Name: Beyond the Hype: How Becton Dickinson is modernizing talent strategy with AI.
Uploaded: 2026-05-19T15:30:14.756Z
Duration: 1 h 30 min
Description: Beyond the Hype: How Becton Dickinson is modernizing talent strategy with AI.

Transcript for "Beyond the Hype: How Becton Dickinson is modernizing talent strategy with AI.": Amazing. Alrighty. We are officially live. Welcome all. Thank you so much for joining us today. We're gonna do the classic. We're gonna give it a a minute or so to make sure that everyone has a chance to hop on before we really get into, our discussion today. Welcome. Welcome. Thanks for joining us. If you all see it, we obviously have a q and a kind of section as well as a chat. Feel free to use that throughout today's webinar and throughout the discussion. And we'd we'd love to make sure that we address any of your burning questions or your ideas, it, throughout the webinar or especially at the end when we have kind of an extra five, ten minutes. Just so the crew yeah. There you go. Adam's getting us started. Get the hang of it. Let's go. Just so everyone is aware, I've opened a polling question. I do not expect anyone to answer that right away. We're gonna leave that open and, and kind of see what our results are at the end. And now we are a minute and a half in, so I'll kinda just make sure, one last time everyone knows we have an amazing chat and q and a function. Feel free to use that, and we'll make sure Lizzie and Sid and Adam and Matt have a chance to answer your great questions towards the end of the webinar. But at that point, I will, pass it off to Matt, for kinda getting our discussion started. Welcome again. Great. Thank you, Tyler. And, yeah, big welcome to everybody. Good morning, good afternoon, good evening, wherever you're joining us from. We're excited for, a good healthy discussion today. Quick introduction. We're gonna introduce our other panelists, in a moment, but quick introduction from me. Matt Jones. I'm the strategy for Cielo. We're a partner of Workdays and delighted to be, a partner with, the team at BD as well. I've been doing this for twenty something years, so excited to bring some of that knowledge to the conversation today. So again, thank you everyone for joining. So I think there's no shortage of opportunity and commentary and content around our topic today. So AI is certainly the ubiquitous topic, for, all of the opportunities to kind of engage in these kind of things. Today, we're gonna be so typically, that is mostly oriented towards, future opportunities, and or risks that we're not, yet ready to realize or to face. The hype is really centered around what technology, and tools like AI can do or could do. But for everybody on this call who's led through any kind of transformational change at scale, you know that this has to be anchored in business challenges, and thinking about what we're actually trying to solve. So that's what we're here to talk about today. This session is not gonna be about kind of general guidance, or platitudes, or telling you that, you know, change is hard, but let's go for it. We're gonna talk about real life experiences. We're gonna hear from our friends and colleagues, from Workday and from BD talking about how, setting up new AI models, and agentic operating models to enable humans, actually flows through into talent strategy. So, our commitment is not to share generalisations as we walk through, the content. I'm sure we're gonna get lots of compelling questions. We're excited to see, the results of the polls, as you all, you know, share your feedback from where you're at today, and really wanna get to the point today where we give you some practical advice and some real, insight that you can walk away with and start to unpack, in your own organizations. So that's what we're going to do and I'll be your host today leading you through. I have some great questions and we're excited for some good, conversation but let's dive into meeting our panellists, and I will hand to Adam first for an introduction. Hey, Matt. Great to be with you. I'm Adam Godson. I twenty three years ago, I started my career as a recruiter, and so I've sat in the seat, but also then spent about a decade building, processes with technologies and piecing them together to make hiring solutions, and then ran product and engineering and have been the CEO of of Paradox, which was acquired last year by Workday. And so now I run all the talent acquisition products at Workday. It's great to be with you and an exciting topic. Thanks, Adam. And Lizzie. Awesome. Thanks, Matt. So like, Adam, I also started as a recruiter, not quite twenty three, years ago, though. I just have to get that in there. And I now head up, all of the global talent acquisition here at Bex and Dickinson BD. For those who aren't familiar, we are a global, medical device company. And we are hiring something around 12,000, positions, a year all around the world, and me and my, team are responsible for making sure that that happens and that we can get great people, into the organization. Thanks, Lizzie. Good to have you here, and over to Sid. Hey, everybody. Nice to join you all today. Sid Radhakrishnan. I'm the senior director of HR solutions here at BD. That means I have an enterprise HR technology mandate. I've been in the HR technology space for about eighteen years now. I've seen different generations of technology come in and sort of the transformation that that brings about, and, happy to be here today. Thank you, Sid. So we have a fountain of knowledge here today to go through the discussion. We have two or three themes that we want to use, and our first theme is to actually think about starting with our business strategy and, you know, not just the technology. So, to frame this, let's really think about AI as a utility and that utility is powering new tools that allow you to kind of access that capability. If we think about it that way, let's think about not starting with, AI or the tools specifically, but actually starting with the business problem that we're trying to solve. And with that in mind, I have a few questions for the panel here. So we're gonna start with Adam. So, Adam, first one for you. Why why is there, such a strong temptation to lead with AI as the solution or a solution? And, conversely, how are most successful leaders resisting doing AI for AI's sake and actually driving and redefining value through, you know, the business problem, instead. Love to get your perspective. Yeah, Matt. I think I think, certainly, AI is is everywhere in the news. And so in some ways, it is a solution in search of a problem where you know you you and there's lots of pressure in organizations to use AI, and, and it can be easy to fall into the trap of using AI for AI's sake. But it's not hard to find use cases that, that can give you transformational results. I think one of the keys that you mentioned is being sure that we're focusing on the end of business results, not in an intermediary talent acquisition result. For example, there was a a case study I saw we we published yesterday with a customer that, had a great reduction of time to fill, which was awesome. It was, I think, thirty eight days to seventeen days, so more than 50% reduction. But they then also translated it to a 4% increase in sales, in the organization, so sale revenue per employee. And so that that second step is