Name: Enable Confident AI Adoption by Changing Behaviors, Not Just Tools
Uploaded: 2026-09-16T18:30:46.929Z
Duration: 58 min 47 s
Description: Enable Confident AI Adoption by Changing Behaviors, Not Just Tools

Transcript for "Enable Confident AI Adoption by Changing Behaviors, Not Just Tools": Good day, everyone. Welcome to this looking forward with Workday webinar. Enable confident AI adoption by changing behaviors, not just tools. My name is Doctor. Liz Pavese and I'm a workplace psychologist here at Workday. I work in our talent management and insights product pillar, getting to partner with our product leaders and managers in the development of our new technologies. For the past two years, we've seen a lot of conversation. We're having a lot of conversation around AI in the workplace. And in many cases, it has tended to boil down to a more simplistic promise of, look how much faster we can do things. That's a really exciting vision, and we're flooded with these pitches of efficiency and productivity and time savings, which is all good and I think great place to get to. But as an organizational psychologist, I'm not just curious and wanting to design in workplaces that are more effective and more efficient and more productive, certainly by the advancements of technologies to help us get there. I'm very interested in in how we really in, like, make that change come to life. Right? How can we truly transform the world of work not just with the tools that we're using, but systemically? So we have to be really focused on the whole journey it takes to get there. And part of that journey, especially with such a powerful tool like AI and what it can do for us, we have a lot of unlearning to do and a lot of learning to do. We're gonna talk a lot today about this topic, but before we get started, I just wanna cover a few quick housekeeping items. First and foremost, this is being recorded so you will have access to this great content, after today's session within twenty four hours. You have a few different, kind of tools at your, fingertips. We have the q and a section on the right hand side of the screen. Please utilize that to send us your questions. We'll monitor those throughout. We'll try to pull some forward throughout the conversation, and we'll have time for q and a at the end. You then see a docs tab, and that's where you're gonna find a lot of different complimentary resources for today's conversation. So feel free to access those and download them. And then the last feature that we're going to use is polls and surveys. We have a variety of poll questions for you. We want to know your thoughts and opinions and get a sense for where you are in your own AI journey. And then we have a survey at the end because we want to make sure that the content that we bring to you through this webinar series is of most value and relevant to, to all of our customers. Last kind of housekeeping is product. Now we're not gonna be talking about product primarily, in this conversation today. But if for whatever reason we we tap into that a little and we say anything that is more future oriented, please just make sure that you are making any purchasing decisions based on what is currently available, through our platform and products today. If you join just a like, a minute or two late, I just wanna welcome everyone again to today's webinar, enabling confident AI adoption by changing behaviors, not just tools. I have an incredible cohost with me today, so I'd like to welcome Ashleigh to join us. Ashleigh McCord is our senior vice president of enterprise technology services here at Workday. So she is one of the leaders that is behind a lot of the the changes and transformations that we're experiencing, in adopting new, AI tools and technologies. Ashleigh, I'll give you a chance to say hello, introduce and share what are you most excited about in terms of today's conversation. Thanks, Liz. As you mentioned, I'm Ashleigh McCord, and I lead what we call enterprise technology within Workday's internal IT organization. I have two main remits. I lead all of our internal global infrastructure as well as our corporate applications, the biggest of which happens to be Workday. So I get the pleasure of leading what we call customer zero, where we really try to be the first and best user of all of our products before they get into the hands of all of our customers. And I'm super excited, Liz, to be able to spend a little bit of time with you talking about the psychology of AI because the and the tools that we have in front of us now, they're becoming the easy part. The hard part is really all of the the people and the softer side around it. So really looking forward to this conversation. Yeah. Thank you so much for joining us. We're just gonna get started then by hearing from our audience. We have our first poll question for all of you. We would love to just get a sense of outside of work, what ways are you using AI? Are you personal planning? Are you learning and problem solving, creating, you know, content? Or you maybe aren't really using a AI much outside of the work context. And it looks like I mean, we have it kind of across the board here. Personal mostly on personal planning, it was and learning. Yeah. Go ahead, Ashleigh. I was gonna say it's it's interesting if you think, what, three or four years ago when Chatuchypt really took off and we all started using it for, writing poems to one another. You know, now now looking back, it seems so basic thinking. about how these tools are now so integrated into my personal life. You know, moving from, I think, writing poems to doing trip planning to now doing full home automation and automating schedules, like, you know, taking a photo of the the calendar entries for my son's school and having those things get created all of the time. It's hard for me to imagine what what we were doing before, all of that administrative work. It's been pretty impactful. Yeah. I'm I love thank you for sharing your story because, I mean, what you're talking about is, like, a lot of the trial and experimentation and figuring out, you know, how do I advance the usage of this to really help me, you know, kind of do things and think about things a little bit differently. And, you know, we can we're gonna close our poll now. Thank you everybody for providing your thoughts. What What I think is really fascinating is that sometimes we take that very experimental mindset and, you know, a little it seems like less risky in our in our personal lives, but we don't always, you know, kind of maybe make the space for that, internally, in organizations because it can be hard. But that being said, that's how we're going to go from, you know, the the quick queries that we did in Google and replicating them in, like, an LLM and then being able to say, like, how how do I reimagine this process that I used to that I worked in before with a new, technology? AI is getting very, very close to kinda human performance or