Name: AI for the Office of the CFO
Uploaded: 2026-08-19T18:16:35.860Z
Duration: 56 min 24 s
Description: AI for the Office of the CFO

Transcript for "AI for the Office of the CFO": Alrighty. Good morning, good afternoon, and good evening to anyone who may be joining from around the world or potentially after hearing this recording. Excited for everyone to have made it here today. We're kicking off Workday Live AI for the office of the CFO, and I'm really excited to spend the next sixty minutes with you, and Brandon, of course, right alongside me to share an overview of how Workday is delivering AI agents and just general AI features to the Office of Finance. Now a couple of quick housekeeping items here. This webinar is available on demand. So as mentioned, if you or your colleague wasn't able to make this and you simply wanna watch it back, it's gonna be available through the same link in which you registered. Additionally, q and a is available. Y'all are on mute by default. So if you have any questions, bring them to the q and a. And if you're unable to hear me for whatever reason, again, not gonna be able to hear you eating lunch or anything like that, so you can go ahead and unmute your speakers if there are any issues with y'all hearing me. And with that, let me quickly introduce our speakers. I'll kick things off. My name is Chris Toledano, and I am a financial solution consultant based in Atlanta, Georgia. Been in the Workday ecosystem for about six years, and in that time, I've had the opportunity to see really how Workday has evolved over those years. And, quite obviously, one of the most exciting parts for me is just seeing our advancements specifically in AI. And so I'm excited to spend a lot of time today talking about what some of those advancements are specifically in the office of finance. And with that, I'll pass it over to you, Brandon. Sounds great. Hi, everyone. Brandon Smith Daigle. I'm an enterprise architect with Workday, all things technology. I'm out of Colorado, and I have been in the enterprise application space for financials and HR for the last two decades. So excited to be with you all. Alrighty. Thanks, Brandon. Well, with that, we can, show our quick product statement here, last housekeeping item for now. And this is just a recognition that during our during the demonstration today, we're gonna be discussing some road map items. We may even cover some forward looking items, and we ask that any of y'all make purchasing decisions based on what is currently available. And I'll try my best to call it out when it comes up. From here, we have our agenda. Pretty simple for today, but we are loaded down with content and expect us to come right up to the hour. So kicking things off, I'm gonna be covering Workday's AI vision and advantage, how we are really starting to move into our opportunity with these different AI technologies. From there, we're gonna pivot to talk about AgenTic Finance. This is how we're bringing some of these different tools into our daily workflow in the Office of Finance and seeing some real outcomes. From there, I'm gonna pass it off to Brandon. He's gonna talk about how everyone can get started with Workday AI. And then to round things out, we're even gonna have the exciting topic of how you can even start to think about building your own agents. And so with that, we're gonna kick off with today's contents being Workday's AI Envision, and I would like to start with kinda grounding where we are today. It's been about a year and change since the market has really got introduced to AI and agents, and there's this common recognition that we're not quite getting the ROI that we had expected with AI. And although the models have greatly improved over time, value has yet to be seen across the enterprises at scale. And I think it's important to remember that this technology, it's still pretty new. And while it has been moving rapidly, we've had to really work hard to get comfortable and start to understand its capabilities first, maybe in our personal lives, and then start to translate that into how we can use it in our day to day at work. Now as we think about what this transition looks like from day to day to enterprise, this is where we start to have the need for things to be secure, reliable, and trusted. And when it comes to, you know, checking all those boxes, it can be quite challenging. And this is the reason why, top AI labs like Anthropic, OpenAI, they're hiring these four deployed engineers to help organizations figure out what can we do to bridge this gap. Well, for Workday, as one of your service providers, we have to be thoughtful in terms of how we're providing that, ensuring that the AI we're delivering is not just a bolt on or a simple chatbot, but rather something that's built into the secure trusted workflows that you use every day. So as we all know, AI is super powerful and helpful. I'm assuming y'all are likely here because you've used it before. But if you're not and it's it's new to you, let me tell you that AI is very exciting about what we have going on. And I use it in a variety of use cases. I use it at work. I use it in my personal life. And one of the early things I started using AI for is to help me recover my backyard. I bought a home about a year ago, and I moved in and the backyard was an absolute wreck. And so I went into chat, and I started to tell it that, hey. I need help recovering my lawn. And I said, okay. Sounds great. What is your grass? And I said, Bermuda. And so, sure. I can help with that. What is the climate? It's humid. It's sunny. It's Georgia. And then it responded saying, this is helpful. Send me a picture of your yard. And, eventually, I got to a point where the chat understood my climate, mowing habits, irrigation, and previous lawn treatment, but it took multiple prompts to get here and a lot of time spent. Now as we think about the context that AI needs to understand your business, this extends beyond just a simple back and forth or a data dump. AI needs to understand how your business runs, how it's structured, and the decision making processes that you have put in place. And so in terms of how Workday has set out to deliver this vision, we have done so by recognizing that there are necessary ingredients to deploy agents in AI in an enterprise context. The first being data. This is really the fuel for AI strategy, and it's underpinned by having our customers' most critical data being that people and finance data in a single space on a single data model. Next to that, we have context, which supports this data and is natively captured within our system architecture through things like always on audit functionality. The system maintains awareness of who, what, when behind every single transaction in the system, and this is gonna provide the AI this baseline that needs to