the one that gets the attention of the CFO, and the people that write the checks. And so being able to take that next step of of why does why could this land on an earnings call, and and why is this the pace setting AI, implementation for the organization? I think driving all the way to business value is the right right piece for talent acquisition practitioners. Thanks, Adam. That's that's great insight. And maybe coming to you, Lizzie, now as the the business leader who's solving some of those challenges. Let's start at the beginning. So what were some of the, opportunities or, business challenges that drove BD's TA approach and transformation? Yeah. I love what Adam was saying there. And it has to really come down to to the business and and what the business wanna wanna hear about and what they care about. And, at BD, we've been going through a huge transformation. We separated from our life sciences business units and are really focused on being pure play med tech. So big focus on growth, big focus on making sure that we are able to serve the the future of healthcare for our patients and and for health care practitioners. And that meant for talent acquisition, we had to be able to evolve and we still do have to be able to evolve to support the business. That's bringing in high volumes of people, that's bringing very diverse and specialist skill sets, some of which we've never recruited for historically. It's new skill sets that maybe us as an organization aren't completely familiar with And when we looked at what the organization was going through and then looked at how we were delivering from a talent acquisition perspective, we were at the time a little bit kind of fragmented in our operating model, and we were certainly reactive. And you have to be reactive. We absolutely need to be agile and react to the business needs, but we weren't able to use technology or data in a way to be proactive with the business, to move quickly, to make sure that we were bringing the right talent in at the right time to business. So that's really what prompted, a lot of the transformation work that we kicked off, about this time last year. Actually, time goes quickly, to really say, okay. How do we make sure that we can support the new BD and the BD that we want to That's great context. I love the connection to patients and practitioners there as well, moving the the ultimate goal of the organization. forwards? I'm curious as well, in the broad with the. broader kind of BD business objectives, were there any specific, stated goals or visions or objectives that you're anchored to with the TA transformation work? Mhmm. Yeah. It's a great question. And and BD strategy, we have three pillars, and I love them because it's super, super simple, so it's also really easy to remember. We have compete, innovate, and deliver. And so that for us was a great anchor point to be able to say, you know, when you translate that to TA, we need to be able to compete, and that's compete in the marketplace, get the talent that everyone is after. Those skills, often very niche skills, very specialist skills, skills that may be a rare in a particular location. We needed to be able to compete for them. We had to be able to innovate the model. We had be we had a, as I mentioned, kind of fragmented model. We weren't necessarily all using the same technology. We weren't necessarily being the most innovative that we could be in getting out to these different candidates, in getting out to town, in working with our hiring managers. So we knew that we really needed to be able to to innovate and deliver. Ultimately, that that is our job in TA. Right? We have to be able to deliver great candidates. We have to be able to deliver, hopefully, great experiences to those candidates and to our hiring managers, but we have to be able to deliver, as I mentioned, about 12,000 positions a year. So we know that we needed efficiency, we needed scale, and we needed a model that would enable us to be able to hire for today. And inevitably, you know, the the future, which is is gonna look different, I'm sure, in six months' time, in twelve months' time, as the world moves so quickly around us. That's great. I love the tension between today and tomorrow, and I. think we're gonna talk about that theme a little bit more as we get to some of the recommendations. Thank you, Lizzie. I wanna pivot a little bit to think about the, the technology or the sort of core of some of this as well. Instead, I have a a couple of questions for you here. I'll I'll lay both out because I think you you may. listen to them both together. But, firstly, when when you think about, BD's broader business strategy, how are you ensuring the technology decisions aligned to that strategy? I'm sure, you know, Guarantee didn't have its own technology strategy. It's probably a broader technology strategy. And then within that, what what did you think how did you think about the role of of data in supporting. that strategy, business intelligence, decision making, those kind of areas? Yeah. It's a great question, Matt. And and I think, Adam and Lizzie both hit on a little bit earlier. I think there's always a temptation to start off with the focus of technology. Hey. We are seeing this new thing. Let's go and attack it. But I think that's the wrong way to approach it. Right? So everything we do has to be rooted in the business problem. What are we solving for? How and then back that into what metrics or what sort of early indicators can we track that are moving us in the right or wrong direction. You then really need to lay it out in terms of saying, okay. What are the people process technology gaps that you may have? And go at it with a more holistic approach. Once you do that, you can then say, alright. Where are truly the areas where technology can bring about a differentiated outcome, and what sort of investments do you need that? We'll talk about it. We'll talk about our governance, but for us, it's more of a core plus plus framework. Right? What I mean by that is start with our existing tech stack. See where our vendors already have solutions before we move sort of more outwards and create this proliferation of, tech sprawl, for lack of a better word, and and that creates its own set of challenges. Right? Now if you think of why that is bad, I think there's a cost of ownership. There's there's there's multiple layers. There's data security, etcetera. But the key thing is when you think of data and you mentioned that, so I wanna sort of hone in on that a little bit. You know, when you think of your data, it really needs to be reliable. It needs to be scalable, and it really needs to be actionable. Right? Like, it's so unless you start off with that mindset, it's very easy to get distracted. And if you if if you're running into issues of people are not aligned on what the same definition is or people look at the same data and they walk away with completely different interpretations, I think that's where you need to double down and see how do we bring everybody into the same page because that's gonna be important when you drive toward decisions. Right? And so when you think of AI, and I'm sure people are already on this journey, if you have a maturing or a mature analytics, organization, but AI just sort of adds an emphasis to it. And so to me, making sure that there's standardized definition, there's