it's it's it's act it's progressing at such a pace because of the kind of intelligence nature of it. And it has so much, potential for solving real complex problems that really allow us then to bring a lot more strategic thinking, critical thinking, creative thinking, to the problems that we're trying to solve rather than staying in more of the tactical parts of those problems longer. But it's not having yet the kind of impact that we are professing and and hoping to see, as I started the conversation today. Right? Adoption is slower, and there's a lot of reasons why that is. Some of that has to do with industry, some of that has to do with regulatory issues, but when you start to peel back some of those layers, a lot of what's overarching to that is kind of this idea of change leadership. I like to use the term change leadership, not change management because there's a lot more components to leading change than just kind of the tactical, things that you do to adopt some some new, kind of piece of tooling. So that's what we're gonna talk about today is the adoption focus, and we're gonna do that, by kind of going through a few different topics. We're gonna uncover some myths of AI adoption, dive deeper into why does it stall, tackle, you know, how do we really bolster trust, and then we'll just cover out on some tips, resources, and certainly q and a. The myth of 30% time savings. So it's not, you know, kind of a little little punchy in the title here, but saving time doesn't really matter if we're draining our cognitive capacity to take advantage of the time that we've saved. And right now, I think because of the earlier AI into kind of existing ways of working and so, you know, we have to get into this path of how are we rethinking what we're doing. We might have to start from a have to start from a different slate, I suppose, is a better way of saying that. So on one hand, while we want all this productivity and and efficiency, we're actually finding ourselves right now at the beginning of this change curve in this work slot situation where we're spending more time fixing low quality outputs. We're kind of just bolting a AI onto these processes and ways of working as opposed to reimagining them. And all of that just creates a lot more kind of reduction it just has a reduction in our bandwidth. Right? So how do we really rethink what productivity looks like and just kind of start with the end in mind and work backwards, I guess, is a better way of doing it? We wanna be able to preserve human time and energy in order to be able to gain what we want out of this transformative experience. And we see this in some of our research. Right? We've put out, well, we put out a report earlier in the year. We have more data coming in around what's the impact of AI at work. And we are losing some time to that rework. Right? If you kind of go in the upper end of this this 30% of time saved, when you cost that out, if it's about ten hours of efficiency that we can gain, but four hours are lost to fixing all that work, that's about a week and a half a year per highly engaged employee that we're losing kind of maximum sort of impact from that person. So it's not just a tool thing. It's about how do we get more proficient about where these tools are applied and that the outputs that we can gain from them. And then, Ashleigh, you have, you know, lots of different experimentation that is going on in your team, in particular. Is there anything in part is there anything kind of, like, that you've learned as you're kind of working through this yourself? Yeah. I mean, I think there's a couple of things that I think about. So Gartner calls this productivity waste. Right? And what do you do with the time, even that you get back from productivity savings? Like, how do you reinvest. that and make sure that you can really, get the value out of that? I think there's a couple of things that folks forget about when we're implementing a new AI tool. And for me, it's it's on the front end and the back end. So on the front end side, looking at what is the process that we're putting AI tools and technology around? Have we simplified it? Does that process still need to even exist? Can we completely change it? And then what you talked about as change leadership on the back end side, all of this means that I need to do something different. Right? I need to stop what I'm doing and think, how can AI help me do this? How do I need to shift what I was doing before? How do I ask AI, you know, to really be able to articulate what I'm looking for? So this whole concept of prompt engineering, if you will, about what am I really asking for? It's it's causing us to get really clear on that. And sometimes I find that even verbalizing things in an AI tool is sometimes more helpful than typing it because I can I can get it out? But those are those are a couple of the key aspects that I think we're spending a lot of time on is making sure that we are using AI on the right processes and then making sure that we really fully think about the change enablement on the back end. Yeah. Absolutely. Which brings us to our next poll question. Right? So you're talking about, I mean, some of the journey that you're taking within your own team. We'd love to hear from all of you. Where would you place yourself in this AI adoption journey today? Are you exploring and researching? We'll launch that in just a second. But many of us are fine on this continuum, piloting, rolling out widely, maybe pulling back because perhaps we launched the pilot in its in in one place where we thought it was gonna have the most impact, but it's not having the the best impact. You know, trying to get to your point earlier, Ashleigh, of are we identifying, like, the right best use cases where we can apply this? And some of those trial and error. You we don't always know if it's kind of the right use case, and that's okay. Right? This experimenter's mindset thoughtfully and, you know, responsibly is is really important. Sounds like it looks like we have, you know, kind of folks across the board here, which is actually really great One thing I can mention as we're waiting for the poll to finish, you know, I'm finding that some of my own personal exploration has been even more effective than these big projects that we've tried to. implement. Just to give you a quick example, I had gotten some feedback from some of my internal business partners that we had outstanding tickets for, you know, various kinds of capabilities that our customers internally were looking for. And so I actually use Asana, which is one of our products, but, you know, it's an AI tool, connected it to some of my ticket repositories, and was able to clear out over 60% of my team's backlog just in two weeks because people didn't know that those those tickets were even out there the way in which they were were trying to source and find that information. So it's been it's hugely impactful, and and also trying to help set