Name: Always-On, Always-Ahead: Adaptive Decision Intelligence Unveiled
Uploaded: 2026-06-24T18:06:12.628Z
Duration: 50 min 8 s
Description: Always-On, Always-Ahead: Adaptive Decision Intelligence Unveiled

Transcript for "Always-On, Always-Ahead: Adaptive Decision Intelligence Unveiled": Hey. Hi, everyone. Welcome to our webinar today, Adaptive Decision Intelligence Unveiled, a really exciting topic we have today. And before we get started, just a few housekeeping items. All attendees automatically will automatically be muted with their camera disabled. The presentation and webinar, recording will be shared in twenty four hours after after this webcast. And then during the webcast, please ask any questions, via the q and a icon at the upper right hand corner of your screen. We'll try to answer as many as we can during the session. For the ones that we can cannot, we'll certainly follow-up, directly right after the webinar. My name is Greg Volpe. I'm with the marketing group at Workday, but really wanna introduce you to the experts on today's session. So first, Ben Pierce, who is the General Manager of the Planning Business Unit at Workday. He's going to provide and kick us off today with Adaptive's AI vision and strategy. Dan Sleimowitz, who is an AI Product Manager at Workday, will follow with a discussion around our innovation with Adaptive Decision Intelligence. Justin Licker, who is a solution consultant at Workday will bring this innovation to life with a really exciting demo, really excited to see that. And then finally, our special guest, Matt DiDonato, a leader in the corporate FD and A team at New York Times, will have a chat with Ben about their his vision and approach on AI and adaptive decision intelligence at the New York Times. So again, really compelling, content today for the webinar. And again, before we get started, just a quick, product statement, our safe harbor statement here at Workday. We will be talking about and speaking to, future road map items here. So please make any investment decisions on Workday, based on current product capabilities. And so with that, I'd like to bring up Ben Pierce to talk about Adaptive's AI vision. Onto you, Ben. Thanks, Greg. Thanks so much. Appreciate it. Hey, everyone. My name is Ben Pierce. I am the general manager of Workday Adaptive Planning here, responsible for our product through customer success of our 7,000 customers. I'm really excited today to to kind of walk through how we're thinking about this. And the first, I guess, most obvious thing is that any planning system should provide clarity for any organization to help navigate and ultimately drive financial results for their shareholders. And I think we can all agree that right now, in the age of AI with it reshaping industries and work changing like it is, that organizations really must drive much faster decisions with, a lot more complexity that they're dealing with. And to talk through some of that complexity, it's not just a about AI automating work anymore. I mean, if you're not planning really for the the impact of AI, not just on the work, but it's really redefining how companies are investing. It's changing cost structures. It's changing really where people are making capital decisions. And if you're not thinking through the AI lens on how it's impacting your organization and how you're financially planning for it, then what you're planning for is really the future that's not going to exist. And part of the problem with creating a financial plan is that I would argue you really can't create a financial plan without understanding how work is going to change, what that's going to cost in terms of investment, and what it really should buy you going forward in terms of efficiency for your organization. And, really, only Workday can unify both with the HCM system of record. But any planning tool inherently has limitations. It's limited in the amount of detail that it can hold. And so a lot of our, you know, historically, FP and A customers have had to eject into some kind of BI tool, but that only tells you what happened. And, inevitably, most FP and A people, and I think Matt would attest to this later, are dumping a lot of things into Excel to fill the gaps. Right? It's the number one most flexible tool that's on the market and allows you to pull data together from any place that is there. And the problem really is not any one of them can independently do what's really needed. And so as we think about, how we can help our customers, we're really going back to the basics of what does FP and A or what do analysts and any planning solution do. What is the work that they really need to do, and then how do we help agentically drive that work? And that really starts with being able to monitor the situation and then proactively diagnose it. So instead of your CFO waking up on Monday morning, asking a whole bunch of questions and causing a bunch of work that inevitably gets delivered on Wednesday or maybe Thursday or, worst case, Friday. It's already delivering, hey. Here's what happened over the weekend. We got these actuals in. I've already diagnosed the situation. But not just diagnosing it, coming up with the ability to actually tell you, here's what you can do about it. Right? What are the different options? What are the different scenarios that you you can take? And then giving you the ability to take action on those. So the decision's only as good as your ability to actually go act upon that. And then, ultimately, with any AI system, the value is in the learning. Just like any any analyst out there, You know, an analyst has been working on a business for thirty years, inevitably has more repetitions and more experience, and therefore is probably better than a brand new analyst. And so every AI system really needs to learn as we go forward. And so what does this look like for us? Really, for our customers, it looks like being able to access this anywhere. So we're gonna show you adaptive decision intelligence today, but we really want our customers to be able to access this from anywhere they are. And we're leveraging MCP and a to a to do