benefits and what kind of KPIs we’re going to use to measure the the ROI. I’m sorry that we jumped here for a second. Let me get back to the right slide, the investment that’s going to be necessary for the organization to pursue this. And that might be the licenses that might be, you know, some professional services. And then the lastly you want to be able to project like your outcomes and next steps. So I I predict a 14th month payback period for this type of an initiative. So a good example would be it currently takes five days to turn around a customer quote. If we had an AI system, we would target one day and and that would allow us to maybe win, you know our projection is 15% more deals. So frame it up in those kind of stories, they’re going to get traction. When you when you frame it up that way, I’ve got a few examples, but I want to rush through this. We’ve got about 8 minutes left and I’ve got 20 more slides to go to. I’m just kidding. It’s close to 20, but not quite an example breakdown of how we’re going to calculate ROI. So I’ve got. I’ve got this invoice processing and I do 22,000 invoices a month. It takes 15 minutes you know to process one invoice and and so if I take 12,000 of those. And map those out. Those are the ones that I can actually do. You know that’s gonna 1/4 hour for 12,000 invoices cause I I split it up and said all these 22,000 only half of them or a little more than half of them, 60% we could actually automate the rest of them aren’t like. Fit for that kind of that kind of activity. So that comes up to 55,000 hours and then take the, you know, 55, you know, thousand 5500 hours and look at that and say I can really only recapture about 3000 of those hours. Because you know some percentage of that isn’t going to, isn’t going to happen. And then that equates to 2.75 full-time employees. Hopefully you can calculate what your loaded cost is for a full-time employee. So at the end of the day, labor savings would be 288 K Now, sometimes that’s, you know, we’re not going to fire the person. We’re going to have to lose that through attrition. As people retire, maybe, you know, as people find new jobs. But that’s the kind of math that you’re going to walk through. Through for your executives and show them each step of the way how you made those calculations. I want to get to some of the technology stuff here too, because I think it’s kind of important to to have a. A good technology footprint. So no expedition is going to succeed without the right gear and the right guide. So here at concurrency we’re we’re really heavily aligned with the Microsoft platforms and there’s a good reason for that that we’re going to kind of show you over the next. Couple of sides, but by using their platforms you get a head start. You typically have licensing that’s gonna support this, and most of the tools work together pretty cohesively and pretty fluently, so you’re not jumping between too many. Connected platforms data is usually a foundation of this, and Microsoft came out with a unified data and analytics platform on Microsoft Fabric. If you aren’t aware of this, it basically combines all the different. Data functions that you would use in a robust data warehouse into a simple into a unified platform that allows you to do the same complex things, but you don’t have to jump between different tools in order to get the work done. So good example. I am going to load my data in some sort of ETL tooling that allows me to copy data from one source to the other, maybe do some transformations and then I want to use Power BI to like, you know, do the visualizations and and stuff like that. You’re not jumping between the different tools now to accomplish those goals. It’s an end to end kind of workspace that you’re gonna be working in and it supports things like data governance through Microsoft Purview and then also has things like copilots that allow you to be able to get your. Work done faster, whether that’s like writing Python scripts or even just asking questions about the data, like what are the top five SKU’s from last quarter in the Latin America operating unit? So then Azure A. In Azure we’ve got kind of a simplified approach of the Azure AI Foundry and that allows you to do model selection, model curating even. You know, even some of the new features that allow you to do automated model selection and you know all inside of a single tool inside of Azure. You can also experiment and generate agents directly in the AI Foundry. And if you find that that’s helpful, you can move it into the Power Platform directly and sort of fluently between them and then on the lower code kind of platform Microsoft Copilot Studio. Is a way for you to build agents in a low code fashion. Sometimes it’s just by generating prompts, but you get a little bit of control to get like drag and drop like I want these kind of actions and sort of like build out a workflow or even like a technical workflow and all this kind of cohesively works together. So I might build a component in a. Pro code solution like AI Foundry and serve that up in Copilot Studio, incorporate that into M365 Copilot and I’ve got everything all cohesively built. I’m going to pause here for a couple of seconds. Is there going to be recording this session? Absolutely. Capacity creation is straightforward as the way to quantify opportunity created. I’m going to skip that question. I think I need to ask for a little bit of a clarification. And then Brandon, there are so many places to start with building agents. It is best to think of building agents for a complete task or to build micro agents. That’s a fantastic question. The short answer, and I’d love to talk to you a little bit more about that, is microagents is the pattern that we’re going to. So all right, getting close to wrapping this up. Quick wins, early milestones, leverage your low hanging fruit. I like to start with low code tools, you know, and deploying those Copilot Studio agents until I hit a barrier or a threshold of complexity that Copilot Studio is not able to to get over. So start small, start quick, build momentum and demonstrate that you can get the ROI and then have you know have a long term vision for you know what the platforms are going to do plan for like Brandon’s question, a multi agent. World and then cultivate the culture for the organization so that they’re they’re bought in and they feel they feel empowered to build with you as part of the enterprise. So I promised we would get to sort of a wrap up here. Turning ideas into ROI complimentary sessions. So I would love to meet with you if you have more questions. I believe there should be a link in the chat for you and and then Paige is going to be providing recordings. As well. So, but I’d love to meet with you, spend 30 minutes on somebody like myself or some of the other solution architects. We can help you understand what it would look like to prioritize your AI initiatives. If you have specific questions, you know I’m happy to do it. And the link that there that page just dropped in the in the chat here is direct to booking. So you’re just gonna get a meeting on the calendar when you can make it and when we can make it. So last question, Brian, we use software written by a third party which is not an Ms. project. So many of our issues involve inefficiencies. These systems, yes, we do work with people in your situation. I would also say that one of the reasons why I am very appreciative of the Microsoft ecosystem is that. They have embraced an open ecosystem. Azure AI Foundry isn’t just Microsoft, Microsoft models or tooling that’s available. They’ve kind of opened that up to other models and ecosystems, deepseek and you know, others. Not everybody is playing nicely in the sandbox. But a lot of them are, and that’s what I do appreciate about the Microsoft. Another good example of Microsoft’s openness is that ChatGPT Enterprise I find a lot of organizations. Were early adopters in ChatGPT. Microsoft Purview supports DLP policies in ChatGPT Enterprise Edition. So just because you might have picked some third party tools doesn’t mean that we can’t make it work in the Microsoft ecosystem. I’d love to hear a little bit more about that use case, so. Any other quick questions before we close? All right. Well, thanks again for your time today. I really, really enjoyed the talk. I wish I could have gotten through all 48 slides, but maybe next time. Next time. Cheers.