understand your specific organizational logic. We also deliver it through multiple other capabilities today such as inline analytics or maybe an invoice that's flagged on the dashboard. This is going to empower our finance leaders to make high impact data driven decisions within their daily workflows. Now the last thing we need here is ensuring that we have compliance and governance, which we deliver through our guardrails. For example, Workday's unified security model ensures that only the right people have access to the right data, and this is going to then extend to our agents as well. And because Workday was built post Sarbanes Oxley, we have zero room for a lack of process, And this is really delivered through our business process framework, ensuring that we have clear parameters for who needs visibility and who needs to take action at each step in the process. And because these parameters are really core to our platform, our agents are going to inherit these controls from day one. Without these controls, we have what the market is now calling lawless agents, systems that are just operating in a vacuum, able to do whatever they want without rules or limits. And this is gonna introduce immaterial risk both financially and operationally. And so as we think about AI handling our critical people and money decisions, it has to operate within the parameters of your business functions to remain accurate, auditable, and compliant, giving you that necessary runway to safely automate your financial workflows at scale. Now with that, this brings us to Workday's architecture, and this is really how we are building these enterprise grade scale, enterprise grade guardrails to the platform that you're already running your business on today. And this is also what's allowed us to turn Workday from a foundational system of record into a system of agents. Let's start here at the bottom with our data in context. So this is likely familiar to a lot of our customers today. And, really, it's kind of recognizing that most of these AI initiatives oftentimes fail because they're built on fragmented data silos or maybe lagging reconciliations. Workday, it's different. We have our unified data model and object model effectively creating a web of context that our AI is going to natively inherit to understand your organizational context. Meaning that if we're looking at headcount spend, for example, it's not just seeing a number, it's able to connect that worker to the cost center and the regional tax compliance without any sort of costly integrations or cost system mapping. Beyond this, our agents don't have to learn your corporate rules. Again, they're inheriting them from day one, so your existing security, delegation framework, separation of duties are automatically enforced from the start. And this brings us up here to our core applications. This is where your accounting and your FP and A teams live and work every day. And so by embedding our agents right on top of this, right alongside these applications, we go beyond just a generic chatbot by instead providing you purpose built agents to shift your team from manual processing to more strategic oversight. Now finally, this brings us into our platform extensibility and ecosystem. I'm not gonna steal Brandon's thunder too much. I'll keep it high level. Really just recognizing that the Workday Data Cloud and Workday Build, we're really starting to open up the same secure architecture so you can connect external data and even go as far as build your own unique agents. Last thing I'll mention here before we kinda get into one of the first demos is Sana. This is our AI experience layer. One of the best ways we've figured out how to talk about it is think about the Gemini to the Google. This is kind of the what and the how you'll be interacting with our different agents. Now as we settle into this agentic future, we're starting to see agents working in roles right alongside humans, and there comes an inherent need to govern that agent in the same way that we would govern a human worker. And so as we want to continue our position as a trusted system of record, Workday wanted to ensure we had the necessary guardrails built to support this future of AI agents, and that drove us to create the agent system of record. And this is effectively how we onboard and offboard our agents. This is how we establish their skills and then the people who have access to them, and this is how we give that total visibility and governance needed to run these tools with confidence. So with that, we can jump into our first part of our demo. We're gonna log in here at in Workday as Teresa Serrano. She's gonna be using a little bit of an admin perspective today as we open up our agent system of record. And so I can go into my agent management hub. This, again, is gonna be our one stop shop to monitor, edit, and manage all agents in Workday. Now when you're coming in here for the first time, you're likely gonna wanna go to your unregistered list. This is gonna tell you everything you have access to based on SKUs you own and what is eligible. Now once you want to actually register a new agent, it's as simple as clicking a button, and then they're gonna appear here in your agent registry where you can take that next step to configure your agent. I'll I'll jump in a simple example here and show our financial audit agent. As we can see, it's already active and configured. And as we jump over to the skills tab, we can start to see and understand what the agent can do. Now within each of our skills, there's a description, security and tools, and we can see that the financial audit agent has two separate school, SKUs or sorry, skills that can be toggled on or off. And so that's going to allow you to really tightly control what sort of automations you wanna introduce into your different financial workflows. Now one thing I wanna mention here as well is that we have same security groups, but they can be assigned independently. So, again, depending on how broadly you wanna roll out these different AI agent tools or maybe having a little bit of a segregation of duties between tasks, you're able to assign different users to different skills within each of these agents. And this is bringing us back to those Workday guardrails, meaning that your agents are gonna natively inherit your existing security and compliance structure by default, and that's what we could see right here. I'll also mention there's a list of tools here. This is really just gonna capture kind of what the skill allows and what functionality it can perform. So, for example, it's gonna autonomously or, yeah, autonomously extract and compile supporting documents for procure to pay transactions to really help streamline your audit process. So just give a quick example of what these agents look like in setup and how we can