Name: From Blueprints to Breakthroughs: Activating AI with Workday Extend
Uploaded: 2025-10-23T17:16:05.828Z
Duration: 58 min 32 s
Description: From Blueprints to Breakthroughs: Activating AI with Workday Extend

Transcript for "From Blueprints to Breakthroughs: Activating AI with Workday Extend": Hi, everybody. Welcome to this looking forward with Workday from blueprints to breakthrough, activating AI with Workday Extend. Really looking forward to today's content shared with our partners at PwC. Before we get started, I wanted to go through a little bit of housekeeping. This is being recorded and will be sent to you within twenty four hours. Please use the q and a function in the chat, and we will do our best to answer the questions during the webinar or we can answer them live depending on time at the end. Anything we don't get to, we will be able to follow-up one on one on emails for q and a. And lastly, there's a survey at the end of this. So once the webinar is over, please stay on and just take the ten, twenty second survey. So I am Amy Brezi. I'm an extend product specialist here at Workday, and I'm here today with Chris Cameron from PwC who will introduce himself, shortly when he's when, in a little bit. But when two times get started. Like any Workday event, product statement, there are some future looking areas in this webinar, especially with AI. So any buying decisions be made based on current functionality and not future state. A little bit of what we're gonna go through. I'm gonna give a high level overview of Workday Extend and some of the AI features you might have seen or tooled around with at some labs at rising. And I'm gonna pass it off to our partner, Chris, to really go through the PwC POV in terms of bringing AI into Workday via Extend and really some of the thoughts and decisions there. Really compelling content, so thank you for joining us today. So for those of you that might not be as familiar with Workday extend, at the highest level, like the name suggests, it is extending Workday functionality. Everything looks and feels like Workday because it is, but you are extending those functions. Same components as Workday, you have your screen you have your presentation component, whether it's cards or your hub. A really common thing I see is using the hubs component to streamline certain processes. Performance is a really common one. Over at this past rising, there was multiple presentations. One from a customer of mine in financial services that is using those hubs and performance management tool with AI to bring things down from disconnected 100 clicks to 10 clicks. Everything's right there. So it's one area of extend. Also using model components, extending the components of Workday. Another customer, great example, they give complex offers. And instead of those offers, there's 50 plus components that were previously in Excel for being able to say what is the bonus, what is it based on, what is deferred compensation. What they did is they built a form with that interface and extend capturing those 50 plus components. So now everything is inside of Workday and they're able to use that to be able to push out for downstream impacts whether it's reporting or the next thing with extend, orchestrations and business processes. Once that example offers me the business process to go ahead and create that Workday doc to later on their onboard make those payments. Orchestration is now GA. It started off in an extent. It's been pressure tested. Definitely go out there and check it. But highest level, custom act development in Workday. And although I'm talking about custom development, it doesn't mean that it's custom maintenance. Just like Workday, everything is future proof backwards compatible as well. So why do customers choose to build inside of extend? There's multiple reasons, but these are some of the main ones that came up from some research that our teams did with hundreds of customers. First one, consolidation. Whether it's to consolidate your technology or the experience, being able to have a security request just for a simple click inside of Workday to clone somebody's access or consolidate that process. Or potentially equity modeling. Maybe you wanna take down this there's a third party homegrown system that's not kept up with, have that inside of Workday and you're able to do that just natively and extend. Next one, agile response. As the world changes, compliance changes, and as we're having to move faster and faster especially with AI, that agile response, being able to build incredibly quickly. I've had customers build an extend app in one day to collect regulate regulatory information from their workers that previously was just done via email for thousands of workers. You can build quickly here. And last off, innovation. Being able to do things differently if it makes sense for you. Being able to bring these experiences together. Be able to tie your compensation, your performance. Donate PTO inside your organization. Those are just some of the motivators for why customers choose to build inside of Workday with extend. To extend, it's you know, I need to update this slide. Been around for a while. Started back around 2018, I was working at cloud platform twenty apps. I think the most recent number I've heard is close to 3,000 apps live in production. And as these apps are going live, customers are getting more value and see and especially as they go deeper in their extent footprint and consolidate the experience. Nasdaq, they will build seven apps in just nine months. Target, it looks and feels with the experience. They one of the main big app that they have, I love is around their employee benefits and discounts. Being able to have that instead of Workday. So you're eliminating fraud. You're able to actually have on their total reward statement showing x amount of money that was saved through this discount program. Otis Elevator, they built an app over a matter of days when the Ukraine war broke out to be able to check-in on an employee safety and what people need. As these extends getting more powerful, so are these apps. And with extend, there is a extend professional, the premium version of extend, and that's really what we're gonna be focusing on today of bringing in those AI functionalities. Workday extend developer copilot, it lets you really be able to bring Gen AI into creating your front end for extend. And there are features I'll go into that is able to natural language chat with your back end, be able to go quicker and build and also upscale your developer as becomes a platform. Workday gateway. This continuous innovation of being able to deliver the AI gateway within type extend. There is a skills recommender and sentiment analysis over there as well. And lastly, we provide a provisioned AWS integration that includes things like Lambda and AI serve and AI services. A common one might be on recognition. So if your onboarding process in that hub, let's say you need to upload an image that is gonna make your badge, you can have that ready day one. I could upload an image and maybe it's me and some friends. Recognition would know that it does not show anything, that it's you know, my friends can't be my badge. So we'll say, hey. This is declined. I upload another one. Those can be all the entire central onboarding hub app as