Name: Finance AI Adoption Moves from Pilot to Scale and Governance
Uploaded: 2026-04-30T18:30:06.838Z
Duration: 1 h 30 min
Description: Finance AI Adoption Moves from Pilot to Scale and Governance

Transcript for "Finance AI Adoption Moves from Pilot to Scale and Governance": Okay. Welcome, everybody. We're really excited to have you joining us today. We're doing a session that's all about finance AI adoption and helping for those of you that are kind of still in the curiosity and discovery phase, moving from pilot to really pushing that towards piloting and scaling and and creating a a governance structure. We will have a few forward looking statements, so just know that we have a safe harbor. I'm Sam Buckner. I am part of the the Workday team. I'm thrilled to have our friends from KPMG joining us, Michael Brady, partner from the KPM advisory team. We've also got a brilliant product leader with us, Nadia Aksha, who looks over our financial management product portfolio. So really excited to have them walk you through some of the exciting trends that are shaping the AI finance adoption world. Quick look at our agenda. We're gonna talk about key adoption trends to start. We'll talk about some of the things that are blocking and creating challenges for those of you actively trying to deploy AI across your businesses. We'll talk about Workday's agentic finance strategy. Nadia will walk that us through some of that, and we'll highlight some of the agents specifically, some of which are available as a free trial, and or soon to be available, for our customers to take advantage of. So we'll talk through that. We'll talk about future design. So Michael will also share some of the deployment and insight he's seeing in customers, that he's been working with. And then we'll also talk about governance and setting up a roadmap for success. So a lot to cover in the next sixty minutes. We also encourage you to take advantage of the q and a capability. We wanna hear from you throughout the session. We'll do our best to answer them as we can. We'll hopefully have room, and then some time at the end to answer some additionally as well. I also want to launch some polling questions and get a sense for where all of you are, before we jump in. And the first is where are you or where are you in adopting, AI, Genentech AI, finance across your business? Love to hear whether you are, exploring, having early discussions, sort of in that early early discovery phase, piloting them, you know, a couple of use cases. Maybe there's one or two. Maybe you're more at the more advanced stage of scaling multiple use cases. Maybe it's part of your everyday at this moment, or you haven't started. So just take a minute to give us a sense for where you're all at. And we've got a really good mix of people in different stages. So early discovery looks like we've got around 43%. Piloting, one or two, we've got around 32%. Multiple use cases, around 15. Curious, Michael, any of this surprise you, some of the results that we're seeing based on your customers as. well? It's it's pretty of line with what we're seeing in the market. as well. Got it. And for those of you that are in the early stages, we'll hopefully have some exciting, use cases that we can can, get you to start thinking about using. So I wanna also do one next polling question, which is for those of you that are actively deploying the one to two or the multiple use cases, where have you prioritized? Is it in record to report, which we know can be time intensive, of course, but critical, obviously, to the business source to pay. Obviously, a lot of opportunity there with accounts payable automation. We've got some contract to cash, investment. And one of which I'm gonna talk about actually is our revenue contract agents. So I'll be talking a little bit about that. Plan to perform would be curious who is looking at using AI from a a forecasting and planning perspective, reporting analysis. Okay. And then we've got none of the above as well. So good mix here as well. So 22%, 23 are and, also, you can do multiple select here. So tell us all of the different use cases that you're looking at. But 24% are looking at record to report, 20%, almost 20% on source to pay. Contract to cash is a little bit less in the six to 7% range and plan to perform. And it looks like reporting analytics, another thing that I'll we'll talk a little bit about, around 27%. So great. Thank you all so much for sharing that. We'll have a couple of other polling questions, later on, but thank you so much. And I think that gives us a good sense of who's in the room, where you guys are in adopting, and a great tee up, Michael, for you and, talking through the the trends that you're seeing. Yeah. Absolutely. Great to be with everyone today. Some context on me. Mike Briggley, I I wear two hats within the firm. My first and primary is I lead our Workday Financials practice. But then my my second, I I lead our advisory intel intelligent processing offering, which is a very vague term for the offering, but but I essentially work with our venture capital arm, as well as our strategic alliance partners to evaluate and activate finance AI applications and and products across our, ERP platforms, but, admittedly, Workday is is near and dear to my heart. So so let's start. Let me change slides here. K. Here we go. Technical difficulty. Let let's start by taking a look at the state of the market. I I think the main headline from my perspective is is that the era of, you know, AI experimentation is is really over or or starting to end for those that are on the leading edge. And, you know, we're firmly in that that age or starting that age of implementation and and really scale, from from my perspective. You know, the the investment we're seeing at this point is is really unprecedented in this space. It's doubling year over year, but this isn't, you know, specifically about the investment. It's, you know, as our early adopters are scaling tiers, their journey and priorities are are really changing. Right? Based on, our research and the conversations I'm having with my clients, we're we're seeing a significant shift in in focus areas. You know, no longer is AI just for isolated tasks. Leaders are really focusing on automating complex workflows that span multiple business functions and, you know, focusing on more tailored solutions that that are specific to their business and, ultimately moving toward creating an agentic network that that really