start to see some other different skills and abilities. Now I use the financial audit agent here as a quick example, one, because it's generally available, and two, because we only have two skills today. Now every agent that we're gonna talk about, every agent you're gonna see has an associated road map. So as we continue to iterate and enhance each of these agents, this list is going to grow. And so, again, even for these agents that are generally available, their list of skills is going to grow over time, and this is going to reduce that need to have a costly reimplementation or maybe upgrade rather these skills are gonna become available for you to then turn on within your organization. Alright. So with that, we've reviewed Workday's opportunity to be a trusted leader in the space of building and governing AI agents in the flow of work. Now to show you how we're delivering this agentic future to finance organizations, I present to you agentic finance. This is Workday's vision for the office of the CFO, and we're delivering this through AI teammates and capabilities who work right alongside your finance professionals, alleviating that operational pressure so your people can focus on strategy and judgment while the agent is focused on handling some of those manual and repetitive tasks. And so to see what this looks like in practice, we've boiled it down to four pillars that you're seeing on your screen here. The first is placing AI at the core. We built AI agents for financial work designed to continuously handle rule bound tasks, maybe labor intensive tasks such as reconciliations, accruals, and variance analysis. And this is gonna shift your team from spending those days digging through transactions into seconds validating that completed work. Next to this, CFOs don't need more complex dashboards. You simply need answers. You need action. And because Workday uniquely unifies people and money on a single data model, these agents are gonna operate with absolute organizational context. And this is going to eliminate those data silos and reporting lags that you might see in traditional ERPs. And your agents aren't guessing. They're gonna be making decisions based on real time data. Next to that, in a volatile market, hard coded automation, it's gonna break the moment you reorganize or pivot your strategy. Work based architecture is fundamentally agile, meaning our agents adapt to your structural shifts dynamically, meaning as you change your business process or your company structure, our agents are gonna continue to work within the frameworks that you set. And lastly, we recognize Workday does not exist in a vacuum. Your ecosystem is likely quite vast, and that is why Workday is open by design. So regardless of where or how you want to build your agents, they're gonna be able to operate with Workday data now without friction. So as we think about how we're gonna practically deploy this vision, this technology, research from a top strategy consulting firm, revealed that finance leaders expect very specific quantifiable outcomes from Majentik AI, not just another tool that we have to learn. And so instead of chasing broad tech trends, the market is focused on investing on three critical themes being delivering insight, boosting productivity, and mitigating risk. And these outcomes are exactly what is guiding our product strategy. So rather than building generic AI and letting the market design its purpose, our teams are anchoring every finance agent on these north stars, whether it's a fully autonomous agent or a natively embedded AI capability. Now I wanna quickly talk about these two terms here. I'll be talking about agents and also maybe AI capabilities. These agents, they're gonna be acting inside of their governed workflows, like our financial audit agent or revenue contract agent we'll talk about later. We also have these AI capabilities. Think, this is going to be our decision intelligence that's built directly into your experience and help you understand and visualize your work to analyze data faster. Now one final thing I'll mention here before we get back into our demos is that all the agents I'm gonna be showing today are either already in our customers' hands or will be in the next six months in either a GA or EA status, highlighting Workday's continued innovation and progress. So to show you how we're turning our product strategy into execution under our fur first North Star delivering insight, I wanna introduce you into a new way in how we're approaching this being decision intelligence. Now oh, sorry. One more slide here. Forgot. As much as I try to encourage our customers, most enterprises don't live on a single system. The, the answers can oftentimes be generated pretty quickly by AI, but the challenge is after the fact, the decision typically breaks down after the answer has been given. And so teams are then pulling data from finance, planning, or their workforce. They're having to reconcile these numbers, chase approvals through meetings in Slack, and then having to manually update the system record. By the time we reach the next cycle, we've completely lost all rationale. No one really remembers what was decided or if it even worked, and we've identified this as a broken loop. Workday decision intelligence closes that loop. Through our trusted data, governed action, and measured outcome, we're creating a governed loop for reliable analysis, and that's going to allow you to have a better outcome in terms of how we can analyze our business. So with that, we're gonna now see it in action. I've created a quick link here where we can jump right into Workday decision intelligence, and this is built to be an intuitive, conversational experience to access information, build dashboards, reports, or start to better analyze and understand your data. Now we're gonna be using our GMS USA as an example for our company as we walk through a couple different examples of decision intelligence. And so it starts with a prompt. I'm absolutely terrified of typing live and doing a demo, so I'm gonna copy and paste it from my notes here. And what we're gonna ask you to do is to create a dashboard that's gonna analyze our total revenue and operating expenses for North American consolidation for March 2026 in USD. So rather than having to build a report or a model, Teresa simply asked the question in plain language, and this is where Decision Intelligence Foundation, being grounded in Workday's data and logic, it's able to actually pull real numbers. It's not guessing. It's gonna be able to provide accurate, explainable, and auditable results from the first prompt. Now as you can likely guess, building a dashboard takes a couple minutes to generate. So while this works in the background, I have a couple examples here that I wanna walk through for maybe what a conversational