well. Just AI develop developer copilot, it has been for generating pages. New features just released allows for scripting as well of just being on talk. I need a page that shows x. Another feature that is coming out r two plus, potentially, this month, I gotta check on that, may maybe next, is API search and discovery. There's over 5,000 APIs and growing every day between soap and rest. I can just sit here for the rest of the day and read them all to you. Being able to go into this Copilot, I need an API for the job change. It provides you with your options. And lastly, coming next year, it'd be a Copilot full for orchestration, be able to use natural language. What is this process that I want to be able to orchestrate in and set information out? Create the orchestration and the documentation, all which is reiterative. This is really where we see a lot of the app development going. In Copilot, we have a lot of investments in to get you more value out of extend quicker and be able to innovate within your teams. A new feature that is coming next year that was available inside of some of the tenants at rising is AI widget. This is to be able to bring your own LLM and be able to customize that. Let's say if you're doing a performance use case, you can load this widget with these are our smart goals. These are our job progression recommendations. So as I'm going in there and I am given performance recommendations, I can have it reiterate in a widget to align my feedback or to align my own self assessment to my company or structure and being able to get the most out of that. And lastly, I'd be really excited about Workday Flowwise agent builder. That is going to be available inside of ExtendPro. We were talking about apps today, but this is the feature building with Zillow and some with the agents. And with that, now we've kind of gone through the lightning round of all the AI features. I wanna pass it off to Chris Cameron to introduce himself and take it away with activating AI with Workday Extend. Thank you, Amy. Super excited to be here and, thrilled to be talking about all things AI, especially in the context of Workday Extend. So let's go to the next slide. I'll keep this brief. A lot of words here, but I've been doing this for a while. I think that's probably the theme, not only within the context of Workday, but in the context of building technology companies. And it really probably aligns very closely to the affinity I have for innovation, having the pleasure to lead what we do from a global innovation perspective at PwC. Building apps is a big part of what we do. Now we have, you know, 26 in the marketplace and many more, sitting in Workday's app marketplace, so built on Workday. It's really exciting, an exciting time to be doing what we're doing and, certainly an exciting time to be innovating. But AI and everything we're gonna talk about here is also central to a lot of this. And and I think it's, again, a lot of what gets me really excited about the power of the possibility. Because, again, being in the Workday ecosystem for thirteen years, there was a time that none of this was really something we can conceive of, and now now it really is. Let's go to the next slide. So when we think about activating AI in Workday, we certainly think about a lot of the things we have. So, you know yeah. Go ahead, Amy. I I just got a message that some people couldn't hear you, so I just wanted to make sure come on to make sure everything's okay. If somebody can drop inside of the q and a or chat if you can hear Chris now, I just wanted to let you know that. K. Anybody cannot be heard? Alright. I'm gonna leave. I'll get back to Chris. Okay. Hopefully, you can hear me. If you can't, let me know. Not sure, if that is a problem. Usually, hearing me is not something people raise their hand and say is an issue with my voice. So in any event, I'll keep it rolling with that in mind. So, talking about activating AI in Workday, I mean, as we just talked about, there's definitely a lot of, options for us, and and Amy outlined a few when we think about extend, and we'll dive into those. But, we have what's embedded in Workday, and and it's embedded throughout more and more. And we can see some of that. We'll talk about some of that. Workday has an AI strategy that centers on a number of key themes, and we see them listed here. And then as mentioned, Workday Extend enhances and drives more AI capabilities. Ultimately, what we're gonna spend time here talking about is what all that means. Like, there's many options and there's, I think, a lot of choices as to what we can go do around these capabilities. I hope that over the next, forty minutes or so, we can drill into a few things that can help you prioritize what those might be. Let's go to the next slide. So, you know, let's start with just level setting. I think it can get a little confusing. We talk about generative AI. We talk about eigenic AI. We have this really rich history. Like, AI, we think about as this thing that we've just invented. In fact, it's been around for quite some time. You know, many of us who have been, you know, fans of the Turing machine, the Turing test, you know, the, Enigma broke down, the code for Enigma back in World War two. It's really fascinating how the, generation and legacy of what we're doing today traces back, you know, quite some time coming up on a hundred years. But where we are today is unique, and I think we all understand that. Then what I wanted to take a moment to do is just kinda level set because we're gonna talk about generative AI and we're gonna talk about agentic AI, and they're two different things. And maybe many of you on the call understand the difference. For those who don't, even those who do, I'll give you a few ways to frame it differently. I think this little, example of the cool little chef here at the bottom because he's baking cookies, which is something I personally am a fan of. You can think of generative AI as that chef who can build a recipe from scratch based on what those ingredients should be and based on how I should bake something like a cookie. And with those instructions to prompt, it generates a recipe and output. In contrast, Aginic AI is a more powerful cookie recipe maker. It's a baker that not only can bake and make those cookies just as the Aginic AI can, but it then goes a little bit further. Right? It's gonna serve them to the people. It will gather feedback from the folks who get them. It'll then iteratively modify that recipe to improve it over time. I consider this a really simple but very clear way to think about the difference between these two technologies because they share some common ground, but sometimes the definition between the two is nuance. And the reason it's important is we're gonna spend time talking about what we're going to go do, what we should prioritize doing with all these capabilities, and the tools we use are consequential. And and as you see and I'll close on this on this slide, we engage with generative AI all over the place. Any of us using Microsoft Office, if I were in a room and say, let's see a show of hands, almost everyone would raise it, and we now understand, and see Copilot built into so many things. Sometimes, for myself personally, to a degree, it's almost like I'm good Copilot. I think I can write this email or I