that example for my team. You know, I have it will it will send them a random old ticket of the day and say, hey. Like, here's your random old ticket of the day to where it's not something that's a nag or, you know, holding them, in a negative way, but just trying to help provide information and prompting them. So that has been another huge, win for us internally just personally. in the past couple of weeks. Yeah. That willingness to try something, right, is is really powerful and to your point the modeling part that really comes with, showing a new way of doing things. You're creating a it's okay space. It's a vulnerable space and sometimes we'll get it right. Like in this case was fantastic and a huge impact. and I'm sure your team was incredibly grateful. And then in some cases like a, oh, okay. Tried it. Lesson learned. Let's pivot and let's try something else. It's it's normalizing this. So that brings us to so why does adoption stall? Right? Like any transformational effort, there's so many levers to pull to get toward the outcome that you want but there's also lots of reasons why these things stall. In a prior webinar, we asked customers, you know, what are some of those top barriers that you're experiencing with, using AI, adopting it, etcetera? And not probably to anyone's surprise, here's what we found. Change management, clarity on the value. Are we even ready for this? And certainly, you know, data privacy, security, governance concerns. These are all very real hurdles and some of the most kind of obvious and surface level ones. But if you look underneath these operational barriers, you really find then kind of this human adoption minefield of what's the experience of people as they're learning and going through something very new. And there are very rational reasons why people are kind of nervous or hesitant to new technologies, especially something that is that gives, like, the promise and power that AI can give to us. Let's talk about the first kind of undercurrent, this kind of first kind of human thing that we're experiencing, and that's about habit and workflow disruption. I'll give you, like, a real very simplified example of what we mean by this. Think about drafting an email. Right? Most of us draft emails, work, personal, etcetera. And if you think about kind of on the left hand side, this I'll call it the legacy way of doing it. You went through a quick ideation. You drafted. You polished. You sent it, you know, maybe a little oversimplified but you kind of get my point and what gives us a sense of like high safety in this state is because we have a lot of agency, a lot of control in that work flow. There's low surprise, and it's steady progress. Right? We see it from start to finish. Now we have, you know, AI, copilots, different things that we might leverage that have kind of AI techno technological capabilities underneath it to create that email. So we go from we prompt what we want to say in the email, and we get what looks like the final draft. But before we send it, we actually might wanna reread it and say, does this sound human? Is it interpreting the thing? Is it is did I prompt it correctly? Did I type it or did I speak it and maybe I didn't quite get the nuance that I wanted in there? And as you can see, it goes through not a one, two, three, four sequence, but a one, six, two, five. This this what seems like out of order and nonlinear process. But in fact, you're creating a whole new way of working, a new linearity. And when we move from that known way of working to this maybe new way of working, it creates it kind of ignites that part of our brain that is fright, fear, threats. Right? So the state that we get put in is, well, this isn't predictable. We like predictability. This is unusual, it's not steady progress. So it just can ignite some of this very light touch what is perceived as a threat and say, I don't know what I'm doing here. So I might just back off and not experiment with it as much. Yeah. And I think you're hitting on a couple of key things. Like, to get from the left to the right means I need to do something different every day, and that's hard. Right? We all have, like, very ingrained habits of, you know, I come in. You know, I look at my ticket log every day. I operate the same way. To think that I could do something completely different and approach it in a different way, it takes time and intention. And so with every single task that we do now, we have to ask that question. Is this suited for AI? And if so, how can I use it in the right way? And then what do I need to do differently? And then letting go of some of that control as you talked about in the right spaces. But you have to intentionally choose where is it that I want to give some of that up, and where do I wanna maintain that agency. Absolutely. Absolutely. So that brings us to kind of the second undercurrent. Right? We talked about this, like, heightened maybe fear or threat response that comes with newness. Very normal. Very, kind of typical when you're unlearning, relearning, reengineering something that you used to do. So what are these different kind of anxieties that are abound amongst employees and managers? Right? Well, when we start to reimagine work and maybe our value starts to shine through in different ways, that can that can make us feel like, well, what is my value? Do I have like the skills and experiences and capabilities that are going to help me continue to progress in my life, in my career? And so on the right hand side we kind of see this ski is like the skill devaluation fear, right? What got me here are these things well now I need to maybe rethink how do I take all those skills and rework them in a different packaging and maybe build some new ones. We talked about the rework, tact. What I also find interesting is this concealed use. So what's coupling some of these fear and threats is maybe we're hiding how we're using AI, for what reasons. It's vulnerable. If I get it wrong, I'm showing people I don't know how to use it. I might not be competent, which is actually not the case. It's not that you're not competent. It's that you're you you're doing something totally new and different. And when we do something new and different, it's it is uncomfortable. We do have self doubt, but that does not make it so that you're an imposter or you're incompetent in what you're doing. So we're, really I I personally lot of this fear of loss, right, or this fear of vulnerability. Go ahead, Ashleigh. You had a. great example. about that. Yeah. Just just to jump in, like, I have seen myself go through this sort of change curve with vulnerability and my comfort level. I think we all know that when when AI is rewriting emails or creating something for you, it always uses em dashes. It's like the the most common way that you can tell somebody used AI. And so I found myself editing my prompts to say, hey. Don't don't let this, you know, show up in any of the the drafts that you're