Name: From Risk to Readiness: Inside Dow’s Approach to Responsible AI with Workday
Uploaded: 2026-01-13T20:30:50.303Z
Duration: 59 min 24 s
Description: From Risk to Readiness: Inside Dow’s Approach to Responsible AI with Workday

Transcript for "From Risk to Readiness: Inside Dow’s Approach to Responsible AI with Workday": Hello, everyone. Welcome. Welcome. You are at breaking the barriers to AI adoption from risk to readiness, inside Dow's approach to responsible AI with Workday. I am Kelly Trindle. I'm Workday's chief responsible AI officer. What that means is I look after our efforts to identify and mitigate risks, that arise with the development and use of our AI technologies. My background, just so you have a sense of who's hosting you here for the next hour, I have a PhD in social science. I'm a psychologist by training. I got my start in this space when I was working at the Equal Employment Opportunity Commission, which is the part of the US federal government that enforces civil rights laws. I've worked in academia. I worked at a startup. I've been at Workday now for five years, so it's I'm very happy to be here with you today. So moving forward, need to give a quick shout to our product statement. We're gonna be talking about some forward looking items as we go through the hour. And so you should make decisions about Workday based on the technologies that we have that are available today. Bit more housekeeping. So we have this webinar will be available to you after the fact. So if you think, what did they say or, should I be taking notes or anything like that, you can just sit back, relax, and listen because the webinar will be available to you later on. If you have a question that pops up, while we're talking, there's a q and a function. We may or may not be able to get to you. We have a packed agenda to cover, so we may or may not be able to get to your specific question live, but we absolutely will follow-up with you if not. So it's absolutely worth your time to pop your question in the chat, and we will follow-up with you if we don't get to it live. We also have a series of resources that are available to you under the docs tab on the right side. So I would encourage you to take a look at those resources. Some of them are ones that I've specifically focused on. And so those are, you know, pretty pretty good resources for you to take a look at if you're interested in the topics we're talking about. And there will be a survey as well because we always wanna hear from you about what you liked and what you didn't like and how we can improve going forward. So with all of that said, here's our agenda. So I am going to in just a minute, I'm going to bring another person onto the stage. He's gonna introduce herself to talk to you about Dow's journey in particular, and that's gonna be a great conversation. So we're going to introduce ourselves at that point. Then we're gonna have some sort of introductory conversation about just what is responsible AI in case you join this webinar and you're thinking, I hope they define this. We'll define it. And we'll just talk about some sort of background matter about the evolving landscape related to AI governance, responsible AI, some of that important sort of, grounding information. And then the real the big part of our conversation today will be a fireside chat with SJ Rowley, who I'm gonna invite on the stage in just a minute, about Dow's governance journey and, you know, lessons learned around that, around working with Workday, around how to be successful if you're focused on deploying some of the kinds of technologies that Workday makes available and and other vendors make available as well. Then we're gonna I'm gonna try really hard to watch the clock and leave time for q and a. So that's our agenda. And with all of said, I'm gonna invite Sarah Gioroli up onto the stage with me. SJ, are you there? Hey. We're so glad to have you. This is gonna be great. So I've already introduced myself. There's a nice, young photo of me there on the left, but would you please introduce yourself, SJ? So I own and manage the relationship between Dow and Workday as well. So I'm super excited to be able to have this conversation today. That's awesome. Gosh. I didn't know that bit of trivia you shared. That's amazing. I'm gonna stop sharing screen here so everybody can see us. And thank you again so much. That's really cool about the legacy. Cool. Thanks. Alright. So let's get into our agenda. So let's start let's start by, like I said, like, if if anyone's listening and they just don't really know what we mean when we say responsible AI or AI governance, I'm gonna give my Workday definition and just see if there's anything you would add to that before we get into the material. So at Workday, we define responsible AI as the alignment of AI systems to AI laws and regulations as well as human values. So that's two two different sort of categories. There's laws and regs, which are important, and then there's values. And as these AI systems get more and more sophisticated and complex, we wanna make sure that we are aligning the systems to both of those categories. That's really important. So have to think about risks, and we have to think about mitigations and aligning to legal requirements. We have to think about our principles and our North Star and how we're aligning to those as well. So I don't know, SJ. Do you agree with that definition? Is there anything you would add just as sort of a grounding before we move forward? Absolutely. And so I want to say, you know, from where I sit, my expertise is in HR and the work processes that HR is trying to do. So as a function, you know, we're not trying to do things in isolation or by ourselves. So our primary goal when it comes to responsible AI is aligning to the expectations that the enterprise and IT have set forth for us. And so where we really need to materialize that approach overall is really within our strategy for the function. And, you know, we're working with highly sensitive data and processes in HR, so we have a lot of responsibility in that space. And so when I think about what our AI strategy is for the function, it's rooted in the things that we were already doing or the things we're already known for in HR technology. And that's being ethical and compliant, human centered, market aware, value driven. Right? And all those foundational things lead us to a place where we establish a philosophy that's rooted in our adoption era. And when we think about the philosophy of an adoption era, I don't know if we have any Swifties on the on the line today, but I really think about an adoption era is not just a onetime activity. It's literally more than a moment in time. Right? It's a journey that we're on. And when we make that commitment via a SaaS model philosophy, that's a mindset shift to know that you bought innovation in the path that you chose. And so we're always