spans the enterprise. I think it's really exciting to see this evolution, but, you know, the reality is the the momentum is now starting to meet the practical challenges of really execution in in what is a new and and sometimes unknown frontier. Right? The great skills reset, isn't, you know, just just a buzzword. It's it's a reality that that nearly 3% of our leaders that we work with, whole, expect, right, humans to be managing AI agents within the next few years. And, as they scale, governance has really started to become the most important prerequisite, to really achieving that scale and and ultimately the value. So the question is is no longer if we should adopt AI or if we should start exploring, but, you know, how do we do it responsibly and most effectively? And this brings us to really what what every organization I I think is looking for, and that's tangible ROI. You know, 79% of leaders tell us that that AI is a top investment priority, you know, even in the face of economic uncertainty. So, you know, they're also under pressure to demonstrate, and I think most importantly, to quantify its value. The the top barriers are technology. They're they're really business, fundamentals with within the organization. You know, scaling use cases beyond pilots, addressing internal skill gaps, and and, again, being able to quantify the value that that AI delivers to the organization. So, I think it's a little over half of the organizations that we're partnering within this space. They've either achieved measurable ROI or expect to achieve that in the next twelve months. And the key takeaway here, is that is that value isn't, you know, just about cost savings. We're we're seeing that those improved analytics for for the c suite remains a top ROI metric moving into this year. And, you know, that really tells us and confirms that that the real value is is in real time, strategic insight that's leading to better and faster decision making, for the leadership group. And, you know, I I think to truly achieve this, you you can't just bolt on AI platforms. Right? You're you're not going to, piecemeal a long term enterprise data strategy. These these outcomes, these solutions, they they need a a stable and intelligent core that they can build on and around. And I think fortunate for for what I assume is the majority of this audience, an investment in a modern ERP like Workday is precisely that and and you're great you're a great first step. So where where's the movement going? Right? You know, based on our work with early adopters, we we've developed at the at the firm a few core hypotheses for this AI enabled future of finance, and I'll I'll probably boil these down into three takeaways. First, we project that up to 80% of today's finance activities will, be automated or at minimum augmented by AI. You know, this this doesn't mean that the finance function disappears. It it it means that it becomes smaller, smarter, faster, you know, more strategic, into the future. Second is that that AI is really starting to become the new operating system for the enterprise. And at the heart of really any operating system is that core processor and ultimately the data repository, you know, for finance function, that that's your ERP. So, you know, a platform like Workday isn't just your ledger system of record anymore. It's evolving to become the agent, the agentic, system and and network of record. Right? This is the the central hub, you know, where where AI can safely and effectively operate with your most critical and and sensitive data. And then lastly here, you know, we're we're seeing the traditional functions starting to converge. Right? The, career arc of your your finance professional, is is being dramatically reimagined. And and the need for strong controls is is only going to increase as agents really start to amplify that that enterprise risk. And, this is where we believe the market's heading, and it's it's a future we we have to design for intentionally and strategically and and upfront, to really maximize its impact and ROI for the business for our clients. I wanna move to to challenges. Right? And I I think one of the the biggest barriers we see with clients who are trying to scale their AI ambitions and and integrations, is is really the state of their data. You know, historically, the the challenge was having that data or being able to access that data, you know, but but the current reality for most organizations is that their data is everywhere. Right? We have too much data now, but that that data is fragmented across legacy systems, spreadsheets, you know, disconnected platforms, and it's often misaligned across those platforms. So, you know, this this isn't just a technical problem. It's it's foundationally an architectural one. You know, I I like to use the the analogy that, you know, you can't build a skyscraper on on a broken foundation. And, you know, a a reactive project by project approach to data, you know, simply doesn't work for scaling AI over the longer term. So, you know, to move, evolve, you know, out of this chaotic state to, you know, an actionable blueprint, you know, you really need that intentional data architecture with that that strong centralized foundation, not only of your your ERP, but also evolving it to be your core master data management engine across the enterprise as well. Moving to to roadblocks here, you know, as as organization move from that ambition to reality, we're we're starting to see a pretty standard set of roadblocks in this space. Again, not not just about data, although that's that's certainly there as well. You know, we're we're seeing a a clear lack of strategy that connects AI AI initiatives to that measurable ROI, operating models that that haven't been redesigned to capitalize on on the new efficiencies. Example of that, you know, we're we're seeing people without AI, but then people are are doing their old jobs just with a new tool, for for example. We're we're also seeing, and this one's really prevalent, a significant workforce skills gap, and then also real security concerns about data privacy, right, when you're using third party models. And, I I think another underappreciated aspect, or or challenge that we're seeing out there is, you know, many leaders are simply overwhelmed, right, by the complexity of the market, the tools, how to leverage them, which what what the sequence is to incorporate and and build this enterprise strategy, and and how to do it with