Name: The CHROs’ Guide to Deloitte’s 2026 Human Capital Trends: Choosing the Human Advantage with Workday
Uploaded: 2026-04-21T10:16:19.340Z
Duration: 1 h 12 s
Description: The CHROs’ Guide to Deloitte’s 2026 Human Capital Trends: Choosing the Human Advantage with Workday

Transcript for "The CHROs’ Guide to Deloitte’s 2026 Human Capital Trends: Choosing the Human Advantage with Workday": So good morning, everyone, and and thank you very much for joining this looking forward with Workday webinar, the CHRO's guide to Deloitte's 2026 human capital trends report, choosing the human advantage with Workday. So if you've ever come to any word of presentation, you'll be familiar with this slide. I have to I have to say this. I have to present it. So if anyone makes forward looking statements during this presentation, it's likely to do to be me about future functionality. Please keep in mind that, future plans can change to base any decisions on currently available functionality. So before I introduce us, I just wanna walk you through, the agenda. So, we're gonna do some introductions. You can see Nina on on screen alongside me. We're gonna introduce I'm gonna introduce Nina in a moment. And then Nina's gonna, has kindly agreed to to walk us through some of the key highlights from the 2026 capital, human capital trends report from Deloitte for about ten to fifteen minutes. And I I'm gonna leave leave Nina to do that. And then once Nina's finished, we're gonna, get rid of the slides, and we're gonna have a discussion. I'm gonna dig a little bit deeper and perhaps some little bit more become a little bit more pragmatic and practical about what these trends might mean for your organization. And then then then we'll wrap up. And and please don't just, zip off because we're gonna share some important information about some really exciting events that, both Workday and Deloitte will be attending in the coming months. So by way of introduction, I'll just introduce myself. So my name is Richard Dobbs. Yeah. I'm the senior director in our HCM product marketing function here at Workday. I've been with Workday for just over eleven years, and I've worked in HR technology for a very large chunk of my quite lengthy career. And it's probably one of the most exciting times to be in HR technology at the moment with the implications of AI, the changing world around us, the future of work. So so it's very, very exciting time. And if I may just introduce Nina. So Nina Kasler, brings fifteen years of experience in tech and HR tech transformation. Nina leads Deloitte's HR strategy and technology offering in Sweden, and at the same time, she heads the work they practice there. So Nina specializes in HR strategy and HR implementations, guiding organizations from setting the vision to empowering people and businesses with the right technology. She's been part of the HR tech journey from coding on premise solutions to today's innovations. She's passionate about helping humans and machines work smarter together. When she's not driving tech innovation, you'll find her enjoying the Swedish outdoors with her family. So welcome, Nina. Thank you very much for joining us today. Thank you so much for having me. Brilliant. So a little bit of housekeeping before I hand over to to Nina. So this session is is being recorded, and it will be shared with you within twenty four hours of, of the session. And we really would encourage you, to share this with your colleagues and contacts as we want to spread the word as widely as possible. If you do have any questions during the session based upon, you know, the conversation and and and what Nina is sharing with us, please pop those questions into the q and a. Obviously, Nina and I are talking and and concentrating on the delivery here, but we have a team of people sat behind the scenes ready to answer any questions that you may ask either directly to Deloitte or to myself or to both both Deloitte and Workday. And then at the end of the webinar, a survey will pop up, and, we'd really encourage you, to just take take a couple of minutes to complete that survey because we take that those survey results very seriously, and we use them to make sure that our webinars are hopefully delivering what you what you expect, and they're getting better and better over time. And then one final thing that's not actually covered on that slide is, you can actually download the, Deloitte's 2026 human capital trends report. There's a tab, I think, with with the dots there. If you click on there, you can download that report directly, and it's quite a big substantial report with lots of great information. So, yeah, download it and have a read of it, when you have some time. So on that note, let me, pass the baton to Nina to talk us through some of the highlights. Over to you, Nina. Lovely. Thank you, Richard. And thank you to all of you joining online. So for me, this HC or Human Capital Trend Report is one of the highlights, of research that comes out this spring. This year's reports, has over 90 9,000 respondents, including over 50 experts and almost 1,800, executives spanning over 76 countries. So there's a lot of compiled insights here. So So big thank you for those of you also contributing to this year's