can compose this document, but it's embedded in in many of our work lives. Let's go to the next slide. So when we think about building the case for activating AI and leveraging extend, I think there's a couple of, components to that, discussion and that story for us that we wanna keep in front of us about almost what we call some north stars. We start thinking about this. I I'd spent time in the world of, early stage enter venture capital and private equity building businesses as was alluded to on my, intro. And I always come back to thinking about things in that context. You know, how we think about the idea of building a case or anything starts with what we're gonna accomplish. And and there's a few things listed here where we can certainly we heard in a few, examples from Amy where some of these clients of Workday, you know, when we think about, a few of the ones that were offered a few minutes ago have enhanced functionality. They've tailored Workday to meet very specific requirements, that they have to hit from a global or a local perspective that wouldn't be otherwise in delivered Workday capability. These capabilities become embedded into our Workday tenant in a very native way. For those of you not really familiar with the extend architecture, I will use the word. It's elegant. And the reason it is I've spent a lot of time in the technology world. Usually, when we're building these applications outside or next to another software system, we're having to deal with, oh my goodness, I have an upgrade. Now I have to go upgrade this thing and make sure it still works. In contrast, extend moves naturally with all of our upgrades because it's working to the rest framework, the API, web services, give us what we need to power those extend apps and those don't change. So just architecture helps us avoid that problem. And then, you know, we are driving more from our workday investment. Right? That's really what a lot of this is about. It allows us to look at opportunities to look at our landscape, our ecosystem of HR and finance, and start to think about creative ways to look at things we can do potentially now in Workday even though it's not part of delivered functionality. So that drives a lot more value for us. It leverages our Workday fluency. It it takes advantage of a single data model, security model, reporting model. So there's a lot of, a lot of strong advantages when we think about what we can bring back into Workday as a partner and technology platform. And then lastly, I think there's an interesting spin on this, MIT study that's been cited a few times. Anyone who was at rising heard it cited, and I've I've seen it, pulled out because it it does identify this failure rate of many Gen AI pilot programs. And if you read a little more closely within it, it really correlates this idea of, Gen AI friction. And there's really three categories of that, and I won't bake those down in great detail. But some of it has to do with the uptake of what we're doing. Right? Ultimately, we're building something that someone has to go in and use. And so how do we think about even human friction around that process? You know, these are the subtleties when we think about the technology married with the fact people have to use it that frankly, I think is interesting where I put this note here, the idea of adopting within Workday versus around it. Right? So our opportunity to look at taking a Genic Ai NAI into, you know, into projects, into initiatives that are going to work within the context of the model of Workday we already have, we're harnessing existing work habits in ways that I think help us address, you know, this very real problem with, Gen AI and and similar projects even if you look at a Genic. So, this is all of what excites me about some of this. Let's let's go to the next slide and we can talk about where else we can take it. So, you know, I spoke to this world of, you know, start ups and and building companies and being in, the world of private equity. I'm huge fan of the show Shark Tank, and, actually, we do something like that internally at PwC called Ideas to Impact. We crowdsource ideas about ways we can enhance Workday, for our clients to make impact in the marketplace, and we do it very much like the show and we run, something twice a year. We get all these ideas in. The reason I frame that is all that comes back to understanding the tenants of really this elevator pitch. The idea of how we look at any of the things that we think should do in a very rational approach. Again, if you go back to some of the things we heard, some of the Workday clients do that Amy mentioned a few minutes ago, Every one of those you can draw back to someone sat down and really measured the value proposition, the benefit of what that thing would be and spent the time to line that up so they really understand how they would know how to assess that benefit before they did it and maybe to measure the outcome of doing this and the benefit they actually derive after it's in place. Having that kind of discipline and understanding the top line, if you will, of that elevator pitch, the value proposition, what we're doing and what we're gonna do that's gonna be valuable and quantify that is really the the hard part. We our cost can be very easily contained. We can understand that because we know if we have to pay more license fees, if we have to pay teams or incur costs internally to develop what that looks like. So I I encourage all of you to think about all of these, and I'm sure many of you do, through this, you know, relatively straightforward lens. But the context for any of this, AI and and the different business cases that can, exist within it are no different than anything else. And the technologies we have to work with, you see here listed in the left hand side of this slide, is a rich palette. There's a lot to choose from. So, that'll make a difference in terms of how we think about costs. If we're gonna use more workday delivered AI, that's changing a cost factor versus, oh, I'm gonna solve for something that doesn't fall into that realm. How am I going to do it? And then what am I pulling from in that left hand side to get there and what cost does it come at. Right? But it all has to come back to the benefits and that's all I really wanted to to hit here. And some of those benefits can be very tangible and relatively easy to quantify as well. In particular, the one I'll call up, there's there's more opportunities today than I've seen ever to be able to look at very significant chunks of functionality, maybe even functionality that has been the purview of a third party application in our Workday ecosystem and say, hey. That is actually something I can now do within the context of Workday Extend, Prism, Adaptive, and maybe even compliment it with generative AI or Genic AI. And and that becomes a very, very simple business case because I'm paying something for that often on an annual recurring cost basis. In addition to having to support, you know, that additional capability that isn't native to Workday that, maybe I need a team to support. So some of those costs can be pulled in. Shadow IT, I see all the time. Right? We work with clients and there's, oh, yeah. By the way, Hal over here has this spreadsheet he uses every month to do this because we don't have another way to do