Name: [Americas] Introducing Adaptive Pro: Unleashing Limitless Decision Intelligence
Uploaded: 2026-05-13T18:31:06.096Z
Duration: 59 min 30 s
Description: [Americas] Introducing Adaptive Pro: Unleashing Limitless Decision Intelligence

Transcript for "[Americas] Introducing Adaptive Pro: Unleashing Limitless Decision Intelligence": Alright. Hello, everyone that's out there. Thank you so much for joining us today. Good afternoon. Good morning. Good evening wherever you might be. I know we've got people all over the world dialing in. This is really exciting, and, thrilled to be one of your hosts today. My name is Ben Pierce. I am the general manager of the adaptive business unit here at Workday, and we've got a great, show and tell for you today. So thanks so much for joining. Let's jump right in. Couple housekeeping items. All attendees are gonna be muted. All cameras disabled. Try to avoid, you know, the craziness and the conflict of dogs or or babies or things like that. We are recording this, and it will be shared with everyone. So you'll have this to refer back to. Throw it through your favorite AI tool or notebook LM. And then please feel free to ask questions. Thanks already for everybody saying hello in the chat. Hello to you out there. But feel free to throw questions in the q and a. And if we can't get to them, we will answer them after the webinar concludes. Alright. And joining me today, I've got two of our great workmates, Kate Bassett, who's senior director of product management, and Nolan Southfield, who's gonna be showing you some really cool stuff today. He's our senior solution consultant. And here's our agenda. I'm gonna talk a little bit about our AI vision, introduce AdaptivePro, which is the first SKU that we've introduced, I think, in close to eight years here. And then we're gonna show you some of the cool things that are in Adaptive Pro, and then, Kate's gonna walk you through the road map and, what we're building and what we're delivering. And, of course, we are delivering things very fast. As you probably were aware, Workday has a release cycle of, r one in in the spring, which we just released in March, and then r two in the fall. But at this pace, as you see, like, Claude announces something probably every single week. We're doing the same. So we're actively launching new skills, new capabilities for these skills on really a weekly basis at this point. So just, take the product statement into into account that, you know, some of the things you're gonna see here are definitely future forward. And so how do we think about, helping our customers, which over 7,000 customers today are are in the adaptive family? About 4,500 of those actually don't run Workday for HCM or financials, and so they're what we call adaptive standalone or adaptive only customers. And as a planning solution, our job is really to help every company by providing clarity as they embark on what is going to be and is already becoming, probably the most profound business and workforce transformation in history. And AI really does continue to change the game on a weekly basis, but it's not just changing work. It's changing how companies are planning their cost structures, where they invest, and ultimately, how they're gonna win going forward. And if you're not planning for that future, then you're probably planning for a world that's gonna no longer exist. And part of the big blind spot that we're seeing across our customers is that you can't build a financial plan anymore, especially maybe in the next twelve months, but certainly not beyond that without considering the impact of AI in your business. And it's not just the impact of AI on work as I said. Although you do need to understand how in AI is gonna impact the work that gets done and what that cost as an investment and what the trade off is there. And and Workday is uniquely suited to unify both of those. But every planning system has a breaking point, and I've been doing this for decades. And as I've told, like, Paul Barnhurst if you don't watch Paul Barnhurst, by the way, or follow him on LinkedIn, he's a great follow. I was on his podcast, a couple of months ago, and we've been kind of telling FP and A, hey. We're gonna give you back the a in FP and A. But we've really just made the piece smaller. And the challenge with that is that not every single planning solution can take in all of the data that is required for FP and A to do their job. And so what ends up happening is they implement Tableau or maybe it's some Power BI, but ultimately, even in those analytics solutions, you don't have the data in the right format or the right view to answer the questions that you need. So where do you end up going? You eject into Excel, which is the, you know, tried and true, planning solution that every single company uses. And none of those solutions really can do all the things that FP and A needs to do their work. So what are we attempting to do and what what's our plan? We're calling this, limitless decision intelligence. And the reason that we're calling it that is because it's no longer about planning, it's about making decisions. And in order to make decisions, you need intelligence. In order to have that intelligence, you, a, need the data in a semantic context window that AI can understand, And then you need, really, AI to be always on, monitoring your business for you. So instead of waking up to an email from a board member of the CFO that says, hey. Why are sales down? You wake up to an email from your planning agent that says, we got actuals overnight. Point of sale data said this. I can tell you right now that here's where all the variances are. Maybe it's this store, this product line. And, oh, by the way, this is how it's gonna impact your forecast going forward. But not only that, it should be able to proactively then provide you with a number of different scenarios or simulations. Maybe it's a full Monte Carlo analysis that says, hey. Here's the spread of things that you could do. Here's the three options that I'd recommend. Let's dig in. Now after you've dug in, well, every AI solution get should get smarter. Right? So as you make decisions and as, you know, you are going through your planning process, AI should be able to mark down and take context of those decisions and then get smarter over time. And that's really the, the vision and what we're building to as we go forward. So how are we approaching that? First and foremost is providing the ability to bring more players into the game. If you are today using Claude, for example, and some of our customers are, they're maybe dumping data into Claude or something. That's usually single player mode, and it's usually only for, you know, the super the ones who have the super admin access. We would like to get everybody into the the game, whether you know a planning solution or not, whether you understand the chart of accounts or not. You should be able to ask questions from any interface, and that could be Claude, that could be, ChatGPT, that could be Slack, that could be Teams. It could be the, you know, logging in through Adaptive or it could be through Workday through their Asana interface. So wherever you are or whoever you are, you should