experience may look like. And so, again, it's gonna start with a simple question here. I'm asking to see a breakout of revenue by divisions with our positive numbers here. And just as expected, decision intelligence is able to provide those details as well as an analysis and a suggested next step. Now while this is helpful, right, we're just looking at simple data here we can gather in a report. Well, Well, this is where we can start to transform this through a conversational experience. So maybe we want to see this from a year over year perspective where we can start to better understand a lot under we can start to better understand and analyze maybe areas where we're growing or even areas where we're actually declining. As we can see here, we're having significant growth across two of our divisions, whereas we're facing decline in others. Once again, this is a conversational experience. It's asking us where we want to dig in to find more. And so in this case, we're gonna focus in on CPG, understand where exactly things may be going haywire. And so rather than having to pull apart this data in a spreadsheet, I can simply ask a question to investigate by customer and tell me what's really driving that decline, and it gets to work. It's gonna specify a report. Or sorry, I don't need to specify a report, filter, or an outcome. It already has all the context behind the data, and and the answer is not necessarily what the top line suggests. We can see that the majority of it is made up by several key customers here. And this is really the difference between just having a number and having the necessary insights is that we're actually able to understand what this data means and even have the information recognizing that we're looking at several customers who account for the data here, those who may have churned or are in major decline. Right? And this, again, is where we can start to bring this into action. I don't have the ability in this demo environment here, but being connected to our financials, our planning, we're gonna be able to write this back into our models. So rather than having to wait until next quarter to see what this change looks like, we can see that change in our models immediately. And so rather than having to, again, wait, we know those details right away. Now this is somewhat of what the experience could look like on a conversational level. We can ask a simple question and start to get some analysis and output. And we can then start to scale this up, thinking about something that might be top of mind for many finance executives being that board package. That's what I have here in this tab. Now part of the fun in meeting to test out new tools is really starting to understand what are the limits, what's possible, and that's exactly has been my experience with decision intelligence. In prepping for today, I was thinking what's top of mind for finest executives, and the board package was a quick and easy answer. Now full transparency, we have a little bit of a longer prompt here that I had AI help me build. I just mentioned this because I wanted to make sure I had all the right components of a real board package. And what you're looking at is going to be shifting this routine package assembly into a streamlined and governed experience. It starts here by giving us this executive summary, our front page, headlining what leadership really needs to know. And this is a dynamic dashboard, so we're able to move from tab to tab, seeing key color indicators, different variance insights, percentages, and analysis. And we can even see specific performance by things like dimensions where we can bring in those key Workday details into our analysis to see a graphical representation of how our organization is performing. And so in this case, this is output. This is helpful. But if we were to actually go into a board meeting, we're not gonna be able to share our screen every single time. And so I could even go as far as saying, can you generate this output in a PowerPoint? See, this is why I don't type live. Took a couple of tries there. So as it's thinking here, I figured this might take a minute. So I went ahead and already generated a different PowerPoint last night, same data, and that's exactly what we're seeing here. I didn't make any edits to this PowerPoint. This is the exact same output now in a PowerPoint format, and it generated it in probably three, four minutes. For the sake of time, I wanted to pull it up here real quick. Bottom line here is that we're able to take this data, take this analysis, and have it automatically generated for us where we can focus more on the review, the insights, and the outcome rather than having to spend all that time manually putting all of this data together. Alright. So with that, some time has passed. We'll double check. And so, look, it took four and a half minutes. Remember, we started here within decision intelligence with that prompt for a dashboard. We have one automatically created for us, and I didn't have to specify any details such as what information beyond total revenue and operating expenses. It knew to include things like operating income, the percentages, as well as different graphs to support this data. So, really, this again captures the power of decision intelligence here, taking a simple prompt, having the context of that worker Workday data, and being able to produce graphical outputs so we can easily understand, share with our team, and use for future business analysis. Alright. So with that, what we just walked through is some of the early capabilities of decision intelligence. We start with a prompt that created a full dashboard with key insights, and then we also review a conversational experience as well as scaling things up and actually looking at a full board package created from a single prompt. And so I'll I'll just say it as it is. We only scratch the surface of decision intelligence. There are other SCs out in the field who could show a really robust demo. So for anyone interested in learning a little bit more, I highly encourage you to reach out to your account teams to see a little bit more of decision intelligence in the future here. Now with that, we're gonna keep driving forward here. I wanna move to our next demo here, really focused on a critical yet manual process that contains loads of risk, being a process of transcribing contract information into financial data. Now typically, your revenue accountants or procurement team may spend hours reading through contracts, interpreting the nuances of the agreement, and then manually keying those details into Workday. And when this happens, your productivity is destroyed and the data with the data entry and the risk is just going to skyrocket as one small error can lead to a misstatement violating things like ASC six zero six. And so these were