confidence. So, you know, the the key takeaway and and why I share these is really to socialize that that these roadblocks are are not barriers, but more of a checklist, of what your strategy needs to proactively address to to ultimately enable its success. This got a maturity curve here. This is something we use to to help our clients navigate this journey. You know, we we use this model really as a starting point to assess where they are today, but really ultimately inform them at a at a high level of what the next maturity tier looks like. Right? It's it's that road map to, or or, you know, road map for evolving data architecture from that that siloed reactive state to, really what what is an AI optimized kind of agentic ready foundation. And I think, you know, the the polling results spoke to this, but the reality of the market is, you know, most organizations today are are at a level one or or level two. They they have some element of fragmented data. They're using a centralized data lake, but that's primarily being leveraged for, you know, that backward looking BI. And so the ultimate goal here is, you know, what's our starting point? Where are we today? And what you know, from a goal perspective and strategy perspective, what what do we need to define and impart into our business to move us to levels three and and ultimately four where, you know, you're reaching that unified governed architecture. And, you know, this is where your platform isn't just storing data, but it's it's actively making it AI ready. Right? I mean, at the highest level of maturity, your your data ecosystem becomes a really rich bidirectional supply chain where, you know, these these autonomous agents can discover, they can comprehend, and ultimately process data, but then, you know, ultimately leverage that of that level of comprehension and sort of the institutional context to produce those strategic insights to to feed to your business leaders and ultimately drive strategic decision making. Outcomes. So I've talked about the importance of of setting outcomes out of the gate and, you know, that that's really because the most common pitfall that that we see, that I'm seeing is is looking at that maturity curve or, you know, looking to set an enterprise strategy and and thinking you need to reach a level four before you can really start to see a return on your AI investments or or initiatives. And that's just really where you find yourself in that state of analysis paralysis. But the good news is, you know, to to successfully scale that strategy, and build that that foundation, you you need to, again, relentlessly, I I think, focus on tangible business outcomes from day one and at each stage, across the four tiers that that I had on the prior slide. You know, trying instead of trying to boil the ocean with this massive, multiyear transformation, you know, our our guidance to clients and the most successful adopters that that we've worked with, really start with a a targeted high impact use case or set of use cases that that offer quick time to value. Right? It's about quantifying, learning how to appropriately quantify, the impact and and instilling that that organizational confidence through, what what are immediate and high impact wins for the organization. So, you know, whether it's achieving a a faster close or or driving efficiency and and invoice processing and accuracy as an example, you you know, the the approach is the same. We wanna tie the technology and initiative directly to a business result that that we can capture and socialize amongst the organization. So, you know, that that focus on from my perspective, those those targeted use cases, you know, they should do two things. They they should deliver an immediate ROI and ultimately build that momentum, the trust, the the practical experience and and skill set, that that's ultimately required to scale AI capabilities kind of further along and and up that maturity curve. So goal here is you're you're scaling your business and your people and your skills with the technology and and not trying to get too far ahead of yourself as the strategic execution and and sequence is really important when you're building that that long term enterprise data, architecture AI strategy. So with that, I'll hand it to to Sam and Nadia who who will dive deeper here and share what what Workday is doing to enable some of these outcomes within the platform. Amazing. And, while Nadia comes on stage, I wanted to just go back, Michael, to a question on the data is everywhere and your if you know I thought that was a great point. If your data is everywhere, your AI isn't nowhere. How do you suggest the, finance teams work together with IT? You know, what what's the the people that have or the teams that have been the most successful, those CIOs, how are they navigating that? And, yeah, would love your advice on that. Yeah. And and I think, again, circling it back to you all and and Workday, You know, Workday the the implementation of Workday, particularly Workday Financials, is really a native catalyst towards that. Right? Just by the nature of the architecture of the platform, you're you're transforming your data model, your ledger. And so inherently, that that core is already there and working, and it's effective. And also through that transition, just through, again, sort of the the operating and and evolution support model of Workday, you know, all organizations know there is a shift. There there is a balanced management and governance that moves from heavy tech in in your leg its legacy, architecture to a balance between functional and technical. Right? So the foundation for for current or future Workday customers should already be well ahead, you know, certainly of of where they started. But it's, for me, looking upstream. Right? You're and particularly on on the AR side. Right? Your your revenue generating upstream applications that are, in in most cases, handling those event level customer transactions, it's the alignment of that data model to to the Workday transformed model where we see some form of legacy be retained. Right? And so looking for a unified data model, I always, you know, advise my customers, look upstream first. Alright? Because that's going to cascade through the transactional life cycle. And so if, you know, from a from a starting point for certainly what is a critical component from the business, you can align those inputs with the inputs in in Workday's data model. It's