be able to access this and you should be able to participate not only in the planning process but in analyzing your business. And in order to analyze your business, you can't be bounded by the amount of data that you can put inside of a cube sheet or a model sheet or whatever it might be. You need to be able to layer in any data whether you have that in your planning system or not so that you can make fully, informed decisions and drive into the amount of detail that you need in order to explain what is happening and then make decisions going forward. And as I said before, it's no longer about just bringing everybody into the game. It's about giving superpowers to all of the users. And to do that, we're bringing an elite team of agents that are gonna continuously scan the environment and actually proactively come up with scenarios, and analysis for you. So how are we gonna get there? The first part is is building something that is gonna unify planning analytics, and we're gonna show you that today. But doing that in a way where it's in a governed governed intelligence loop. And what do we mean by that? As soon as you take data out or you provide data maybe even through MCP or or a, APIs to Claude, you lose a lot of the context and that's in the system built in the semantic layer that's already in your models. And then on top of that, you start to lose governance. Right? Who can see certain things? And we've seen from some of our customers that they've gotten their employees have gotten access to certain things they shouldn't get access to, as they've collaborated on things leveraging the AI tools. And so we're providing a not only the semantic layer that's within adaptive, but also the ability to bring in and model data that's not an adaptive together and create that semantic layer across both of those things. And we call this doing this through enterprise rails, which basically means, like, it's already governed, it's always secure, and you can collaborate on it with other people, so that it's not just single player mode in Excel or cloud or something like that. And then lastly, we've got a massive effort going on to drive towards what we're calling workforce intelligence. And so every Workday customer, you may be out there and you have HCM, you know that Workday or any HCM system has been built on job profiles. And a lot of those job profiles then break down into certain skill sets and what levels of skills they have. Going forward, AI works based off of tasks. Right? And so we need to understand what are the different tasks and increments of work that people are going to be doing, and then what portions of those could we make trade offs in to allow AI to take over some of those tasks. And then what is the the business benefit? What is the trade off of doing some of those things? So welcome to limitless decision intelligence. This is what it's gonna look like. We've always had the calculation engine, the Elastic HyperCube. And one of the benefits of being an adaptive customer, hopefully, you've recognized is that with the Elastic HyperCube, you're not breaking the problem down into multiple different cubes that are disconnected that have to move data back and forth. It's all in a singular model, which means you can change a lever and automatically see it ripple through every single formula and calculation all the way down to net income, EBITDA, or whatever, down to net income, EBITDA, or whatever financial metrics that you might need to look at. In addition to this, we recently just launched the planning agent in March, and there's a number of different skill sets around that, which are inclusive of data exploration, so help me understand this number, variance analysis, why what is driving, the differences here between maybe our actuals and our plan or maybe it's different versions of the plan. And now with adaptive decision intelligence, which we are going to be launching at Gartner here in a couple weeks, so we're giving you a sneak preview of it today. It's an it's a workspace where you really can do ad hoc work, in a in a environment that's actually powered by cloud, but has all of the context, security, and all of the collaboration and control that enterprises need. You're gonna see that shortly. And then, of course, the ability to leverage that across any different, user interface, whether that's native and and adaptive, that's across Sana, for Workday customers, or it's across Cloud, Chachipity, Slack, Teams, as I mentioned. Okay. So with that, I think we're ready to see it in action, and I'm gonna call up Nolan Southfield to help us jump into it. Alright, Ben. Thanks a lot there. And, hello, everyone. My name is Nolan Southfield. I'm a senior solution consultant here at Workday. I'm based out of Chicago, and I support Workday Adaptive Planning. Prior to prior to joining Workday, I've spent over twenty plus years in the in the in the world of finance going through probably more month end closed processes than I care to admit, but I did end my career as a director of FP and A whereas lead in building teams. Before we get into the demonstration, I do wanna kinda set the stage of who we're going to be demoing as and that's going to be Sarah Chen. Sarah is the Director of FP and A for her finance team and her work spans multiple different lines of business, geographies. She's pulling data together from multiple different source systems to build the models that really help her leadership team make informed decisions. Right now, a lot of that work is painfully manual. Her nights and weekends are spent tracking down variances, trying to determine the why behind the numbers, and then manually building scenarios just to understand how today's changes impact tomorrow's budgets. By the time she has the answers, unfortunately, the question has usually changed by then. So let's see how this looks when Sarah leverages Adaptive Planning Pro to do the heavy lifting. So as Ben mentioned a little bit earlier, Sarah starts her day a little bit differently than in the past by not opening a spreadsheet, but by opening alerts. So overnight, the always on planning agent variant skill detected a problem in our underlying data, ran analysis to diagnose it, and then has flagged that net revenue is down and salaries and wages are trending above plan. So instead of Sarah having to hunt for the insight, it's delivered to her. So in this case, we can see that net revenue is down and which products and customers are actually driving that variance. Along with salaries and wages, our average salary is trending higher and which countries are actually driving those variances. So right away, she has three things. She knows what's off, which is gonna be her revenue and salary, but also where it's off, product, customer, and country. And finally, she has a hyperlink directly back to that report so she can do further analysis. But our leadership team doesn't just want to know that there is a problem. They want to understand why it's happening and what Sarah recommends. So let's jump into Workday after Planning. And once Sarah is in Workday planning, this is where she can now notice that she has some additional alerts that have been notified that that people have maybe