Name: Workday & Deloitte - How AI is changing the world of HR
Uploaded: 2025-07-07T12:46:12.999Z
Duration: 1 h 24 s
Description: Workday & Deloitte - How AI is changing the world of HR

Transcript for "Workday & Deloitte - How AI is changing the world of HR": Hello, and good afternoon, everyone. Hello, everybody. Thank you all for joining, and a very warm welcome to everyone. Thank you for joining this webinar organized by Deloitte and Workday. My name is, Raimond Rovers, and I'm a partner at Deloitte HR transformation practice. And the topic of today is how AI is changing the world of HR. And looking at the number of attendees, for this, webinar, we think this is a, you know, a topic that's really on your mind, and it should be. So it's really, really great to see you all here. Before we dive into the content, I wanted to share with you that this is not a one off webinar, but what we're looking for is to set up and organize a community around this important topic going forward, so with a series of connects. So stay tuned also after this webinar, and we will follow-up with you, for more connects and more sessions in the near future. So if we look at the agenda for today, we're gonna start off with some some nice insights on how AI is changing the world of HR. And that will be followed by how Workday is enabling this with their solution industry side. We'll then open it up for q and a, so questions and answers, so that we make it also a bit more interactive. So a full packed agenda. So let me then introduce you to our speakers of today. Our brilliant speakers for today are Bo Vialderkse. She's an associate partner at Deloitte and a thought leader around workforce transformation and HR transformation, and she will share her insights on how AI is changing the world of HR. Followed by Jens Lehrmacht, who is the CTO of Workday EMEA, and he will share the latest and greatest around AI, coming from Workday. And then our special guest, you know, from the industry is Evtimio Sindros, who is the global HRIS director at Philips, and he will be talking about how Philips is already experimenting with AI in the space of HR. So with no further ado, I would love to hand over to Bo, and we'll kick it off and dive into the content. Thank you so much, Laimond, and thank you for your kind words. How AI is changing the world of HR? I teach a course on this. It takes me ten months, and I do it together with IE University. So let's see what we can get to in ten minutes. Bear in mind, we will probably only scratch the surface, but I will give you some insights, and we can take it absolutely further. I love this quote to set the stage when we think about the impact of AI in work. And this is a quote from Roya Mara. You guys may have heard of it. We tend to overestimate the effect of technology in the short run and underestimate the effect of technology in the long run. And what we mean by this is sometimes new technology comes along, and we think it will change everything. When Excel came along, people thought that accountants would be a thing of the past, but completely the opposite happened. And when we started space travel, when man first landed on the moon in '90, in '69, what happened then was we thought that space travel in the year 2000 will be a commodity that everyone will be in space. That's not a thing that happened. Right? We're not all taking trips to the moon just yet, but all of the advancement that we had in the space, technology, they really influence their daily lives. So things like an MRI scan, a CT scan, a reliable pacemaker, the wearables that we have, having your cordless vacuum cleaner and even things like baby formula all improved because of that new technology. And now that's the way that we should also start thinking about AI. This is like our business is being catapulted into a new era. It's like we have invented electricity. Now what would that change to the way that we run businesses? It is like Henry Ford, designed that conveyor belt. Thinking about AI as an enabler, how would that change the way that we deliver work? And many people, many leaders recognize this big need for change. According to the Deloitte research, close to 80 of leaders see that this will drastically influence their business in the near future, and 75% of organizations recognize that they need to put more investments in technology. But here's a very interesting number to me and to probably a lot of us at HR professionals. Only 20% of organizations believe that their people are ready to take on that change and innovate effectively. And that is a massive discrepancy in where we need organizations to be because in order to lead, you really need your workforce to get along with you. And I will start explaining you a little bit why. Now if we look at the scope of HR, it's a little bit different. I would like to invite HR professionals to start thinking about how we influence AI within HR beyond what we typically look at at. And we're very good at seeing how will AI optimize talent acquisition or learning or talent mobility. Those are all great facets, but HR has a bigger responsibility. Basically, I would see it in three buckets. So HR has a a responsibility to redesign work. It has a responsibility to find the right workforce. Typically, we would say, like, who do you build by borrow bot? Right? And now we think about what is your human workforce and what is your AI workforce or your agentic workforce in this? And then how do we enable work? What are the structures? What are the technologies? What is our organizational design that helps us deliver that work effectively? I love this slogan that Deloitte has trademarked, which is making work better for humans and making humans better at work. That is what HR should be doing. So I'm inviting you to look beyond the HR, typical scope and look at how do we reconstruct work in a way, how do we reconstruct the workforce, and then how do we enable work. Now if we take this as the scope and, actually, for anyone working on an HR strategy, I think these pillars will be fantastic. I would love to see HR strategies that are more more focused outside of HR, than inside HR. If we now say this is a scope of HR in 02/2025, then what is the role that HR should take on? And Deloitte has a fantastic visual for that. There's five different roles that HR should take, which is HR is that strategic workforce architect. Who are the humans as well