it. You know, for us, it's a really interesting opportunity to question all those points across our ecosystem to say, hey, where are we doing this sort of thing and why? What can we do to bring that back maybe into Workday in a way that not only makes it a little bit more native, by the way, gives us all the power of it's not sitting, you know, on on someone's desktop, but it's sitting within the transaction engine of Workday. It's giving us the compliance control we want, and so there's loads of benefits there. But let's go to the next slide. I could do elevator pitches all day. So let's let's step into the world of the generative AI. I gave you that definition a few minutes minutes ago of the chef and and my cookies again. But as I mentioned a few minutes ago, and even at the top of how we looked at the legacy of where we've been to, where we've come to today, this is embedded in how we work. It is. It's there. Many of us are interacting with it daily. I'm sure many of you are aware of it, but these models are are simply delivered in ways that are now native to our work habits. We are now maybe adopting the purpose. So here at PwC, we have Chat PwC. We obviously have made an we have made a very large investment in the, AI marketplace in partnership with OpenAI enterprise partnership, that we have adopted internally. And it it is a tool I use heavily. I've I've been, certainly leveraging that more and more. I'm sure many of you have. But whether I'm going so far as to using a chat GPT model or whether we're using, something like Microsoft Copilot built into how we're working or something else, how we leverage it is now becoming a more native part of how we operate. I'll say this, and I think many of you may have heard it, mentioned in the news. The statistic for the first time ever, Google has tracked a decrease in Google searches. Why? Because it's rotated to ChatGPT. I do 90% of what I look at with, my my ChatGPT open AI platform. And I know I've spent very little time doing search, and now we you've heard, by the way, there's gonna be a new search capability that'll compete in the market around all of that. I think it's a really interesting, idea about how we think about these technologies. Now in in this case, all of these can do lots of things for us in the context of business use cases because this is what it all comes back to. Ultimately, it's thinking critically about our landscape of how we're delivering, technology, how we're delivering and enabling HR and finance related service and capabilities and where some of the generative AI use cases can fit. And there's a few listed here. But whether we're using and leveraging something like text and chat, which often come in the form of, obviously, chatbots. Workday has a delivered one. You there are ways to harness, ones that are outside Workday that can interact with Workday and other data. These ideas of semantic or what we just call natural language discussions with these bots is really super interesting in ways of the system of engagement. I've spent years thinking about ways that we build technology that creates a very fluent and frictionless engagement layer with our our end user community. Generative AI presents the opportunity to do that in ways that are really, really new, compelling, and and kind of reset the bars to what that can be. I can certainly envision a future where much of what I do when I engage with HR, with certain elements and processes and finances, go through some sort of AI step or interface that really interacts with me very differently than just going into a UI the way I do today. But it does do things for me that not only deliver that, but I can deliver process improvement. It can start to look at ways or generate, create, for example, job descriptions. Maybe translate them in volume, and get them prepared for posting to the marketplace. Obviously, ideas of interview questions and other things you see listed here are just idea starters. We don't have enough slides to list out all the possibilities. I think the point becomes each one of these categories, I hope to leave you with thought starters around things that you can do with these technologies. We've we started as we were kinda coming down that list of technology. We've now come into generative AI as an area that we think about, you know, very deliberately because it's already there. It it's embedded in the ways we work. Let's go to the next slide. So when we think about finance and the generative AI models that can be applied to use cases there, there there are several. And I think there's some really interesting landscapes for that. And I've had a chance to see some of it, and you see some of these listed here. And I won't necessarily read off all of them. But I I think the opportunity to use this technology can be effectively, applied across, like we talked about, the summarization and, you know, synthesizing data is something it's really good at. So we can look at, for example, Evisort is a technology that can synthesize contract data and pull that in. It's incredibly compelling. I had an opportunity to co present, be on stage with one of the cofounders of Evisort. We talk a lot about the the capabilities of what that brings to the landscape of how we can work and how we can now leverage something like AI built into a technology like this to take what was a very burdensome process of understanding, not only our landscape of contracts, but you couldn't really get creative. Right? Now I can bring all this data in. Often, when we think about what for a finance team might now start to inform us as to what our future financial obligations look like, guess what some of that comes back to? Contracts. So our ability to break all of that down, and this is just one example of many that you can pull off of this slide, into a structure where I could actually objectively understand very clearly what sort of financial impacts or financial obligations or financial modeling that would be interesting to do around the types of contracts I have with vendors, franchisees, suppliers, even, with our workforce, I think is really interesting. So in any event, this is where the idea of using generative AI and applying those models within the context of finance can be really compelling. Let's go to the next slide. So this is I don't wanna call it too much of an eye chart. There's a lot on here and I certainly don't need you, to memorize it. There will not be a test at the end. But what I will say, so you wanna think about your investments. Coming back to that, idea of the elevator pitch as to where you wanna categorize that value proposition. Do we have some quick wins that could be relatively light lifts to land something that can do something impactful, but might actually have, you know, a relatively low cost because my efforts lower? Or are there things over to this right hand corner of the slide that have this higher potential, impact. You know, contract management was one I just spoke to. We can see it on here and listed in these, bold ones are capabilities that Workday delivered within Workday to day, whether it's within a product like Eversort or elsewhere. We can now get different insights right out of the gate. Match exceptions. Right? So that's on the finance side of the fence, you know, the classic three way match. We see that listed a couple