tagged her in as part of the budget process. So in this case, she's gonna quickly scan and review all of her historical comments or all of her historical notifications and she also notices that she has a brand new alert from Logan McNeil from this morning. She can quickly scan the alert, understand what has been requested of her and then quickly jump in and see exactly any additional details or context that had been provided. She can then execute upon this request, update her targets, and then respond back to Logan McNeil tagging her in this particular comment where Logan will be notified either via email or through Slack and later on this year, Microsoft Teams. And once she has completed it, she can resolve this commentary and move on with her day. Over on the left, Sarah is also leveraging her AI planning hub. And planning hubs are a curated experience for people like Sarah who have budget and budget responsibilities. It's all gonna be based upon their roles, their responsibilities, as well as as well as their security. And within her overview of the planning hub, she can quickly review any sort of instructions, documentations, key dates, or even any hyperlinks back into internal source system records like travel policies, CapEx policies when it comes to budgeting and forecasting. In the center here, machine learning is looking at her key accounts, identifying the trends, so she can spot any anomalies, dips, increases and maybe do some further research around those particular data points. And with our advanced configure workflow, she can also address all of her tasks that she needs to complete. She can jump in, submit them for review and approval once completed. And Sarah is also an administrator of Workday Adaptive Planning. So when it comes time to kick off the budget or forecast process, she can quickly and easily start to assign tasks to individuals using simple drag and drop functionality to be able to pull those tasks in and then assign them and then have any sort of due dates. So in this case, for our twenty fiscal twenty seven q one forecast process, we can see we've started it, but we may need to bring it in a new approval task. So we're just simply going to drag and drop it in and then we can start to edit it, move it up and down, add instructions, what they need to review or approve, who's going to do it, and then that duration. In the future, we're also going to have conditional routing based upon dollar amounts, approval levels, and then maybe record based approvals as well. And then for her existing workflows that she's responsible for, she can dive in and see exactly where people are as part of the process and maybe reach out for those individuals who might be causing a bottleneck and understand why they are a little bit behind their particular task. So in this case, we may want to take a look at reviewing our OpEx targets. And she can see those individuals and reach out accordingly. But before she gets too far along in the process of diving into understanding the alerts that she had received, she also wants to come back in and take a look at some of the ways she can leverage machine learning to be able to build up certain aspects of her budgets. One of those being maybe setting global targets for the organization. Now Workday Adaptive Planning has had machine learning built in for a number of years using various algorithms to be able to identify those trends and be able to forecast out for certain accounts, groups accounts, to be able to set things like our global targets here. But we always have our our human in the loop aspect. So in this case, where Logan McNeil has requested Sarah to update her budgets, she can always toggle off that Intelligent Plan, make the unnecessary adjustments, and then save the data. This is going to allow for people like Sarah to be able to make adjustments for things that happened last year that were one time or add in those new additional one time items like we have for this year. Additionally, for end users where we maybe don't want them to spend a lot of time, as part of their budget, we can identify those particular accounts, leverage those same algorithms to be able to prepopulate those budgets for them. This way those end users are not spending all their time building up their budgets from the bottom. This shifts their jobs to reviewing and validating and adjusting the assumptions, which is gonna dramatically shorten your cycle time and improve the overall quality of the budget. But there is always that human in the loop, so that way if they need to, they can always manually adjust their budgets. In addition from a revenue planning perspective, we may also want to bring in some external data that's gonna help us forecast out our our budget. So in this case, we have multiple different algorithms that we are comparing and contrasting just to see what sort of impact those algorithms have. We can see from a bottoms up perspective, everything is in alignment versus those particular algorithms. Where we do need to bring in additional data like our consumer price index here to impact those algorithms, we can bring that data in. We have one client bringing in weather data to be able to update their weekly forecast data so that they can see what sort of impact weather is going to have on their foot traffic decision intelligence can now start to surface additional things she may need to take a look at, like around our q three revenue forecast. She can drill in, start to see some leading indicators of what what's actually driving this particular variance along with maybe a headcount reconciliation where she can identify which departments are currently running above or below plan. She can accept this, edit it, or maybe even make a response. But she does want to come in and start to bring in more detail than what she currently had within Adaptive Planning. So in this case, I'm going to bring in some additional headcounts and pipeline data to start to do that additional analysis to understand what is happening with my revenue as well as my expense. So we're just gonna ask it based upon my headcounts and planning and my pipeline data, connect to adaptive planning, and build me a model. From there, Adaptive Decision Intelligence is now going to connect to Adaptive Planning. It's going to go through my files that I've attached, detected any schemas along with different items that I may need to provide some input for. So Adaptive Decision Intelligence is not just going to create me a model, it's going to review the data that I have and then ask questions that I need to confirm before moving forward. So in this case, we do have a couple of things that we need to complete. What is our revenue? How do we join our month in our region? As well as do we want to actually track attrition? So in this case, I am going to confirm that, yes, that is revenue. Join on region month, and, yes, track attrition. And once we've confirmed the the items that Adaptive Decision Intelligence has highlighted us for, you can see on the left hand side, it's building out the detailed model, and I can now start to review things like how is it going to actually calculate revenue variance? Where is our attainment rep, where is our attainment