good governance, and it's really supported by simplified processes and intuitive experiences is is is sort of the road map that people need to build towards. Thanks, Sid. That that's great. I think people get scared off by master data management plans, and you you've just described it in a really simplistic way, which is, you know, common language. Yep. And someone I work a lot with likes to say AI eats data for breakfast and so, I think, you know, what you're sharing here is really important, like, getting ensuring the data is consistent, is in one spot and is available to to use for the advantages of Scrape. Okay, we're going to shift on to the next, theme now, so thinking about, the the tech stack itself, integrity, integration, how these things all play together. So, I'm actually gonna start with Adam. And, Adam, we we hear a lot about and see a lot actually about kind of platform native or built for platform solutions, that that help, you know, unlock value and deliver better results to, to organizations. So I'm curious. Couple of questions on that. How does or how do platform native AI orchestration layers differ from point solutions, or or how does that platform native differ from, you know, the multitude of point solutions in the world today? Yeah. I certainly, I said the other day that, what we used to do from necessity, we now do on purpose, but it is it is sort of building, a a technology stack of of the tools that are needed to get the the job done. It used to be doing lots of integrations to to to do this, and and that has some fundamental limitations to the the long term scalability of getting work done. Mainly that, every time you do an integration, you get to maintain it forever. And, by using a native stack, that that responsibility then goes back to the SaaS provider to think about the underlying rules and permissions, the security model, all, the the new features that are going to come out in a SaaS platform. That's what software as a service is is for. And so having having that, long term extensibility of the platform on a, you know, common set of, of foundational rules, permission security. But, also, as Sid mentioned, the data, you don't have to go and think about what the data means every time and have these data dictionaries and these cross platform translations and the ETL layers, those types of things. You you just have a single platform where those are reused and standardized. And so I think seeing that as a as a better way to get things done in in the AI era. So I'm hearing less complexity, less requests to folks outside of your own organization for, for support. What what are the that? yeah. Yeah. Well, you you have a few of those tokens to use each time, and we'll talk to Lizzie and Sid about that in in a moment. What what are the advantages then for those kind of native or fully, platform integrated systems for governance and compliance? Like, how does it how does it help us, keep the right side of our friends in legal and IT and other areas? Sure. Re really, it's about using a standardized model so that you don't have to redo those processes for every every vendor, every integration. And and and also that, not having to try to predict the future, no one can tell me with any degree of certainty what integrations are going to need in five years, barely five months. And and so just not having to to continuously repeat the foundational rules and permissions, this the security structure of of the the platforms and every integration, that's, it's worth its weight in gold. K. Great. Thank you. We we're gonna stay on this topic actually and just sit wanna come to you and just, learn a little bit more about, the decisions that BD made. So, you know, doubling down on the Workday stack, including hired score. And so understanding that would be the first question, and then sort of link to that, how you how do you think about maintaining the integrity of the stack while adopting and delivering these new AI capabilities? Yeah. It's a great question, Matt. Because as I was mentioning earlier, for us, it's all about looking at our existing tech stack first before we go out and say we need to look at other technologies. Right? So when you start with the business problem, we can I'm gonna sound like a broken record, but you break it down into people, process, technology, and really evaluate where does technology truly give that differentiated outcome. So in this specific case, when we were looking at some of our TA processes and where technology can help us, it was very natural for us to talk to Workday. And, obviously, they had an offering that really helped us with our, problem that we were trying to solve. There's some advantages to it. I think Adam hit on it, but, like, the way I think about it is, look, if it's an existing vendor, you probably have an established process and governance with them. You'll you you you probably end up having a lower cost of ownership. There's a simplified security model, so you know that there's integration there that you don't have to constantly have that at the back of your mind. And again, data flow and consistency. Right? So that these are just certain things as when you look at your core platforms, you just naturally get some advantages there. When you think about our integrity of tech stack, you know, obviously, there are more there's more demand. And, look, we're lucky in an organization that is hungry for AI capabilities and and asking, like, how you know, here's the business problems we think AI can solve it. Tell us how do we go about it. But we have a fantastic governance framework here at BD. Right? It's it's a cross functional group of folks. We have legal. We have privacy. We have cybersecurity. We have IT. We have compliance. We have the business owners. All of us come together and evaluate each use case, and that's the big thing that I wanna make sure that people walk away with. Each use case is gonna be different because each use case is gonna look at specific data points, how does that data get processed, etcetera. And, therefore, it's important that your governing body feels comfortable approving something. With that, automatically, you you avoid the AI sprawl. You go into it in a very methodical way, and and and that has really worked well for us. So bringing them on early, make sure that there's a cross functional representation, and and I think the AI sprawl, will take care of itself automatically. Thank you, Sid. We're gonna stay on that topic actually, so specifically setting ourselves up for success when it comes to, you know, governance, responsible deployment, thinking about kind of AI, adoption as well. And, actually, you know, I think we we all want to make sure we're delivering positive human experiences. We're ultimately working towards a business goal. We've heard BD's, patients and practitioners. We're we're we're trying to drive to deliver better results there and make sure that the AI deployments we're delivering are responsible. We drive adoption. But we know we can't talk about agentic or, or AI, deployments without thinking about our, our governance friends and talking to our legal teams and all that kind of cross functional work as well. So we wanna spend some time sort of thinking about that. I mean, I know, from the CLO end, I've spent the last two or three years speaking to nearly every, governance team in our