as the technology delivering this work? And is that your internal or your external workforce? And which parties and partners do you work with? We will dive in a little bit deeper in a little bit. Then a very important component, ethics and trust. Trust is something that will be very much stressed and tested, specifically when some of the workforce that we're going to work with is not human. And then what is the data that goes into, some of our decision making? That will be an important component, ethics as well. How do we make decisions? How can we track these decisions? And do we know that we made truthful decisions? Future work navigator, how is work decomposed? And we will dive a little bit deeper into it, but you should think about. And I I love this entropic, a quote from the CEO from entropic who said, for any new hire that you're willing to, bring on, show me why AI would not be able to do this. And this is actually thinking about it. And from a workforce perspective, why could AI not do this? But from a work perspective, what are the outputs that we're trying to deliver and how do we get there? HR is very comfortable being a reskilling and learning leader, and they will need to continue to innovate, inspire, educate, mobilize their entire workforce to continue to push the frontier, when it comes to I see some of the slides shifting. When it comes to AI, and then there is that data driven decision maker. Definitely something that we, have been practicing with for a while that we need to get much, much better at. I'm gonna use a Peter Henson quote who said you need to collect the dots in order to connect the dots. And I would love to add to that. We also need to structure the dots to make sure, make sure that humans and our authentic workforce can make sense to this. These two specific elements, strategic workforce architect together with the future work navigator, we're going to dive into a little bit deeper. So when you think Future Work Navigator and how AI is going to support, there's basically three big containers of how we say work is going to change. So AI can support us in automating some of the work, augmenting some of the work that we do, and create net new roles. And I've collected three different examples, and we could have had many, many more. But three different examples that I have heard in, in in recent, months that I think are very interesting and also help you think around what are the types of examples that we can bring to some of our leaders to give them a little bit more insights. Verizon example. I was lucky to see the CEO of, Verizon, Hans Vestberg, talk about how he is leveraging AI within Verizon. And rather than saying AI is an opportunity and we're going to push for productivity, he recognized that AI would give him an opportunity to improve employee experience. And I'm a big believer, Josh Person, was my my former boss. So I've been a little bit coached around this that your employee experience eventually is your customer experience. And Hans Vassberg, agreed there, has a similar philosophy, and he said we're going to improve our employee experience so that we can have a better customer experience. And the way he did it is focusing on where they have high volume. They have around a million and a half calls coming into the call center from customers at Verizon every year. And they had a lot of trouble getting these people who were dialing in with all kinds of challenges on the right, call center agent. And that was super troublesome both for the people calling, for the customers, but also for the people working there because they have to say, like, oh, I don't have the right response to that, and then forward them to another agent to another agent. You will get people on the line who are super upset, and the quality of work wasn't great, not for customers' subpar experience and for our clients' software experience. So they honed in on that. They said we have an opportunity there. Let's look at technology, AI enabled technology, that can help us elevate that experience for both our customers and our employees. And they found the technology that can then look at all of the different data points coming into one single phone line. And before an agent picks up already through phone number, location, understanding how that phone number links to certain services, they can estimate what this person is going to dial in for. And because of that technology shift, because of that routing difference, they've been able to get 80% right on their first call, resolution, which is tremendous impact for Verizon, but also for people working at Verizon because they now feel that they have more job satisfaction because they can actually deliver value, which is what humans ultimately want to do. So when we look at AI, see how we can automate, but there's a very important component here. How do you get people to work on top of their license? How do you get them to work on, higher task, higher order tasks? Second one that we're gonna hone in on, augment. We can make services better for our internal, employees or internal customers by doing some things differently. Now Deloitte has built a portal. Deloitte's a fantastic organization to work with because not only do they have strategic advice on PowerPoint slides, they can also help you build certain things. So Deloitte has built a portal, UX portal, that brings together all of the different talent services. And what it does, it also gives you a line of sight in based on your previous courses or based on your performance scores or based on your development goals, you can get certain recommendations for learning. Now what that does, it doesn't only enhance your, in your experience as an employee. It also changes the role of what an l and d professional would typically do. Because rather than coursing all of the learning out, you're now testing to make sure that the algorithms continue to work. You're now looking at, can we make sure that the right learning is available in the right moment of need? You're looking more at your source documents. So it's completely augmenting the role of an L and D professional. Now if we shift forward to a completely different new role generation and how that is going to impact HR, we are going to be managing not only humans, but also agents. So there are certain agentic solutions, and Jens is gonna talk about a little bit more. But you can do, like, your micro