gonna make this scale up, but much more accelerated in in your overall journey. Got it. Thank you so much. Okay, Nadia. Over to you. Awesome. Thank you. Hi, everyone. My name is Nadia Oksa. Sam mentioned, I'm one of the product leaders in, AtWorkday. I've been, on the financials product for for ten years now. And what my team and I spent a lot of time on is talking to customers like yourselves about their AI transformation journey. And, there's a lot of excitement around it, and I'm really glad that we're touching on things like outcomes and governance because that's that's really top of mind for for a lot of organizations. And I'm sure it's a it's a big reason for for why you, chose to, attend today and give us an hour of your time. So let's sort of set the stage first. Right? In these conversations that we have with customers, what's evident is that AI transformation is not no longer just a proof of concept conversation. Organizations are moving from isolated pilots to scaling AI across their finance functions. And with that shift comes a need for governance, and frameworks that ensure trust, control, compliance. But to get there, we first have to clear some of the barriers, to that are slowing down the adoption that Michael mentioned. Right? He, highlighted some persistent barriers to AI adoption in, in enterprise such as workforce skills. Now this one in particular is one of those things that's really organizational challenge. No product alone can, you know, solve for something like that. But several of the challenges that KPMG's research has highlighted are where you really start to overlap between what a platform offers you versus what decisions you you make in in how you approach this transformation. So, you know, the strategy you take or maybe lack of strategy, the platform selection, the security concerns, misaligned operating models. So these are exactly the things that Workday is designed to help address. Because when you have an ERP, as Michael noted, that where your data, your workflows, your agents all live in a single governed environment. You're not choosing between speed, and and security. Right? You're not stitching together the best of breed tools and hoping that they talk to each other. Right? You're not starting from scratch on on a strategy because the platform, like Workday already gives you the rails to be able to sort of, go from there. And so that's the foundation that Workday provides, so that your organization can focus more time on, an energy on the roadblocks, but also on just your business and the outcomes that, you as a business need to deliver. And if you wanna see what happens when an organization tries to shortcut the platform problem, this is what it looks like. This is a pattern that that we see. I'm sure you've seen as well. You see posts about this on LinkedIn. A team gets really excited about AI agents. They wire them into a data lake. They extract data out of the ERP, and then they try to reconstruct, the the business rules through manual scripts, one off logic. Right? And then it gets too complex, and, you end up with something that's just vibe code. Right? It's, AI generated code that that nobody fully understands. Maintaining that is, is a nightmare. It's not trusted. And on top of that, the data is already stale, the moment that it left its source. So what you've essentially built then is a shadow ERP. Right? And and this is the hidden cost. It's not the AI license. It's the engineering debt, the overhead that you've created, and the governance vacuum that you've created around your financial data by doing that. And the reason that this happens is exactly the deterministic versus probabilistic distinction that I'll come to come to in a moment. So when you're pulling data out of a system like Workday, to run it through an external agent, you're losing those deterministic rules that make the financial AI trustworthy. Right? You're so you're left with a probabilistic system trying to reconstruct the accounting logic that it was never it was never meant to own. This is why Workday's recommendation is to keep agents inside the platform where the data lives, the rules live, the controls that you've already defined. Now the agent works within the system of record, not around it. But having said that, we have options that customers can leverage for also building their own agents or or leveraging agents built by our partners, but with the guardrails. And I'll and I'll share that, in a bit here. So when it comes to the distinction between deterministic and probabilistic AI, I think the architectural decision is what everything sort of depends on. So the central challenge that's in front of us is how do you correctly couple probabilistic computation with deterministic enterprise systems. Think about what an enterprise system was meant to do. No CFO wants their revenue recognition treatment to vary between journal entries. Right? It's meant to be deterministic. The internal control policies, the codes of conduct, all of those things are mechanisms, that exist because human cognition is probabilistic. And we need to make it reliable enough, to run a company on. Right? That's why those were created. Now we have computational cognition. We have a reasoning system that you can spawn and and direct at virtually any task. But what's missing here is, like, what's the equivalent of segregation of duties controls to an agent? What does materiality mean to a to a model? Right? So our answer to that is not to abandon, AI and finance, obviously, but it's rather to leverage everything inside the platform, the controls, the data, the business processes, so that, you could responsibly take your organization, into the future, with AI. How do we deliver on this? Well, we think about this in three dimensions. Workday is a agent system of record. Just as Workday brought consistency and auditability to your human and finance workflows, it does the same for your agents. So the controls are not bolted on. They are foundational. They've existed from day one. We are also opening the ecosystem so that you can build contextually rich agents on top of those guardrails that we already offer. So you're not starting from scratch, but rather you are building on a foundation that understands your business rules, it understands your org structures, it understands your financials. And through our partner ecosystem, we are extending those same guarantees across the broader Workday economy. So whether it's a