customer base and is instead of had the delight to work with your teams, over the years as well and built some great trust and, and and conversations there. But wanna just double click on that with this group a little bit. So, Adam, I'm gonna come to you first. Curious about your experience. So I'm sure you've presented to just as many, if not more, AI governance boards, as I have in your role as CEO at Paralogs and now GM at at, at, Workday. So so what are some of the key lessons you've learned and advice to the group that we're speaking to today, to to be able to speak the language of of risk and compliance, and how do you prepare for those, those sessions? Yeah. I I I think most often, it's it's a TA leader that is going to the AI governance board for the first time, and and many of these governance boards are somewhat new as well. And so they're they're figuring out their processes. So my my advice is typically, you know, get the questions or what needs to be decided in advance so you can prepare, so be able to understand what you're going in with. You use your vendor and your your providers to help prepare you. They've been in front of many of them, so have them help with data, strong answers to questions, to to help you prepare for, for for that event. And then be really clear as the TA leader in your business problem. What are you trying to assault to solve? What do you hope to achieve? And then why is a AI specifically, unique and important way to solve that problem? Because what you don't wanna do is get pushed back into AI could be slightly risky. Go do this the old way. Being able to show the the right, the way that you're seeking transformational business results, in a way that is safe and and compliant. So a little homework goes a long way to making that happen. Thanks, Adam. Okay. Lizzie, we're coming to you to actually share a story of, you know, how you moved through this process in real life. So, you know, I would categorize you as an innovative leader. You're in an organization that is regulated, so there's risk awareness as you as you move through. So how did you how did you first champion AI adoption and get that initial buy in from all the people you needed in your own organization. It's a great question. I think Adam did an awesome job of of outlining probably how I felt, going through that for for the first time as well. And and we should be risk aware when it comes to AI. Right? I think Adam and I both shared we're recruiters by background. And by by nature, therefore, I I care about the interactions, and decisions that and hiring decisions that we're making. We we need to make sure that those are the right decisions and that we're using AI in a responsible and and ethical way. So, first of all, we shouldn't be frustrated that that we have to go through, you know, the these governance reviews and boards. They're they're there for a reason. So so my advice, Sid mentioned at the beginning that we should always look at our existing tech stack first, and I am probably the worst customer for Sid because I get all very excited about all the shape shiny new AI stuff that's out there. But what I think have actually really helped us as we went through these processes with, hired school, with Paradox, obviously, both Workday products, is we're using and leveraging existing systems, relationships that we've had for a long time, data, and and kind of models that we are familiar with. And so, despite the fact that I did get very excited and still do get very excited by all of the advertising that's out there and we can't avoid it now, by the way, about all all the things AI can do, leveraging existing partners and existing systems did really enable us as we, you know, went on this AI adoption journey. And the other thing is I like to think of myself as somewhat technical, but some of the conversations around AI as a TA leader absolutely and completely goes above my head. And so what I would say is rely on the experts around you. Adam talked about kind of educating and equipping the TA leader, and I think that's right. But you do not have to be the expert in LLMs and all those different things and words I don't understand what they mean because you have people both on the vendor side and in your internal organization who can do that. And so feeling comfortable to say, hey, we're gonna connect these two people together and have a conversation that I absolutely won't track with is absolutely completely okay because we will also make sure that we're kind of speaking the language, that that the people need in the room. And bring it back to the reality. Right? Here is the hiring problem that we're trying to solve. Here is the hiring opportunity that we have, the business problem, the business opportunity that we have, and here is where AI is gonna be able to do that. And and let's be open about here's the risks that come with that. Absolutely no decision is risk free. But equally, here are the mitigations that we can put in place. And so BD feels very strongly that we should absolutely have our own bias testing, that we should absolutely be doing these things ourselves. And and we we feel very, you know, strongly and as we should about that. And so making sure that we're upfront and honest about here's the risk, here's the mitigation, and making sure that everyone is having the right conversations with existing trusted long term vendors, I think made our risk aversion, you know, us to be much more open to take, some of maybe what feels like risks on paper. But, actually, they're really educated decisions that mean we can move quicker, we can get better talent into our processes and our systems, and ultimately serve the the business better, in a much more credible way. Excellent. That's great advice again. Would you mind sharing actually, really practically, what does without giving away anything that is confidential to BD, but, like, what does that interaction really look like? So, you know, thinking about some of the work we've done together of, you know, assessing and bringing new tools, like, how how does that work in in reality for our audience? Like, what does the what do the interactions look like? Who's involved? How does that go? Yeah. Absolutely. And I think Sid mentioned that BD has a a very, well established kind of AI governance board. And so for us, that means that we are going through, first of all, demos with that cross functional board. And so, making sure our vendor comes to give a little demo, and I do think that demo is really important to actually showcase here is the impact, here's the functionality of of the tool that we're talking about here. And then having our privacy team, our risk team, our legal team, our, IT and digital and cybersecurity teams, really ask in detail all of the questions that that they have and that they feel that they need to understand to be able to get to the bottom of it. And so making sure your vendor, and in this case it's you guys, so so that's good for us, is really well prepped to be able to talk to that and understands the questions that they're gonna be be getting. And what we've also found is for a lot of this, it is new for a lot of companies going through this for the first time. And so being very open to have these conversations to say, hey. Can we do this together? Would we be able to do this? It's some of the really practical things that that