rate, our revenue per rep, and then different assumptions that it may take into consideration as well. If you want to take a look at it instead of a list view, we can also highlight from a diagram view where we can see where those unique keys are and how they are aligned across the different data points. But on the left, we can now see it's not just spit out a model for us, it's actually building the narrative along the way. It's identifying the sources of data it brought in, some key key metrics as part of that data, but also highlighting where we want might want to dive a little bit deeper into our data. So in this case, it's identified that EMEA is a $187,000 below plan. In this case, once we're ready and everything is in alignment to what we we want and we're happy with the model, we can go ahead and start to yes and investigate that particular data point. So again, we're gonna be able to see exactly how everything's getting built out as part of the data structure, but also some additional visualizations around things that we need to take a look at. We can see what our revenue is, how much of a variance it is, what percentage and where it's actually located. Then any data point, we can click on it, we can highlight that formula to confirm exactly how the calculation was created. If we need additional details, we can drill in, we can see where our sources, our inputs actually came from. So in this case, our actual data came from Workday Financials. Our budget came from Adaptive Planning and then our source data along with any assumptions that that Adaptive Decision Intelligence made along the way as built as it was building out the model. Again, we have additional detail to the left where we do have additional narrative that we can start to review, which has now identified that the sole driver of the shortfall is EMEA, and then all the other regions have practically zero variance. If we need to do further insight, we don't necessarily need to actually type a question. We can leverage the visualizations, which where adaptive decision intelligence will actually create what we are calling a chip that we can leverage to drill down a little bit deeper into our media region. And just like before, Adaptive Decision Intelligence is now starting to build out that narrative around exactly why EMEA is lower using that that headcount data that we brought in specifically. So in this case, we can see that EMEA had three reps that were terminated or had left the organization over a three month or a two month period of time. We can see what sort of productivity they were generating for each one of those different months, along with which products were impacted most when they left the organization. And then finally, we have a narrative that highlights that it's not a market problem or a product problem. It's really comes down to a capacity problem. We can see that our our reps have left the organization, and we can highlight that trend over time along with that product impact through various visualizations. But Sarah might still have some concerns over not whether or not headcount is actually driving this variance. So in this case, she wants to do a little bit more of an advanced, statistical regression analysis. This is something in Excel that might take her a day or two to put together. But with using Adaptive Decision Intelligence, she can simply ask a question to run a regression analysis. Adaptive decision intelligence does the heavy lifting for her and now does that statistical probability and identifies that, yes, for Mia, the variance, 94% of it is actually driven by headcount. We can see the confidence level. We can see the visualizations, and then we can see those predicted values for the upcoming months. In this case, now that we have confirmed that headcount is actually driving that variance, we do wanna run some scenarios around what are our recovery options. So let's see what sort of recovery options we have to make up that particular shortfall. So in this case, using our our data that we've loaded in, our historical data points, adaptive decision can decision intelligence can now start to identify how we can make up that gap. So, obviously, there's really only two choices here that we can make. One is hire our three three replacement reps and then also maybe increase our our win rate by 5%. Adaptive decision intelligence also identified a third option where we can now start to maybe combine those two options together. And this is the preferred method, the the preferred the the preferred scenario that we want to take a look at because that gets us closest to closing that gap. And a lot of times, we wanna kinda see what sort of range of options we have with these scenarios based upon different levers that we have within our levers and assumptions within our budget. So in this case, I do want to run a Monte Carlo simulation to highlight those those predictions. And again, this is something where in Excel, this could take, you know, an extreme amount of time to run based upon the number of scenarios you're running, the number of inputs, things of that nature. But with adaptive decision intelligence, this is only taking a couple of minutes to pull and produce a Monte Carlo simulation. Across the top, we can see exactly what was run, what our median recovery option is, our confidence level. And then down below, we can kind of see from a percentile breakout what is really kind of the the most likely outcomes for these particular scenarios. And now we can report back to our executive leadership team with pretty good confidence that we can cover most of the gap, but we're not gonna get all the way there. And in our narrative, we can see what was run. We we have our headline result, what our median and our mean recoveries are, along with the confidence interval that goes along with that. We have risks that we've now flagged to our CFO in terms of worst case, best case, and really the the key driver of what's actually going to predict any sort of risk in these forecasts. In this case, the ramp time for our sales reps. The sooner we can get them up and running, the soon sooner we can close the gap. But now we want to run and compare those scenarios altogether. So let's line them up side by side and just confirm that this is the direction we want to go. So now Workday Adaptive Planning is highlighting what is going to be our baseline, what that scenario looks like by hiring three reps, the increase win rate by five points, and then what it looks like to combine them both. Visually, we can see the outcome a little bit later on in q four of exactly how everything's going to line up. And down below here, we can see exactly that combined scenario is kind of where we want to land and and start to report upon within adaptive within Workday Adaptive Planning. But before we move forward, we do need to generate an executive summary, for to send to our executives. So let's generate that executive briefing. And in this case, we are gonna come up with a nice narrative that we could quickly copy paste into an email, but also various slides that we can present as part of an overall meeting view. We can also export this as a PDF to maybe attach to an email with our overall