of ways. But now we can do that with more intelligence. Expense reporting, variance analysis. And variance analysis across anything. Obviously, ledger analysis, really and stuff in these context. And again, even though some of this might be delivered, every one of these is a project, and I'll I'll speak to that more in a second because we just can't think about let's flip the switch and say it's on. I mean, we have to think about being delivered about ways to leverage what these capabilities can do. Let's go to the next slide. And in particular, when we think about what these capabilities can do, and here's some in the HCM and some additional ones in the finance lane, it's also thinking about the change impact, the human impact on how we work. Whether it's taking on some of these capabilities and delivering them through the context of a project to turn them on in Workday, you know, build up the change management function to make sure they're being adopted effectively and efficiently, or along with it, you know, doing the work we need to do to be able to test it in environments and get it out the door. Let's go to the next slide. All of those things are are very consequential. You know, the last thing I'll say maybe in this lane when we think about what Workday's AI capabilities can do, because we listed off a lot of really, really good examples of what's out there and what we can do. We can also think about it from a persona perspective. When I speak to these AI capabilities, this all drives back to some of the things we can do with extend, and we're gonna talk about that a little bit more detail in a few minutes. This is specifically where we're able to leverage extend capabilities that think uniquely to solve very specific business cases. But in the context of those business cases, we can think about the value they drive around how we're able to support certain key persona engagement, and this is just another lens when we think about that business case. Again, coming back to that theme of how we evaluate what we're going to prioritize to do. It's overwhelming. You know, we we have leaders asking us questions about what we're doing with AI, what does this mean, and, again, they're mixing generative AI, with the idea of a Genic AI, like it's all AI, but they're very different as we talked about. But then what are all the things we could potentially go do? They happen to hear that there are another company that maybe is appears doing something, whatever that is. We need to do something. But what we don't wanna do is just something, and I'll speak to why we don't wanna do that in a minute. But I think these are other ways for us to frame what that something could be. Now we can look at folks who are in talent optimization, folks that we are working with scheduling and labor optimization, or we just think about the employee experience, and we can start to think about those personas as a lens to frame the type of engagement changes we might wanna do. And I spoke about that earlier. The idea that AI can craft and change fundamentally our means of user engagement is an important thing because that's what every one of these things do. The these things we list on here are things that otherwise people have to go do without this technology in a highly manual fashion, and and often one that degrades the quality of the work that we're deriving out of it. And so now we can do it faster with higher quality, and that that isn't just about either way we don't need a bunch of people anymore. It just changes how we work. This is really a technology, and all of these are technologies that make us work more effectively and better. It doesn't necessarily replace everything we do. So I think that's a thing that gets lost today because there is so much about what AI can do to replace everyone's work. We hear about that and and sometimes some of the, headline news stories. But the reality is where it fits today, it isn't necessarily just about that. It is about augmenting our ability to work in ways that, in my opinion, when we think about any of these personas, leads to higher job satisfaction. To me, that's one of the outcomes from all of this. I get to do some other things in the context of my work, my persona, that one, are more impactful than just doing some of the root work that these technologies will manage, but two, that I might enjoy more than just doing some of these things. Because maybe, again, I'm back to that, you know, shadow IT Excel workbook that would help me figure out the right skills to suggest on our job requisition. Now I can have AI drive that. It can quickly not only do that, but, again, if we wanna put a job posting out, get an updated, job description, make sure it's compliant with all the necessary regulatory components of that job description, translate into three languages, and do that on a button push. I mean, that that's replacing a process that takes people a lot of time that now they can rotate that focus on really screening and finding the right talent. And that to me is a better use of what I do and I think more satisfying. Let's go to the next slide. So a couple other things I'll close here when we think about the the business case again. There's a top 10 list. No. No. I guess maybe no presentation is complete without one. So I'll leave this with you. I won't read them all off. But, again, how we reimagine work? What are the things that we can do that we're able to really drive, you know, the NPS net net promoter score, the idea of creating improvements in how employees have an opportunity to engage with, you know, the employee experience. We see that in left hand side. We can go from left to right to see how each one of these things has a quantified impact. These come off of off of research that's been conducted in the marketplace to say on average, what are the outcomes that get driven back into, back into the enterprise when we adopt some of these things? So, you know, for me, I look at a landscape like this, and then once again, I think about ways to prioritize what I want to do. We will not be constrained about how to do it. We'll have a lot of options, and hopefully, we'll provide guidance to this. And, certainly, any anytime anyone wants to talk about ways to do things, I I enjoy that. But it it's really about finding the right thing to do and then what's most impactful. This is one more lens to that to that view. But as we go from left to right, we can think about whether it's folks internally or internally or as we're bringing on new hires or if we're looking at, you know, functional or business unit leaders, what are the things we can do that can be very impactful and drive those types of values? And that gets back to that top line value proposition. Let's go to the next slide. So let's talk about extend. You know, that's one other tool in this landscape. It's it's right in the title of why we're all here today. And extend provides very, very specific value to the idea of how we can harness AI and agentic AI and the framework of solving for some of these compelling business problems. And some of it's listed right here. This really, you know, I I see extend amplifying any a AI strategy because it gives us the flexibility to create these tailored AI delivered, driven experiences that complement what Workday does natively. And as I mentioned