writing for me because I didn't want people to think that I was just always using AI. And I've sort of shifted my tune in that because why not? Right? Like, this is a tool. I'm using all the other applications and tool sets that I can to do my job more effectively to make me more productive. Like, why why am I trying to hide behind this? And so now I've actually shifted all the way the other way. And anytime I have an agent running that's sending someone a message, I put a little emoji and say that this was composed and sent by the the AI tool of choice. And so that really, I think, is helping to to make that transition to show people that, hey. I'm comfortable for this. I saw a question popping up. An m dash is the really long dash that, you know, sometimes you would use a colon before, a small dash. It's just a long dash that that AI typically will will use in. communications. Yeah. To connect two thoughts. Right? And it's typical in writing. Like, you see it in books. You see it in articles and all that, but there tends to be an over usage of it. Yeah. I think that's really great. And, to your point of, like, our own vulnerability curve, one of the things that I so I've been working on a a research project. And, classically, I would use, like, all these I'll call them older statistical software packages and all this other stuff, but there is so much power now in, you know, cloud and, like, all these other ways that we have put in analytics in, in AI tooling that I've had to re I've had to relearn, like, how am I going to analyze data and parse through that data and really think critically about that data? How do I load data? Like, all of these new things that I've never had to do before, because I used to use what was called IBM SPSS. It's just I guess it's just a single software package. Very, very old. You So, it literally in the last few? months, What I've had to, like, re reengineer how I analyze data. Very new and different. yeah. One of my favorite things is I am a huge Excel user. I've always been, like, creating all kind of, code behind Excel. Mhmm. And now having it write all of my formulas for me that would take me hours to do. Incredible. Like so, yeah, those kinds of analysis things are so powerful now. It's incredible. It saves so much time, right, Mhmm. to do the bigger thinking about what is what does this all mean. So software doesn't change. culture. Right? Like, it's not going to quell all these fears. It's not gonna naturally, like, get rid of all of these things that are kind of at underneath the surface for us. Behavior does. Right? We have to be willing to try something new and different. Small actions. It doesn't have to be big. And we can address a lot of these fears as well with clear guardrails, transparency, guidance on right use case, not right use case, and ensure you know, you had mentioned this, Ashleigh. Where in the process is it most important to maintain that human control and where is it? So brings us to the kind of the third undercurrent and and a little bit of what we've already been touching on has been, you know, this devolves into some myriad of, like, kind of constellation of trust issues. Right? I could be overwhelmed by the tools. There's a lack of clear guidance sometimes. How do I use these tools? When do I use these tools? It creates a lot of uncertainty. And so how do I know that the technology itself is working? How do I know it's effective? How do I know it's trustworthy? And likewise, how do I also know that my organization is applying them at the right ways in the right place is at the right time. All of those kind of trust issues get magnified by, you know, how we process all of this information. If we don't know how things work, if there's this black box understanding, or kind of black box around the the tool or the technology that we're using, we're less likely to trust it. We're more likely to take a step away from it. We have conversational AI now. Right? There's so much around, there's so much around, you know, does it sound human like? Are we making it approachable? And that's actually a very helpful thing with adoption. But when it gets to kind of this point where it feels creepy, it's, like, almost too human like, but I know it's not, that's another thing that creates this, like, negative or kind of withdrawing response. Mhmm. And then the third thing that can happen and as humans, we tend to have what's called this attribution error. When something goes wrong, I'm more likely to blame or attribute the outcome to external factors rather than seeing what's the internal or my contribution to it. This can be exacerbated with tools and technologies that are now recommending things, maybe acting on your behalf if you have a simple agent that's kind of resolving an issue or resolving a problem. And what that can really do is is really undermine the human component in the whole system and process. So we have to realize that change is not just this operate this type of change is not just an operational shift. It's a negotiation in value. Right? Where is the value most placed for technology to really do its thing and create the outcomes that we need? And where does it really, really matter where humans play a role in that and the value that we can bring from a strategic and creative side? That's why it's so important to think about the responsibility, the build of these tools and technologies. And if we think about the responsibility, the explainability, clear sources, where humans show up, we're going to guard against then what makes us most valuable and the valuable piece that we bring to the equation. Right? What motivates humans is having agency, autonomy, control, feeling like I belong and I'm connected into this team, into this organization, and my my contributions are being valued and that I have skills and abilities to bring to this, to bring to the table and that I'm probably growing my skills and capabilities. So as we are developing developing these tools, as we're implementing these tools, as we're applying them in new contexts, it's important that we really design it with a human centric piece in mind. It's a design and governance challenge as much as it is a kind of tech challenge. So let's go to our third poll. We wanna hear from you again. What is the biggest unspoken barrier in your organization? Do you find that kind of the trust issues in terms of, like, maybe black box and some of the things we shared are challenge, job insecurity, or career insecurity? People are just feeling very confused and overwhelmed. Maybe there's this personal contribution value fears. Where's my value now? How do I add value? Is that coming up for for people mostly? Or is it just operational readiness, which that's definitely a big part of it? Seems like more votes. went, about the operational piece of it there. yeah. I agree with with all of these, and we probably have some aspect of all of these even at Workday. Mhmm. But one that's not on here