focused on adopting and reducing our technical debt, working with vendors like Workday who do the research and know the trends, but also recognize that we're also gonna have some custom needs. You know, there are things about Dow that make us special, but we don't need to dream up every solution either. So I wouldn't say we have a responsible AI philosophy in isolation, more that we're aligning to what the enterprise and IT is asking of us, but really focused in on what does that mean from an actual strategy in HR. Absolutely. I love that. I love that, the ethical and responsible aspects and the transparency. And I think you said something there that I think I just want to underline it. The alignment to business need, business direction, like, that is I I would highlight that to our audience. I know we have folks who are joining us from all kinds of different perspectives. And so those are a few things to really kinda keep in mind, like, the the alignment to what does the business need, what are we trying to do, what's our direction. I think that's really important that you raised that, SJ. And so, relatedly, as we've defined what we're talking about here and kinda set the table for the rest of the conversation, Is there anything you would like I I like to point out to sort of misconceptions about the space, about AI governance, about responsible AI. Like, for me, the biggest misconception that that occurs to me is that it's sort of related to the the point I took out from what you said a minute ago. Responsible AI AI governance, it's not about slowing anything down or, like, stopping progress or sort of being overly risk averse or anything like that. I think if if you're doing it right, everything is all aligned together. Like, the business is headed in a certain direction. The folks who care about AI governance and risk identification and mitigation, we're all moving in the same direction. So that's one sort of misconception that I wanna clear up right up front. Is there anything that that comes to mind when I when I mention that, SJ? Yeah. And I think this goes back to, like, how are we thinking about this in the function? And I actually take a little bit different approach. One of the misconceptions that I see rampant is, you know, that the only reason to go forward with AI is productivity, or that's the primary reason to go forward. And I think that's a super easy play. And if people have heard other talk tracks from me, this is something that I double down on. I think the conversation around productivity is the easy play, but I don't think that alone is enough. Productivity is the catalyst, but leveling people up is the reason to go forward. And when we think about, well, what do we mean by leveling people up? Well, it's about making them exceptional at the things that they're already super good at. So to do that, you have to relieve people from work that's unfulfilling or super time consuming or maybe doesn't bring a ton of it has to be done, but it isn't necessarily, like, the most value and fulfilling type of work that you wanna do. How do we level people up? It's about bringing people into the space that bring human perspective to HR processes for us. That consultation space, relationship building, reputation. Right? All of those things isn't something that AI can do for you, but what it can do is give you the right information to level up at those things that require that human touch. So, I mean, that's kinda like my tag phrase right now. Productivity is the catalyst, but leveling people up is the reason to go forward. I love it. I love that focus on like, I I can't remember who said this, but whoever said it, I owe them a debt of gratitude for the statement. But, like, AI done right, responsible AI makes makes work better for people, makes people better at their work. Like, let's remember what what is the North Star here? Like, what are we trying to accomplish? And I love how you're focused on, you know, how how do we, I don't know, put the human back in the work. So if there's something that we can give folks, right, to make their job better and easier and kinda automate some of the the types of tasks that have to get done to your point, but maybe they're not super fulfill fulfilling, those are just the kinds of tasks we wanna give to AI, right, and sort of keep the real exciting stuff, the human stuff, for people. So I I really like that. Ask any what what would make me better? Time is always the number one. For me as a human being, like, as a person, it doesn't I don't know if I I don't care if I'm talking about work or or my personal life. Right? Like, if there is one thing that, like, it doesn't matter what I do, I don't have more of, and that's time. And if I had more of it, maybe I could be better at the things that, you know, are fulfilling to me and bring me. joy. I love that. That's exactly that's exactly how we should be thinking about moving forward with AI. Cool. So before we move into our fireside chat proper, because so far, we've been setting the table, defining terms, talking about, you know, kinda where we're coming from on all this. I think now might be a good time for me to just give a small bit of background about how we approach responsible AI at Workday. So I'll be quick with this because I I really I have some great questions for SJ. So I wanna move on to that. But super quick. So at Workday, we have a program that I I remind myself to talk about the four p's because that reminds me of of what all the different aspects the program are. So we have principles. I think a lot of folks now have AI ethics principles. We treat those like our north star. Whenever we have a question about moving forward with something or not moving forward with something and risk versus reward and that kind of thing, we look to our principles. So that's number one. Number two is, you know, principles are great, but only insofar as you put them into practice. So we also have a robust approach, for our responsible AI practices. I've put out a lot in the resource material, in case you're wondering, how does that work? What does that look like? There's a lot for you to read there. I would, point you in in that direction. But the the take home message about our practices are really kinda it's risk based. It's a risk based approach. So we take a look at all the different AI technologies that we provide to customers and that we deploy here at Workday as well. And we think, like, where are we introducing potential risk to people's fundamental human rights? And, you know, if we're doing that, what are our mitigations for those risks? I think when folks hear risk, they get nervous, but I don't mean it in the way you may have heard it elsewhere. It's it's like, you know, if you're utilizing AI for certain use cases, like in an HR context, for example, since since we're talking to an HR leader here today and say and SJ, if you're using AI in spots like