before, also reduces this notion of, you know, the Gen AI friction, the idea of bringing, something like this forward without really being able to drop it in the pace of how we work. We've already got that framework. Let's find ways to leverage more of it. Workday Extend gives us that. And you can see some of the gateways here that it can do different things, whether it's skills mining or forecasting, document intelligence, or sentiment analysis. That's really interesting. So what are business cases for that sort of thing? I I think it's, it's certainly a good, it's a good lens to think about what you could potentially do. And I would encourage as I go through all of this, I know I'm unpacking a lot and I can't go back and forth with you out there in the audience, but if you have questions off of anything I'm saying, if you have questions about things you think you might want to do, put them in chat. You know, we've got folks keeping an eye on that. They will pull those questions forward. I'll I'll leave a little bit of time at the end to to address them. But I I really encourage you to use the time to do exactly that because two things. One, I'll do my best to answer it, and provide some guidance. But two, I think it also goes to the, outcome for everyone here. Nine times out of 10, the question you're gonna have might help someone else who's participating in this webinar. So really encourage that as something if you have an opportunity, drop those questions in the chat. Let's go to the next slide. So sentiment analysis, what can we do with it? It's interesting. We we had an opportunity to participate in a, Extend in in AWS, married up competition to build an app. Right? But we don't wanna just build an app in search of a problem to solve. We wanna solve for something that's a problem that we really can quantify, and they've said a few times, and we can come back and build something to support it. One of the things that we know very much about in the market, and this is just one example. Right? I'm just giving them as we go, that the value of post termination feedback. There are 63% of folks will change up their exit interview answers if you ask them the exact same questions after they've left the company. Right? That's really interesting. Almost all of us conduct these before folks leave and that means that the majority of the time the feedback we're getting to guide our business around by the way, here's what we have to maybe, do a little differently so we don't have regrettable losses in terms of terminations, people leaving we don't wanna have leave. We should change some things. But we're not really getting maybe the best insight as to what's really happening because folks won't be as transparent and forthcoming in their responses, and it's just human nature while they're still employed. So what do we do to potentially solve for that? That became an interesting opportunity for us to engage, extend professional, and do something about building an app. Let's go to the next slide. And so that's what we did. We built an application that would give us better insights into attrition data in Workday. Right? We have, first of all, deployment challenges around even communicating with terminates, the term that we use when we look at trying to communicate with folks who are no longer active workers in Workday. We have to communicate with them often for a lot of different things. But the idea of being able to send them, like, a questionnaire, like, this gets complicated and there's other things that go on within Workday when we have housekeeping around terminees. So for us, you know, we we look at this as an opportunity, in our case, to be able to leverage Workday Extend Professional to be able to, first of all, build the ability with extend forms to be able to interact with the Termini and gather information back. We harness AWS sentiment analysis, that very pipeline you saw a few minutes ago we used. And anyone who uses Amazon to buy anything, if you look at reviews on your app and you look at, you know, four or four and a half stars, there's a summary of thousands and thousands often of those reviews. That's the AWS sentiment analysis engine in action. We harness the same thing, except we applied it to the sentiment analysis coming back from these attrition, surveys that we were running to the terminees. And we got really, really cool insights. We got really simple ways to look at the data that came back to us. And, ultimately, if this is applied in a real enterprise, it's a great example of how we could quantify back to the value proposition what this does for us. So we have a clear problem statement. We can identify a clear value proposition from doing this and doing it better, because if we if we avoid how many regrettable terminations, how much does that save our enterprise? How much value is that to us? We can quantify that, and then we break it down to, by the way, with, you know, Xtend Pro, we can use this pipe to do this. We can use Xtend to go run these other steps that we outlined in these deployment challenges, and we have a solution. So I think that's a really interesting business case. I have no doubt many of you on the call can think of others that are, even more compelling. Let's go to the next slide. So I'm gonna transition into a few key takeaways, and and kinda re, assert a few things I'd like you all to think about. I'm gonna leave some time here, probably even more than I planned, at the end of this to, field any questions you might have. You don't have to have any. But if you do, I'm happy to field them. But I sent this a few minutes ago. Matt Wood, our global and US commercial tech and innovation officer here at PwC, said something that I I think I alluded to that AI really amplifies our expertise. Right? It is not replacing our ability to think. It is making us better thinkers, in a fashion because we're still the ones thinking about what we should do, and then we're using these tools that are available to us to improve both the efficiency and effectiveness of the outcomes of the thoughts and the things that we wanna go do. And and I think that's how I look to engage this, these technologies routinely. Now the other thing that I think is interesting, this little, black circle is was with some studies that were done that identified that when companies fail to really understand and clearly communicate the purpose and impact of AI initiatives, it actually can start to breed mistrust and fear among employees. It's even worse than friction. It results in many cases of high performers in these enterprises choosing to leave. They get concerned. They feel like, you know, leadership isn't really on top of how this should look and how it should work or maybe even starts to unnecessarily threaten what they think they're doing as opposed to compliment it as we see listed here, and so they go elsewhere. And so I think this is just something else that, I I would keep in mind as you think about some of these key takeaways. It is the importance of the human equation. Right? We can't lose track of that when we think about these initiatives because while the technology itself is obviously incredibly compelling for all the reasons we're increasingly aware of almost every day in the news and as we've shared some of the very many of these practical examples we can