automation, if you will, on certain things that are super high volume, high churn, or tedious for people to do, such as continuing to update your job descriptions to make sure that they're ready for, more skills based organization, or have that skills readiness into them. If you have agents working on proactively update updating all of those job descriptions or proactively updating some of the meta tagging that you have on your learning, someone needs to make sure that those agents continue to do the right thing. Train them, but also verify that they still have all of the right information. Make sure that you approve some of the choices that they make. Dive deeper into. We're literally going to manage the agents. And those are some of the net new roles that we haven't had before that we will definitely have. This is just a selection of some of the use cases that you can think about. And as you can see, I'm trying to encourage everyone to think a little bit beyond our HR scope and really also for what's impact for the organization. And already, given that there is so much interest in this topic, we might want to do specific deep dives on, let's say, talent acquisition, learning, mobility, etcetera. Now if we look at specifically what's happening within the workforce, and I just wanna take two slides, and then I'll hand it off. The innovation that will happen will need to come from your people. There has been a big shift that we have a lot of intangible assets within organizations. We're no longer building things that are very tangible. The majority of organizations is actually the human connections and the human services and the elevation that are driving your brand and are driving the value of the organization. People are important, and people are overwhelmed. I'm not gonna talk you through all of the stats on this slide, but I wanna lift out one specifically and all the way in the bottom. Organizations feel that only 10% of their people have the curiosity and the imagination to advance AI initiatives. And that is a number that we need to shift because we need your frontline employees to understand what the potential is of AI. This cannot just be a top down initiative. It needs to be top down together with bottom up. And as an organization, you need to get into a very creative spirit. Now a couple of closing thoughts, and then I'll hand it off to Jens who's going to give us some more information on where Workday is going. If I can recommend you this, it is think big, start small, and learn fast. What we should not be doing is talking about how AI is going to change HR on PowerPoint slides. We should really roll up our sleeves and learn fast, feel fast. You need to start doing it so that you can start learning, so that you can start applying. Then continue to educate yourself. This technology is advancing at such a rapid pace unlike anything we've ever seen before. So educate yourself, but also educate your organization. And that's my third point. Make sure that within your organization, lift that 10% definitely to a 70 or 80% where the majority of the organization is very interested, not afraid, does not have FOBO. Right? Fear of becoming obsolete, but understands we are evolving. We're building this conveyor belt, and together, we're going to lift this organization in the next century. Those are my three tips. I would love to deep dive, with this on you. And if you have any specific questions, please put them in the chat for later on. With that, I'm going to hand it off to Jens. Thank you so much, Bo. Really setting the scene, you know, for an incredible important topic and many topics that's already stimulating all of us. Well, let's have a look. As you know, Workday, we are a provider for exam, financial management planning software. And, as such, I keep it short. Don't worry. It has a special intention. It's not about all the history and the great successes that we celebrated as a company. It's more about, you know, our history. We started in 02/2005. We back then, we started already as a, what we call, a multitenant of best service. In easier words, it doesn't mean nothing else than all of our customers are actually on the same versions of our software, which, you know, when we anticipate now and catapult us to 2025 is is still the same, the same guiding principles, which is an important prerequisite because if customers are willing to work with us, they feel having benefits from our air functionality. We really have a huge amount of data because all of our customers are transacting against this uniform data model. Just last fiscal year, worldwide of 1,000,000,000,000 transactions that serve as the the perfect input of high quality, data to feed into our, the, AI capabilities. We started with AI actually 02/2013, a couple of acquisitions. So we tried something ourselves, but then also acquired some companies. And through these acquisitions over 02/2014, '17, and '18, there's actually one, solution area resulting with immense value for our customers, which is the Workday Skills Clouds. Many of you know it. It's about, an application of deep learning machine learning capabilities to support companies and their skills based transformation. Rather managers having check ins with their employees, it is really scalable self-service approach to support employees in their development, in their career, and foster this environment of growth. Over 2,000 foreign customers using it with this critical mass, hopefully, more and more over time, you know, there are certain benefits also, for example, keeping our skills library up to date. Well, what are the advantages that we see, you know, of us approaching AI, our competitive AI edge? It's really about this trusted data that I already said that is there and the required quality and the quantity. We're focused on HCM, financial management planning only, so we think we have a really, proficient and very deep domain expertise that help us support our customers. And, also, later, we have a look in our partners and also our customers themselves to solve these these problems, really to drive these these tangible business outcomes from AI. And last but not least, we are applying a platform approach to AI. AI is embedded into our solutions. And in the background, we made a scalable AI development platform to easily work on on onboard new AI capabilities. Well, and with that, you know, it's a little bit over