commentary, which is going to include our variance, our recommended action, and any remaining risks that we have as part of this particular option. Then when we're ready, we can now commit this back to adaptive planning. We don't need to go in and recreate this within Workday Adaptive Planning. Once the model has been created, the scenario has been created, we're going to let Adaptive Decision Intelligence push this scenario back into Adaptive Planning for us. But before we do that, the human in the loop is going to have to confirm that yes, this is what we want to do. We can see exactly what is going to be written back to Adaptive Planning, how it's going to get mapped back into our dimensions. Then once we're ready, we just confirm what we want to do and be able to push that data back into Work Data Planning. And now I can jump back into it back into Adaptive Planning. I'm gonna log in for security purposes. I'm gonna jump into my AI Planning Hub where we were just at a little bit earlier. In this case, I'm gonna come into my scenario planning dashboard where now that that particular scenario has already been created for me. I can bring it into my report, run it, and now start to analyze any variances that have been that are driven off of our actual data, our budgets, or any other scenarios that we've already created within Workday Adaptive Planning. So that wraps up today's demonstration. So over the last twenty minutes or so, we've seen a very different life for Sarah. So instead of jumping around chasing variances across different systems manually manually building and reconciling scenarios and trying to connect planning to real demand data from Salesforce, Sarah has an always on agent, detect a problem overnight, diagnose it, and send her an alert. She used conversational data exploration to drill into the root causes, exploring expense and revenue variances across any version or time period. Then she used Adaptive Decision Intelligence to build, analyze, multiply scenarios to help choose a Q3 gap driven scenario that's driven by the EMEA region. This is what it looks like when planning is not just the static process, but an intelligent continuous conversation with your data. For leaders like Sarah, this just means fewer late nights hunting for the why, more time proactively shaping what's next. Thank you for your time and attention, and I'm gonna turn it over to Kate Bassett now to talk about the road map and how to get started with Workday Adaptive Planning. Great. Thank you so much, Nolan Southfield. It was it was great to see the product working in in action, and it's crazy how real it's become in a a very short period of time. What I'd like to cover with all of you next is some more of the specifics, regarding what specifically is included in the pro offer and share with you the rich road map that we have for the rest of this year. Before I get into that, I'd just like to address upfront that none of our existing base offers being taken away from you, our customers. You get to keep your implementation as it is. You can still utilize existing AI features like predictive forecaster or anomaly detection. Another key point for all of you is that we are not abandoning the care and the keeping of our base product. And a great example of a new innovation that we made available for all of our customers in this last release is is hubs. And we've seen just wild, wild adoption. So we will nurture our entire customer base, regardless of of this new of this new offering that we're introducing to you today. So how I really recommend that you think about Pro is that it's an offer for those of you who want to get a lot more out of your planning application, whether that's significant productivity gains from the teams that use your manager tool or the insights that are otherwise very difficult to spot with, with the human, naked eye alone. So to be very specific about what make these gains possible, the capabilities, some of which you saw earlier, are advanced to dynamic workflow, the planning agent, adaptive decision intelligence, and some very soon to be released tools called application life cycle management as well as, MCP and and a to a. And I'll talk you through what, what specifically those things are in just a minute here. So stick with me. An important distinction in these offers is that if you are on the seat based offer today and you decide to go pro, we did embed flex credits directly into the offer for you so that you can get up and running with the planning agents, in a in a single SKU. It it makes it really simple for you to get up and up and running. If you're an FSE customer and you decide that you want to upgrade to pro, you'll still need to work with your account team to accurately size what you think your consumption's going to be, but they'll help you through that process. And again, I'll I'll talk to you, a little bit more about the end on on how to take steps to do this. So I'll come back to that topic and let's, let's dive a little bit deeper into the, into the road map. So as you saw in Nolan's demon Nolan's demonstration, excuse me, the new planning agent is the it's the cornerstone of, of this offering. And currently, the planning agent can perform data exploration, and it can analyze variances. And these are what, you heard him call as the agent's, skills, if you will. And why skills matter for you is that not only do they allow the agent to do more types of tasks, so we can teach it more skills and it can do more things, but they matter to you and that they keep the agents and your user operating on your organization's governance rails that that Ben Pierce was talking about in the in the opener. So if and when you decide to adopt the planning agent, you're in full control of not only which users get access to the planning agent, full stop, but also which agent, skills they can use or cannot use. So that's it's down to the user level granularity if if that's how you choose to roll it out. So this this provides you a lot of, a lot of control. It ensures that your users can't abuse the agent as a bypass for tasks that you wouldn't want them doing on their own. It honors contextual security, and, of course, keeps you in full control of who can consume those flex credits and, for what purpose and to what end. The agent is fully context aware. What I mean by that is that it understands where the user is in the application when it's engaging with, with the agent or with the user. And it respects the data access rules that you have in place, all making it possible, to engage through a very simple and an easy use natural language interface. So these are, three key points. There are advantages, of using the planning application that has AI infused directly into its workflows, and and that's something that a a stand alone, agent outside of planning simply cannot provide provide to you. So, speaking of, of workflows, as I mentioned, data exploration is that very first step, but it's certainly not the last in automating key planning workflows with with AI. The planning agent will ultimately augment the four critical FP and a and planning stakeholder roles with naturally some some overlap, of course, based on, each of the nuances of