consider with them Workday to day, at the end of the day, it's still how we're going to work and that involves people. And we we have to keep that front and center, you know, along with maybe I'll start at the bottom of this, the importance of responsible AI practices from day one. Right? So when we think about these initiatives, I think the framing for it all, while we can look at it, again, the value proposition, the ROI in terms of what we're gonna get, what it's gonna cost, all of that still has to drop into framing of how we think about responsible AI. There is the potential for us to build systems ultimately because AI at its core is built on algorithms, essentially, technology making decisions about different things by leveraging data, and and those decisions and suggestions are only as good as the data it uses. We're well aware of the potential for bias and hallucinations and all those sorts of things. But we wanna think about how we foster ethical AI when we engage in this because I think it gets back to that, you know, circle to the left. The more we can make that thematic and how we think about these projects and actually bring them back to our workforce, I believe strongly the more our workforce will be excited that we're being thoughtful about how we're doing this. And it's all about adoption. Right? And that really helps us reduce that idea of, you know, AI friction. But going from the top for the other three, and the idea of where, again, that MIT study kinda broke down for those 95%, it it was around that friction, but it's because they weren't really thinking about ways to start smaller and to work with these well defined tasks where we can maybe go through and get some of the quicker win, and we can build from there. Right? When you think about it, once we get one or two with traction, whether it's GenAI or we actually, you know, harness an agent and do a Genex, we can start to see how that gets adopted. And we can also, count on the power of people reacting to it within the enterprise that, by the way, I'm using this thing. It's amazing. It's saving me so much time. It's good branding if it's done right, and that's the key thing. So, you know, the tech is important, but I do not wanna lose focus of the the fact that, again, responsible AI, human equation. Two other things I'll mention, is that when I think about any of this, right, whether it's generative AI or if we think about Aginic AI in particular, where now we have agents, when those technologies are accessing sensitive data in our enterprise, so they have the ability to go in and do things that might, you know, traverse sensitive landscapes in these enterprise systems, We have to think about our governance and security model. Right? And that that, I think, almost goes without any saying without any of this. But what we've created is an additional risk. Right? We've got another point on our ecosystem, our landscape, if we're building something, you know, around Workday that comes in and out of it potentially that now or it's relying on data if it's GenAI models that aren't necessarily just in Workday. We've created another point of potential risk, and I don't wanna call failure, but it's access and usage of data, which inherently means how we think about security. Now we obviously have a Workday security model that we can harness and leverage to make sure folks are doing, not just folks, but technology is doing what it should and when it should and how it should. But, I I think being deliberate and thoughtful about governance and security with these projects upfront and early is gonna be highly beneficial. And that means not just obviously for the project itself, but what's the governance model for how this is gonna work for us? And then then maybe lastly on this, and I'll close on this point, which is that we think about engaging our stakeholders early. So who are the key stakeholders and what these initiatives will look like? How can we get engage them early so we get their buy in, we get their response? We we learn from them how best to communicate this back to everybody else. Getting back to this core idea that the human equation is essential, for success in this just like most of these projects. But I think with AI, it's particularly sensitive. And it also makes sure that we're really pressure testing what we think the real pain points are. You know, that we're not, taking our best guess. We're coming back to that top line and the value proposition and retesting that equation, that question of what is the value and how do we measure it with the right folks at the right time who have the right information. And then, you know, the last piece becomes packaging all of that up with a very thoughtful plan to how you're gonna communicate the benefits of all of this and and how you're gonna train folks around it, how you're gonna build trust and ease the adoption, and the potential adoption friction that you otherwise might experience because all of that has to blend together for folks to be able to use this successfully and and for us to be able to realize the outcomes that we framed in that initial business case. So whether we're leveraging the power of extend or extend professional, where we've given some examples of that today, or whether we're leveraging delivered AI capabilities, or now we're leveraging this increasing landscape of Aginnic AI capabilities that are either being brought to us by Workday across, you know, many of the, technology investments they have made and they continue to make. Or we look at, again, an agent system of record framework, our ability to harness, nearly anything we wanna build in terms of Agenic AI and have it add value to, you know, any of these ideas we might come up with. So we have many, many options. The key takeaways, I hope, will provide some thinking around how you evaluate not only, how you're gonna do what you do. We talked about choosing the priority of what to do, but I think these really help us think about taking that priority and dropping in another lens that brings us closer to execution. So with that, I will I will stop with about ten minutes left and just see if there's any questions that came through in chat that anyone would want to send my way. I could try to address them. Absolutely. Thank you, Chris. That was great. I like to especially the key takeaways. I get asked a lot of where to start, how to get started, and I like to say, how do you eat an elephant? One bite at a time. And you've provided a great framework for where's that priority of that bite? Where are you getting that, you know, high value quick wins as you go deeper in your journey? A few really good questions. One I really I want to address, is around, you know, from Chris, is Workday extend mental support Workday's internal AI capabilities or a gateway to connect Workday with external AI resources? And he gave an example of his work in higher ed as a tech analyst and involved with building customer apps and chatbots for their employees. Before I pass it to Chris, I like to view, you know, Workday delivers has delivered AI and Workday has delivered apps and, you know, various modules. It'll simply extend it will extend that Workday functionality. It can extend those Workday AI capabilities and plug into your own ones. AI widget that is coming on the road map. I have a