hiring and recruiting and things like that, it doesn't mean that's bad or you shouldn't. It just means it carries a greater risk, for fundamental human rights. So that's where you have to really focus the governance aspects and and think about the different things that could come up. So that's practices. Then at Workday, we have, people. So that's the third p. We have a dedicated team that does this kind of work. That's the team that I lead, but we also work with this is I think this is gonna come up in our, fireside chat as well. It's not just the dedicated team. It's all the people who do the work across the organization, like our, legal and compliance folks, you know, our folks who work on those sort of higher risk AI products, like engineers and product managers, the folks who deploy those kind of products, like folks who have roles like SJs. But here at Workday, we spend a lot of time with them as well, and their input to the program is very important. So it's there's a dedicated team, but then there's also a whole enterprise approach to the people. And then finally, there's policy. So you if you've joined this, webinar thinking, are they gonna talk about things like the EUAI act and things like that? That's what I mean by policy. We're, super hooked in with our public policy team here at Workday, and they're out there working with the lawmakers to introduce new laws and regs. And so we pay a lot of attention to that. We give feedback to the regulators about, you know, what makes sense, what would be easy for us to bring into our framework, etcetera. So that's the fourth piece. So I talked about principles, practices, people, and public policy. So that's kind of the nuts and bolts of the responsible AI program at Workday. But what I really wanna do now is move into our fireside chat. So back over to you, SJ. My first question for you here is I would really love to hear about your AI governance journey at Dow and any lessons that you've learned along the way. So what occurs to you for for our audience to on that topic? I mean, I think this could have been, like, a three hour conversation. Right? So let's actually just start with just the responsible AI governance piece. But I wanna just build on something that you just said. And I think what's important and I think this transparency moment is helpful to people is that we also depend on all the things that you just described that Workday does on behalf of their customers. We do depend on your reputation. We do depend on the markets that you're doing business in. We also depend on the fact that, you know, 65% of Fortune 500 companies are Workday HCM customers. And those global organizations are in many of the same markets we are. So we do do some of that level setting to say, you know, what's the feasibility of deployment of a particular feature or adopting this more advanced capability and what are others doing? So I don't I think there are parts of our organization where we are willing to be first, and then there are other parts where we're like, well, what are others doing? Right? Let's do get a little reality check here because we don't always need to be first. Sure. But, also, it's okay to go forward in a cohort and learn a little bit along the way. So I just wanted to say, like, in addition to some of the things that we do that are special to or specific to Dow, you know, we do depend on those things about Workday getting involved in legislation and governance at a global level in understanding how their tools can be compliant, how you can deploy those make those tools available to us in a compliant way. Because we're just not staffed to figure all of that stuff out ourselves. Right? Yeah. So I got a little sidetracked there. But responsible AI governance. I think the most important thing that I can say about this is as a as a function within a broader organization, we literally cannot do this without our partnerships. And our partnerships I mean, I'm always gonna forget somebody, but we have exceptional partnership with our IT team. Our HR technology team working in lockstep with our IT team enables us to go forward in so many ways. We have an exceptional team, and we know we are some of the best at what we do. And and part of that is because of the different skills and perspectives that we bring. And and doing and having that relationship with IT allows us to make those easy connections to, you know, the enterprise level responsible AI teams, our risk teams, legal, the local the local teams. All of those organizations coming together make everybody feel comfortable is a fundamental part of our responsible AI governance as we apply it to HR. So with that in mind, we did establish an HR AI enablement committee here internally. And that's had a couple iterations already too because, look, we've never done this before. And I'll say this again, I'm sure, but I wanna start by saying, like, I'm actively upskilling in front of an audience as well. Like, I don't know. I don't pretend to have all the answers. Some of this is literally operating on instincts and not being afraid, but also using your experience to understand where maybe something goes beyond your tolerance threshold for risk. Yeah. So all of these come together, you know, we it's it's not about operating by consensus, but it is about operating in a way that doesn't make people feel, you know, run over or not informed or not understanding so that we can go together, especially in HR processes because they touch everybody. Like, everybody's getting a look at what we're trying to do. Right? I think the other thing that we really focus in on is that responsible AI is framed as in an innovation enabler, not a blocker. So it allows us to align the technology that we're deploying to not only the laws, but our own internal values to make sure that we're bringing solutions that align with who we are as a company and what our identity is. And really designed with human in the loop as the imperative. Right? Like, if if we're we're not trying to give, AI agents autonomy at this stage in the game. We're trying to give people the right information to make the right decisions. So what does that mean? Like, that's a lot of things. And so I wanna make sure that I'm speaking in a way that people are like, what are you really saying? We're not activating AI in a AI in a silo in HR. Outside in perspective and being able to review processes with the experts makes it so that we don't have to know everything. Uh-huh. That can feel cumbersome at first, but we're learning the language. We're way better at answering those questions today than we were three months ago. Right? So we're in a better position to anticipate what is the risk team gonna ask us. So we've got that relationship. We have a good cadence. We know what they need to know to feel comfortable. I think the other thing is we're really transparent about what it is that we're doing over here. Whatever