the leading drivers for what caused us to create the revenue and supplier contracts agent. Now I'm gonna focus on a quick example here, centered on the revenue contract agent. And so this builds on Workday's contract intelligence, allowing us to ingest documents and have them seamlessly transcribed into Workday contracts here. And so although these agents are built to drive productivity for these manual tasks, we can also see how we can drive them to ensure we have accuracy and completeness of data. Maybe where it's unclear of a specific sales item, it's going to route this the user ensuring that we have that human in the loop rather than just guessing with the data. And the same thing will apply as we go throughout our sales item as well as making sure we have specific details like our billing schedule all set up appropriately so we're accurately billing our customers. And so this experience is built to be intuitive, but that doesn't mean you're not gonna have questions about your data. So we can use AskWorkday using plain language to simply understand the data decisions, and you're gonna be provided with the necessary information and context to review and support it. Now beyond creating contracts, we can also help you spot issues as accurate billing is paramount for pretty much any organization. And in the case where maybe you have something like a discount rate table, the agent is gonna be able to identify when that criteria is met and suggest an installment to automatically adjust. And this is all possible because these agents are built in line with that Workday data just as we saw in the previous demo, providing them with the necessary context and data to deliver this automation. Now the same build approach is what we use for our supplier contract agent, really designed to work as an agentic teammate to own some of the manual processes that are really key to organizations and align your accounts to focus on more strategic work. Alright. So closing things out here. I wanna make a make sure I save some time for Brandon. I'm gonna focus on one of the north stars that's key on every leader's mind as they consider AI investment being risk. Now for our agents, they're natively mitigating risk by having the necessary context and making sure we have the human in the loop for critical activities. But risk extends beyond just, you know, trusting an agent. It also comes into processes like the financial close itself. So, you know, in many cases, our financial close could have hundreds of checklist items, manual reconciliations, and, ultimately, you have a controller who's having to stitch it all together under a tight deadline. I'm gonna be walking through how Workday is able to move you from really a chaotic checklist process to one that effectively manages itself. And so I've gone into a new demo environment here, really capturing a vision of where we're hoping to go within our period close, in the near future here. And so at this point, it's time to close the books. We're gonna be logged in now as Sarah, the account manager, who's gonna start on her daily brief, where she can see it's already 83% complete. Now we can see all the work that's already underway. We can see different activities that may require our act action or visibility, and we can also see different agents that are in line with this process, surfacing up key places for us to go in and then take action. Like, for example, our financial audit agent running tasks behind the scenes here so we know where we need to actually focus for the day. Now in a process as critical as close, it's gonna be important that we have the auditability and insight throughout. So Sarah may wonder, okay. Where does this 83% come from? At any point in time, we can drill in, understand the details that's gonna capture how our closed agent is reading and interpreting the organization's closed checklist, Reading all the different tasks, statuses, dependencies, and work that's been completed, it's able to provide us this clear list of our closed readiness. And so we know really at a glance what is complete, what's in review, and what is yet to be started. Meaning, could Sarah can really start her day knowing where the close stands instead of having to spend the first hour of her day really figuring that out. Now at the same time, we're never gonna strip away that visibility. You can also drill into any work stream here, and you can see some of those details specifically. Like billing and receivables, for example, we can see has 24 completed items, and we can see the activity, the status, and who's been involved in the work stream, giving us a clear audit trail throughout. Now at any time in the closed process, we can actually also check out our full closed checklist, which is going, in this case, live in a worksheet. And this would be this is gonna be available whenever Sarah wants to jump in and review it. However, the opportunity with our closed agent here is kind of removing Sarah from the spreadsheets and allowing for her to focus on actually understanding what are the key tasks that may require her attention. Now I had mentioned this earlier. I wanna call it out again. There are several agents taking action here to provide our daily brief. Again, for example, with our financial audit agent running different controls in the background, we can see that we are actually up against a missing revenue accrual that's gonna put our close at risk. Now this is typically where that spreadsheet would be spreadsheet work would begin, gathering volumes, maybe applying new tiered, pricing, handing these calculations of unbilled fees across our different, our different accounts. And so this is going to be a spot where we can start to drill in and see what's actually taking place here. In this case, our agent has identified a 142 mandates that'll arrive a little bit too late for the current billing cycle. It also takes it a step further where it's able to use the tier rates to calculate the revenue that needs to be accrued in order for this to actually, close during the period. And while keeping a human in the loop the whole way, the agent has identified this need as well as it's already recommending the necessary action to be taken, where it's going to have staged an accrual, radish the controller, and it's gonna pause the EMEA consolidation based on this necessary dependency. And so we not only have the necessary recommended next steps, but we have that complete visibility and insight as to what is going on. And so for a risk that may have been surfaced days later, burdens out of consolidation, we're able to catch it before it even gets there. And once again, we're not at a point where we can blindly trust AI. We ensure that we have the human in the loop so we can always scroll down and understand what the agent reasoning is. We can see cases