that across Sana, which is our front door to work, or if that's Claude or Slack or Teams or email, wherever you're at, this is where it should meet you. And then it shouldn't be limited by the only the data that's in a planning solution. You should be able to get access into the core systems of record. And if you own HCM, great. But if you're an adaptive customer, maybe one of our 4,000, that doesn't run on Workday, that's not a problem either. You should be able to get access to all that data and ultimately make sense of it, though. And to make sense of it, you really need to understand the semantics of the data underneath, and we'll we'll spend some time talking about what that means. And then delivering really not just, you know, an agent, but a team of agents that can do the things we're talking about in the last slide, be able to monitor, be able to diagnose, advise so that you can take action with a human in the loop and then continue to learn on that flywheel. And to get there, we're focusing on bringing together planning and analytics. Our CFO, I met with him a couple weeks ago, and he said, we're called FP and A, not F and P. And so to do that, we need to bring back the analytics, which means bringing any data together, providing the ability to reason across it, and then providing all of the different capabilities to be able to dig into that data, run future scenarios, Monte Carlo simulations, whatever it might be, in a collaborative governed environment so that you can have all of the same security and audibility, but really understanding what the data means. And that's where we're gonna talk about SymantecLayer, and Dan will talk about that next. And, again, we really feel like you cannot figure out what the impact of AI is going to be on your organization and therefore can't plan for it unless you really understand the underlying workforce, the underlying tasks, and what needs to happen in order to transform your company with AI. So at this point, I'm gonna turn it over to Dan to, introduce Adaptive decision. Alright. Thanks, Ben. I'm Dan Slomowicz. I lead product for Adaptive Decision Intelligence. So I've been working really closely with FP and A teams on this product. So I'm gonna share what ADI is, why we built it, and what we're hearing from early customers. Let me start with the shift that's driving everything we're about to show you. Analysis is commoditizing. Ben kinda hit on this, but I'm gonna be really direct. Any AI tool can summarize a variance, spot an outlier, generate a chart. That's table stakes and it's getting cheaper every quarter. If you're evaluating AI for planning based on who has the best analysis features, you're asking the wrong question. The real question is who connects that analysis to your actual plan? Because a variance explanation in a chat window doesn't know your model structure. It doesn't know what revenue means something specific in your adaptive instance with specific dimensions, specific assumptions, specific approval chains. And it definitely can't take what you learn and commit it back to a plan version anyone trusts. That's what adaptive planning already does. It's your governed model, the place where assumptions become commitments and the commitments become the numbers your CFO signs off on. Decision intelligence sits on top of that. Investigate a variance, model the response, commit the reforecast, one workflow with full governance. No exporting to a spreadsheet to run scenarios, no copying results back to adaptive and hoping the version's right. The analysis really flows directly into the plan with audit trail from insight to action. So every decision that you make deposits back into adaptive and that were compounds instead of disappearing. And that's what we're building. And I'm super excited soon for Justin to show you what that looks like. And really back to, the other slide here, you know, here's the reality. If you ask an FP and A analyst where their time actually goes, it's not about building the budget. And we did a pretty extensive research study too. We talked to so many of our our wonderful customers, and we really learned it's all about wrangling data to figure out why something's off and deciding what to do about it. So adaptive for us is always where the plan lives. It's your system of record for the future. But that investigation work, it always still happens somewhere else. Right? And Ben really hit on this point. So you export, you build something in Excel, and you manually bring the answer back. And that round trip, it always takes days. And that analysis that drove that decision, well, it's a spreadsheet sidecar. Right? No lineage. There's no connection back to the source. And by the time it's in the deck, you know, the numbers have already moved. And so really the the thesis here is ADI is bringing work back into that governed planning workflow. So it connects directly to your adaptive plans and actuals, lets you explore, model, and test scenarios without ever leaving that governed environment. So first, you know, you're exploring without exporting. Right? That analysis that used to require that spreadsheet now happens in a conversation connected to live data. Second, you're not locked into a rigid model upfront which is sometimes the problem with existing planning solutions out there. Right? They were, you know, beautifully designed to kinda lock you into that structure and that's that's really on purpose and by design and that's what we want. And you can always but now you really have this flexibility to add data, change assumptions, work at a different grain than you're used to, right, and keep the analysis moving. And then finally, every number really is traceable. So you can see how any result was calculated down to the data and assumptions behind it. And we're building this closed loop. So approved scenarios, they'll flow back into your adoptive plan with full lineage. And one of our customers actually put it really simply. You know, they were talking about how they're spending so many days, and all this time just wrangling the data. And they were saying, hey. If you just solve