Workday, native, agent or a partner built solution, or something that you built on your own. The governance travels with it. So govern, build, scale, that's how you move from AI that's impressive in a demo to AI that's actually reliable and useful and helping you deliver on the outcomes that you are responsible for. So speaking of being responsible, and so this is all of this is connecting directly to what, CFOs and, chief accounting officers are responsible for and are actually being asked to deliver on right now. Right? These mandates haven't changed, but the tools to achieve them, have. So the first one, greater efficiency by redefining your productivity, looks like. So not headcount reduction, but rather redeploying your best people away from those low value tasks towards decisions. That's agent skills for action. The second one is around delivering more value to the business through deeper insights. Not just reporting on what happened, but surfacing the why and the what. That's the agent's skill for insight and through some of the things that Sam will show earlier, you'll see examples of how we are doing this. The third one is the guaranteeing of the integrity of your data and mitigating the risk when you're adapting to change. So especially as regulation shift and business models evolve, so that's the agent skill for control. Talking about data, Michael alluded to this earlier. If you're a Workday customer, you've likely heard about our Intelligent Data Core. It's our ability to let you bring operational transactions, your business events from external systems into Workday and marry them with the data that lives natively in your ERP. When you connect that, you don't just have data, you have the marrying of that structured and unstructured data together. In Workday, that's blended to create a single source of truth for the business and for for AI, to build on top of. Right? That trusted layer, that granular data, that rich context is everything that the AI needs in order for it to be relevant, for it to be contextual, for it to be useful. To sort of share a little bit about, you know, how much data are we talking about. Right? Workday handles over a trillion transactions per year across 75,000,000 users. Right? So this is massive amounts of data. But it's not just a quantity of data that's important here. It's data that's structured. It's it's reliable. It's the same data that you report your financials off of. You trust this data, and that's super important. And then along with that, we also offer zero copy access. So meaning you can use, your reporting tool of your choice like Salesforce or Snowflake, without the risk of delay of moving moving this data around. Now beyond that, we think that agentic finance only works if you have the richest financial dataset underneath it. So that foundation. Right? We bring the data that really matters to finance, whether those revenue events, CRM events, in a system like Salesforce, your contracts, your documents, your billing records, all of it flows through that, intelligent data core or other products, and it comes into form this the solid foundation in our platform that is for people, for money, and and also, for agents. And of course, more importantly, all of that comes with an inherent flexibility and auditability that apart from like Workday offers. I'm going to hand it off to Sam now to move on to talking about more specific use cases across finance that we're solving for. And I'll come back later to talk a little bit more about how we're building for ROI and outcomes. Awesome. Thank you so much, Nadia. I wanna do one more polling question before I go to some of our our demos and walk you through a little bit more of the use cases we've been building, specifically around the everyone's favorite topic, which is ROI, and how do we measure that? And I'm curious for the use cases that you for those of you that have that deploy the one to two or multiple use cases, have you kind of had strong quantification, some pockets of ROI, maybe not enterprise y or kind of still too early to tell, maybe limited. It seems like some have, limited or no measurable ROI and some are not applicable. So just give it give a take a second to to weigh in. And I can see it looks like many of you are seeing still still too early to tell. That's so far and away the biggest one, biggest response. And then want to close that poll and do one more, which is around if you could measure some of that value, where would you be looking at, you know, making those investments and and selecting those use cases? Is it time savings, direct cost savings, quality control, data quality? We talk a lot about data today. Strategic resource allocation as well as just not not there yet in measuring. And it looks like productivity is the big the big focus. So thank you so much. Super helpful. I am going to now close that poll and share some slides. And kind of build off of what Nadia was talking about in terms of our agentic finance capabilities, the data, the governance and the control. And before I go into some of our use cases, I want to just paint the picture for those of you we had a couple of questions that come into. So for those of you that are new to Workday, welcome. We're excited to have the opportunity to help you learn a little bit more about us. There's a question on is Workday, an ERP solution. And while we don't call ourselves overtly an ERP solution, we are, in fact, a services centric ERP solution that brings the HR component into the powerful, financial management system and and solution that we have today that's powered with AI. And it it breaks the mold in terms of rethinking traditional recorder report as a business process, for example. It's traditionally linear. It's batch process. There's data silos, which as Michael alluded to earlier, when your data is everywhere, AI is nowhere. And that's exactly how we actually architected Workday from the beginning to have an intelligent data core that unites data together that rather than traditional ERPs that strip data away as you go through the recorder process, we maintain all of that data. We have contextual insight and that is, you know, something that we have from a data perspective. We have a unique business process framework that threads all of your business processes together, not just record to report, but contract to cash, procure to pay. And that is what creates the combination of rich context with business sophistication, business performance