report. For those of you who remember, last year's report was all about the tension due to rapidly changing environments, questioning a bit, where we're going next. This year's report is about that we have now reached a tipping point, so we need to start making some really important decisions. These decisions are not purely a matter of HR. It's actually decisions that challenges how challenges how business works as a whole. The three tipping points, we're going to talk about today is going from human plus machines to human times machines and the dynamic orchestration that demands. We're also gonna talk about going straight strictly from cost efficiency, to value creation. So now let me deep dial into a bit of the tipping points and also the underlying trends. So on the slide here, you can see that we have the three tipping points in green, and then you have the trends with the, with the small pictures on the side there. And the first tipping point, is about going from human plus machines to to human multiplied or times machines. The result in this report, shows that there's a massive gap between what we invest in AI compared to the realized value. And what the research shows is that technology itself won't bridge that gap. This is due to the lack of reimagining and redistributing the work between humans and machines, including both processes and workflows being designed without intention. Organizations will likely need to redesign the work between, human and machines, moving by young, human and machines working side by side. This includes rethinking of culture, decision rights, and the trust in data itself. Since the boundaries between humans and machines are blurring, and as an example that we expect by 2027 that 50% of all decisions are either augmented or automated by AI, it's super important that we don't lose the human agency. Organization needs to start including disinformation security alongside with the cybersecurity to create both guardrails and guidelines for the usage of, AI and data. It's no longer about the most advanced AI. It's about the most trustworthy data and the most capable people making decisions about that data. So rewiring the way we work today. There are some fundamental questions we can ask ourselves in relations to this tipping point. It could be how our culture evolves when people and AI work side by side. Who has the authority to decide when algorithms act or when human intervenes? It could also be how organization protect themselves against misinformation and untrustworthy outputs in a world where AI is both a collaborator and a risk. And then if we're moving on to the second tipping point, which is about moving from only focusing on cost efficiency into focusing more of value creation. Relentless of relentless cost pressures and changing consumer and worker behaviors and the geopolitical shifts, have pushed many organizations towards efficiency at all cost. But as that model tips, the focus should shift towards value. And this means evolving functions that are fit for purpose, reinvest in innovation, and prioritizing growth through adaptability rather than simply reducing cost or expense. At the same time, the geographical shift and disappearance of workforces are making human capacity itself a limited resource, elevating the need of invests where human creates, unique and irreplaceable value. Organizations that succeed will likely not do so. Sorry here. Lost myself a bit. But organization that's a seed will likely not do so. To automate the fastest, it's about how they channel the efficiency to reinvest fueling new forms of value creation and worker performance. One interesting finding is, that only 55% of respondents think about how AI impact, people today without assessing what organizations does do about it. And if we don't do that, it will mean that we automatically erode the adoption to AI since many are still, mistrusting AI. We basically need to start connecting culture, trust, and AI. This tells us AI should be upfront design choice where we focus on humanity, transparency, especially since AI does not only impact how work gets done, it changes how culture is reinforced or broken. This is important since it will determine if organization will scale AI or whether they stop. So then for the last and what I find the most interesting tipping point is going from static plans to dynamic orchestration. So the future is both here and unknown, making curiosity of core a core, organizational capability. Staying relevant means continual reimag continual reimagining how workers change, learn, and grow. A worker is experiencing about 15 major changes yearly, including change of tech, operating models, shift of strategies, business models, and how we work with clients and customers. So there is a lot going on. This never ending change demands organizations to grow from reacting to change to adopting to change. And according to the report, 85% of leaders recognize the importance of developing adaptiveness. Organization needs to start seeing adaptiveness as a muscle rather than a project. So traditional change management, training, and learning methods are not enough. Development needs to start happening in real time, empowering people to learn and adapt as part of the job every day. Only 8% of the respondents believe that their organization effectively meets the continuous, what we call, always on learning need. So here's great opportunity for improvements. As strategy and execution merge, organization will likely move beyond structured jobs and workers, orchestrating capacity and capabilities to meet shifting demands. 