that I think, you know, I think about a lot every day is embedding AI in the flow of work. Because if I have to stop and go to another tool and then stop and think, how would I use this? What question do I ask? How do I prompt this, you know, this this thing that I'm working on? It causes me to stop what I'm doing, and I kind of break my flow of work. And so for me, that's another one that that I think that I am spending a lot of time in in our internal IT department on is how can I embed these capabilities in the flow of work by predicting what they're doing on their computer or, you know, understanding the context of who that person is to be able to deliver an experience that is in the moment and that can help them get to the next stage of whatever that work is versus having to stop and go somewhere else and do something different? I know that's such a great example, and it's, I think, just one of the the structural challenges that we have right now is, you know, we're we have all of these different disjointed tools and technologies. It's now figuring out how you bring those together to your point in the flow of work. So it's not, you know, we already have issues with, you know, employees, you know, trying to navigate a whole bunch of different tools and interfaces to get their work done today and that is the hope. It's that some of us can really consolidate and streamline that. Right? So it is much more efficient and effective. So it's such a great point. Thank you all for your comments there. We're all on the journey and in different points in the journey and facing different challenges along the way. And the bottom line is is that, you know, all of this is it's progress, not perfection. I think it's one of my favorite, kind of kind of sayings, that I adopted when I first became a manager, many, many years ago, and it just still stays true. So trust is an engine. We what where do we really how do we continue to bolster that? I touched on this a little bit. First, it's certainly in, you know, the design of the tools that you are adopting. Right? Are we being judicious in how we're vetting and experience vetting the tools that we're using and that's something that we take very seriously here at Workday, right? You host talk a lot about responsible AI that it's not just this kind of layering on top of but a foundation to kind of our AI strategy. And if you have the confidence in the tools and in the approach and the way that that is building, that's that's gonna get you very, very far. Right? It enables us the confidence to innovate. It ensures that our customers can adopt, you know, AI, in a trustworthy manner, and you can build from there. And that's part of that kind of organizational trust piece. Right? Like, is my company? Is my environment making, you know, sound choices in the tools that we're bringing in? And are they creating that space and environment where we can thrive that that that safety and vulnerability, that you talked about earlier, Ashleigh? Are we creating that kind of experimental sort of environment progress and imperfection? When we have, then we can build, you know, onto into that what's called cognitive trust. Right? People can really start to trust these tools. We can trust that, you know, the the outputs that we're getting are reliable, transparent. There's confidence indicators and they're showing us what is the, you know, continuum of reliability. And then most critically, where humans show up in and on the loop really creates that emotional connection to really resolve kind of the three pieces of what trust looks like in organizations. Yeah. Maybe I can share what we have done, at Workday that kind of touches on some of this. We had a program. called Everyday AI that we rolled out. It's been probably a year, year and a half ago now where we really tried to put AI in the hands of everyone. We did a lot of enablement sessions to say, here are the capabilities that are available to you, how you can use it. Here's what we have gone through all the security risk and trust kind of from a compliance perspective on to give people that comfort that, yes, they can use these tools and here's how they can do it effectively. So that has been hugely successful in just getting our organization comfortable with using different technologies, thinking about a different way of working. And then now something that we're working on, we call it work insights, which is an agent that allows any employee or workmate to go in, and it will help them look at the different tasks that they do in their role based on their job family and their their current assignment and help them identify tools or capabilities that they could be using, helping them think about what to do differently. So not just providing the tool, but also helping them think about for them with their context of who they are and what job they're in, what they could be doing differently to to make their their work more productive. And it's such a great example. Right? Because that's, like the enablement part of it. Right? Is giving people more knowledge and information to figure out where are the right places that I can use this. And that brings us to you know, that is the most important that is another important ingredient of how, you know, this human AI collaboration can thrive. Right? Is really identifying the most important areas that can make the most material impact in any one particular job. In most cases, we see that in like creation and exploration kinds of tasks, but we are become we are seeing so much more applicability in more complex decision making. So it's about identifying the right places for that impact. What I find really interesting, right, and you this was like such a beautiful thing you just said, Ashleigh, around the enablement of of all of this. So in our in our, research report that was released earlier in the year, we found that nine in 10 organizations say that fewer than half of the roles that they are kind of either recruiting for or have currently, in place are updated to include AI competencies. We need to be capable. Right? We have to be capable. And but just merely asking for those capabilities doesn't mean they're going to manifest and take root. Right? We have to put the right the right learning, the right knowledge, the right, you know, kind of environment in place for you to test, learn, get feedback, apply, like, that whole learning process that happens. But what this also shows me is that we are still on that kind of lagging element of redesigning work and roles. Right? Part of that comes through that learning process, but part of it is also maybe taking a leap and imagining how things might look differently even if we don't know if that's the right way they need to look differently. Yeah. You know, even even last night, Liz, I was reviewing some job requests that had come in, and I declined a few and sent them back to the hiring managers to say, you know, where are you asking for this person