and processing that AI can truly learn from rather than having AI look across multiple silos. So we believe not only does work to have the most granular set of insight compared to any other ERP in the market, but as a result of that unification of data, the best AI outcomes and as Nadia spoke to the best deterministic outcomes, that's what we want all of our customers to be able to achieve. And so rather than as well Michael was alluding to kind of use case, specific investments that people are making. We are with AI, we're looking across a business process like recorded report and building AI across that to transform how it works to interrogate data. There was another question around data quality and how do I protect that. One of the use cases I'm going to show you is actually how we interrogate data and processes to remediate to surface them up as they occur, to remediate that risk, actually give you the ability to resolve it in the in Workday directly. So we're constantly looking for ways to test and elevate the quality of your data as well as well as mitigate operational risk. We're looking at how as well we can take that really rich contextual data and make it available for narrative reporting, making that easy to share. But also we've we've had this concept of real time insight in Workday. Identik AI is taking that one step further because of the rich contextual insight. Not only not only are we giving you that real time insight, we're helping you spot trends from the inception that they start to occur. It's a real game changer for when you're looking at ROI and elevating your strategic impact. You're getting to spot those trends from the inception so that you can do something about them right away. So I want to highlight three different use cases that are, you know, transforming the recorder report process. Financial analysis agent, this is where we're able to take a Genentech AI layered on top of your financial data, financial financial processes and allow you to query and ask questions of your data and even get prompted, on an upcoming trend and emerging trend that you may need to to mitigate. And one capability of that agent is also to help draft narrative reporting. When you have automated drafted, narrative reporting, that can lead to 58%, faster reporting cycles. That's an external benchmark that we've cited. I'll also show you document driven accounting. This is a concept that is expanding our automation capabilities and applying them to documents and giving you the ability to extract insight from those documents and transform that into action to mitigate risk and ultimately to have even richer, more accurate accounting that starts from that document. In the revenue contract example, we're seeing, potential value for our customers, in that they can achieve a 35 faster or improved time to billing readiness. And then I'll talk about the test suite. I mentioned that already, which is the ability to interrogate data. There's also another component that allows you to automate audit evidence collection, and we're estimating that our customers can save upwards of 80% of the time they used to spend on collecting audit information. I know everyone's favorite thing to do is a side job. I'm saving 80% of the time in order to do that. So I'm going to show you some quick demos that are bringing some of these agenda capabilities to life. We're also going to be opening up early availability programs for these for financial analysis agents. Revenue contract agent is actually available in a free trial. We'll talk a little bit more about that. But for those of you that are existing customers, we can happily share information on how to get involved in that as well as an upcoming test, test suite e a program. The audit agent is actually going generally available this summer in June, so we're really excited to have that available in mass. So but the first agentic capability I'm highlighting is actually the financial analysis agent. And what happened, like, it was quickly moving, is we are alerted to an emerging trend back to what I was mentioning, the ability to spot trends from their inception via Slack. And that's alerting the CFO to come in and look at that emerging trend. And despite the fact that this business, which is a retailer, has higher sales in the San Francisco office, it's also citing the fact that there's an emerging off contract spend issue as well as labor cost. And in this case, San Francisco store had a marketing campaign that was far more successful than they had expected that drove up additional hiring. So outside of the norm, outside of the plan, hiring and labor costs as well as additional construction building in the, as a result to support the campaign and in that specific store. So what we're surfacing is one, you have all of this really rich contextual insight in the application. The agent is alerting the finance team to the fact that there's an emerging trend. And you're able to drill into the root cause of that information, to ask questions to learn more immediately so that the CFO, the controller and team can quickly move Thio resolving that out of contract spend and even introducing new tests to surface that even earlier. Now in addition, let's say you're a post close, you've allocated costs, you want to use the financial analysis agent to write up a narrative of the store, how it performed, how it brought some of those those unexpected costs under control. You can use that, as I was mentioning, the ability to expedite narrative reporting. You can use the agent to draft that right away. You can create slide where to share that information. So not only are you getting the alert on the emerging trend, you can resolve the trend with with some of our other capabilities. But then you can draft that narrative. So you're sharing that across different store locations. And not only are you up to date on the drivers, the root cause, but you're able to share that out and create learning, opportunities for growth and making that bigger impact as a result of having that information handy and Agenca AI helping deliver on that. The next use case is one that I mentioned that document driven accounting capability. And so this is where if you're looking at productivity improvements, ways to bring productivity to revenue contract processing. And what Workday's done is applied to Genentech AI to our contracts. And what you're seeing is a side by side comparison of the original customer contractor order