customer that before AI widget came out, they plugged into their own LLM for some of the performance reviews. I had chatbot, the really common one being able to keep your workers just asking a question in chatbot and not super hard go in. But we love kinda your POV on that, Chris, as well for Workday's extend role with AI overall, Workday, and, third party. Yeah. No. I think you gave a good answer there, Amy. That's that's definitely on spot on in terms of how we think about what does extend do? Is it able to access and leverage and augment AI that Workday delivers, or is it meant to go off or maybe leverage other AI? And, again, whether it's a generic AI or generative AI, to to the benefit of a a particular business case. And I think, in fact, it's both. Right? We have the ability with extend to do, you know, do projects that can leverage capabilities in both directions. So there might be circumstances we saw a few minutes ago. We wanna harness the the sentiment analysis in engine, which is a generative AI capability that work that AWS has in their platform. We have other cloud platform partners that have all kinds of capabilities that we're harnessing will harness more of within context of extend extend professional specifically. But that, I think, is really where extend, distinguishes itself. Because as Amy even explained explained in the beginning, it gives us by its really clever branding, extending our ability as to what we can do with them work there. Right? And so we, once again, are coming back to that equation of extending functionality and context to this specific question. Yes. It could be used to to curate and develop forms and user experiences or processes. I mean, we built custom business processes using extend. Those business processes look and behave like a delivered Workday VP. Create steps, I can add condition rules, I can do all those cool things. But maybe within that custom VP I built within my new extend VP, I go do something that actually goes and talks to an agent that doesn't even reside in Workday. Maybe it's one that IT uses to provision something. Right? So it really becomes some interesting questions, and that's an example of where that could be leveraging EIA within Workday to be maybe, engage in onboarding experience, then go out and fetch and use through ASR an agent that might be external to Workday to go do something, bring it back into play, and then I'm still experiencing as an end user a very fluent, experience within Workday. Any other questions that have come through that we wanna try to tackle in the last couple of minutes? Alright. Well, hopefully, we hit a lot of the important marks. If I would, again, summarize everything in closing, I I'd certainly encourage you to take a moment to step back and, you know, again, think about, how you're gonna evaluate, what to do, and when to do it. So, you know, we've given you some ideas for a framework for that here. Hopefully hopefully, they are helpful. And, I guess as I sign off, I'm gonna say, by the way, I'm also AI. No. I'm just kidding. That was just made very popular by a stunt, with The UK documentary that did exactly that. I'm real. So if any of you actually wanna talk to me for real that that's available too, I'd be happy. Happy to do that. Looks like we might have maybe one other question on hallucinations. I don't know if, someone can queue that up for me on on the speaker here. So, Chris, it's on how to mitigate the risk of AI hallucinations and strategies you recommend? Sure. That's a great that's a great question, and that all comes back to the data models you're using. So, I I you this with anything that's using the, data models or, again, any kind of custom LLM, the thought starts from the ground up, you know, right around that data model. How how is that being curated? How well is it structured? You know, where is it pulling data from. Right? Hallucinations come out of data models that, you know, sometimes are curated over the public domain and we get, you know, data in there that maybe hasn't been called through embedded to the degree we want. But, you know, the the key thing is, you know, thinking about two things. First of all, what does that data model need to do? Maybe it's three things. Two, how are we gonna source that data model and from where with enough volume to make it meaningful? Because none of this works with enough without enough data volume. And then what's our governance over the over the course of time? So, again, hallucinations might not begin, but then suddenly our LLM is producing hallucinations to the outcome of what we're doing because data has continued to grow. And I think that's another, another challenge that when we think about it, it always comes back to how he was offering some of those closing thoughts. What is our governance model over that LLM, and and how do we get it set up right and make sure it stays on the right track? And then just the last thing, Chris. There is a earlier question around accessing PwC solutions on the marketplace. Can you point the everybody on for where to where the marketplace and kind of some of the solutions out there, how that contact can get a hold of you? Yeah. I'd be happy to do that, and thrilled to talk more about those with anyone who's who's interested. So if you go to if you go into any search engine or if you go into ChatGPT and type Workday marketplace, you will land, on the Workday marketplace. And from there, you've got a couple of facets on the side. You can just pick, us as a partner and PwC, and you'll see, I think, 27 or so applications that we have in the marketplace today. As I mentioned, we have many of them, sitting in the built on Workday marketplace or the app marketplace now as it's branded that are really addressing some significant enterprise, challenges and really giving us an opportunity to bring some third party enterprise software into Workday in areas like pay for performance and incentive compensation is a good example of that, as well as many other capabilities down to connectors, down to, you know, very specific application to solve for regulatory compliance, and and for that matter, global requirements because our library of what's out there has been curated by the global team I get to lead across all of our member firms. So we have many ideas that can have come from our German firm, our Canadian firm, Netherlands firm. It it it's a it's incredibly exciting collaboration that I get to engage in, that we engage in the marketplace where we're bringing the best and brightest driving ideas from our clients. We just don't come up with these on our own. These are all often driven by partnering with the client out of the gate to build these things. So for me, that's what really you'll get a chance to see in the marketplace. That's how you get there. Go and look for the Workday marketplace, filter on PwC. And if you have a question about any of those apps, you know, I will ultimately be probably the person you get a chance to talk to. No. And with that, thank you so much everybody joining. Thank you, Chris and our partners at PwC. As a reminder, this will be shared out with you. And with that, there'll be a quick survey that pops up once I go. And please just take a few moments to answer that survey so we can continue bringing content like this. Have a great work day, everybody.