to have some AI competency? Like, I need them to be thinking about how they're using tools in a different way and thinking differently about the problems we're now trying to solve in our organization. So this is hard, and it's not just like a a, you know, a light flip light switch that we can flip. It really takes, like, constant iteration to say, I need to challenge every single one of the leaders in my organization to say, are you thinking differently? Are you thinking differently about the people that you're hiring? Are you thinking about the work that that person is going to do? Because I wanna make sure that everyone I'm hiring, I'm very clear on that expectation, but also setting them up for success. And then I've thought about redefining that role before I bring someone in to do a job that, you know, existed a few years ago, but we're changing today. Absolutely. Yeah. It too. It's not like an overnight thing. Right? It takes time, and we have to we have to be comfortable with the time that it takes. So this leads us to our next poll question. This is the last one last poll that we have, for the content today. Looking ahead, which business function is your top priority for AI expansion? And these are focused on, like, really core functions of your organizations. A lot in HR and people ops. Okay. That's that's great to see. Finance. Absolutely. There is I mean, I you know, in my earlier career, I worked in telemanagement consulting, and I worked in internal OD. And the opportunities from across kind of the life cycle and how we think about kind of the strategic foundation of work, you know, we're planning planning for roles, planning for work through talent growth, development, and retention. It's unbelievable, like, what we can do and how we create, you know, and how we really support not just the infrastructure of people, but the material contribution that people can have. Yeah. You know, some of these areas are what we have been digging into as well. We have, just something as simple as an agent that will help solve support questions for all of our, you know, HR, IT, facilities, all of those types of requests that employees have that we have had many, many humans going and answering. And it's not just the human cost of answering those questions, but it's the delay. It may take twenty four, forty eight, you know, Mhmm. or or a week to to respond to those. We're now deflecting a significant percentage of those requests that can be answered in the moment, can be self served by that person so they can get back to to what they were doing. Absolutely. Absolutely. So this takes us to then so, you know, what do we do about this? What can we practically do? Because some of what we're talking about is large systemic multi year change. But to change behavior, we have to start today. We we can do do small things, take small steps that really create then the kind of infrastructure around the this multi year technological transformation. So when you think from a leadership standpoint and Ashleigh you shared a lot of great examples of the things you're doing and the things that you're seeing, just within Workday, right, here are three kind of simple things. Maybe, easier said than done in some cases, but how do we reframe the narrative to a capability narrative? Right? Really thinking about, you know, framing AI as this is the time to change skills and talk about growth and learning in a completely new and different way. Pilot smarter. Right? Committing to experimenting and like kind of lower risk but also like maybe very impactful places. Right? Kind of big change doesn't usually come from big change. It comes from very small incremental changes over time, quick and smaller wins. And then sharing your journey. Right, Ashleigh, you talked about kind of your own vulnerability and sharing your journey about your wins and maybe I'll call them fails, but setbacks or just the learning opportunities of where we can really, make the most of this. From a frontline manager or kind of wherever you are in the management, kind of landscape and people people perspective, I like this idea of really treating AI as your toolmate. I think there's a lot of language floating around in a lot of different organizations of, you know, what the role of AI is in the workplace and what does it do alongside an employee, but it really is a very powerful tool in your toolbox and I think that that's an important, you know, thing to to maintain. Normalize unlearning. As much as we like to talk about learning something new, we have to create the space for that new thing in our mind and in our body and until it becomes muscle memory. So taking some time in in team meetings or, you know, learning sessions of where did I stumble, like, how how do we normalize the unlearning component and create career rules for where and when to not use AI is really really important and valuable. One, it doesn't all it doesn't necessarily, you know, it gives clear guidance but it also helps people think about the response their own responsibility in in in using and judging, you know, this new, this new capability. Can I add one more on that, Liz Pavese, Yeah. Please. on the the manager playbook? Please. So I would also add, for one, it's not just frontline workers and individual contributors who should be using these capabilities, and it's not you know, we shouldn't think of it as a weakness when we are as leaders. I think that's just, like, a standard thing that that people think about is, well well, my team should be create using this to code differently or to do analysis differently, but I I should be too. Right? And so I think, you know, I mentioned how I'm trying to set some of that example, but it's also helping me think about strategy in a different way and and up leveling. And what I would put on this slide potentially is get really uncomfortable personally because the more that I have done that at home, the more that those things have started to translate for me personally at work. You know, I do some cross stitching, and so I have it take a photo and create a cross stitch pattern. Right? And and, like, thinking about doing something creative in that way. I mentioned some of the home automation and, like, personal reminder kind of things that we have set up in my personal life. That kind of inspired me to think about how I can do that with my team as I mentioned with with how we're working our backlog of tickets. And so the more that I've pushed myself there, the more that I have understood and learned and just have gotten more bright ideas of, oh, wait. I should be doing this at work. Why am I not? And it's because I've been trained for twenty plus years working in a corporate environment doing the same types of thing. My operational reviews look the same. My, you know, quarterly business reviews with my business partners have always looked the same, but they can look different now and they can be better and more effective. And so, you know, get