where it used different policies or different agents to actually come to its results. For example, here, we can drill into our revenue recognition policy. And if we wanted to double check the agent's map or better understand the underlying policy, we can always confirm the source of reasoning and then go back and verify the result. Now once we've gone through, verified our evidence, we're happy with the treatment, we can go ahead and start to resolve this issue here. All we have to do, we can verify what the journal looks like. Again, the accrual has already been automatically staged for us. It's simply going to be a review and approve process, and we can see it has all of those necessary comments and work tags included. Once I'm set here, I can approve my accrual. We can see the details confirming that this accrual has been posted. And when I jump back to my dashboard here, we can see several updates have already taken place for us automatically. For example, our close readiness, it's moved to 85%. And as we look down below, we can see that that action is now resolved knowing where we've taken action and what is next needing our attention within this period closed process. And all this is being orchestrated by our closed agent, ensuring that we are up to speed. So, really, just a quick summary. By leveraging this leveraging AgenTic Finance, we're able to quickly transform what is typically a delayed and panic period close into a streamlined governed process through the closed agent and how we could surface some of these anomalies and action items. So let's transition now to yet another key piece of our period close being reconciliations. In this case, we can see that, Workday's already auto prepared 35% of our reconciliations along with generating a summary as to what is complete and what needs to review, meaning that your reconciliation team already has a third of the work done before they've even started their day rather than having to start from a blank workbook. Now we can drill into our first item here, better understand it, drill into our trade receivables, where we can see some of the key details here. In this case, we can see that we have, the ledger and the supporting balance are in agreement with no difference, as well as any necessary supporting commentary as to some of the key findings and insights. And here we can see specifically we have DSO that has increased as a result. So rather than having to typically, we'd have to switch screens, maybe jump in back into our spreadsheets, start to understand what is going on here, Sarah can simply ask the agent. She can use a simple prompt, say, I like to see the DSO trending, and, you know, understand if it hadn't been late, what would be the result? And so in a case where, again, we have to step out to better understand what's going on, Here, our agent is going to model the trend, and it's gonna give us a direct answer saying that the increase isn't necessarily a collection problem. It's because we were actually missing specific billing that was meant to take place. And so that's really kind of the payoff here is the agentic regioning. It doesn't just report the number. It connects us back to the original root cause. Now with the support of Workday's agentic teammates, we can really start to better understand trends behind the reconciliation, and the process stays informed and governed throughout. And once again, because we want visibility of the agent's actions and reasoning, we can always review those details just as we had in the previous task, understanding which policies were applied, and we're always able to drill into them directly to see those relevant details. Now with all of the amounts tied out with the necessary policy information included and all that commentary and understanding kind of what is the source of these details here, we can go ahead and approve this reconciliation. So we can see that it's moved to the status of approved. And once again, as we return to our daily brief our daily brief, we can see the statuses of updated once more. And throughout, our accounting manager didn't have to go in and live inside this closed checklist. Rather, our closed agent was facilitating and supporting us throughout this process. And so as we move forward in our close, all this underlying work that was completed and approved has automatically been tracked for us. Now I'm gonna bring us to a close here today, really just recognizing that we just saw what goes beyond a faster close checklist. It was an agentic powered close that tells you where it stands. It shows its work, and it dynamically updates based on the progress, taking place throughout the period close. And so this is just one of those ways in which Workday is turning insights into readiness and starting to give your productivity back from your spreadsheets and help late help eliminating those risks before they ever reach your auditors. And so what we saw across our vignettes, this isn't just a conceptual road map. These are gonna be the key steps and direction that Workday is building towards with the agentic finance. By bringing together your people, your pennies, and your agents on a single platform, we're positioned to deliver these three core outcomes, our better decisions, service through insights, and a simple conversation just as we saw within decision intelligence, productivity through our revenue contract agent where we're eliminating that time consuming bottleneck of having to enter that data, and lastly, mitigating risk through our financial audit and close agents as we move your organization from random transaction sampling into a continuous close by catching those misstatements before they ever reach your consolidations. So these are gonna be the key elements that we're set to deliver with AgenTic Finance in order to free up your finance teams and enable them to focus back on judgment and strategy. And so with that, I've shown you all some of the powerful ways that we're moving towards agentic finance and Workday. And some of y'all are probably wondering now, how do we get started? I'm gonna pass it over to my colleague Brandon to kinda answer some of those questions. Off to you, Brandon. Great. Thank you, Chris. And thanks to all of you who've added questions in the chat. Please keep them coming. That's why we're here. That's why we're live. And so I'm gonna talk to you a bit about how can you take all of what Chris showed and bring that to life. So let me just make sure I got my screen rolling here. Alright. Excellent. Okay. So there are three major steps for you all to take to be able to start harnessing the power of what Chris was demonstrating and sharing. The first is you would sign the universal main subscription agreement. So we'll talk a bit more about what that is. If you're already on that agreement, wonderful. But for those