of your ownership model because every customer is different. And across these critical planning stakeholder roles, the planning agent will provide incredible value, including planning transformation, improving strategic focus, and also enabling better decision making and empowering all of your all of your planners. So for this year, our big focus is on the analyst and on the planner persona. And our plan is to deliver automated variance analysis where we bring those variances to you rather than you going and asking the agent for those for those variances and also the scenario planning skills. So we're making great progress. We've already got these in the hands of a a very small group of early adopters who are giving us some really great and rich feedback to help shape the product direction. And I know it there'll be unlimited availability state for pro customers before we know it. So all of the skills that we're planning for this year and and in perpetuity will be included under the planning agent entitlement. And, again, that is a cornerstone of what is behind the new, the new Pro offering. Alright. You also saw saw Nolan demo the new adaptive decision intelligence capability. That was that marquee feature that you saw at the end. And that's our new AI powered workspace within adaptive, and that gives strategic planners the freedom to explore scenarios and perform root cause analysis and model those really complex decisions conversationally. And, we see this workspace as a complement to the scenario planning skill of the agent. It still operates on those governed rails, but it does remove the highly structured constraints of your planning model. So it's really putting you back in the garden of Eden. And this is a type of scenario where we would traditionally see our users go offline to model in Excel because Excel doesn't enforce, governance rules and and definitions of the model. So, we are able to strike that balance of of security and governance as well as, extending or rending the rules, if you will, of your model on all in a in an online planning application. So we are enabling this out of the possible workspace directly in the application, and you can still reconcile it back in your, in your plan as as Nolan showed you, and rather than leaving it to some poor unfortunate soul who'd have to manually hack, an offline solution and and put it back into your into your plan to be able to move it forward. And, again, this is included under the pro offer, and it's currently accepting early adopters if you decide you wanna move forward, right away. And much like the advanced, workflow feature set, not all of the capabilities included in Pro are purely or inherently, agentic. So another example of this includes application life cycle management or ALM as I'll call it going forward. And that allows you to move configurations from a nonproduction environment to a production environment. So if you're a customer who's building out another model or you make frequent enhancements to your existing model because your your business is always requesting lots and lots of changes and enhancements, that's great. But you've probably felt the pain of having to build and test changes in your non prod and then have to rebuild them directly in your production. Or maybe you you have a multi instance implementation and there are unique artifacts in each instance, and there are also shared artifacts that you wanna keep consistent for your entire user base. You have to manually replicate those and maintain those across each instance. So not a great use of your time, comes with some risk. ALM takes a lot of this manual effort off of your plates. You can build and make those changes in your model in one environment, be it a nonproduction, you know, sandbox instance, and then put together a package of those changes to migrate to your production environment or another, another production environment if you're multi instance. And you can run validations. This is a really important point. Those validations ensure that you don't break anything in production. You don't break logic. You don't erase metadata. These are very powerful tools, and we've we've done a lot of, a lot of intelligence in how we built it to ensure that, you keep production super, super stable every time you introduce a change. So, obviously, this is a a huge time savings, tool for your administrative teams, and it also, of course, reduces risk to destabilizing production every time you make an enhancement to your deployments. And then I guess the final point that I'll leave you with is that you have a nice sound audit trail of exactly how those changes got introduced to introduce them, and when as you're using a nice, a nice package solution to be able to port these things across. And, again, bears bears repeating that ALM is indeed also going to be entitled under Pro, and we do expect to see it released sometime in the twenty six r two time frame. And we'll likely accept early adopters and and potentially design partners as well in the coming months here. Okay. As we continue expanding the role of AI in planning, you're taking a very major step towards making adaptive planning an integral part of the broader AI ecosystem. And, specifically, that major step is model context protocol or, MCP as it's commonly known in the industry. So for, MCP being mail made available to you, our customers, you can securely expose adaptive planning model context to external large language models. This could be ChatGPT. This could be Gemini. This could be your cloud. I know we've we've talked about them quite a bit today. And this is going to enable your users to query planning data, create scenarios, put data also if that's what you want them to be able to do, or just explore business logic using natural language within their preferred AI workspace wherever that might be. Agent to agent or a to a, a very similar type of technology, will enable adaptive planning agents to collaborate as autonomous peers with agents across your enterprise. A nice example of this, just to illustrate what I mean, might be for the planning agent to talk to Snowflake Cortex or in QuartinXcess when you're analyzing a variance. You know, if you wanna see which store is this coming from and you need it down to the granularity, that information might live in a data warehouse or it might live in a in a Salesforce. And being able to have our agent talk to their agents to give you those really accurate fine grain answers, is enabled through agent to agent. So, using the published planning, agents a to a card, this planning intelligence becomes fully discoverable and actionable by any agent in your ecosystem, and that could be built again by another software company like I mentioned above, or something even custom built by by your organization as well because I know lots of our customers are seeing them build their own agents. So it's it's fully compatible. In essence, Adaptive becomes a strategic AI powered teammate. It's how I want you to think of it. This is a teammate, that connects plenty directly into the flow of your enterprise decision making. Customers approach should see MCP become available sometime in twenty six r two, and '8 a will likely be