88% of the respondents in this year's report recognize the need of this orchestration, yet only 7% believe we are making progress to address it. This means building systems for continuous learning and experimentation and reinvention where workers are not adapting adapting to disruption, but also are empowered to shape it. We need, basically, to reimagine the way we work today, not using AI as a add on because that only creates incremental gains. We need to truly integrate it. We need to move, We need to truly integrate it. We need to move away from static allocation of talents to dynamic mission driven orchestration, creating speed and fluidity where we combine human insight capabilities with AI. Allocation, we can see a bit, like playing your part while orchestrating is conducting the entire performance. A shift AI is forcing inside of organizations today. Purpose, values, and culture should evolve from static statement into living parts of an organization, anchoring them while providing the freedom to adapt, compete, and thrive. And for me, this last piece, is why I enjoy my job so much at Deloitte because we get the opportunity to help organization navigate in this quite tricky terrain through advisory, sustainment lab, road maps, or frameworks like trustworthy AI, to mention one, which I hope many of you online can recognize. And then finally, the tipping points are interconnected and all are pointing to one thing, the need to to make intentional choices about how to use technology to unleash the human potential. So Richard and everyone online, I hope I didn't scare you away with these tipping points and trends, rather that I made you as a human more empowered and motivated to go out there and make wise choices, about how humans and machines can multiply. So I look forward some for some questions now. I think both in the chat maybe for for those behind the scenes and then also from you, Richard. Yeah. Fantastic. Well, thank you so much. So let me ask you guys to start let's stop sharing so people can see us. That's brilliant. So it's a it's a hefty report, Nino. You've done a fantastic job there summarizing all of that in in just over ten minutes. So that that was that was brilliant. Thank you very much. So, let's now so the the the big focus at the beginning there was around, you know, moving away from this sort of human plus machine to human times machine. So moving from a sort of additive thinking to really sort of the multiplier effect of AI and technology on on on the productivity and capabilities of the workforce. So, you know, what's the most critical first step a HR leader should take to intentionally design this human AI collaboration, you know, especially given all the risks of things like work slot and disinformation and hallucinations and so on? Yeah. So I do think so. I would say that most companies today have some sort of machine there, either if it's Copilot or it's some sort of agent or AI embedded in an in some of the tools that we're working with. But for me, it's coming back to the intentional design. And when we talk about intentional design, it's about choices we make on how humans and machines collaborate. And there's one way of doing it, and I didn't bring it up. I just mentioned it briefly in the in the summary of the tipping points. It's about looking at intentional design on both macro and micro level. So when we talk about it on macro level, we're talking about it on an organizational level. And on micro level, it's closer to the core to the teams. And there we talk about, rewiring, and we talk about hardwiring and softwiring. So from a hardwiring point of view, it's about, you know, making sure we have the right governance in place, the structures in place, so that we actually can work with AI and data on the right within within the right frameworks. You can call it, like, the the guidelines and the guardrails that I mentioned earlier. Then you also have the softwiring, and there we talk more about the culture and behaviors. How we want, our colleagues and employees to to behave not only towards AI itself, but also among each other. Because now, sometimes you consider a person that use AI heavily that him or her is kind of cheating, and that's where you get that work slot that you mentioned earlier. Then on a more, team level, so the micro level, when the hardwiring there, what we talk about is more like the workflows. How do we actually integrate AI into our day to day processes? So it doesn't not only become a layer on top. And then from the softwiring, it's all these skills we need to build, the trainings, and how we empower our people to both trust and question AI. So it's not about only building a layer on top of our foundation. It's actually coming back to the integration. And then to come back to the question you mentioned there regarding the disinformation, because I think this is also a super