of you that aren't, we'll cover that. And then we've moved to a new model called flex credits, which allows you to try new solutions with Workday, without paying anything, get exposure to them, make sure they're right, and then consume what we call flex credits. And finally, our platform console, agent system record, and other vehicles to make certain you understand exactly how you're using the Workday artificial intelligence capabilities. So if you're not already on the universal main subscription agreement, this agreement gives you access to new innovations faster than ever in a way that you have access to play around with them in nonproduction environments, learn how they operate, ensure that they are exactly what you want. We've also consolidated a lot of commercial terms to simplify it so that your contract team will have less time reviewing things and ensuring that as you, activate new capabilities within Workday, you can do so with less administrative burden. Now importantly above all, with the universal main subscription agreement, this does not in any way obligate you to adopt AI capability. So we like to emphasize that simply because you can move forward with your legal teams, raise your hand and begin this process and know that it's not a commitment to start using AI. It's simply opening the door for you to do so faster and, with greater agility than ever before with Workday. We have a migration kit available to you all so that you can have conversations with your legal team, start to work through that process, make it really streamlined and simple so that your functional teams can start to play around with new agents and features like Chris showed and start to, to adopt those. The QR code in the bottom right will take you to that set of resources. And that includes videos, presentations you can walk through with that team, with your internal teams to make them confident and begin the adoption process. Flex credits is a new concept, not just for Workday, but of course for many enterprise vendors. And it's a way for you to be able to pay for what you consume. So rather than perhaps subscribing to the financial audit agent, and looking at that based on the number of seats or other, consumption, this is based on what you use. And so we have aligned to this particular process for new agents as a way to make sure that you're paying for the value you realize, that it's predictable and fair to you. You can forecast your usage, and you can test in nonproduction. So, again, being able to play around with the financial audit agent, with decision intelligence, with other solutions in a way that you can ensure it's exactly right for you, and then move to production where you'd be able to predict your consumption very reliably. So flex credits new part of the model to gain access to these amazing capabilities. And then finally, you have access in line of sight to how you're consuming flex credits via a new capability called the platform consumption console. So this is going to help you with understanding your use, understanding the rates for the flex credits, what you have as part of your entitlements, and then importantly, at no time do we just cut you off. So if for whatever reason you exceed your credit balance, just know we're not going to interrupt service for your teams. Now just to show you a bit more about adopting, a common aspect we know is you need to socialize what are these AI features with your internal governance teams, bringing them to the same level of comfort about how Workday approaches AI. So we're gonna show you the fact sheets on community. And in addition to that, how can you manage contribution where it is necessary for a feature and ensure you're confident what type of data is de identified and used in order to use the feature. So with that, I'm going to change over to share a different part of my screen here. One moment. I'm not as slick as Chris is with swapping over, but we'll get there. Alright. Here we go. Alright. So for the fact sheets on Workday community, you can find fact sheets for every single AI feature that we have. In particular, this is the financial audit agent. And so your team is going to have clear line of sight to what the feature is for, what inputs are involved, what outputs, what's the technique of artificial intelligence or machine learning employed. If it uses a model, does it train? What type of training? Do we do bias mitigation because it's, important for the feature? Not all features are in the point of an employment decision or financial impact that require it, But this is your one stop shop. You would take this and use this as the conversation piece with your legal organization, with your, governance counsel, whomever it might be that you need to ensure is highly confident that Workday above all gives you control, transparency, that your data is secure, and that it's auditable. So these are available for every feature. Highly encourage you refer to them as you are deciding what features are right for you and you're starting to opt into them. Now speaking of opt in, the other aspect is how you go about that. Chris showed you the agent management hubs, so that agent system of record. But in addition to that, we also have our data contribution experience. And what you're looking at here is the ability to wholesale opt out, should you so choose, to a product line to say, we don't want to contribute any data for this particular area. Naturally, you can do it at another level of granularity. So if we were to say, let's look at the financial management area, for example. Hopefully, I haven't logged out of my tenant. In each area, you will have the ability to then traverse even further for a given area to say, if I opt in or opt of particular features, what specifically, date what data is contributed? So here we're looking at say financial management for supplier invoice processing, just as an example. And we're able to see what data would be collected if I remain opted into this. And so your team has that full visibility to see what data is, and you will notice that it is really not going to be person names and sensitive information. These are going to be identifiers necessary for the machine learning and artificial intelligence to process and provide recommendations, to do the analysis for that feature in a way that is always based on a responsible AI feature. And of course, you have the ability to indicate if there's a country for which data should not be contributed. Certainly the case in some countries where you want to review with a works council or other organizations, but you were always in control of this. That's the important takeaway here is your teams control that. You're not obligated at any time to use the capabilities of the AI unless you choose to. K? So you saw how easy it is to understand the feature, to