that data wrangling problem, we're gonna be in a much better spot. So just to kinda wrap up here, just I wanna make sure we get to the the demo on a bit. From questions to govern action really, like, our our thought was, you know, finance decisions rarely stop at the first answer. Right? Once you know what happens, the real work is then deciding what to do about it, comparing options, and then understanding the risk before you commit. And so we built ADI around this progression of detect, model, and stress test. So when it comes to detect, it's really about what's driving the margin depth this quarter. And ADI can decompose the variance by volume, mix, and price effects. So, you know, a lot of AI solutions will tell you what changed. ADI is all about telling you the why. Then it comes to model. Right? So once you understand the problem, you need options. So ADI build structurally different recovery paths in minutes, which you'll see. You can compare trade offs side by side with full transparency into those assumptions. And then lastly, stress test. Right? Before you commit, you really wanna know how confident you should be. Traditional planning workflows give you a point estimate. One number, one answer. I think we all know being in the space for so long, it's never one number, one answer. Right? So for us with ADI, we're gonna give you a distribution. So we'll run thousands of simulated outcomes, Monte Carlo, so so you know the probability of hitting your forecast before you put it in plan. And that's really a fundamentally different way to make a commitment. And these three steps, they all show up as a single conversation. So, really, you understand what happened, you model what to do, and you carry that decision back into adaptive. Alright. So soon, you'll you'll see the workspace where planners do thinking, and you'll see the agents that operationalize that intelligence inside your plans and across your enterprise. But here's really what makes this all actually work. So Adaptive was built on a foundation of trusted data, and this isn't a feature. This is the architectural truth of how the entire platform is designed Because the quality of every AI answer, every recommendation, every scenario is really a direct reflection of what the system and the AI can actually see. So if you give AI a narrow view, then you get narrow analysis. Right? You give it the full picture with operational detail, the historical context, the signals that live outside the financial model, and then you get something fundamentally different. And so that foundation that we've built is called adaptive data foundation, and it really does three things. So first, it's connected to every source your business runs on. So cloud systems, on prem sources, flat files, third party APIs. You got prebuilt connectors, plus native real time connectivity to, our Workday financials and HCM as well. So you've got financial data, you've got operational data, you've got people data. It's all unified in this trusted environment. And second, and and really this is the differentiator which is our semantic layer. So most systems, they can reach your data. Right? But very few systems can actually understand your data. So our semantic layer powered by Encarta really understands the relationships, hierarchies, and the business logic that finance teams already use. So when AI reasons over your data you know, let's give an example. So the reason is the way your finance team would. Right? So raw, a c c t underscore four five zero one. Right? That becomes software revenue. Right? Department underscore h q becomes headquarters. Right? The semantic layer is really what makes AI defensible in a finance context. And then third, it's it's really governed, secure, and audit ready. So every answer is traceable, every model is defensible, every decision your CFO has to defend is grounded in that same truth with enterprise controls, finance, and IT already required. And then, of course, with with everything adaptive, all this is running on our elastic hypercube technology, our in memory calculation engine. So AI is not just delivering insight. It's delivering at the speed and scale that enterprise finance actually demands. And then, of course, and I'll close on this just all of our Workday's trust boundary. Right? Your data stays governed. It's never used for model training, and it's never exposed to third parties. And so this is really what it means to be built for AI from the ground up. Right? Not bolted on, not retrofitted. It's really engineered for the way that finance actually needs AI to work. And with that, really excited to pass it over to Justin for a live demo. Thanks, Dan. Appreciate it. By way of quick introduction, Justin Licker, planning solution consultant here. Been here for about four years, but thirteen years prior to this was in FP and A. Used a whole bunch of different systems, a lot of Excel, a lot of ad hoc analysis. You really want them on a portrait today is what Ben says, putting the AVAC and FP and A, becoming the strategic partner of the business, not just to say what happened in the month, but the to answer the questions that were always out there. Like, what macroeconomic factors, how they can factor in, you know, what is, where is our opportunity lie? Where does our risk lie? What is the likelihood of those things happening? And what are those next steps action plans in the short term? Thirty, sixty, ninety day plans. How do we identify all of those? So, if I quickly jump out into our demo environment here, we can analyze any sort of, what we call kind of understanding of the business, very high summarized level output from our teams. It takes a lot of time, etcetera, etcetera. Where Adaptive Decision Intelligence comes into play is the ability for us to have those preset connected interfaces. Basically, what it is is it sits on a ad hoc player, sits on the guardrails of core Workday. Right? We're able to leverage the security, the work write back, the workflow, the version control, the compliance, all to close that loop at the end of the day to be able to read from all of our infrastructure as