marketing, but it is super relevant. That's our ambitions, really. We as Workday, we wanna be the AI platform that manages helps our customer manage people, money, and the agents. Similar like Bo was mentioning before, agents, if not yet, they will be there, and it's good, you know, to have a, right approach to this, from a strategic point of view, but also the right tools in order to receive the required and the expected results. So a little bit back, look back before we go forward. Right now, as of now, with that release, we have over 85 already capabilities. And we need to differentiate between Illuminate, Accelerator, System, Transform, and I'm briefly gonna give an overview about these capabilities. The accelerate is more about, you know, singular tasks that are supported by AI. And so it's really the traditional more traditional field of applying ML, deep learning, or Gen AI, to, to problems. You know? Like, for example, creating a job description sometimes, you know, was kind of challenging in the past. Some may use, JetGPD or other publicly available large language models. We're trained in own large language model, you know, fed by the high quality data of our customers who are willing to share this with us. We really do have these these high quality results that our customers are expecting. Always with a human in the loop, doing further adjustments in order to get to the right quality. When we go beyond the singular task, there's another area that significantly was impacted by AI over the last years, which is what we call eliminate assist. As this conversational access of users, to information tasks, really the findability of of information to make it such more easier, with taking also taking to mind, you know, that an average larger conversation have over 200 different systems with it, 200 different user experience is really an important path to to improve, how users are working with the service and and also not only stopping there to find things. You see this with contextual guidance. We also improved of, the the way how employees are guided or how users are guided to actually complete the task. It's like prompt suggestions that use the context. Let's use machine learning to really proactively, you know, provide values, for the users to confirm or on a sidebar to provide additional information. So with that really, a significant increase of of, people just getting the work done. Next up is Illuminate transform, and this is, you know, for sure, the exciting world of agents. Definitely, when we worked on machine learning in Gen AI and also when we have a look in the future with, agents, it's really about this measure of business value. We just don't do AI for the sake of AI. We wanna apply it to real world problems that our customer having that we can anticipate and and really, provide, a value with our solutions. And we differentiated in three different areas. Typical thing of AI, that's the promise of driving productivity. So that's the first area. The second one is and this is really something where it's also a huge potential there, you know, to, have processes and interactions with the service to foster, being more consistent to audit and compliance. And the last area is really like when you are doing and working on something, you know, to proactively, bring out the right information to make better decisions. So what is available and what are we working on? Because, as Raymond said, you know, we're on the road to to rising, with it also a couple of more activities of this this group. We have four agents already available, recruiting agents, talents, mobility agents in the area of HCM, and two two more agents, which are a little bit more in the contract management area in terms of having intelligence insights or support for negotiations. When we look at the road map, others in the area that are coming, specifically an employee self-service agents, which is, you know, extending, the capability and the impact of our Workday assistance, and also very super exciting and interesting a frontline agent frontline agents. Can imagine the typical store manager that has to take care of so many things, time checking entries, scheduling of employees, making sure enough stuff is there. So really to take out the burden of these repetitive tasks and let these, the frontline managers focus on the more important ones. So really exciting things coming up. More to come for rising, in, in November this year. Well, and as we as we know, really, it's it's all about, you know, driving this value. And so, where Generative AI was was kind of limited in terms of the impact that it have. You know? Or, like, your opening statement from Hugo overhyped at the beginning. Well, you know, expectations fell a little bit short, but still, you know, I am still a big fan of of creating, incremental value. So smaller things also can, you know, to lead to bigger things that also, you know, create a bigger result, you know, also the agentic AI. But for sure, the the world's future is really having these role based, agents that go beyond, you know, specific limited tasks to more mimic humans and the way how they work and how they their, they do their work. And with it, this is something a new area that is coming up. We really need to prepare ourselves, you know, to have a workforce, employees, contingent workers to work hand in hand with agents. And this is really also following up with a lot of different, sorry, not brothers, but really, things to take care of, especially, you know, having a governance about, you know, which agents to use, how to onboard them, how to to configure them in terms of the skills that should be used, and and also, then the on governance and compliance. What does it entail? Having providing the transparency, what agents are doing to have actually the human always in the loop in terms of, you know, having a look what what are they doing. Are they doing it the right way? And to also have that impact on the reasonable capabilities to train them doing maybe the work in a different way, in the future. Governance and compliance also has an aspect that is quite interesting in terms of, in terms of the the value that they can provide. So the the, the metrics actually that in the AEMEFAC can also help, you know, in planning the future workforce. Where does the company wanna go? To which extent? Can also agents can help the humans in their work to achieve these