we were able to do. And so, we went through those very, thorough review boards. And then on top of that, made sure after the fact that we have really clear follow ups from a project perspective. Right? That's weekly project calls. That's looking at the metrics on a quarterly basis, looping back with our colleagues from a cross functional perspective to make sure that there's executive kind of oversight of that. So for us, it's not just kind of ticking a box and making sure that we're, you know, going through, hey. This is what we need to do, and let's tick the box and move on. It is really making sure we're doing this in the right way. We're building trust with the organization and and ultimately with our candidates who who are now using these tools as they come into our hiring process. Great. Thanks, and thanks for sharing a little bit of the the detail, Lizzie. That's, always good for others to hear exactly how it's working and and, and see some similarities in their own organizations. Sid, I wanna finish this section with another quick question, for you. We've covered some some of the topics here, but I'm I'm curious from a sort of technical and, platform perspective, what lessons you've learned around kind of data integrity, building trust with, you know, compliance stakeholders to make sure that we get to this, like, nice move launch and actually we're solving these business problems. What are some of the lessons you've learned there? Yeah. One of the things that I would say is walk in with the mindset of a student in this initially because an AI implementation is very different than a SaaS implementation. So if you walk in with sort of the same mindset, you're likely gonna face hurdles that you did not expect. We've been lucky, like, with when we partner with Workday. That was the first thing they said. Look. An AI implementation is different than your SaaS implementations. Here are the questions that we get asked. Here's what I think is gonna be important, etcetera. So the first thing is walk in with that mindset. Be prepared to answer questions in terms with from your legal and privacy and how your data is used, where it is used, etcetera, because that's extremely important to establish the trust and people understand sort of where your data is being used. And, again, like, when you get into this process, like, from when I when I think looking back, one of the things that was very early on is we said, look, think of AI as any other process. A human is always evaluating and making judgment calls, and that is extremely critical. So the technology bit is one thing, but how you making sure the process supports that, that the human is always making sure that they are using their judgment and and making decisions. Right? I said a governance process was, again, extremely helpful. So so at a high level, those are the things, Matt, that when you walk into it, it's it's gonna be slightly different, and and that's how you maintain sort of your data integrity when you think about these implementations. Great. Thank you, Sid. I mean, I have to give kudos to to both you and Lizzie for, you know, being a great example of how to navigate through these kind of responsible, deployments. I know, here on the CNO side and I know on the Workday side, on behalf of Adam and team, we we're always delighted to work through these things, with you. So kudos to the leadership that you provide in those areas. Good good model for others. Okay. Let's, let's move away from the super exciting topic of risk and compliance to business outcomes, and actually think a little bit about, actually back to where we started. This is a utility or a tool that we're looking to deploy. You know, it has a great role, and it could be potentially transformatory, and start to think about, like, how those business outcomes, start to show up. And I think everybody's in the early innings right now, so I think wanna think a little bit about even at the early stages how we map, key indicators that are sort of leading towards the business outcomes. So I know we wanna go for those business outcomes, but we also wanna plot a journey towards that success. A couple of questions for you, Lizzie, and then, something for you, Adam, as well from from your experience. So, Lizzie, straight up for for you, what are you what are you seeing now in in terms of, early indications of some positive business impacts or the the sort of trajectory towards the results we're we're aiming for here? Yeah. It's a great question, Matt. And you're right. It it's early days for, I think, probably for many companies who are on this kind of AI journey in talent acquisition. And it is really, really tempting to make all of the big claims, especially when you're going through all that governance we just talked about and say, wow. We're gonna see a 100% recruiter efficiency gains in month one. We we definitely have, you know, not not done that. We've learned our lesson from previous implementations, of various things not not to overpromise on things. So it's early days for us, but I think what we're starting to see now, through the AI that we've got in place is, certainly increased top of funnel. So we're seeing more candidates from more sources. So we're starting to see again pretty early indications that are making that meaning that our outreach, is much more efficient. We're able to get out to a lot more people and starting to see also the quality of the candidates that we're getting through. I think most of us in talent acquisition know that this is quite an unusual job market at the moment and and for lots of us will actually be getting a huge amount and a very high volume of applications. And so looking to say, okay, how do we make the most, of our recruiters time to not be looking at, you know, hundreds and thousands of applications, but really looking at where are the true quality applications of people who can come in and really make a difference within our business, making sure that we're able to identify those quality talents much quicker, and then move them through the process, and and and convert them throughout the funnel as well. So, we're not overclaiming. We're not kind of declaring victory at this point, but seeing that recruiters are able to be that little bit more efficient, to get to the high quality candidates quicker, be a little bit more targeted, That's really the early indicators that that we're seeing, and hopefully then starting to say, okay. Hey. Look, business. This really means that you're getting access to the best quality talent, in a much more efficient, and quick manner than you did before. That's the ultimate aim that we're hoping that we're able to to go out to the business with, in in months to come. Yeah. I mean, that that it's great to see that early success at that at the start of the process as well. because you gotta kinda feed deep ingredients into the recruiting machine in order to, to actually get the quality hires and deliver the business outcomes at the end. And by the way, I was a recruiter as well when I say the recruiter. I've also run a desk many, many, many years ago as well. So so, Busy, we've we've got the model up and running. Mhmm. Workday have we've we've implemented. CLO is your partner operating alongside you to, to use a lot of this tooling. Absolutely. How are you thinking about tracking that workforce adoption to ensure we're