interpret it. It's not in highly technical language. Have that conversation. For those few individuals in your organization that have access to that data contribution screen, they'll be able to, to understand the data contribution and socialize that with your organization. So our responsible AI is something we take very, very seriously as I know all of you do. The QR code at the bottom right will take you to specifically a, a resource for your team to understand Workday's responsible AI approach. But just know that we have ISO 42,001 certification by a third party vendor. We aligned to the NIST standards for AI, the EU, AI act, and other regulations because we are, your data processor and you are the data controller across many very highly regulated industries worldwide. So what's next? What's on the roadmap? Chris showed quite a few things. Just know that we are continually developing and maturing our capability, and we have a very feature rich roadmap across both our workforce management, human capital management, talent, as well as finance, financial planning, procurement, legal. So please do keep your eye here for, and I should say also our annual Workday Rising Conference on Workday Community for announcements on what's available. The things that are in dark blue, those are in early, Brandon. access yes. I'll I'll stop you here not to hit a source spot, but we're still looking at your data contribution screen. Oh, goodness. So you for telling me that. That is a. So make sure we flipped over. here. Thought I. had, we'll get there. Apologies. Good news is we can recover from this quickly. That's why there's two of us. Alright. You can see it now, Chris? Let me see. There we go. It's coming through. Nicely. done. Alright. Okay. Alright. So feature rich road map, keep an eye on our conferences and, announcements on Workday community. A lot coming to make sure that your teams can save seconds, minutes, hours of their days across many different aspects of the business. So last part we'll talk about is how can you build your own agents? How can you govern those and put them to work with the Workday platform? So our vision is to ensure that at all times, you continue to harness the power of the data and the context within Workday so that your agents are never off the guardrails that you've already established. Building on top of those core applications you've come to know, trust, and work within Workday, but bring the teams of agents so that whether it's a agentic HR, finance, IT or legal, your organization is able to operate more efficiently, effectively, make decisions and importantly, more than ever interoperate not just with Workday, but ever other solutions. So Workday's data cloud available this fall, it's gonna allow you to have zero copy interoperability with supported platforms and interconnect with AI and machine learning solutions in a governed way more than ever. Our workday build capability, which we'll touch on a little bit more, allowing you to build your own agents and use agents to build solutions. And then finally, as Chris showed, governing all that with the system of records so that you're always certain you have one more layer of security on top of your existing Workday security as to what solutions can interoperate with Workday in a conversational way, what skills are they having access to. And then of course connecting in the panes of glass that makes sense for your teams. Whether that's productivity applications like Slack and Teams, Copilot, SharePoint, etcetera, or even Workday, Sauna enterprise so that wherever your individuals are within finance or otherwise, they can get answers, get insight, and move on with their day. Workday build is the capability that allows you to both build agents on Workday platform with capabilities like Sonae agent builder, orchestrations, extend, but also build with Workday so that you can, build your intelligent capabilities with Workday data cloud, also interoperating with agent ready tools like MCP and a to a touch points, and always with those guardrails within Workday. So you know there's no back doors, everything's secured. Now a key capability is how do you build an embedded application Workday that understands your organization, can help meet the needs of your specific organization in a way that doesn't require you to create a bolt on or, another barnacle solution in another tool. So that's where our developer agent actually allows teams to create robust, sophisticated, multi step applications in a conversational way. And so that's what you're seeing with, our evolution related to AI machine learning. So if you wanna learn more about that, that's part of our Workday extend professional developer agent, incredibly powerful way to build agents, to build applications on top of Workday, work with Workday, and extend it. Now overall, Workday has spent decades ensuring we build products and partner with our customers to ensure that we establish a leadership position in the market as a way to further create value for you all. We've been listening to you for decades, ensuring we invest in places that are going to move the needle for your teams. And so we're very appreciative to be the leader in the magic quadrant for ERP finance. We take that very, very sincerely as a way that over twenty one years of partnership with customers, building solutions, ensuring that your datasets are high quality, that we embed capability into the Workday tech stack and into the experience, but at the same time bring that so that if your users are in other panes of glass too, you're gonna be able to connect, be efficient, effective, and ultimately successful with your workday investment, for the entirety of our portfolio. So with that, if you wanna learn more about agent capabilities that Chris showed so well or even more capabilities across our footprint, You can scan this QR code. We have tons of videos, resources you can share with your colleagues internally. You can share this webinar. It is recorded and you can share it after, but, just know all available to you and we're very excited and eager to partner with all of you as our customers, for a very bright future ahead. Chris, you wanna cover this one? Yeah. Sure thing. So as y'all likely know, we have rising coming up, and looks like we got a little code here. But, Brandon, this is your slide. I don't wanna steal your thunder. So, I mean, No. just let you go. Apologies. I yep. So we have a session at ITC Vegas. You can scan this code, save someone, the registration. And in addition to that, of course, our Workday Rising Conference also coming up in October. So we look forward to that. So we appreciate everybody attending today. Thank you for the questions. Always here for that. And with that, have a great rest of your work today. Thanks, everyone, for joining. Take care, y'all.