get uncomfortable personally to make you more comfortable at work would just be another one I'd add on that list. Thank you for adding that because I think one of the it maybe sounds cliche, but, something we would talk about, so, Shauna, was leading a lot more change, internally is leaders go first. Right? We're setting a model. We're setting an example. And and I think sometimes managers and I think I could speak it from from my own perspective being a leader before is sometimes we're felt we feel that pressure of, like, well, we have to know the answer and we have to show we have to have it all right and good, and that's actually not true. Right? Like, we need to show that we learn too and we don't have all the answers, and that's okay. Right? And I think the more that we do that, it creates that it's okay kind of culture. Yeah. This is this is also new. We're all still learning, Liz. 100%. No one fully knows what they're doing. And then from, like we'll go back then to, like, kind of we're all end users of this technology. Right? But so we do have our own responsibility to take in in learning about it, in in in trying to, you know, not just take kind of the words of other people, but really immersing ourselves in it. Like, to your point, Ashleigh, like, get comfortable being uncomfortable. Learn about it. Make sure that we're verifying. You know? It is incredible the stuff that comes out of some of these, some of the answers, some of the, kind of banter that you can do back and forth as, like, a as a thinking partner with, you know, some of these LLMs. But that is your human judgment is also super essential. Right? You have a context, a a set of experiences that really adds a tremendous value, and you have we cannot dismiss our own critical human judgment, because that will also atrophy if we don't use it. Be open about successes and failures. Share it with others, because that is how we learn, especially as adults, is by learning through stories and sharing with our peers. And because this is all new and it's growing and it's evolving and it's, you know, it's evolving technologies, it's important to also call out, like, where we might be seeing these technologies miss misstep, you know, providing maybe not the right recommendations or, you know, flagging some of these solutions so we can all be learning together and we can all, you know, progress, progress through this change. This has been an incredible conversation so far. I want to pause now. We're going to have some more time for some stories and questions. We have a lot of we have several really great questions, coming in through the the q and a, so thank you for providing those. Let me just take a look here for a second. How okay. Here's a here's a here's an interesting one. Sure your perspective on this, Ashleigh. There's just a tremendous amount in the media and news around, you know, AI that can affect adoption, right, personally and certainly here at work. How I'm curious how you take that into account or how you think about that, in relation to then, you know, kind of continuing to build this environment of of trust and experimentation and and how, you maybe quell some of the maybe Yeah. fears that, you. know, could be exacerbated by what's happening, you know, in news cycles and things like that. Yeah. I can talk about it from my side. I'm sure you have some of the psychology aspects of it as well. So, you know, what we have really you know, I I own and feel accountable for making sure that the work force here at Workday feels confident in the tools that we're using and feels enabled to do that. I I feel a personal obligation for that. And so some of what we are are doing is ensuring that first of all, I feel like I have full confidence in the tools that we are using. So I'm working with different vendors that we are enabling those tool sets for and ensuring that our contracts are solid, that we have intentionally, decided where we do not want a training on our data, just as an example, or retaining our data. So we have some basics of trust that and and privacy that we have built into all of our our vendor relationships. So that's that's one thing that we we do talk quite a bit about. And then I think it's it's just helping them, you know, understand that this is a change journey that we're on. I think with with everything, you know, it kind of reminds me of when we moved to the cloud. Right? It was very unknown and very uncomfortable, and we had to help people through that change journey. I think you will always see hype, both positive and negative, with every technology change that we go through, and we've got to help folks understand what's real and what's just hype. And, you know, we we go back to those foundations of where we have built trust into the technology and where we have trusted partnerships that we will continue to to rely on, and we we work those relationships, very diligently. Absolutely. I think that's such a I don't have much to add to that other than, you know, kind of our own personal responsibility in the changes as well. And, you know, I think the more that, you know, the more that you kind of immerse yourself and you try things out yourself, it can help you put into context a lot of the sometimes noise, that that may be clouding, you know, what truly is, you know, trustworthy and responsible and and all of that. So I do think that there is we cannot divorce ourselves of being, you know, maybe good kind of consumers and testing things out ourselves because there's no greater teacher than lived experience. And until we start to really try things and and and have our own kind of experience with it, sharing that with others, calibrating with other people, then you start to really see, like, what the truth the kind of the truth of the experience does start to emerge. Yeah. We're kinda treating it a little bit like we treat our developers. Right? So before you can develop on our product systems, you have to go through certain training and certifications and things, and we're we're starting to do the same in the AI front more so from an education perspective just so that people understand the risks, understand things like data classification and all of that so that we are handling all of that responsibly and people understand, the capabilities of the tools. Yeah. Great. That's great. I wanna turn to, you know, there's there was we talked a lot about, you know, kind of time savings, the productivity. You gave a great example of of, how you have applied and approached, some of the kind of new ways of working within your own team, Ashleigh. And I'm wondering if you have any thoughts or advice for for leaders in how they can define instead on this productivity sort of message that we we consistently hear an efficiency message, What advice might you might you give leaders to kind of focus on, you know, other outcomes or other metrics of kind of success, I guess, or or other kind of goalposts to focus on? Yeah. I think