actually gonna realize those those gains? Yeah. And it's it's really easy sometimes, I think, to sit in in the seat that I do and say, okay. Awesome. Right now, Cielo, you are our global RPO partner. Workday, we've got all this tech. We wanna a 100% adoption. But, Matt, you you gave me a good reminder then. Right? So I've been a recruiter, and as I said, maybe not twenty three years ago like Adam, but about fifteen years ago. And fifteen years ago, if someone came to me when I was a recruiter running a desk and said, use all this technology and AI. I mean, we wouldn't I don't maybe the word existed. I certainly didn't know what AI was fifteen years ago. I I I wouldn't have known what to do with it. And so I think it's naive to suddenly think now because it's there and it's out there that everyone is gonna jump on the bandwagon and say okay amazing yes I'm in for it. Obviously that's what we want to happen but we also need to support people on this. So we are tracking adoption, are people using this first and foremost? That's pretty easy to to track. Are the people who are using it, is it working? Is it making things quicker, better, more efficient? Right? It's it's lots of different outcomes that we want, our recruiters primarily to to gain from this, but we also have to support people on that journey too and people have to understand why is this going to add value and why is what I was doing before which maybe by the way has been super super successful not the path moving forwards and I think that for me, personally, the way that I find to be able to do that is most of us are now used to using AI and certainly you kind of very efficient automation and technology in our personal lives. Right? In how we shop, in how we do things around the house, whatever it might be. And so, you know, there's some synergies that you can put across to say, and here's now why we're gonna be using it in a work context, and these are our expectations. So it takes a little bit of time to to get there, but we're really making sure that we have regular, kind of reviews in place, a lot of governance there, and really importantly in in the CLO team have been really good at this, getting feedback from the recruiters too. Why is it not working? What is it that's kind of preventing you from getting out there? And sometimes that is simply change management. Other times that's saying, okay. Great. Let's see if we can make some tweaks and changes in the system actually so that it's working better for you because a lot of these tools right now are focused on recruiters. Let's make sure that it's working for them so they can get the most out of it as well. So, early days, but we need to be able to move from just logins, kind of people using getting in there and the tool being used to. This is changing behaviors. This is adding value to our recruiters, and then, therefore, it's adding value to our candidates and ultimately to our business, longer term as well. But it it doesn't happen tomorrow. It takes time to really get embedded and and become, part of everyone's kind of daily workflow, to to ultimately see as you see the the the real world gains. I like that phrase. Yeah. Thanks, Lizzie. That's great. And and, you know, this is all happening in the real world. So there's, you. know, hiring managers and candidates who have needs and other things that are happening at the desk level and, you know, it's important to be holistic in how we think about adoption and and and moving forward. So, Adam, I think you've probably seen this movie a few times before with some of your clients, previous, previous years. So when you think about this kind of early innings, optimization phase, you know, launching to to see early results and and and ultimately grab the business value, down the line. How do you think about the leading indicators and and what are the what are some leading indicators you suggest organizations should celebrate? Yeah. I mean, ultimately, you wanna get to the hard business metrics. You know? How do we help the company save or make money? That's gonna take some time. And so in the interim, you've gotta make some some claims of of of your wins. I recommend celebrating the first time you make an AI hire or the first time you schedule an AI an interview with AI and and just giving some some celebration to that moment to say, okay. This is new. And then you can start down your your time to x, metrics. So how long is it it used to take six days to do this part of this subprocess. Now it takes two days. And and starting to get some energy around that, whether that's the interview, scheduling cycle, or whether that is a job opening or whatever part of the process, just focus on on on that. And even candidate satisfaction anecdotes, hiring manager anecdotes are fine. You wanna you wanna be able to stress that it's saving time and money and, it is and that people like it that they like it. And so so I think trying to get those those, those stories told before you've got the metrics is is an important part of a a winning rollout. Thanks, Adam. I like the idea of celebrating the first AI hire. I actually remember a piece of work Cielo and Paradox did a number of years ago when, actually, we watched the conversational AI doing scheduling. We we celebrated how many candidates were actually saying thank you to the conversational AI. That was a that was an interesting, thing to watch, but but celebrating the small things. I actually just wanna be focus on the the left hand box on the screen here at the moment. We we all know that, most CEOs, or for the last two or three years, most CEOs have AI as a core strategy and lever, and that tends to get sort of passed down through the organization. And so do some AI often becomes the or what's your AI strategy for HR or for TA? And I think this is really important. So moving beyond we have x number of AI tools deployed or we've we've converted this amount of SaaS traditional SaaS spend into this amount of AI spend, moving beyond those metrics to actual business outcomes is gonna be critical and important for this next phase as well. And so thank you to, to Adam and and Lizzie for sharing some of the stories of how we can do that together. Okay. We're gonna move on to the last, section just thinking, thinking really about how we can get started. And and, Lizzie, you'll be somewhat familiar with this framework because it's something that Aclio and BD and Workday use together to think about where we could go, in your transformation activities. And I think at the moment, sort of standing still is ultimately kind of moving backwards for organizations. So we start to think about sequencing, the now being how we can sort of build momentum while keeping a clear line of sight on the next and the later for your workforce evolution. So let's live in the now for a moment, and then we'll talk a little bit about this framework, in more detail. But, Lizzie, a couple more questions for you. I'm curious back to the sort of BD business strategy translating into TA. What were some of the immediate wins you were prioritizing when you were thinking about, like, this transformation? What were some of the now things that you you felt like we can we can do this? This should be this should be in the now category? Absolutely. I I think for us in the now category was was probably that efficiency gain right now that we were