we're trying to move to production, there are no secrets. We're really trying to help people understand because it's to bring benefit. Right? So we're trying to help people understand where and why we're using AI and not for the sake of using AI. And I think the other part of that is the incremental approach. So being okay with getting some of this wrong pairs really well with that incremental approach. because you back things out pretty easily when you're not constantly doing these huge program efforts. Right? And incremental steps do lead to transformational experiences and tool availability. So we have strong support both from our partners and our leadership team. That's another, you know, big important part of our responsible AI governance is that leadership team encouraging us to keep moving forward. I think they're also not afraid for us to get some of this wrong and knowing and trusting us to be able to pivot and correct where necessary, but acting in an ethical and compliant way. Yeah, absolutely. Gosh, there were so many gems there in what you said that I think is so important. And I keep thinking about our audience and kind of what they might how they might be reflecting on what we're talking about. And one of the things I've heard as I've talked with various folks out there trying to make this work is that, like you mentioned, a committee or a council that you have in place, and you mentioned leadership participation. Right? And I think one of the important things I wanna pull out there is that it is very important, as you've been saying, SJ, to, like, bring folks along, involve other people, like, from other functions across your organization, absolutely take an incremental approach. You know, don't be, know, don't be bothered by the fact that you don't know everything. I mean, I've got a PhD after my name, and I'm the chief of this thing, and I don't know everything either. I mean, it's it's a fast moving technology. We're all learning. If you say you know everything, it's a good indicator that you don't actually. And so, like, the more you're open to the fact that, yeah, the tech is fast moving, the regs are fast moving, you know, there's going to be more to learn, and I should talk to other people who know other things because I can educate myself that way. That's really important. I think the trick there for for our audience potentially is that when you kind of are trying to balance that sort of moving forward with innovation against the risks that come up. Right? Because that's what this this game is about. Like, you wanna get the productivity gains. You want to get the benefits from AI. You want to support innovation. There's also risks, and so you're trying to balance it, and you're trying not to be a deer in headlights. You're trying to be open and embrace, you know, learning and all that stuff. I think the trick is you can get a little stuck. So, as I'm thinking of people sort of listening to this conversation, they might be thinking, I've got a committee or I've got a council, and I can't get anything through the committee or the council. And so one thing that you said, SJ, that I think is really important is that highest level senior leadership participation and kind of, like, noting who's got the d, if if you know that phrase, like, who can decide? Who's the decider about moving forward? So is there anything you would add to that sort of balancing of the risk, how to move forward, how to keep things moving given all this participation from across the enterprise? Anything you would add to that? That's it. I like to remind people of this. No one has done this before. Right? But that means no one's done it better than us trying to do this. So we are literally the best who's ever done it, and I mean everyone on the call. If you're trying to move your organization along this change curve, no one's done it better. And so it. knowing that you know, we're on the climb right now, but no one's done it better. Nice. I love that. So despite various challenges that have come up on your journey and this whole balancing innovation and risk, you've continued on. Right? And so I think it's related a little bit to what you were just saying. But if you could sort of mention anything more about lessons or just what's kept you moving forward, through, you know, the various challenges that have come up. I think our audience would love to hear that. Yeah. Of course. And I think we all want to go fast. Do more. Go faster. We don't have enough resources. How do we get cross functional alignment? You know, there's ambiguity and rapidly changing requirements. I was having a conversation with someone yesterday where they reminded me of something I said before we left for Christmas or the holiday break. And I'm like, oh, but I've learned since then, so now I feel differently. Right? So let's rewind that. I'm glad we didn't tell anybody about that because I feel differently today based on what I know about that today than I did then. And this is part of the the environment that we're in that, you know, being able to deal and navigate with that ambiguity. And also, you know, at Downward, navigating a global environment where we work really hard to provide consistent global experiences for our users. But if I if I had to talk about lessons learned, I would say, for me, what the I didn't necessarily learn this through this, you know, journey so far because we're not in the past. We're actively in this. I kinda knew this, but it solidified for me that relationships and reputation matter. And an HR director said to me recently, the HR technology team's default is collaboration, and that's why you are able to make progress. And that I think that is true. Mhmm. I can't think of one person on the HR technology team that their default isn't collaboration, and we work together to move to a common goal. We don't always all agree on how to get there, but we leverage that relationship and reputation. And, you know, we like to think there's no other team on the planet like us. We have many of us have been on the team since the Workday go live, but it really collaboration really does matter. But, also, I think the other thing is be loud about what you're doing. People this is the world according to Sergio. I don't have a PhD like you, Kelly, but I think people are afraid of what they don't know more than what they do know. And so if you're loud about what you're doing, it comes off as way more authentic and transparent. And I think that the louder you are about it, the more people can support you. But to that end, we can't get anything done without the right support. Challenging the status quo is definitely on brand for me. I've been doing that for about forty years. You can ask my mom and my current boss and everybody in between. It's very on brand. So when I was asked to take some of this on or the opportunity was presented to me, my leader knew what he was in