well as to write back to that infrastructure. So we can have different connectors, things of that sort, where we can connect to today, your adaptive planning instance forthcoming very soon, be able to connect to other parts of Workday or other various connectors. You'll also be able to bring in any sort of third party attachments. So thinking like things like GDP or or benchmarking data or publicly available data like weather, all just to cross reference that data and bring in a set of, set of data that you need to analyze your business. K? So in this instance, we're gonna say get, head count from HCM, get pipeline from adaptive, data foundation, get actuals and budget from, adaptive, and build a model. K? And so what's gonna happen here is in large language, very simplistic conversational methodology, we can an end user, an analyst can ask questions of adaptive decision intelligence, and it's going to start building that model, doing joins, blending datasets, often which is very challenging. I like to consider this as application to be a blend of a data scientist, a data architecture, a statistician, and a senior analyst all in one. So when I ask it to do that data, it's going to ask me before assuming, right, and and saying, do you wanna join on this? Do you want to show attrition? Do you wanna track revenue? And we're gonna answer those that very quickly by saying, yes. We wanna yes. It's revenue. Join on those two in attrition. So it's keeping that human in the loop, but allowing you to define the model that you wanna build in the way you wanna build it, k, without the knowledge the coding knowledge needed. So now it's running through a set of building that semantic layer for you. But while it's building that semantic model for you, which is in itself usually indicative of pivot tables, trying to figure out how to join an Excel, a pivot table on a pivot table, blending data just to hopefully get that data to get some output at the end of the day. It's right out of the gate kinda suggest things or make sure you're okay with things. Hey. We're not including terminated reps, or we're only counting active reps, or we only matched on 22 reps. If you're good with those, again, human in the loop to be able to understand and make sure your model is running correctly. And then as the last part, right, being able to visually see the audit trail of what is happening when those joins are happening from the relevant data sources. So really cool. It's always gonna give you that governance, that safety feel. But right out of the gate, it's gonna surface up some analytics. It's gonna say, hey. Very quickly before we really analyze, we see there's a a revenue gap in EMEA that's causing your variance analysis. K? Or what's going on? Do you wanna investigate? And so we're gonna say, yes. Investigate. And as it's doing that investigation, right, we can actually see a focus, interaction with the data, insights being generated for us in a very visually pleasing type of methodology. K? Hey. It's coming from EMEA. It's fully attributable to EMEA. And then we can start to interact, not just through conversation, but through clicks. Hey. Let's drill in a little bit further into EMEA. Right? So immediately, we're starting to scan the underpinnings, the dimensions, the different layers of our dataset to give us that true root cause analysis, not just the the very high top level summarized data that we often get and take a while to get from our teams through, you know, asking questions, picking up the phone, calling all sorts of people. It's gonna break this down by product month and rep, identifies that we have three sales reps that departed, in in the particular period. And and because of that, an average of 13 k per month, we're actually seeing an impact starting here. K? So we can visually see it, but then it's gonna do revenue and rep kinda combinations to see really what's impacting this. And the real narrative here says it's not a market problem. It's not a product problem. It's not a pricing or pipeline problem. You have a capacity problem. So identifying the what but using a true root cause analysis with it. Now maybe suggestively, we wanna, run a statistical regression that it's going to surface up those suggestions to me. In my past, being a stat major, I would love to do that and it does it really well, but we're just gonna explore, some recovery options, real quick. And by doing that, right, it's going to present me with actionable insights or recommendations on what to do. K? And and the the the benefit here is, right, it's giving risk aware knowledgeable scenarios. Hey. If you hire three replacements, understand that there is a ramp period associated with them, and we'll close this much of the gap. Or if we increase the win rate for our existing population of employees, we're all enclosed 25% of that gap. So, suggestively, it's gonna say, we think doing some sort of combined approach may be relevant. Now what Ben talked about and what Dan talked about in this Monte Carlo simulation, that is to really understand the probabilistic outcome of hitting one of these scenarios. So let's say run Monte Carlo, simulation. And what is really cool about this is this is running 10,000 different scenarios to understand the frequency, the the likelihood of outcomes, something that is very, very challenging, in to do in Excel or other applications. So within here, we can run those simulations, see mean and medium outcomes, confidence intervals, probability of hitting different cases all the way from worst case to best case, and then ultimately landing at a recommendation that says, hey. We think we should do the most likely case. It involves a 50 k residual. Right? Do you wanna see these scenarios side by side? And, of course, we wanna do that. We can always compare these scenarios in a very visualistic way. And the the outputs of this from a chart standpoint are very, very easy to understand where we're looking at those different rates visually within here, surface up our recommendation in a highlighted format over here. And then, again, just to talk about really what that next step