saying, okay. We are a large organization. We just moved to a global operating model for talent acquisition, which is super exciting. And so kind of right now, let's say, how can we be a little bit more efficient? How can we scale up? How can we use technology and AI to scale up and and kind of get some of those efficiency gains? That was probably kind of the the first phase that that we've been focused on, and and being able to say, okay. Recruiters are now have access to all of these tools, and that's, by the way, something that you should celebrate too. Like, we actually just have access to these. We've turned them on. You go through an implementation. That's a big deal. But now recruiters have access to these tools. That's really the the now that we have been focusing on, starting to build that credibility, starting to get access, and things turned on for our recruiters to start to be able to focus initially probably on the efficiency side of things. That's great. And and curious as to what are some of the capabilities you're most excited about in the next sort of twelve to eighteen months as well. What are some of the things you can't wait to to get to? Oh, everything. Everything that you and Adam promised me. Right? That's what I'm most excited about. And now you're in this forum. You have to promise it. So what am I excited about? So I would say what I'm probably the most excited about is we we you know, we've implemented now several AI tools. And and, really, that is no easy fee, and that is something that we're very excited about. At BD, you know, we started to get, as you can see on the slide there. Right? Some of the efficiencies, we've got the governance set up. We're we're kinda moving in the right direction. The next twelve to eighteen months, what I'm really excited about is that this truly becomes embedded within our talent acquisition process. It's no longer this kind of, oh, I've got my process, and, oh, and now I need to kinda remember to use the new tool that's here. And that's okay to start with. Right? Again, it doesn't become a top to do overnight, but to say, okay. I think you call it unified workflow. That's a very fancy way of saying this truly becomes the way that we work every single day. And recruiters don't even need to think about this anymore, and hiring managers don't need to think about this anymore. This is just how we recruit at BD, and we are used to the speed, and we expect the speed, and we expect the efficiency, and we expect all of those things because that's normal for us at at BD and how that we we recruit. And I think what we really wanna be able to move towards is saying, how can our recruiters, our human recruiters, add the most value with the relationships that they have with our business, with candidates, with the industry knowledge, with all of the experience that they have that is so critical and so valuable, and we want them to be able to focus there because our technology and our AI is taking care of all of the other stuff behind the scenes. And maybe that's a slightly simplified way of putting it, but as a nontechnical person, that's the way I look at it. I want our recruiters and our humans out there adding the most value they can because technology enables them. It works for them, and it makes their life easy. Is that not what we all want technology to do for us? Right? Make our life easy. And the same applies to candidates and the same applies to hiring managers. I want candidates to have the best hiring experience whether they're hired or not when they come with BD because they're working with awesome recruiters and awesome hiring managers, and their process was just super, super slick like they would expect in their personal life with any kind of consumer thing, product, or or service that they're interacting with. Yeah. That's a great answer. Thank thanks, Lizzie. We it's all about we want the humans to be more present in the human moments. Right? And. so, I'm really excited for the journey we're all on together and some of this moving. I mean, ultimately, the next becomes the now and the later becomes the next, and we just add more and more innovation and capability. So, okay. We promised some time at the end for questions, so we got the last couple of, things we wanna share. Actually, Adam and I wanna spend two minutes maybe just talking you through this, model. I think this is something we'll be able to share with, with the group afterwards as a way of thinking about how you move through, the the evolution of your TA functions. This is really based on the thousands of organizations that CLO, Workday, have worked with. You know, we spend a lot of our time, I know Adam does, I know I do, talking to, heads of TA, CHROs, recruiting leaders around what to do next. And so we've built this set of common scenarios. We don't have to over index too much on the language, but but the I think the scenarios should give you a practical way of thinking about moving through the next step. So together, Adam and I are just gonna talk through the the model. Alright. So, Adam, I think you're gonna talk about it now, to get us started, and then we'll we'll talk about the other two categories as well. Yeah. Really. I mean, the the the so the now is about stabilization. You know, if you're an early adopter, you're likely auditing tools to fix fragmentation. If you're an optimization leader like Becton Dickinson, you're you're calibrating, tracking real adoption over the vanity metrics. And for the, conservative guard, now is a 100% about governance, you know, building the AI council to earn your license to innovate. Great. Thanks, Adam. So the next, this is where you actually get to kind of accelerate. So we're moving to those unified workflows that, that, Lizzie was mentioning earlier where AI is kinda native to your platform and to your process. So it's not a bolt on. It's not an additional thing. The six bullet point in your presentation is actually core to to what you do. This is the kind of speed and trust phase. So we we are more comfortable and more confident letting AI handle some of the manual heavy lifting or certain parts or segments of our working and and and what we're doing. But we're keeping our people in place, for the kind of governed oversight as well. So this is the first sort of evolution into, human centric AI, delivery. And this is where, actually, you're winning the hearts and minds of the organization to show that this is a capability that can be trusted and can deliver, you know, superior performance and outcomes in certain areas to complement, And the human approach. K. Adam, take us home with the later. and then and then, you know, later than the destination. Right? You know, this is the shift to agentic talent acquisition where recruiters are managing a fleet of AI agents. You know, it leads to dynamic resourcing that matching the skills and needs across the total workforce in real time where there's there's very little friction in labor markets at all. You know, this is scaled trust. You know, the administrative burden is gone. You know, human judgment is back at the center, and and human connection is what what powers this. So that that recruiter job changes from being a facilitator of a process to being kind of back to basics, to twenty three years ago, to building relationships, to to thinking about how that deeply human aspect of it, makes