goals in a much better way. So it also has this link from a more IT perspective, to a workforce connection. I'm gonna jump over some slides here to make it a little bit easier and also leaving just enough time. So it's really about us anticipating the future work together with the feedback and the guidance of our customers, but then also giving tools to our customers and partners to, to also let them innovate at their own pace, to to to to innovate alongside with us. So our agents, having extended agents by Workday extends, having additional skills, but I'm also working with a couple of partners in order to, include also those agents even the non Workday, but having them in the same environment, in Workday in the agents' small records and having a light form of an orchestration and really an agent to agent, communication. So really exciting time is ahead, and and, really, it's on us, the community of vendors, partners like Deloitte, thought leaders, at our customer side like that just to focus on really what we wanna achieve. It's about the outcomes. It's about the value. And despite there's still a lot of ambiguity, it's amazing to see, you know, how the industry worked with a lot of different other major players on the market are actually working together hand in hand to define the new standards and having a better workforce in the future. And specifically excited about this, community, this activity to bring in together vendors, partners, and the knowledgeable, customers around our side to work together and creating the future work with it. Thank you. Handing over to. Hello, and thank you very much. I do get a bit of background noise, so I think now it's a bit better. And good afternoon, everyone, and what a great story. Right? Thanks, Bo. Thanks, Jens. It already shows all the thought leadership. Right? Both out there in the academia, in our partners, but also in our technology vendors, showcasing at least the direction that we are going towards. Now, for us, at Philips, we are still a bit on the path of learning how to walk. Right? The overall story line around implementing AI comes with tremendous opportunity on one hand, but on the other hand, also with a significant risk. Now internally in Philips, at least many people would look at me as a person that says no to a number of opportunities. But, the story that I want to share with you today is not necessarily where do we say yes versus where do we say no, but more how do we walk the talk, how do we get to have that end user impact at the end of the day. Briefly sharing, also on Phillips just to for everyone in this call to understand the context of, where I am coming from. For us, one of the most important pillars of our culture and our strategy is driving impact through innovation, of course. And our company has a significant proof footprints across a number of countries ranging from white collar to blue collar and significant investment in r and d. So innovation within Philips is part of our DNA, and slowly also AI is becoming part of our DNA in our products and in our solutions. The next few slides that I'm sharing with you are not slides that I build. They are not slides that, we are trying to define within the people function, within HR. There are slides that the entire enterprise is following. So you can think more as a guideline that we have in the company of how do we want to approach this significant space of AI. So there are three areas on how do we approach innovation. And, of course, the key element there is the customer and the consumer. We are approaching it with a targeted, focus, towards scale. So we want to make sure that we are not stretching ourselves too thin, not going in too many directions, trying to over invest in a number of areas that afterwards will be very difficult to bring together. At the same time, we are trying to work backgrounds, so we are trying to understand that why. So we're not necessarily focusing on this on the what. Trying to understand the why, trying to understand how do we want to achieve that why is a significant area in our approach. We are not doing that on our own, so we are collaborating. We are cocreating with our partners, with our vendors, with our peers, And we are trying to do that across the overall continuum of the service fees and the solutions that we offer. First, on the focus and the scale, we are trying to identify where we do see the biggest impact. Focusing on those big impact areas, we know that there is a significant appetite, but there is also a significant maturity. Not only a maturity for us as an enterprise to adopt a solution and to keep it sustainably running over the coming period of time, but also that there is maturity out there in the market for the solution to be offered. Together with that, we are also looking to marry that solution with where the customers needs us. And that marriage of those two elements, of course, is targeting better outcomes, And at the end of the day, more time for employees and managers or people leaders, as we call them, which leads, of course, to improve health and well-being of people everywhere. That is, of course, part also of the Phillips storyline, and and we are investing significantly in this approach. Last, as I mentioned, collaboration, cocreation. Right? You do not need to work alone. So we are using our partnerships in the business, so our internal customers as a people function with our vendors. And today, we have one of the major vendors that plays significant role in the people's function ecosystem within Philips, but also in our industry partnerships, be it with our partners such as Deloitte, be it also with our peers. So we are continuously investing in this cocreation, in this innovation, and we are targeting also, let's say, to simplify this approach because there are many dimensions that are at play when you start discussing AI. So in order to simplify our story line, to simplify our approach, to make our message more digestible by the end users, by our teams, by the entire ecosystem, we are targeting three areas. And those three areas, let me quickly clicking too fast. Apologies for that. Are focusing on the new technology, so artificial intelligence, and I will touch in a moment why I'm referring to technology only. We are looking at workflows, and, of course, we're looking at the product life cycle. And I think there is already a very busy parallel between what