for, but he has never backed down from the support. And I get some pretty wild ideas. Right? I might have to temper a little bit, but that support has allowed us as a team to kinda thrive around the art of the possible, but with the appropriate guardrails at the right level for Dow. Yeah. Yeah. There's so much to be said for having that sort of executive level kind of sponsorship. Like, at Workday, for example, our chief legal officer, Rich Sauer, came from, Microsoft before he was at Workday, and he had set up a similar, he started to set up a responsible AI program over there before he came over. And so, you know, lucky for me, he already had the kind of back down to think about it a bit even within his much larger, focus of being our, you know, highest level GC, basically, at Workday. But having that, like you said, that sort of spot I think anytime, probably, we could tap out for just a minute from responsible AI and just talk about or AI governance and just talk about, like, if you're leaning into something new, you've never done it before, it's never been done before. If you're in that space anywhere, really, it's important to kinda look around and say, like, who can I look up to here? Who can sort of help me from that kind of hire? Who can sponsor this work from from the highest level? Right? I think that's kind of really important. Right? Would you agree? Like, kind of career progression stuff. Absolutely. Absolutely. Yeah. Cool. Alright. So as as we're having this conversation again, I I keep thinking about the listener and just various things that I've heard from other companies trying to get things like this moving. And earlier, I mentioned the whole thing about who's got the d and, like, how do we how do we move something how do we keep moving things forward, like, through committee and council? So we talked about that. Another thing that I hear coming up a lot is resources. Like, for example, whenever I talk about how we have a dedicated team at Workday who does this work, I often sort of met with, yeah, we don't have a dedicated AI governance team or responsible AI team at our company. So how can we move forward? So I assume, SJ, that your resources are not unlimited, neither are mine. So how how do you what's your philosophy on, like or or examples of kinda how you've kept things moving even in the real world of kinda limited resources? Yeah. And I would say we have moved from relentless prioritization to ruthless prioritization, and that is cleanly aligning to what is it that we need to do to support Dow and what is the enterprise asking from us and how do we move that needle. You know, again, I said this already, but I think it's really important to double down on it. We're going through our own upscaling. Like, all of these new features coming available, sometimes I go to webinars and I walk away and I'm like, oh my goodness. I have no idea. I I don't get that. I have no idea what we're talking about here. Right? So I have to take some time to go back and learn new language and new ways of doing business and kinda get out of my own way. And so part of that is is trying to figure out what is gonna get us to that next level. But when you're not skilled in the latest, right, availability of technology, you're trying to learn that as quickly as possible. So that prioritization, we're constantly going back and reprioritizing, not our big wins, not our strategic bets, right, but the work that continues to keep the engine going, we are constantly reprioritizing those things and doing that ruthlessly to make sure that we're being impactful to Dow's overall success. I love that. From relentless to ruthless. I might write that down. Guess that's a good takeaway. I love that. Yeah. So the prioritization piece, absolutely. Like, what's everybody working on here, and is it the is it the key to moving forward, both for, you know, if if you're in a position like if you're in the audience, if you're in a position like mine where your focus is the governance or you maybe your focus is the legal and compliance or whatever, what's most important to you and your team, and how is that aligned to the overall direction of the business. Right? And so that'll help you get that, ruthless, relentless move move forward. I would say another thing just as I was listening to you talk, you know, how do you deal with, limited resources? Something for the audience to consider. One of the things that we do at Workday is we use the technology to help us govern the technology. And so, for example, like I mentioned earlier around our responsible AI practices, we a big thing that we do is risk tiering. So, like, is this does this fall in a more sensitive AI kind of category? If it does, we pay a lot of attention to that technology. So we can actually use AI to automate that analysis. How do we decide what is in a sort of higher risk or more sensitive tier? You know, we use automation technology to run folks through a set of questions, for example. Does is the technology designed to do this, that? The other thing, conditional logic that makes that list of questions as short as possible so that we can determine very quickly. Okay. This is a low risk tech. That means you can move quickly. There's not we don't have a lot of safeguards or guidelines for you. Go ahead. Or hold on a minute. We we're gonna have to spend more time documenting the safeguards that are aligned with this thing. So, that's one of the things to think about when your resources are limited. Just how can you to to SJ's point, how can you ruthlessly prioritize? And to my point, is there anything you can automate in the actual process of governing the thing? Right? It is kind of an important thing to think about. So, SJ, I'm interested in hearing a little bit more about how you've worked directly with the Workday team, that you spend the most time with. Like, how do you work with vendors, including Workday? Any other vendors you wanna mention, I think, would be fine too. But the point is, like, how do you work with the vendor to ensure that this AI governance thing is on the right track, that that it's working the way that it should? Yeah. And so one of the things that we do is focus on transparency. And, you know, I've noticed, I I am engaged in our enterprise level HR relationships, with vendors. And one of the things that we've learned through participating in those ecosystems is that not all customers are pulling back the curtain and being and being really upfront about what it is that they're trying to achieve. Now the partnership I have, with our Workday account team, the Dow's Workday account team, expands beyond just myself. So I am the relationship owner. But for example, our functional leads sit down quarterly with our account team and say, Here are the things that we are trying to achieve over the next three to four quarters. How do we get there? Is anyone else talking about this? Are we adopting