is, the action plan, not necessarily the the, the whys or whatnot. Okay? So when we come in here, we can see all that. At the end of the day, where this sits on the guardrails of of Workday is the ability to say, hey. Do you want to, well, let me back up a step. Right? When you think about now that we understand and we have a suggestion as an analyst, let's go talk to our executives so we can say generate executive briefing. And why this is relevant is, like, we don't have to spend all the time in Excel and PowerPoint pointing together the value analysis. It's going to surface up in a very legible, conversational type of way. What is the situation? What are the drivers? What do we recommend as a business? Why do we recommend it based on that Monte Carlo analysis? And then the ask overall. K. Hey. We need some folks. We need to hire some folks. We need to upscale some folks or adjust our targets. K? And at the end of the day, those guardrails that I've just mentioned is how do we have that ability to write this back to adapt it to a governed model, one of our models where we're planning all, planning all of our business, but write this analysis that we've done so it sits and resides somewhere that's relevant to the business. It's gonna surface up what that audit trail is. Right? Hey. Here's our baseline. Here's what our new forecast in that gap is. Here's the source of where it's going to go, the full audit trail when we write this back to Adaptive, which which should be available in a couple weeks here, how we submit this and push it back. Right? When we approve and submit, it gives that extra layer of approval. And then once it's submitted, right, that is writing it back to a governed scenario that resides in Adaptive. It knows the dimensionality and knows the level structure and understands everything that sits within adaptive. And now you've written that in a very governed way back to adaptive. Now one other example in practice that I wanted to showcase is the world is your oyster with this thing. So if you wanna I I there's one, large steel manufacturing company I did some work for, recently, and they're basically heavily end product to consumers. As you think of, you know, the inputs into building or manufacturing steel, highly energy dependent, auto product based, cost of goods. But I wanted to see what the impact of a forward looking, you know, projected, cost of natural gas was gonna be. Right? With all the changing economic landscapes of the world, we wanted to see how that impact and they worked on a cost plus basis model. Meaning, anytime a change happens in a cost, in an input, or into, one of these methodologies, it pushes back to the customer so that they're made hold on their margins. So what we did, we joined that model. We brought in some different datasets actually generated from Gemini as an example, And we're able to project out, hey. When is the peak of our expected energy, you know, pricing going up? Right? What are my products, my inputs that are going to go up that are most heavily impacted? Right? And then, ultimately, what are the customer combinations that drive my margin risk? And so it's key finding. It's true root cause analysis again within our infrastructure. It's starting to break down high, low, and medium risk. But, again, the output of this is what matters most. It's, hey. What's what is happening in our business? What are the recommended next steps? Prioritize surcharge outreach by segment. Model of phase surcharge for spot approach. K? Which customers are gonna drive that? So we can break this down and start to understand the customer combinations of where I need to address in the future. Maybe I wanna look at by customer segment, which was not in here previously. Again, all within the confines and understanding that we want recommendations of next steps. Initiate outreach sequence now. Get ahead of that curve so you can get that revenue retention and that margin risk to your business. I even asked to tell me generate a letter. It said, generated me a letter to outreach for this surcharge, which, you know, should be expected contractually to this business. We're able to see that output here, see maybe what are some examples of ways we can push back the savings so it's not just all surcharges we're pushing. So it's gonna actually help me identify, in a very simplistic way, maybe what are some areas that we could look to minimize or, the customer sentiment here. Variable surcharges, cross product rate relief, price lock commitments on lower natural gas products, volume loyalty discount. If you're doing such a volume, maybe you don't get the full pushback of the surcharge. Right? Recommendations for real action plans. Follow the story. Right? Look for certain customer types, issue price lock letters, actionable insights on what to do. At the end of the day, I asked it to give me a flexible scenario type output where I can play around with different surcharges and different volume growth assumptions, exclude and exclude certain products, all in an interactive format, which ultimately could again be written back. So kind of the world is your oyster with this thing. Really, really cool. You know, I appreciate the time, and I wanna turn it over, and I think we'll talk over to Matt and Ben and have a good conversation on how they're using, this technology today. So thanks, everybody. Hey. Thank you, Justin. Great, really compelling demo. Really, really interesting to see that that in action. So, next section, I'd like to bring Ben Pierce back to stay back on stage with Matt Leonardo, who's a, one of the lead, heads in the corporate f and a team at New York Times to have a chat about Matt's vision, New York Times' vision on AI and adaptive decision intelligence. So welcome, guys, and I'll turn it over to you. Thanks, Greg. Appreciate it. Hey, Matt. Thanks for joining us today. How's it going? Hey, Ben. Good morning. Thank you for having me. I'm good. How are you? I'm great. I'm great. Congrats on the Knicks winning, by the way, since the last time I saw you. Pretty pretty awesome event that happened for you guys out there in New