Jens was mentioning before, between the assist and so all those different use cases that some of them are plug and play, some of them are foundational. Some of them come and go, some of them might be replaced. But, nevertheless, if you approach those three areas, artificial intelligence, how that applies in your workflows in order to drive value and impact. And then how do you maintain it on the long run, in our view, makes a solid approach with, of course, a lot of small letters underneath that detail out what and how do we need to do that. Now my next slide had to do a bit with a transition on how did we move as a company from the past to the future. But the transition is not there anymore. At least I cannot share it, so I will just go with a busy slide. Apologies for that. But, nevertheless, it shows a bit how did we transition as a people function from a point to point to local for local HR back then in 2007 to a fully integrated landscape where we were consolidating capabilities. And I really like that also. Jens used the name capabilities because those capabilities are anything from small tiny capabilities, one AI solution here and there, up to strategic investments, which include multiple capabilities in one solution. And for us, that consolidation did happen in Workday together with apparel being foundational for our ecosystem and the build up of that ecosystem together with all the verticals that we have integrated. So all those value add components that we are connecting to in order to drive the impact. As you can see, multiple years to get there. Right? Because it didn't come on its own. It's not that you are going for a technology implementation and suddenly all your problems are solved. And I'm pretty sure that it will be the same also with AI. It's not that we implement AI, and tomorrow, AI will be doing the work and the thinking for us. Like, unfortunately, at least I wasn't able to call that agent yet, but, I'm working heavily on that. So looking aside of technology, it is about implementing both the framework to build that rolling line of adapting and adopting abilities within the space and marrying those capabilities to your existing infrastructure. So that framework and, of course, there are multiple frameworks that you could use. In my view, it doesn't really matter which one you're using. What matters is that you are using a framework, that it does give you clarity that when you're implementing technologies, not only about that technology, but it is also about having an operating model to support it, having the talents. And already Paul mentioned nicely that there are some talent gaps in that space and that people, despite having the curiosity, they might not be enabled to adopt AI in a number of spaces. Technology, of course, is an a an enabler, an enabler that at the moment, there is a big hype about. Some cases are very mature, so we need to tread carefully on how do we get there. But then you also need to have the right data to apply that AI. Right? Because if you do not have garbage data in, most probably the result will be similar. Building also on a road map and having activation around that road map, is significant. So this pyramid is something that we want to implement as a continuous framework for adopting sustainable AI. On the right hand side, you can see a bit of journey and that again, this is not, you know, any special journey. It is our journey, where in the past, we were focusing over the last few years on some niche applications where we saw AI and machine learning adding value. Again, treading carefully to ensure that we minimize risk with those implementations. Today, we do believe that as an enterprise, and we are, an enterprise that likes to do its due diligence, we believe that we are ready for enterprise wide adoption. So that means global code lives, big bank approaches. In the near future, so over the next two to three years, we do see that agentic disruption coming. And that agentic disruption, in order to happen in the next two years, the foundation is to be laid today with the framework, but also with a number of other capabilities to enable the transition. Our road map and, again, I'll apologize another time because, next slide will be a bit easier. But our road map or our tentative road map, and, of course, this is changing every day because there is simply so much happening out there in the industry, is at least targeting the next three years on how we're going to approach some of these foundational components. And I see some things moved around, unfortunately. I think it's the scaling of this webcast solution, but we do see that the main element in our AI road map, and this is not the exclusive technology road map. Right? AI is only part of that. Our road map is targeting a growth in maturity. You can see there the maturity levels moving from layer two to level three up to level five in twenty eight plus. And and we see at least significant investments in some of these areas, some of the investments being spot investments where we are adding one capability on top of our existing infrastructure. In other cases, we are talking about full infrastructure investments, right, because we need that layer in order to orchestrate AgenpiKi in the future. We need that system of record. We need the orchestration. So all those solutions, we are trying to, yeah, rationalize them. We are trying to create at least an illusion of control. Right? But we are having, a very clear roadmap on how do we need to approach each of these components in our technology stack in order to drive that why at the end of the day, so the impact towards our end customer. I'm not going to dive into any of these details, of course. Happy to share more details afterwards in the q and a, but I would like to close also a bit on a positive note. Because for us as humans, we we tend to be a bit usually on the positive side. At least that's what psychology is saying, that we always tend to be a bit overly ambitious or overly positive. But I I saw this example recently, which for me, it was a great example, in the medical journal since, at least for Phillips, these are articles that we're usually looking at or research that we're looking at of and this was an example of how, AI in, the medical space, confuses accuracy with efficiency. So it doesn't mean necessarily that if an AI is more accurate that it can drive more efficiency. And after a