the right things? Are we focused on the right things? Is this what's going to bring us the most value from our contract? And really being honest with them about why and for the things that maybe don't have that obvious reason to go forward, we have the conversation that says, well, this is what we think it's going to do for us. And sometimes the team is able to help us swap things out, get us connected to other customers, have have us talk to product. There are lots of opportunities for Workday to support you in your journey, but you have to tell the company what it is that you're trying to do. That's the only way that they can be helpful. I think the other thing that we do with the Workday team is we've set actual measurable success pillars. Now you think I could now that I'm mentioning it, you'd think I'd be able to, like, list them out. But the first one is nurture bidirectional relationship. You know, Workday, what do you bring to the table for us? Dow, what you bring to the work to the table for Workday, and how do we nurture that relationship and partnership? The second one, I believe, is near term road map enablement. What are we trying to do right now? Then the third pillar is innovation and impact. What are where are we trying to glean innovation? How are we impacting the enterprise? Where can we get involved in your long term road map and get and understand where you're heading so that we can do the right things to prepare? But the only way to do that is to have that level of transparency. Now that might not be right for every organization. That's what's right for us, and it has been a game changer for us to establish not only that expectation, but also that level of partnership. Yeah. It's so important. Like and I wanna connect what you're saying now to something that you said earlier. When you talked about the fact that we're all learning, none of us have all the answers, this is a new area, I would just point out that Workday, we also learn from our customers. Absolutely. Like, so to your point about that sort of bidirectional kind of input and openness and transparency, you know, we're learning like, for example, I'll I'll give everybody in the audience a really good, solid example. When we built the responsible AI program at Workday, we really started with the products and technologies that we make available to our customers. Like, how do we govern that? Once we got that up and running and set up and and feeling good about that and its alignment to laws and regs, and we did things like went out and got third party attestation that our approach is aligned with things like the NIST AI risk management framework, and there's an ISO standard now for AI management as well. Once we got that up and running, then we said, alright. Now we also use AI at Workday just like you just like you do, right, in HR at, Dell. And so how are we gonna like, how can we take the governance program that we built on the product side and make it make sense on the deployment side when we deploy AI, whether we built it or acquired it from elsewhere? Because Workday doesn't build everything, right, that our business is such, then we have a bunch of use cases as well. So, anyway, I raise all that to say the sort of open back and forth that we have with fantastic customers like you. We learn as much as we provide as far as, you know, how do how do we how do we move forward in the best possible way. So incredibly valuable. Thanks for that input. So, you know, so now I'm gonna move to, like, the hardest question, that I get when I'm in conversations like this with customers and just with others who are interested in the space. I think a really hard question is okay. So you talk about AI technologies like bringing efficiencies and automating certain processes and and doing things like this. And as the technology gets better, it gets better at that. It gets better at bringing efficiency and automating, know, things that you we used to have to do manually. And so the question that I get that's really hard is, what about AI replacing human workers? Like, what do you see when you look into the future, and and what is your, you know, approach and kind of philosophy associated with that as you get all these efficiencies that you've been talking about? So what comes to mind for you on that one, SJ? Yeah. I think I probably bring this is probably the most human part about me, is that I like to think of myself as just your average person. And how do I think about the impact of this transformational moment in tech? And what I think about is everybody becoming a manager. And that's not you know, you I you know, I'm getting that from other market inputs as well, but I already talked about leveling up. I've seen org charts, not here at Dow, but just out like, on LinkedIn. I've seen org charts, you know, and we're starting to embed agents, for example, in our org charts and agents showing AI agents showing up as peers. And I actually don't envision the AI world in that way. When I think about a redesigned organization, I think about everyone leveling up, and now you are the manager for that AI application. Right? And you are accountable and responsible for anything that that AI or agent does in the system. Right? It's not acting on your behalf, but the data that it collects or the process that it helps facilitate. I believe you are a manager. And so I think everybody kind of becomes a manager in recognizing that we are responsible for what happens, in our own processes is a really important part. Now I always get a chuckle about this too, and I can't help myself but share this. I always also think back. I'm from mid the Midwest. I'm in Michigan. And I think about how when you know, we're a big auto industry area. I think about how when the car showed up, Yeah. how all these people must have been like, why do I need this this car? I have perfectly fine horse. Right? I don't need this car. And this horses are still a thing. They just have a different role, right, than what, you know, a car a car has a different purpose for us. And once we realized the efficiency and accessibility was increased and value to individuals, you know, was actually realized, Not only did, you know, the car take over as the primary mode of transportation, all new industries showed up. I mean, insurance, infrastructure, mechanics, you know, safety protocol, road roadway governance, like, just this whole ecosystem just shows up. Right? And I don't think in that moment that people knew what they were in for. Right? And I think AI's a lot like that. I don't I I think that there's a lot of benefit here. I think the way we do things or the way we work is different. Mhmm. I don't but I think the skills you need and things like that are different, but I think it's also part of the evolution of us as a society and as people. But that's the way I break it. I know it's a silly example, but that's the way I break it down