form compared to the transaction that's been drafted in Workday. So you can verify that the information is is is, correct based on what's been drafted in Workday that's done automatically. Huge opportunity to eliminate error. But as you're processing revenue contracts, you're looking at an invoices. You as a controller have to diverse multiple documents. And so what you're seeing in the demo was not only do you have the ability to compare for accuracy as as a result of using that agenda capability and extracting the data, but it was surfacing the fact that there was a discount missed. And it was going back to the original source of that information, the MSA, to say not only was a discount missed in the original invoice, let's point to the exact language in the original MSA and then let's rectify and re update the invoice to be processed appropriately with the discount volume, the volume discount applied. So a lot of automation there, but driving greater accuracy on dso something that our customers are using in early in as part of our free trial now and getting a lot of great feedback on the last use case. Going back to our data quality and integrity discussion today in the webinar is our ability to test and, look for errors in the data. And so our financial test suite is spotlighting the fact that there is a duplicate invoice that that has been identified and nobody wants to pay the same invoice twice. So not only is it flagging the that fact that there's a duplicate invoice, it's giving you the ability to communicate with the supplier to indicate, hey, this we've actually already paid this. Let's let's stop payment. So you're not having any erroneous spend going out the door. We're communicating with the supplier there. And then we've also got the ability to introduce additional tests. If there are other risks that you might be wanting to to mitigate, you can create or leverage a marketplace of tests. You can also stand up new tests and configure those tests. And then ultimately as well, we have the ability to automate audit evidence collection. So really, really, exciting. And ultimately, those three elements are one from a financial analysis perspective elevating impact because you've got access to that data and insight. You are elevating productivity with revenue contract agent to drive faster processing, more accurate processing. And then with the test we are looking, you know, to test and pervasively interrogate your data and processes. So delivering a new level of control and governance. So really excited about those use cases. And as I said, we're happy to give you more information about how to start taking advantage of some of those, right away. Okay. Nadia, wanted to expand a little on building out, ROI and and some of the things that you and your team have been, working. on. So I'll hand it over to you to to expand. Absolutely. So this topic is very close to my heart. There's a lot of noise in in the market right now about what a model can do, how many agents can you spin up, how fast the technology is moving. It's just a it's a it's a race. Right? And we we know that the outcomes at the end of the day, the outcomes of finance and accounting organizations care about have not changed. You still want a faster month end close. You still want invoice processing's cycle times to be reduced. You want your DSO down. Right? You want your journal accuracy to be better. Sendbacks to be reduced significantly. Like, none of these are new problems. They're the same problems that your team had before all of this, and these are a lot of the same metrics that your organization is being measured on right now. So an example of how we are building for ROI from inception, I'll talk about is the revenue contract agent that that Sam showed. Right? The outcome that we're solving for with that one was reducing the time to billing readiness. So how quickly a contract becomes actionable, and, and creates an accurate accounting event with the help of an agent. And critically, it's it's not just new contracts. It also, addresses amendments because in the real world, as you know, contracts change constantly. Right? So scope changes, terms get extended. And and today, when that happens, when that amendment happens, it triggers a manual process. So someone has to go and figure out downstream the impact and and go and update that billing schedule, the rev rec schedule, and draft the corrected entries. That work is time consuming. It's error prone, and it happens a lot. So we're building the agent. In fact, this is in the hands of a a number of customers today that proactively handles these these things. So when that contract changes, it captures the downstream impact automatically. It updates the billing schedule, updates the rubric schedule, drafts a journal entry. It makes it ready for for that human to review, so human in the loop, still very important, before it gets posted. And so your team isn't chasing that amendment and doing all of that grunt work downstream. The agents already doing identifying that and and doing that for you. So that is the model the agent drafts, the human approves, the system posts. The clarity on outcomes we think is critical to the ROI. We spend a lot of time with our customers to really understand what's most valuable. Right? And and the scope of these agents and what we should build and shouldn't build and so on is is directly a result of what we hear, from from our customers, our design partners, or early adopters. Now earlier, Michael highlighted some of the persistent barriers to AI adoption in the enterprise, change management, lack of trust in technology, the burden of getting IT involved just to get started. These are not new challenges, but there are real ones. There are ones that a lot of our customers talk to us about, and and they're the some of the reasons why so many promising AI initiatives, you know, stall or never really reach end users. So as an ERP, we are, actually in a unique position to be able to address some of these barriers head on. So we, because we already sit at the center of our of our customer's operations. Right? We have the infrastructure. We have the data context and the customer relationships. Right? So what does that look like in practice? So on the change management side, we can embed AI directly into workflows rather than asking users to go and adopt a new tool. When the agent shows up inside the screen and processes that people already use every day, the adoption