few conversations and also some speakers that we had within Philips, we're linked to thoughts of the utility. So practically, it is fitting the solutions in your existing ecosystem, understanding what can drive the most value. It doesn't mean that one of these three elements are key differentiators because in some spaces having low accuracy is perfectly fine. In other cases, having low efficiency is perfectly fine. But having those two as low scores doesn't mean that that solution is not very useful, that cannot be utilized by the entire enterprise. So low accuracy doesn't mean low utility, low efficiency doesn't mean low accuracy, and vice versa. And that's why we do see that there is a lot of room for opportunity in this space. And having this sustainable approach towards adopting AI, in our view, is the most significant part, because it is coming. And at least we are also scouting inside the organization, and there is a lot of curiosity, a lot of willingness. Maybe that 10% that Paul mentioned before, maybe in Philips, it is a bit higher, because with everyone that we're talking, they're very excited, and fearful at the same time. Right? But with the right enablement, I think people want to go in the direction of learning on how to use it and how to leverage it and how to be augmented by it. So with that in mind, I hand over back to Raymond and team, and, happy to answer any questions that might be in the chat. Fantastic. And and thanks to our speakers for great inspiration and great story. It's great to see that, also in your story, Feet, that you're already applying that. You know? Start doing it and learn faster also with your road map and the maturity levels that you presented. So I think that's great to see. We already got some questions in the chat. So let me read out a few so that the panel can can take those questions. One of the first question that was submitted is how about organization design, blending humans and agents, into a governance? How would you govern then the agents? What is your point of view on that? Who wants to take the question? I'll take a stab at it, and then if Tim knows who's probably working through this in real life, so then he can think about what what they are thinking at Philips' side. A couple weeks ago, I heard someone say, we're probably the last generation of managers who are leading just humans. The next generation of managers is going to lead agents together with humans. And I would argue that we're already managing technology, but maybe not to the extent that we should. We're very used to implementing systems and then just letting them be, and not necessarily continuously looking at the data. How do we feed it? How do we govern it? And that will need to be a shift. I do not necessarily agree with NVIDIA CEO Hanson that IT is going to be the one who's going to manage the agentic workforce. What I think will happen, if you look at the application and also what we see within applications, affiliate with different organizations as well, Agents really make sense close to the front line, close to where value is added, and it will be those managers actually managing the agents. So we need to make sure that everyone within the workforce who is leading is comfortable leading humans as well as agents within the framework that the organization sets. But, Estimios, maybe you have some more thoughts on that one. So for us, this is part of a road map. It was under the strategic, investments, because it's not only a question about designing your job catalog, right, and your job architecture. It's not only a question of designing your operating model and feeding this component in. I think, philosophically, if you look at it, it is not necessarily a tool that you're using. Right? It's not that you just use AI. It's a tool that you apply it on top of. There is much more to it. And the less we see AI as a tool, and the more we see AI as a new layer that is morphing our way of working, I think the easier it would be to start absorbing and translating that information into an operating model. Now it won't come with one transition. Right? Saying that, yeah, next year, we'll build a strategy. In '27, we'll have a new operating model. Right? Everything. Yeah. I think it takes a bit more maturity from the industry, from the enterprise, from ourselves to get there, And that's why building that the the foundation, the building blocks, in my view, it is key. Because once you have the building blocks, you can make it sustainably, and you can adopt it into all those different aspects that you're targeting. But I think it is a very good question because no one really has the answer. Right? It's like, we are talking now about being a manager, right, like the recent Microsoft study, being a manager of agents. Yeah. Great. Right? And then what? Like, then you need to make it work. Right? Then you need to keep maintaining it. You need to keep those agents talking to humans, then you need to keep that model alive. And that's why, in my view, we haven't really answered all those questions. But the more time you spend as a human in those topics and you think and you absorb that information, I think it won't be AI offering that answer. I think it will be us offering that answer. So still, I'm answering a bit with a few additional questions, but, happy to philosophically go through that conversation. Maybe the thing here has some great ideas. No. Let's but maybe also coming from my side, you know, what we really see, you know, in the past when we talked about digital transformation was always the business working together with IT, especially when it comes to the new agentic, workforce where humans and agents work together. So once again, it's in the intersection about, you know, people, their development, their their enablement, and then in terms of achieving new skills, the new workforce composition. And then for sure, also a lot of the ties. So once again, IT. So once again, an intersection. The easiest thing is to create a new role. I don't know if you heard about the chief work officer, which really is like putting together the dots between HR, IT, maybe, you know, also, you know, chief, ethical officer or, you know, just embedding this thought in the collaboration of having in mind, you know, what it really needs, what kind of, thoughts it needs to come to this new workforce. An exciting time. A lot of challenges, but let's see where