in my brain, how I rationalize it. Sure. Of course. I mean, it's such a good point. It's such a good point. The the the fact that if there's some automation of pieces of jobs that exist right now, it kinda has to be weighed against the new jobs that will be introduced, frankly, from the technology. Right? Like, like, my job did not exist. None of my team's jobs existed, before AI. Right? And so that's a really good example to your point when you talked about, like, the car insurance industry was not necessary before there were cars. AI governance and people who do that work, not necessary before there was AI. So I I really, really like that example. And I think whatever technology you're looking at, you can look at it through that lens. Right? Like, we're all carrying around our phones these days. Right? And that was not a thing back in the day. So that brings up all kinds of new things to consider. Math teacher, you're not gonna have a calculator in your pocket, and you're like, I do now. Alright. That's absolutely right. Right. Right. Love it. Love it. Cool. Alright. So then I'm sure there's something I should have asked you that I didn't ask you. SJ, I wanna give you the last word. Like, what's one thing that you would say to this audience that you definitely want them to take away from today's conversation and just from your philosophy and perspective in this space? Yeah. And over the last year, my focus has really been on tolerance and grace. And that's for the people I work with, myself, all of us trying to navigate this new environment. But I've also been challenging myself to kind of be like, okay, well, that's nice. That's a nice sentiment. But how do we move to the next level? And part of this, I kind of already alluded to, and that's getting out of our own way. And so I think we are all called upon to be aware enough to know where our legacy philosophy not be applied to the future ready capability available to us. And so where do we need to rethink the way we've done things, the way we've always done things, how we've operated? You know? We can't always apply that same mindset and approach to the totally new ways that we're trying to work. Uh-huh. And so that's really where I've been at. So if anybody wanted to remember one thing, Yep. that's that's my tagline for 2026. Legacy philosophy cannot always be applied to our future ready capabilities. Yeah. Love it. Love it. Love it. I like to say I mean, I don't I didn't prepare an answer to this question, but I'm just thinking as you're talking just that future's bright. The future's bright if you're paying attention to this kind of space and you're leaning in and you're, you know, open minded to it all. Like, there's so much more there's so much more cool stuff that's coming, And it's a great time to focus on it and, you know, build up your expertise and your knowledge there. So one of the things about the future's bright that that you see on your screen here is that if you would like to take advantage of some of the technologies that, you know, you're hearing about that Workday provides, that that folks like SJ have been able to take advantage of at their organization, one of the things you wanna think about is migrating to Workday's UMSA. And so you can work with your Workday team to, you know, better understand what that's about. So the other thing the next thing I just want to kinda reiterate is that there are these additional resources that are available to you. So make sure you have a look at those. There's a lot of really great stuff that's out there that's that's available for you to have a look at. So with all of that said, I think we might have time for one question from the audience. So let me just have a look to see what our first question is. So we already talked about there was a question here. Thank you for what's your one takeaway? We actually hit that one, so lovely. The next question I see here in the queue, SJ, is around how can organizations start small when they're introducing responsible AI frameworks? I think we talked about this a little, but is there anything else you would mention about that starting small when getting started? Yeah. I definitely think starting with those cross functional partnerships well, you know, it depends on if you're trying to do this at the enterprise level or within a function. If you're at the enterprise level, right, make sure we're establishing clear criteria for the enterprise. But if you're trying to do this at the functional level, I think creating that that that clear alignment between the expectations of the enterprise and IT and then establishing that strategy is really important. But it if you're especially if you're in a functional technology team like an HR technology team, it's not fundamentally different. It's likely not fundamentally different than who you are today. Uh-huh. So I would I would I would really do that. And I would actually use features that are nondisruptors as your catalyst to kinda say, well, here's what I'm kind of envisioning for a governance process. Now here's the feature that I'm planning to go forward with and bounce off the criteria right from the feature to the governance that you're trying to establish and and see if it passes the test. You know, we started SkillsCloud was our catalyst for that conversation. It's nondisruptive from a just purely SkillsCloud perspective, and that really, I think, got us excited about what was possible. Absolutely. And I'm going to move on to the next question. But just so I don't forget, I just want to show you this while we're sharing screen. There's another great, webinar coming up February 5. And so if you're interested in joining the breaking the barriers to AI adoption session, five behaviors of high performing companies, that's coming up February 5. You can scan that QR code, for more information. So I just wanna make sure I got that in there. I'm gonna stop sharing my screen so we can take another question. So the next question that I see here is around the EU. Think I I mentioned it earlier, so I may have seeded this this question. So the European Union, has, put out an AI act EU AI act. And it's I would say I mean, I'm not an attorney, as I mentioned earlier, but I work very closely with attorneys at Workday. And I have an attorney on my team who's focused on this and and other developing regulations. And what I would say about the EU AI act is it's kinda like the GDPR of AI, basically. So it's coming from EU, and it's sort of the first big regulation that that takes both the position of you know, there are developers of AI technology. Like, we've been talking about Workday as a developer of AI technology. There are deployers. We've been talking about Dow, and also Workday itself is a deployer of AI technology. And so this law, the EU AI Act, is a is a regulation on both sides. And I you know, it's the first big one. Like I say, GDPR for privacy is to EU AI