a very fast, very fast follower of that. We will likely do another session sometime later this year to show some good examples of how we expect this tool to be used in practicality. So if you'd like to know more about how to get started with Pro, I really recommend that you start with your CSM. Your CSM can guide you towards the necessary steps to try out the planning agents free of charge in your nonproduction instance if you simply want to just test it out and and play, play for yourself. And And if you're not sure who your your CSM is, that's also not a problem. We just launched a new customer success desk earlier this year, and they can guide you in the right direction and help you take those steps or point you directly to your your CSM who can also help you. So either one is an is an option to you. If you prefer to test out the planning agent directly in production, right, on your production data, you can also choose instead to to contact your account executive, and they can guide you through steps to receive, 5,000 or more potentially complimentary flex credits to put towards the planning agent directly in production. So this is a great option for you to choose if you feel like you need to test it out on your real production data or you've got decision makers who don't, don't engage and you're on prod and they wanna be able to try it out for themselves before making the decision, this is a great option for, for you to explore. Again, it's it's no charge. These these credits are entirely complimentary. If and when you decide that you are ready to move forward with Pro, you don't have to wait until your renewal unless you want to, of course. Your AE can help you perform an off cycle SKU upgrade. So when you're ready, we can upgrade you to, to Pro. As upgrade happens in place, there's no redeployment needed. Services efforts are not required to turn these features on. We've had a lot of early adopters, well over a 100, and they've all self configured these these features. So it is very, very simple to, to do this upgrade not just from a commercial perspective, but also from a product and implementations perspective. So I really just wanna drive home the point that you have a lot of options available to you to try this product out, before deciding if you, if you wanna move forward. That timing is totally on your terms. Obviously, I'm a product person myself. I feel like earlier engaging is always better because we have limited availability programs going on throughout the rest of the year, and this will give you a chance to shape our product direction and give feedback. But, again, that timing is fully up to you, and we'll meet you where you are no matter where you are, no matter what. And I'll close you with, a a shameless plug. We will be doing another webinar in, in next month in June. We'll go deeper into the adaptive decision intelligence technology that you've got a little snippet of from Nolan. And we will also be talking about our new adaptive data foundation powered by powered by Encarta. I know I talk to customers very often. Everybody's on a different, a different, different step of the journey in terms of their readiness to take on AI, and a lot of our customers are still trying to wrangle their access to timely, quality, rich data. So this is a fundamental offering that we are also introducing just to help you get really good, rich data help to feed your, your AI adoption AI adoption journey. So feel free to to, to check into that, as well. I think that, that concludes what we wanted to cover with this group. I would love to turn it over. I think, Ben, you're gonna be the one to moderate our our live q and a. That would be me. First of all, thank you everyone for all the chats and all of the q and a. We're trying to answer them. We've actually got a team behind the scenes, quote, backstage trying to answer as many of these as possible. So I'll just bring up a couple that have been, kind of repeated over and over again. And and one of them that's really interesting is just around, accountability and AI hallucinations. So, the question is typically come up, hey. What data are you sending into LLMs? Is, like, Chad GPT or Claude or Gemini or whatever LLM you're using gonna be learning from my data? Will I have exposure to that? And then on top of that, like, how do I make sure that this thing's not hallucinating? So, I'll turn it to Kate to jump in here from the product side. But we've had a 100 customers on the planning agent prior to the release last, a little over a month ago, testing it. One of our customers who's pretty, in-depth about security ran the same, query or the same prompt and, like, they told us a thousand times across the platform, and it gave the exact same answer back a thousand times. Now the reason why that it would do that are twofold. One is, AI systems are probabilistic, but the elastic hypercube is deterministic. And so you might ask it a question like, hey. What's the variance on revenue of products in these stores? And maybe it accidentally picks a different store, but it's not gonna calculate it wrong. And it's not gonna calculate it wrong because it's using the logic in the model, and it's using deterministic calculations. It's not AI coming up with the calcs. And so there's actually no way, in the solution to be able to do that. Now we do run everything through evals. So as there's a closed loop. So as we're running it through and then we get, you know, feedback from you that says and this is another question. What happens if it's wrong? Can I give it feedback so that it, you know, makes a better decision next time? A A 100%. And that's what we've been doing through thousands and thousands of iterations with hundreds, 100 plus customers. And we've got it's not just one agent. It's actually an orchestration of multiple agents, with an eval feedback loop to make sure we're constantly improving in terms of giving the the best answers, possible and and making sure we're airtight on, you know, what pieces of the model we're selecting to to bring together. Kate, any comments or other questions just on around ensuring people's data is safe and and Yeah. today? I I think one one thing I'll I'll add to, to your comments. I think you did a a bang up job, Ben Pierce, of of talking about how we give better, higher quality answers and and our AI get smarter over time to the to the question around security, right, and and ensuring that folks don't get access to things that they should not get access to. The the fundamentals of how we built their our agent and and specifically the skills that I was talking about is built entirely upon our contextual security model. So, I personally saw the architecture diagram for, how we how we navigate and how we evaluate security. There's a number of checkpoints that happen throughout the process at various points in run time to evaluate what is the, the agent allowed to do, what is the user allowed to do, what is the user allowed to do with the help of the of the agent. Right. And that's done with the permission and the authorization level as well as well as the data access level. So it's it's pretty airtight, I can I can tell you, if if that's maybe some additional context into the