with every AI initiative that we're doing, we have to start with what's the outcome that we're looking for. I saw a question come in earlier around costs. This isn't free. Right? What we're doing is not free. There is a cost to it. And so, you know, making sure that we have clarity on what do I expect to get out of this, and is it going to be worth more to me than the cost of actually doing that work? Whether that's done by an agent or by a human, we should have the same kind of diligence with with everything that we do. And so, you know, ROI, I think, for every initiative, it this is hard. It's challenging. It's not something that is always just easy to calculate, as as you know. But it's extremely important because there are many ideas that we could do, but those things take work and they they take, costs to do it. In terms of how we manage costs, so the the question specifically is how do we manage the cost of AI? I would say a couple of ways. I talked about some of the vendor relationships that we have. So we have been intentional about what technologies we will use. We have, in some cases, curbed the spending. So in terms of, like, giving people guardrails that they need to stick within around, like, token allowance for certain. technologies as an example, and do some basic awareness of, you know, do you know that what you're doing here is costing you this? Like, just making making that more available for folks. But that's really the the key, I think, is is being clear about what are the the the tools that we wanna use and then how do we govern the usage of those, just like we would any other expense that we have in the organization. We've got to have eyes on it. You know, I mentioned cloud earlier. It was a very similar, concern then. Right? As we were moving a lot of, capabilities to the cloud, we saw very similar, concerns coming through. And it's just part of the maturity of the technology, right, where we are in that maturity curve, and these things will change, over time. But But we still need to be responsible about it and not not. using those those things where we don't have the results on the other side. Yeah. Absolutely. We talked about I'm gonna pivot to another question here. We talked about, you know, one piece of the puzzle is kind of this people afraid of losing their value. What how they can contribute in meaningful and value add ways. And you gave already some examples, you know, that I think hit on this within your own team. But how do you help your team and even, like, broadly across Workday? Day? You know? Kinda see AI as this partner that elevates work rather than something that is, threatening and replacing. Now in many cases, it will replace a lot of tasks, a lot of meet kind of lower lower value add but important tasks that that help us. run effectively as an organization. And that's a reality. But, you know, in terms of this kind of widespread narrative of, you know, gross replacement and things, how do you help people see their value in how it can get elevated? Yeah. I mean, for one I mean, I don't know about you, Liz, but I feel way more capable and smarter and more competent than I did without AI at my fingertips. Right? I can answer questions better. I can speak more articulately. I can, you know, have that banter with a kind of trusted partner in a way to help refine my thoughts and, like, elevate myself. I can do work faster. There's no way I could have done some of that cleanup that I talked about with my ticket backlog manually. I didn't have enough context. But using this tool, I now have that ability. So Yeah. I truly believe that I am more capable today because of that and trying to help people see that. I think we all have an option. No one knows what job is going to be here in five or ten years. We could we couldn't have known that five years ago before AI. Right? Yeah. But we have the option of either stagnating or growing and learning. And I'm always gonna pick the side of having a growth mindset and getting curious and learning and pushing myself to learn and develop more because it's always the the, you know, the better pathway in the end. Yeah. And I think today, we we have no idea what the the ROI and the outcomes of all of this will be. We're we're learning and experimenting and growing, but, Mhmm. you know, I I can't help but think about one of the most, like, credited productivity tools in The US in the past, like, recent history. It's actually the washing machine. And, you know, because it took time away from manually washing clothes every day, like, people could put put in the washing machine and go on and it saved, you know, dozens of hours per week. And the outcome of that actually meant that more women entered the work force. Right? You you might not see that correlation in the moment, but you see it. after the fact. And that's what I get really excited about and why I really wanna push into to the learning and the growth and the the expansion of of all of my my team and myself, to see where this is gonna lead us. Absolutely. It it really does. Like, it is a lagging effect. And, you know, I think the more that you use new things and try new things, value starts to get redefined in that process as well. Right? You can't always know what the end goal is because you because you're learning. Right? And we're. figuring. it out. So we're going we have a few minutes left here and just a couple of things I wanna draw people's attention to. So hopefully, there's one thing that you have taken from, you know, the conversation, today that's just kind of resonated or help you thinking help you think a little differently about maybe what you're leading, how you're leading in your organizations around this change and transformation. But here's just kind of a simplified kinda like thirty, sixty, ninety day action plan. Like, how can we start to scan what we're doing, ID those maybe high leverage but low risk, opportunities and search to really streamline and coalesce around strategy versus sprinkling kind of AI everywhere? Because I think, you know, to Ashleigh really great sound advice, like, when you get focused on something, you can really then start to evaluate the impact and the value, and then it allows you to think about the next thing that can happen. Lots of, resources available. It sounded like maybe some folks are having some, challenge accessing that, but we'll make sure that you that you have access to all of those great tools, materials, information. And just so we can make sure that all the content that we can bring to you in these looking forward with Workday webinars is of most value, please, make sure that you take the survey at the end and don't forget that we have another one coming up, on September 23. And with that, I would just like to say thank you. Thank you, Ashleigh, for joining me, being my partner in this, in this webinar. Thank you everyone who joined and for the questions. We hope you have a wonderful work day. Thanks, Liz.