York. Why don't you start by just telling us a little about yourself, a little bit about your role at New York Times? Yeah. For sure. So I've been with the New York Times for about six years now, primarily incorporated p and a in my time here working on all things, total company forecasting, budgeting, board of directors materials, and things of that nature. And prior to my time here, I worked in the, you know, retail industry for Estee Lauder and and Columbia Sportswear. So, being in the media industry now has been, a nice a nice change of pace and a great time to be here. I, was also a part of our adaptive and Workday, integration team here. So working very closely as one of the power users, bringing that on board from our prior systems, and, currently happy to be partnering with your team as an early adopter of, EDI. Thanks for being a customer, and thanks for partnering with us. Appreciate all the feedback that you and our other customers have given us to help us improve the product product here. Let's just start not product. Let's just start talk about how New York Times has been approaching AI and and Genentech innovation overall. Yeah. So first and foremost, I think you mentioned this earlier, just the human led approach. So making sure that we have a human and, at least some aspect of everything we're doing on the automation or AI, work front. And just trying to think of how we can be more efficient with the the same resources we have to today in the same amount of time that we have. So thinking about how we can make ourselves better with all these tools that are are coming out, but recognizing that everything is continuing to changing almost every day. So making sure that we're, you know, promoting accuracy and not trying to be first because that's not necessarily the best thing to do. And and what's kinda, like, your, I guess, short term, long term vision for how you incorporate AI into your planning processes? Yeah. Make making sure that we're just defining any constraints upfront. So anything that we're not willing to sacrifice, whether it be in strategy or principles, and try not to think of just what's coming out tomorrow or next week, but thinking long term, even next year or next decade. Right? This as it's rapidly, evolving technology, we're just trying to make sure that we we keep up with everything and make sure that we're using the proper tools for us and trying to recognize that there's no, bad idea right now at this stage. So making sure every team from the ground all the way to the top is involved in in being creative. Like, finance is typically not somewhere where we get to be too creative. So trying to make sure that every every idea is, given given a chance. That's cool. I can't imagine planning for ten years from now given everything that's changing every, like, three months with these frontier models out there. So what about, I guess, you know, what you see is maybe couple of the largest values that AI and and since we're talking about decision intelligence, what do you think decision intelligence can bring to your organization? Yeah. I think there's a lot of, of value, especially with ADI in in the AI space that think about everything we're doing so frequently in in FP and A with variance analysis and, you know, reporting that can be automated or, any type of visualizations now that we can create and then make persistent and push forward so that we can share these across teams and kinda do the lift upfront one time instead of, like, you guys had mentioned earlier, going into Excel almost every cycle you do something, clean the data, bring it back in, and then try to find that trail again. So if we can define all that upfront and move forward with a distinct more, I guess, auditable trail where, especially in ADI. Right? We have all of our data, clean, in adaptive, connected now to this very powerful tool, and we can automate those processes and, kinda free up the time. I think Dan had mentioned earlier, another client had said kind of flipping the the days to minutes from prep to decision making, and I think that's something that we could see as well. Well, you you've been part of kind of the, guiding the product here and and helping us kind of give giving us some feedback. One of the things that we talked about in our vision was just the ability to do not just, I guess, monitoring, but also learning from it and then executing routines and and playbooks and things like that. I mean, just curious what your thoughts are on on that kind of approach. Yeah. I think the more specific you are with the prompts and the tool and, obviously, the natural language prompting is super helpful. You can start by giving it as little or as much as you want, and then over time, the tool will begin to, I guess, learn from you or recognize what you'd like and and know your data. So if if you're giving very specific prompts and consistent data and the workflows are kinda similar or able to build off each other, I think the tool becomes more powerful for you and then building, you know, those agents together. Right? So different workflows can now turn into multifaceted things where it's learning, it's growing, and, it's kind of becoming more powerful for the group overall and freeing up all that time in finance. So, like, I'm thinking of a great example of, like, our our month end process. Right? Like, so we got our financials out of out of Workday that feeds into Adaptive, and then we, let's say, put some sort of visualization or reporting together. If we can do a a one time lift upfront across the entire group that we can filter by department, We now have kind of a centrally built model or dashboard that can now be shared with business partners. You could schedule that, like you said before, via, you know, email, Slack, through the system itself. And now this takes a step of, like, all the data gathering and formatting and reporting, and it's pretty much automated all that and freed up FP and A for all the analysis at the end so we can come over the top and provide the context and make the tool even stronger and provide that to our business partners now