interesting topic. You know, we talked a lot about cybersecurity and the firewalls and access managements and so forth. Now we need to go beyond that, and that's to manage then this disinformation. And in the report, we talk about three ways of doing so, and that's about authenticity, agency, and critical judgment. So, we need to trust and we need to be know who's accountable and what data we and decisions we should question. And all in all, this comes back to the intentional design, clear governance, and then, of course, coming back to what I mentioned there at the end, that the keeping the human, in the center of decision making. So that would be, like, my broad answer to your question. Yeah. No. That's great. I mean and if I can just add a couple of points, some sort from the word data perspective. I mean and I I noted it down there. As you said earlier, guardrails and guidelines, I really like that. So when we think about so we think about AI. AI is, you know, is probabilistic. Right? It's making predictions. It's a prediction machine. And when we look at HR and HR processes, you know, do we want payroll to be probabilistic? Do you want to be 95% sure that we're gonna be paying people the right amount of money? No. Don't. Obviously not. That has to be a 100% right. Yeah. A 100% of the time or as near as close as possible. So we recall so that needs to be a deterministic process. Right? You have a business process. These are the steps. It's predictable. We know what the outcome's gonna be. And then, you know, Workday HR system is is deterministic. It has a business process framework. It also has data security. It's GDPR compliant. It has all those elements that an enterprise requires. But, of course, we want to take all the value and benefit that AI provides. And by by embedding that in our platform, the AI can now operate within that governance framework, within that business process. Right? So within those guardrails. So so sort of the the system of record, the HR system is is that providing those guidelines and guardrails, and the AI can operate within that. And at work, they obviously get combining the two. And then just one other quick point. You know, the AI is only as good as the the data that you train it with. Right? Yeah. And and the context that it understands. And when we think of, you know, you could you sort of the OpenAI, anthropic, Gemini, you know, they're amazing. Right? They've read every book that's ever been written in the history of civilization. However, how much do they know about my enterprise? How much do they know about my business processes? How much do they know about the talent in my organization? And so on and so forth. Very little. Right? So that but but Workday has that institutional knowledge in it. It knows your business processes. It knows your people. It knows your history. So with that data, we're able to produce that or to to to leverage that data with our AI, with the AI that's embedded in the in the platform, so that it it so that we can have that trust. The employees can have that trust in that AI. So so yeah. No. Those are just couple of points that I I wanted to to add there. Let's move on a bit now. So also the value creation point. Right? They see this is Yeah. A lot everyone's a lot of money is going into AI at the enterprise level, but there's lots of question marks over, how much value is being delivered. So my question to you, you know, where do you see the biggest opportunity for HR, maybe even finance and IT to come together around a common data and and AI platform, you know, to kind of shift the conversation away from, you know, how do we cut costs to actually how do we create new value for the business and the workforce? Yeah. So in the report so there's I I really advise you to go to this document documents and download the report and and and listen to it. And you can even, you know, leverage the tool to get that into a create a podcast out of it. So there are great opportunities there to also leverage AI in that sense. In the report, we're also talking about, you know, separating, the activities, when it's about running business and growing business. And here, I think, where we can do, like, find efficiencies so we actually can focus on value creation, it's when we can package to run activities in a good way and automate those. And here I think Workday is a great example because it's basically one platform and it's a single data source. So if we can get, finance and HR and IT, for example, to to focus on running the business through Workday as a platform, then people, can then focus more, intentionally on on value creation because that and the growing the business because you will free up time by automating those, tedious or admin related tasks. But, again, as you mentioned, regarding your first question here, I mean, being intentional about when human play part and when AI does, I think that's super, super important. But here yeah. So Workday is a great tool to make sure that you create value and also at the same time, of course, then focusing on the cost efficiency. Well, I was about to say, actually I mean, I think everyone's gonna be focusing on, okay, how can we use AI to drive efficiency, better productivity, which ultimately