becomes incremental. It's not a whole new change in behavior. Right? So a great example of it again is a revenue contract agent. When we went to, market with it, like I said, we we we put it we lowered the burden to adoption by putting it in customer tenants in a safe way. But at the same time, what it we didn't disrupt the user's flow. So if they were gonna be creating that contract manually, they could still do that. Right? But, along with that, they had the option to do a free trial, which, by the way, is still running if if any of our working customers who are on the call and use our revenue management capabilities are interested to reach out. But they are able to have that option to try it in that way. Now on the trust and transparency side, we can provide explainability natively. So surfacing why the agent made a recommendation, showing those audit trails, giving finance and compliance teams the visibility that they need to feel confident in the output is super important, and this is core to our design and how how we're building. So for example, we're building a cost and profitability management agent. Right? And it recommends allocation rules. It you you can drill down to see what is the allocation rule that it recommended, what are the calculations that it's based on, what is the rationale, and you can amend it. So that that explainability and transparency is built in from day one to help you build that trust and to and that goes a long way for for removing the barriers to adoption. Another one is around integration complexity. So with that one, we kinda have a structural advantage. Right? Because we we own the data model. We don't need a lengthy integration project to connect agents to the right sources. That work's already done. It's built into our platform. And then as far as getting started is concerned, like I mentioned, this is something we've been investing heavily in. The revenue contract agent isn't is a great example of that. We're really trying to find every way where we can deliver these capabilities to customers directly in their environment, fully functional with no configuration required, no IT involvement so that customers could experience the value immediately on their own. And when I say customers, I really mean getting as close to the business users as possible, but with the option for the the user or the admin to clearly opt out where it's where it's not something that they they need or they wanna turn on or expose and so on. So it's we're trying to find that that balance and and working very closely with our customers to make sure that we reach that balance every time. So the through line across all of this is the same. Right? We're not waiting for customers to clear the hurdles on their own, or really trying from our side to remove those hurdles by design so that you can get through this transformation with the help of a board. So with that, I will pass it on back to Michael. Thanks, Nadia. Really tried to get Sam to schedule this for Tuesday, for the last we've got, Taco Thursday here. You know, the this Taco diagram is really just how we distill down the concept of aligning, a tool to a use case in in a simple format here. And this is really just a a complexity progression. Right? Moving from simple, you know, single task executors on the left to to your highly sophisticated multi agent systems on the right that, really enable collaboration to address complex problems. And, you know, understanding this progression is is really critical to success in my opinion. It it it helps our clients certainly match the right type of agent, to the specific use case, really with the goal of ensuring that you aren't over engineering for a simple task or, you know, conversely, underestimating the infrastructure that's gonna be required for a more complex one and and to, you know, function effectively, especially as you move up that complexity curve. An agentic system network really requires an anatomy of of what we feel are eight core components to be successful. It needs defined goals. It needs a planner, reasoning engine. It needs orchestration. It needs tools to take action. It needs a knowledge base, memory associated with that, and and most importantly, governance, overseeing it, and evolving it. So it's it's really an entire ecosystem and not just a simple script or a model. Right? So we have this agent, these agents. We have this network. But what happens to the organization? Right? What happens to our people? The the skills that built your finance team historically are, you know, no longer the skills that will drive it forward. So that that's where, you know, we we align to this term, the the diamond workforce. Right? The the bulk of your human workforce shifts into that middle tier, and their daily reality is no longer about creating or processing data. Right? It's about interfacing with and and managing the AI and and its outputs. It's about handling those cons the the complex exceptions, validating those outputs, interpret interpreting insights, and and really acting as that strategic adviser to the business. You know, and and it's not something in in terms of evolving it that you leave to chance. Right? You have to intentionally redesign career paths. You have to think of talent acquisition, certainly upscale your existing teams to manage and interface in this new human machine collaborative workforce. So it's it's a complex, you know, cultural and operational transition. But but it's also, you know, one of the most critical conversations we're having with our clients today. Right? Because getting the people side right is the only way that that your AI technology investments will actually scale. And so addressing the business, the underlying operating model, and parallel to the technology is is really how our clients have been most effective in scaling those tiers. Awesome. Michael, thank you so much for sharing so much knowledge. Nadia, thank you for all that you're doing in building these incredible products for our customers. We are at the end of our time today. We had a number of questions. We tried our best to get to them as we went throughout the session. We'll follow-up where we were not able to get to some of them. The slides will be available. We'll have this on demand if you wanna watch it again or share it with some friends. And, thank you so much. We'll be having another webinar soon on some of our other upcoming AI agents. So thank you so much. Enjoy the rest of your day.