accuracy of the answers and the, the privacy of, of the answers? It's the, again, the beauty of operating on, the Workday rails rather than dumping data out and sending it somewhere else where you you lose control over all of your, your governance rules that are put in place by your security team today. There's been a pretty simple one, but everybody's kind of asked, like, how do I get access to the planning agent? Why don't I see this in the sandbox or non prod? You have to be on the, universal MSA. So we've converted over, I think, over a third of our customers onto the UMSA. It's an it's a very small addendum. It's, I think, one page, and most of our customers as they are renewing, just sign in it. And then as soon as you're on that, we can enable everything AI into your tenants. So if you do not see, like, an option to turn on the planning agent in non prod or sandbox, then it's probably because you're not on the UMSA. Once you are, you should be able to turn that on in non prod or sandbox and play with it and and see if you like it, and give us feedback, please. Okay. Another question is a lot of questions around the adaptive pro skew. So what is the licensing? What does it cost, etcetera? And what the heck is a flex credit? So every one of, Workday's agents, run off of flex credits. And if you're a Workday customer across more than just adaptive, that's great because you can buy, like, a set of flex credits and they can be used across multiple agents, which is a nice thing if you have, like, multiple different Workday products. If you just have adaptive, we still use flex credits. We use the exact same, like, meter it's just a metering system, for customers that are on, like, a user based license. So we have two licensing options unlike most other companies. We have an FSC license, which think of that as just like unlimited license. Like, everybody in your company can use as much as they want. That license, you just buy a set of flex credits on top of it, and then your users can can use them as they as they like. And then, obviously, we have, like, the reporting in place so you can see what your burn down is and who's using what, things like that. If you are a on the user base license and you're a stand alone customer and all you run is adaptive, we with the upgrade, we are giving what we think is a, like, really generous entitlement of flex credits that come with the SKU. And if you hit the if you hit the the max on that, then, we're not gonna throttle you, but we'll probably call you until you you cross the line. Probably actually call you before you get to the line. But each customer is gonna get I think it's 8,000 per user. So if you had, let's make it easy. If you had 20 users, that's a 160,000 credits across your entire user base. We found that in our estimations based on our early adopters, we think our customers will use about 4,000, 4,500 a piece per year. So we just gave that a ton of headroom, and then, you know, that's for every user and it's pooled. So if you have some users that use a ton of them and go beyond the 8,000, that's fine. You'll probably have some users that, you know, barely use any of their credits. So we think our customers will, on that side of the house when it gets bundled in with the Adaptive Pro SKU. We don't think you'll ever cross that. And in terms of pricing, the Adaptive Pro SKU is of, 40% uplift. So we actually sold this, I think, 20 times in January before, we even launched it and did a huge promo, and the average uplift was, like, 25%. If you're a customer that's paying, let's call it a $100, at 40%, that's $40. The idea is if it saves you from hiring one or two analysts over the next two years, then ideally, that's like a pretty easy trade off to make. And we're gonna keep improving on this and keep making it better and better, and you just saw adaptive decision intelligence, which is, I think, hopefully, something that you're not seeing across, the rest of the industry, bringing planning and analytics together. So, last question. I'll kick this one, to you, Kate, because I thought it was really interesting was, we already talked about the data one. We already talked about access to it. Oh, MCP. So, yes, you MCP and a to a, that is part of the AdaptivePro SKU. So that'll be coming out here in a couple of months. We're, you know, putting finishing touches on it now, and then you'll have access to it through the pro SKU. Oh, one of the questions, Kate, was with ALM, does that mean that you're gonna have a, like, a change log with that too? So for example, if you're doing. things. Yes. You absolutely will have a change log of, who created this package, what things are in this package, when did they move this package, Did it successfully complete? What are the artifacts that are now in production? So, yes, you'll have a change log or an audit trail as as we're, as as we're calling it. Speaking of of audit trails, also to make the the package building a lot easier because I'm sure there are folks on this call who've done major phase two deployments that had a ton of artifacts. We'll have a conversation, a conversational interface on that tool as well where it'll, it'll help you build that package based off of when were the last things that you created in a in a sandbox and and by whom and, use that as a way to get, get it building up and running. If anybody if you've ever used change sets in Salesforce, it's manual hunting and pecking. We'll still offer that as well if if that's how you like to operate. But we're using audit trails of what was built to help you create the package as well as give you a nice tidy audit trail of everything that was moved so you have, like, that real, true soft compliance, over your over your implementations. Yeah. And safe harbor, I mean, we're working on it right now to allow it to, you know, you to be able if you're running I saw some people running chat g p t or something like that. So if you're running that, you'll be able to enable that for your users. So if they're logging in to chat g p t, they can just say, oh, there's another skill in the search bar. I wanna, you know, use this skill. It's adaptive planning. And I wanna ask questions about variance or forecast or run a new scenario, and it should be able to execute that from there. And that'll be through not MCP. It'll probably be through a to a. The the reason why is because you think about MCP as a tool set and, think about a to a as, like, coworkers. So if you're gonna ask most users are gonna ask a question. You don't want to just get, like, a hammer and some other things. You want somebody to actually go, you know, build the wall and show it to you. So that's what that's gonna happen there. Okay. I know there's a ton of other questions. We're overtime by a couple of minutes. I just wanna say thank you for everyone for joining us. We'd love to talk to you. We'd love to show you custom demos, higher ed demos. I saw that. Wherever it might be, we've got a great team here that is excited to engage with you. And, please just reach out to us. If you don't know how, just, do successdesk@atworkday.com, and we'll we'll follow back up with you. Thank you, guys. Have a great, great day. Have a great rest of your week.