almost you know, around the clock as opposed to them waiting till after a day five close. Yeah. That's really cool. So speaking of, I guess, your business partners and just overall, I mean, what do you what do you think is gonna be the biggest challenges or barriers today to to your finance team or other planning stakeholders and kind of embracing AI overall? I mean, training, skill sets, you know, data quality. What do you think it might be? Yeah. I think on both fronts, it would be the the training and the skill sets. I mean, it's almost becoming a requirement for financial jobs now to be able to have at least basic, of knowledge of an understanding of what AI can do for you in in our part of the business, but also just getting trust as well, not just from finance who's producing the numbers, but from the business as well. You know, some people are still hesitant to to take a dataset or trust that there was human intervention. So making sure that we have that that step, very clearly outlined that there's a human in the loop, whether it's at the beginning, middle, or end checking the data, providing the data, or in some functions so that there's, that human element not just from, like, a check perspective, but also, like, you don't want to see that, you know, a robotic email sent your way as a business partner. You also want to know that your FBNA partners are still having their actual touch and input there, and I think that's where, like, the context piece I mentioned comes in where it might seem a little scary for somebody to just get a dashboard emailed to them. But if they know that the data or the context provided is coming from a human still, I think I think that helps with the trust. Yeah. You said a couple of key things in there, which is, like, still want the kinda human in the lead or human in the loop and FP and A, providing more context around it. And then, man, trust is such a big thing. Right? I mean, it's huge for finance data. You stop trusting finance data, and you start ignoring, you know, things. Right? So that's that's a huge one. Alright. So any advice that you've got for people, you know, as they maybe go down the path of ADI or just AI in general? Yeah. I think my favorite analogy is that Sports one is a big sports fan. I think just getting your reps in. Right? So like with anything else, just practice makes perfect. And whether that's on the beginning of your journey, just starting out with proper prompting or trying to iterate on any models or dashboards that you might have already tried to build in a in a platform like ADI, making sure that everybody has has access and time to develop those skills and, you know, continue to to iterate and try. Like, the first take is probably not gonna be right every time you get in there, but the more that you work at it, I think the better the better it will become. Well, thanks for being a customer. Thanks for joining us today. I give you a handshake, but we're virtual, so virtual high five. And, appreciate everything you do, Matt. Now I'm gonna bring up Greg here, and we're gonna jump into some q and a. Hey, Greg. Hey. Hi, Mike. How are you? Thanks. Thanks, Matt. That was really, really insightful. And also, I want to bring in Dan as well. So and then Dan and and, lots of lots of questions coming in today during the webinar. So really appreciate everyone, participating. And again, any questions we don't get to, we will follow-up with you immediately after the webinar. So couple here. So, you can take I guess this one for Dan, first off. So Dan, one of the questions that came in was how is Adaptive Decision Intelligence different from a general purpose AI like chat, GPT, or one of the others? Yeah. I've gone gone right for the hardball questions here. Thanks. Yeah. So, really, the difference is that ADI is connected to your governed planning model, not just your exported data. So, you know, your chart of accounts, your consolidation hierarchy, your security contacts, your metric definitions, it's already there. So the general purpose AI, you're, you know, connecting to this data source. You're uploading, you know, your Excel sheets. You're uploading a CSC. You're starting from scratch really every session. And so ADI inherits that structure from your system of record, and really that's what makes your analysis accurate. Not the model alone. While these frontier models are incredible and and we use them, of course, the government context around it is really what makes that shine. Right? And then really, you know, the other pieces I'll say is is the same Workday security. So ADI respects who's allowed to see what, It keeps the audit trail managed within Workday, adaptive and not in external chat log, and then you've got your closed loop. So the trajectory is really that when you land on a scenario, it writes back to your adaptive plan. And so a general purpose AI gives you that answer. It's in a chat window. It's kinda like we talked about. You're still in that kind of sidecar spreadsheet mode, whereas ADI gives you an answer finance can defend. And, of course, you know, like I said, as as the models get better, then EDI gets better. And so we ride that curve, and we don't compete with it. And I'll just say one last thing on this too. Like, one of our early adopters is a ChatGPT enterprise customer, and they were really frustrated about having to explain their chart of accounts, their levels, their dimensions to chat GBT over and over again. And then it kept forgetting, and then it wasn't considering the reality on the ground within their adaptive instance. And so that's really where we start to shine. Great. Thank you. And I guess for you, Ben, so one of the questions came in. So one of the attendees said, we know that Adaptive can support really compelling scenarios and configuring scenarios. Why should they do that with an Adaptive Decision Intelligence versus Adaptive, right? I mean, what's the difference? What can you do more in ADI versus, that what's the value there? I kind of look at ADI as supercharging the already awesome infrastructure we built with Adaptive. And so one of the most actually