Act for AI. And so the question I see is how do you approach concerns real oh, sorry. I'm looking at the wrong one. How do you how are you navigating the EU? Yep. And so. do you have a reaction to that one, SJ, or shall I jump in? think we do this the same way we do any other approach to our HR technology deployment and feature adoption. So we work closely in partnership with risk and legal teams to assess impact as well as data protection teams, and then also engaging the local teams. And there will be times where maybe feasibility is low to deploy a feature globally. And so I think we've had to reset our own thinking in that way to know that there are gonna be cases where a particular feature may not be well received received in a particular country, and we have to be okay with knowing that there may be some level of disparate experience, but still serving the organization in a way that makes a lot of sense and brings a lot of value. I'm not trying to give a non answer. I'm just saying I think there have historically been barriers where that globally consistent philosophy was so important to us that we were willing to go without if we couldn't get approval everywhere. Yep. And I think going into 2026, we know that may not be, the only path forward. And so that's still the overall desire, right, is globally consistent processes. But we do know where required we may need to make exceptions, and I think we're willing to do that. And, really, it's on HR technology to provide the consultation on, where that makes sense and where it doesn't and make sure that you're still creating things that, you know, local teams can support while also making sure you're maximizing value of the contract and the technology that you own. Yeah. I love that. And you're actually reminding me when I talked before about, as I say, working as a developer and our risk tiering approach and the safeguards we apply more to sort of higher risk AI technologies. One of those safeguards that we apply when we build, and provide the higher risk AI technologies is what we call location exclusion, and it's for this exact thing that you're talking about. Like, if you if our customer wanted to, say, deploy, like, a more sensitive AI in particular locations but not others, so that might be Germany or somewhere in in EU or it might even be in New York City or, you know, California or or whatever the case might be, We know that our customers, might be thinking the way that you hear SJ talking about, and so we wanna guide our development teams to think about that so that when we provide these technologies, these higher risk, more sensitive technologies in particular, those kind of capabilities are built in. So that's something to consider whether you're thinking about Workday as a vendor, or I know you have other providers of AI technologies out there in the audience. So that's important. But one thing I wanna mention related to both questions that that we've seen from the audience so far, the first one was I'm just scrolling back to make sure I got it. Start small or start you know, how do you make it easy to get started, whatever that wording was, and the EU. I actually think this is a good example of sometimes regulations are helpful, says says the non attorney. But, like, EU one of the things in EU AI act is higher risk or more sensitive AI is not the same as sort of lower risk or less sensitive AI. Again, thinking about fundamental human rights and how AI can potentially impact people. I think that's also practical if you're just getting started. Like, there's the laws and the regs and how to think about it. But from a practical business perspective, limited resources, what do we wanna focus our attention on from, like, a governance perspective or or ethical or, you know, that that type of approach? Think about the technology and the use case and how it could potentially impact people. If the potential for impact is low when the technology comes into contact with human beings, you probably have to spend less time thinking about safeguards associated with that, and you can move forward kinda quickly. If the potential for impact is higher, then you wanna ask more questions. You want to think about what are the kinds of safeguards that we need, who do we need to involve in reviews, all that kind of committee, counsel, senior executive. Who's got the d on this? Like, who could make the decision about go forward or not go forward? That's where all the governance stuff becomes important, and that starts you down the path of alignment with EU AI Act anyway. So I think that's sort of an important thing for our audience. Let's let's take one more. Let me see what we got, and then and then we will break. And if you put your questions in the chat and you didn't get an answer, we are gonna follow-up with you. So the very last, question is let let me go to this one. Looking ahead, SJ, what does the next phase of responsible AI adoption look like for large enterprises? I'll give you the last word. What do you think? Yeah. I think it's really specific to us, it's maximizing value based on what your entitlements are. There's always little nuggets in there that you're like, oh, I didn't even realize we didn't have that turned on, or I didn't know that was, available to us. I think maximizing that value. And I think the other the next layer to that, I'll take it in a step further, is deciding where your entitlements are not going to meet your needs and where do we need to customize, and and then do the test about, is this actually special to me, and is this something that I need to build or buy? And and what value is this gonna bring to me? But just I think for all of us in the organization before we're going to market or trying to build custom things that we really need to make sure that we're leveraging first what's available to us and minimizing technical debt in what we own. And then beyond that, now, what is special about us and what do we need? Excellent. I love it. Alright. One more plug for join the next, session in this series. If you like February 5, there's the QR code for that. Keep an eye on the various channels where you found this one for that for a reminder about that as well. And I just wanna say, again, SJ, this was fantastic. This was a fantastic discussion. I'm so grateful to you for joining. No one would wanna hear me yammer on about our program at Workday for an hour, so I'm so glad we got to talk to you about not only how we develop these technologies, but how they're deployed at fantastic, partner companies like your own. Thank you again for your your expertise and your wisdom. Thank you for your spirit in all of this like we talked about. Once again, you don't have to know everything. This is a journey out there in the audience, a journey we wanna take together with you. So thank you again, and hope hopefully our hearts. will cross again. Take care. Thanks a lot, everybody. Bye now.