we're going individually together. We have time maybe for one or two other questions. The questions are still coming in, and maybe we can answer those questions also, later on, coming back to you all. Bo, question for you. The data shows that you also presented is that well-being in the workplace is at an all time low. And also Gartner research shows that, you know, the connectivity between humans, in, you know, in at work is is, is is slowing down. The question here is how do we ensure human connection is valued and built into the new world of work, including the agents? Yep. And, beautiful, beautiful question because this is something that, I really think about a lot. And every conference that I visit where we talk about AI, the conversation quite quickly shifts to people. How do we get people ready? How do we get people adopting to the change? How do we make sure we continue to crust trust each other and that we have that collaboration? And I'm going to pivot from that question a little bit, by talking about what Google is doing in this space, Google Tech First organization. I had lunch with the chief learning officer of Google two weeks ago in Stockholm, and I asked him with everything that's changing within the learning space and how much you're using AI, how are you measuring the impact of learning? And he had a very interesting thought. He said, we measure trust. They have a trust index. And the one thing that they measure is, do employees trust their leaders? Do leaders trust their employees? And do employees, in return, trust the organization? And that's a very powerful metric if you think about it because from that, innovation happens, collaboration happens. It's a completely new way to think about return on investment or return on AI, and that's probably where organizations are heading. Not to say that we should not be doing initiatives to improve trust, but we should be measuring trust and then having initiatives to support that. So that would be my response there. It's a beautiful topic. Yeah. Thank you so much, and it is. Alright. Let's take one more question. This one is addressed to F. Timios. If you look at the AI journey, at Philips, how do you reflect the human experience? And they gave an example around recruiting. So there's a lot of debates around, humans owning that recruitment process versus also AI being involved. How how do you make sure that, human experience is reflected in in everything you do around AI? And and I'm also processing a bit how to answer that question. Not not the first time. I I get it. I think experience comes from the eye of the beholder. Right? At the end of the day, experience is what the customer wants or what the candidate, in this case, wants. And, I think it's great, you know, if you have a conversation with any recruiter in any organization and ask them how much they enjoy scheduling interviews or how much they enjoy, you know, scanning through a thousand CVs. So and and that naturally also brings the question of what is the quality of that work of a human that is not doing it twenty four seven. So and and there's also in some in the chat, you know, a few other linked questions on, you know, the MIT study recently that show decrease in cognitive abilities. 20 other studies that show a decrease in x y zed. Right? So there is or there are definitely a ton of reasons where why why we shouldn't use AI, and we can definitely do that. At the same time, the transition is very similar to moving, you know, from using horses to using cars. Like, when the first cars appeared, no one really saw the value in them. Right? They were loud, expensive. They were smelling bad. They were dirty. And so we do not know today what AI would be offering in five years from now. We do not know that. But the potential that it brings or at least, you know, if you look at the what philosophers are saying about what AI can bring, it is a fundamental redesign of the way that we're approaching work. And that's why I like so much the previous question. So it is fundamental that we start learning and informing ourselves about it, that we try exercising. From a user experience in Philips, I can say very simply to answer the question, the original question, We are using it in recruitment for, at the moment, those repetitive tasks which no human likes to perform. The intent is to start choosing use cases which can drive more volume Because at the end of the day, the candidate will be much happier if you get back to them in one day and not in ten days or in two weeks. Yeah. So in our view, the user experience is improving by doing your due diligence and having a structure. Because at the end of the day, the human needs to choose where AI will be applied, the recruiter. And so in our case, we don't do it that yet. I choose. Right? The technology function chooses or IT chooses. No. It is a functional owner, the personal owns that process that drives the design. And in some cases, it makes sense to automate. Right? You do want your offer letter to go auto automatically, and you do want to use esign, right, to get it back immediately. You don't want to go through that two week back and forth. Yeah. What other areas might suffer. Right? But it doesn't mean that we need to slow down the pace of innovation, and it doesn't mean that we shouldn't try. So And the priority failures. Right? I think if we read the news, right, there are legal cases. There are all kinds of other risks that are popping up. So it is something that we will learn. Yeah. But that learning comes only through this experimentation and through informing ourselves. Right? The more we know, the better it is. So I have to thank you. It's just on your mind. I will stop you down quickly. Answer, maybe a bit inconclusive, but in my view, there is a lot of opportunity in user experience generated through AI. Thanks, Thanks a lot for this this comment. And, really, we have limited time left because we've been kicked out automatically. I wanna thank you, both of you, Eftis, Bo, Raymond, and also the work team behind that to come together as a team. It's it's a time of change. We're inviting you to be part of that community. Some of you will reach some some communication here. We're really looking forward to work together and see you exchange on the Workday on the way to Workday rising. Thank you for joining. I will Thank you so much. Thanks to everyone. Thank you. Bye bye. Bye bye.