adopted, capabilities that we've ever launched is personal what ifs, because it allowed all of our customers to not have to, like, actually create a copy of an entire scenario to actually make changes and see what happened. This just takes that to the next level. Right? And instead of you having to manually go in and make changes and then see what the impact is, you can literally just do this through a conversational interface and say and and you don't even have to pick what the changes are. That's the most beautiful thing. You say, like, hey. What are the five things I should be testing here? And what you know, if you okay. Great. If you were to go test these things, what are the 20 scenarios? And then can you run a Monte Carlo across this to see, like, what the real full spectrum is? Back into your environment. I think it's just kind of like it's not either or. It's like them working together, and and forming like Voltron, like a much more powerful beam. Yeah. You mentioned Monte Carlo. I've seen that it's really, really powerful. So excited to see how that kind of gets in use with our customers. Dan, a question for you. So, Justin also alluded to this during the demo. You talked a little bit about this in terms of data types and data sources and things. So one of the questions that came in, what types of data and data sources can we ingest into adaptive decision intelligence to support our modeling and analytics? Yeah. So today, really, it's it's live adaptive plans and and actuals and and your forecast and and all of that. And really, it's covering the most common patterns. So blending governed planning data with supplemental sources. So, you know, you can it's not we we've kinda learned, like we said. Right? You can't just rely on the data that's in your plan, to go make some of these decisions you're trying to make. And then what we're coming really soon here is native connectors for adopted data foundation, Snowflake, Salesforce, with more following. And so our thinking is this architecture that we're building is really extensible. So each new connector we have, it'll plug into that same framework. And so the principle for me really is that you shouldn't have to choose between your adopted data and the market data or CRM data that really completes this full picture. So bring it all in, ADI, you know, the the main technology behind it actually, it builds a unified model behind all that data, and then you can analyze across everything and really take advantage of of the power of AI and where it's at today. Cool. Thank you. Alright. Looks like we have time for one more question and then, I'll redirect this one to you. So, question is what size company and customer type is this geared toward? Well, I think Dan can talk a little bit to this too, and I'd be interested in his perspective. But thus far, the companies are all over the board. So we've seen some really, really small customers take advantage of this. And I think in some ways, you know, the the mid market or or SMB customers are gonna get even more value from this because, it can enable them to scale and grow in ways where they don't have to add headcount. And then what we're seeing from the largest customers is and the fun part about getting really, really big, like, Fortune 50 customers on this and testing it is you figure out a whole bunch of other scale, questions and problems that you've never had to address. So what's really fascinating is we have, you know, the smallest customers all the way up to, you know, Fortune, even 10 customers, on this and testing it out, And we've seen pretty amazing results. I mean, Dan, you're running the EA program with all of our customers in it. What do you think? Yeah. I mean, I'm just I'm grateful that Matt has was on today, because as a product manager who's been in the space a really long time, I always say when I'm leading an EA program, I always want the biggest names. And and I say that because the reason they're the biggest names is because they invest in EA programs like this and they invest in innovation. And so they have consistently been the best partners to go work with. But just overall, yeah, I agree. Like, it, you know, work it's available at any adaptive customer. In the EA program, like, we've specifically had, the range from mid market to large enterprise. So Fortune 500 manufacturers, tech companies, financial services, and then the use cases really scale naturally. So we've seen, like, a mid market FP and A team of three, you know, gets the same variance investigation and scenario modeling as a 50 person planning work. And the difference is really volume and complexity, not really capability. So if you're on adaptive today, really, ADI works with your data as it is. Yeah. Before I oh, sorry. Go ahead, Ben. No. I was gonna say for you mentioned the e a EA program. Is there a way that customers or any any and customers that might be on the Webcast, they can sign up for that? Or Yeah. Absolutely. We've got a link and a page up on community. Really, that's the fastest path right now. So express your interest there. Our team will follow-up. You know, we we do prefer assigned you, m s a, to be eligible. But, you know, let's reach out to your CSM or account exec. We can help get that in place. And, for us, AdaptivePro, really, those customers have been getting priority, but it's not a requirement. And I'll just say we'd love to have you. And we're gonna push this thing into GA as fast as possible, by the way. Yes. We are. Yes. We are. Yeah. Well, that's about it. That's all time we have. Again, we'll we'll try to follow-up with any other questions. Thank you, Ben and Dan. And most importantly, thanks everyone today who was able to join. Thanks for taking your time out of your day. I want to say special thanks to all our presenters, Ben, Dan, Justin, thank you for your great demo. And especially, Matt, thank you so much for joining us today and providing some insight, on yourself and New York Times and your approach to AI and adapted decision intelligence. So thank you again, and we will see you next time. Thank you. Bye, guys. Thanks, everyone.