will, you know, will will drive down costs. And, obviously, it's going to I mean, the interesting thing is now so the the sort of work day that that that what historically has been a system of record that has recorded work is now becoming a system that does some of the work. Right? You know, which is a general HR, a Genesic AI, and so on. So and I think Yeah. Can I just add one thing here that just popped into my mind? And that's what the the nice thing also with the with having Workday like the as one platform is also when you come to analytics, reporting, dashboards. You know, I'm used to going to one place to find a cost center, one place to go and get one report. But if you have that as one single source, there's also a lot of this cross functional, you know, admin and work related processes that you have as a one stop shop with I which I believe is is really, really great. Yeah. Absolutely. And I mean and and thinking about you know, I think it would be awful to work for a company that's just focused on reducing cost. Right? That, you know, that's an awful place to work. It's a race to the bottom, isn't it? So but you do you do that cost control, that cost minimization, that automation, but at the same time, driving growth. And and, you know, at Workday, we definitely feel that HR should be, you know, a prominent part of that growth engine because, you know, companies are gonna grow by hiring the right people, by upskilling people rapidly, by ensuring that the reward strategy is is aligned with the business strategy. And and AI is gonna help in all those different elements. But, you know, HR is absolutely a key component of of of the growth of any any organization, more so now than ever. Even though we have got more more technology than we've ever had, kind of counterintuitively, people are becoming even more important. Brilliant. So let's move on to another area of the report that I found quite interesting, which was around that whole dynamic orchestration. So so the report says or states that only 7% of organizations are making great progress in dynamic orchestration. So what does dynamic orchestration look like? You know, what are the biggest barriers to achieving this orchestration advantage, and what practical advice can you offer, HR leaders to overcome them? Yeah. No. So it's it's coming back to that, it's all about the adaptiveness. So ensuring continuous learning, for workers on a day to day task, on a day to day basic, and by being able to quickly, you know, adjust to the flow of changes. And that does not only relate to to to the development of AI. It comes to, you know, orchestrating the people, the skills, the data, and technology in real time to respond to the business, business need need of speed or at speed. So there are of course, depending on what type of industry or business you're in, things differs. But one big shift could be that, you know, you start hiring people instead of hiring people for specific jobs, you start hiring them for their capabilities. So that, you know, as I said, you're gonna go from a more static view of talent to more into this dynamic, like, task forces. And then you also to be able to do so, you also probably need to move away from this annual planning. Instead, you should have real time visibility into skills and gaps. And then you also have this today, you hire people into locked roles. But locked roles, sometimes if you have this, dynamic orchestration, it means that people needs to be also be fluid. So you probably need to hire people into an organization with that skill set rather to a specific role. And this is not a shift taking place with inside of organization. It's also a shift that needs to take place of us as candidate applying for jobs. So, yeah. It's an interesting shift there. Yeah. I mean, this is sort of skills based organization, something that we've been talking about, you know, for a while. And Workday, we've been building out that skills platform with the skills cloud and so on. So I 100% agree with you. It's so important to know what skills have you got within the organization and what skills do you think you're gonna need and where are the gaps and so on. One other thing actually to add was, obviously, if you look at dynamic orchestration, I think it's it shouldn't necessarily be limited now just to our human employees. We need to think about the digital employees, these agents potentially. And and that also makes me think about what they've introduced recently, our agent system of record, which comes with the core HCM platform. And and the idea here is that in the same way as we need to know what, you know, what employees have we got on the books, what skills have they got, and and and, you know, and so on and so forth. We need that same sort of information about the the agents that we may be Yeah. Deploying within the organization. And we need to know because an agent, just like a person, will have different skills and capabilities, so we need to have a record of what those skills and capabilities are. We also need to be able to, sort of have an idea of who who who should be able to interact with these agents, what teams should these agents be linked to, and so on and so forth. So that's why we built out this agent system of record, which is a, an interesting development as as we and and we did you could, you know, have some kind of registry of your AI agents in some IT system or or you know? But but to be honest with you, it's much better to keep them in your HR system because you also need to control what data they can access. You need a lot of governance and control over these agents. Otherwise, they could run a mock. And, also, unlike people as well, you know, you need to manage their performance because you can't just assume that an agent that you deploy deploy internally, just like an employee, are they actually gonna deliver value? Right? So you need to measure the performance. And that sort of the metrics we would use against different agents, say, a recruiter agent versus a self-service agent. The metrics that we use to measure the performance will be, you know, potentially quite different. And, obviously, that that's something that at Word Day, we're we know that we're delivering now. Yeah. Okay. So another question, another area. So that this I I was interested in this whole thing about the or concept of the knowing doing gap, you know. Yep. What what's one key takeaway or action action item you'd recommend for HR leaders to bridge this gap and move beyond sort of experimentation into a strategic architecture in an AI in an AI powered world? Yeah. No. For me, it's again, I I feel I'm repeating myself a couple of times here, but it's coming back to that intentional design between humans and machines. Because we need to ensure the human agency, and having a structured approach when using AI. And that means both from a governance point of view of how to use AI and when to use it. So for the intent intentional design I mean, AI itself is no longer competitive advantage if your people don't know how to use it and if you don't integrate it properly into the current landscape. So, I mean, you won't maximize your investments if you're just laying, as I said, before, like, on top of, of your current foundation. And then from the human agency part, we need to determine, and train our people on our critical judgment. So we will still be at the core as humans, or that's at least what I believe. And we should leverage AI for what where where AI actually makes sense. Because in the end, I mean, humans will also be accountable. So far, I have not yet seen an AI agent be taking, the blame if something goes wrong or or so forth. So, I mean, we should always have that human in mind. Yeah. I think that's the two points I I would like to bring forward for this one. Yeah. No. Absolutely. I mean, again, repeating a point that you've made, but it's so important. You know, it's almost the technology is the easy bit. It's it's it's the people. It's it's it's bringing the people your your employees along for the ride, you know, making sure that they know how to use these tools, making sure they actually use these tools. They have confidence and trust in these in these tools that they're gonna be collaborating with to achieve that multiplier effect. There's also something, you know, quite sort of, quite basic, but but what we're finding is a lot of our customers are now looking to really redesign their their job architectures because people's jobs profiles, the way their jobs are defined, probably still reflecting how people did work, you know, in in 2015 or something. Right? You know? So jobs people's jobs have changed, but the the the sort of the the way in which those jobs are defined hasn't changed. So you need to update those jobs. You need to define, you know, what should the person be doing versus what should the AI be doing? What skills does the person require? And so on and so forth. Because without that alignment, you're gonna struggle with with that adoption, I think. I agree with you, Hamak. One other comment I would make is, because I I spend lots of time, you know, talking to customers and and and analysts and industry analysts and so on, and partners like yourselves is, you know, I think, I would say. So I think some of those customers are a little bit too slow to adopt. Right? You know? Also, I'm from a vendor. I am very keen for organizations to adopt technology and adopt it quickly. But I think that with AI, it's a little bit different now In terms of first of all, as well, when we look at how these, AI tools and solutions and agents are being made commercially available, you know, the great majority of of vendors are moving to a consumption based pricing, so you pay as you go. You pay as you use it. Like with with, ChatGPT or Claude or whatever, you know, you have to buy these tokens and use them up. It's gonna be the same. You know, it's the same with with, AI in in, Agentech AI in in Wordday. So that means you can test these things out. You can run pilots. You're not committing to a multiyear contract for something that maybe you for every reason after six months, it's actually it's not right for us. You can actually test these things out. You have the metrics being, reported on within the agent system of record showing you how well are these agents performing. You can and and, really, what we're looking at now is delivering value outcomes because that will justify the ongoing, investment from the customers in those in those agents. So so I think and it's easy now to to run pilots, to localize those pilots, to define who's gonna you know, where we're gonna run this pilot. Because maybe some AI you can run-in The UK, but you can't run it within the EU or you or in The US or in wherever it may be. You have complete control over all of that. So I think, you know Yeah. I think it would be great to have a lot more sort of experimentation. And in fact, just maybe on to sort of checking the clocks. We probably need to to wrap up. Do you wanna keep people too? But but one final question, Nina, because I was talking about experimentation there. And I'd like to, dig in a little bit into how about how organization move beyond experimentation. So if you were advising a HR leader, you know, using word day to day, two or or three very specific moves, you know, what would you tell them to prioritize in the next twelve months to activate this human advantage we're talking about? You know, things they should stop doing, perhaps start doing, and scale up quickly. Yeah. Funny you're asking because we just had, two days ago, a breakfast seminar in relation to this human capital trends, and I had a CHRO from a famous Swedish company also actually having Workday, approaching me with a bit of distress after, and she was a bit like, where to start? What to do? And here, I think, I I'm gonna advise you and also the audience here online the same. And I think we need to to use the the human judgment here, and we cannot do all at once. And advice here that not trying everything at once will ease up in a sense that if you focus on one thing, you can take a pain point. Or we talk a lot about what is trending is the whole employee experience in relations to AI. Take one of those, start small, get a win, build momentum, and then scale. And here you have the opportunities in Workday with there are agents there already that they can try it on and try out, as you said. So the most important thing is that we should we should try. And it's okay if we fail, but we also need to be willing of let go so we can jump on the next. So that was my advice to her. And I think also here, if it's scary doing it by yourself, leverage Workday, contact me or any of my colleagues at Deloitte. I mean, we do this in many places across the globe. There is no limitations to industries. And it could be us helping you throughout the full journey of doing this, but it could also be only getting guidance on how to start. So I think that's a great way. Like, if you don't feel comfortable yourself, reach out to somebody that can support you. That's brilliant. And and just just to add my 2¢ there as well, you know, if you if you're doing this on the Workday platform with Workday AI, you know that there's a whole government's framework around that. You know that it's GDPR compliant. You know that your data is safe. You know, rather than just picking up some random AI startup tool from goodness knows where. You know, you you can be confident in that in that that foundation, in that framework, and the governance, and the auditability of it also. So it's a great place to start. So great advice. Thank you, Nina. So let me just, in fact, before I before I wrap up, have you got any sort of final parting comments, Nina, for our audience today? How would that be? No. For me, it's it's, it's it's not about the most advanced technology. Dare to try, really, but be intentional with what you do. So don't just put it as a layer. Like, really integrate it with your workflow and look at the end to end process. Because as you mentioned, it's not only a shift to the infrastructure and technology piece. It's actually impacting how we humans, play or how humans can multiply together with machines. Yep. Yeah. If that would be it. That's brilliant. Thank you. So so, Nina, I'm just gonna pop up my last slide here so people have a chance there. So, Nina, I mean, thank you so much for your insights. I mean, it's it's been absolutely brilliant, and and it's a great report. I look forward to reading it every year, and it's great having you, give your insights and and your summary and and and your your pragmatic practical advice. So thank you very much. To our attendees, you know, thanks, for joining us today. If you enjoyed this session, just want to highlight that we have a series of events coming up across Europe. You can see them there, where both, Deloitte and Workday, will be will be present, and presenting at many of those events together. We've got some great speakers, great content, great venues for these different events. So if if you would like to join us or would like to, you know, register your interest to join us, plea please please do so. It would be great to see you all there. Please do, again, just to reiterate, download the report. It's it's a really great read. It goes into a lot more detail than we've been able to cover today. And, what else did I want to say? Oh, yes. Don't forget to fill in the feedback survey, please. We we'd love to hear your feedback, and and we'll leverage that feedback for future web webinars. So thanks very much for joining us today, and, you know, have a great work day. Thank you very much, and bye bye. Take care. Bye.