sense. And, Matt, close. us out. Yeah. Great. Well, I'm I'm gonna just throw one last one to Lizzie. When you when you look at this map, how does it align with how you sequence things at at BD to sort of balance, like, impact and taking everyone on the journey? Yeah. I think we we are probably in in kind of the optimization leader. That feels kinda cool to say as well, by the way, that that we're in that that, that category today. It it completely aligns, right? Fragmented tools, that's absolutely where we were and and and I was absolutely part of that. Right? Because I got very excited with all the cool new shiny tools that are out there and certainly needed to be educated by colleagues around actually having kind of that seamless workflow is really important and and adds a lot of value to to all of the stakeholders, that that we go through. Kind of saying, okay. You know, now we we have this, you know, kind of in in theory kinda seamless platform. We're not quite operating like that yet, but now we're integrated. Now we're starting to build up that speed. We're starting to build up the trust as well. And I really like that that bottom one. Right? Piloting focus cases. We absolutely love doing that, BDM, for maybe those on the phone. We're a big company, and it does take a long time to implement, you know, these big scale projects saying, okay. Where are they some of these pilots that we can take opportunities with and and try things and see if it works and tweak before rolling out across the whole organization? So I think this represents absolutely the right steps to go through and where we have kind of been in some of them and where we're going. Hey. And by the way, I think in this world of AI, like, what we're presenting here for later in six months' time, by the way, it probably looks a little bit different because something new has happened and and come out as well. So, I I think this is an awesome road map. I think we all need to remain agile as the world continues to transform around us and new solutions and new products that are brought to market in in the hiring world. It's it's a pretty exciting world out there and a place to be right now. Thanks Lizzie. And this sort of concludes the, curated part of the webinar. I think we may have time for a couple of questions. We've overrun a little bit, we're all very excited to share our experiences together. I actually want to say a big thank you to, Izzy and Sid, from BD and Adam from Workday. On behalf of CLO, it's an absolute, pleasure to work with you on this journey and continue to kind of share this journey and start to think about the next and the later together. And, but but I think we're welcoming Tyler back to the stage and we may have a. couple of questions, that we wanna oh, we're gonna look at the polls together. first. Pretty interesting. I'm curious kind of Adam and Matt what what you guys are thinking. This is the first one we have. So does your organization have an AI governing board? So 25% of our attendees say no. I'm sure this hurts a little. 35 say yes, but, HR and TA is not represented. I'm just curious. Kinda Adam, is that is that what you're seeing kind of with a lot of our customers or, folks you talk to? Yeah. I mean, t TA is often a guest. Right? And so, it it's often an IT led led or a risk legal. Those those folks, they're gonna have the background to lead that. And so I think, we see lots of these, but oftentimes, TA is someone that, comes to present a particular solution, not not with a long term seat. I would, Yes. we see the same. I would just add that, this is better than if we'd done this poll two years ago. So we're sure. we're. moving in the right direction. Still some way to go, I would suggest. I I also, Lizzie, Sid, any any thoughts on this one? Yeah. I think we're in the camp of of TAs not represented all the time, and I think, Adam, you said it perfectly. Right? We we're a guest because lots of time there's by the way, AI is used across the business. Right? And then maybe it doesn't make sense for us. So I think, making sure that you have that seat of the table when needed is important. But I also think to your point, Adam, it's often a IT led board. That's absolutely the case at BD with a cross functional representation. Understanding who those players are, the, you know, the things that are important to them is really important before you're going as that guest for the, you know, maybe the first time or the second time that you're going. So, again, we talked about it so you can talk the language, of of those participants, and and don't just come as that kind of guest with with maybe no context, of of those around you and what's important to them. Awesome. Well, before we jump into the last poll, I wanna make sure we have time to answer, Tony's question in the q and a for the group. So how are your governance bodies, thinking about change management for AI adoption and the changes needed in your culture for AI to not just be adopted, but actually valued by employees? So kind of kind of a big one with with only a minute left. But what are what are kind of initial reactions on, how how kind of your governance bodies are thinking about change management? It's a really great question because we talked about it from the lens of TA. You know, that recruiters aren't just tomorrow suddenly gonna be, I think Matt said it well, AI natives. Right? That just doesn't happen. And I think from our governance board, kind of as they look at AI bigger, they're very aware of that too. So there's a lot of effort happening at BD to say, how do we kind of educate the organization on what is AI and when, you know, people throw out things like agentic AI and, yeah, what does that mean? Right? Compared to conversational AI and then hang on. AI existed twenty years ago, but did it? I never heard anyone talk about that. What does that mean? So I think there's a lot of effort going on to educate people. What is AI? What do all these terms mean? And then how do people how what is the expectation that BD have of how people use AI and how we don't as well, by the way? Of course, there's that element to it as well. And how can we all become a little bit more comfortable and confident with it and what to do when we're not? So I think there there's a lot of effort around change management and meeting people where they are. Naturally, there'll be some people who love this and are completely natives in a day. There's some people who will absolutely resist it and then probably a bigger camp of people in the middle And so making sure we're talking the language of all those different groups of people. I think Bede's done a really great job and continues to to do that. And specifically in the TA world, right, we're translating that a lot to saying it doesn't ever remove the human, that's never gonna be our intention. Yep. This is the journey that we go through, and here's the benefit for you, you know, dear recruiter, of using this new technology and the change management that we need people to go through. And celebrating those little successes in seven the buzzer. seconds. Yes. Exactly. Celebrating the successes. That's a really important thing to to build that trust and change management. Okay. The the education piece is is huge. Mhmm. Absolutely huge. So thanks, Lizzie, for for mentioning that. Tony, actually paralogued now Workday and see how they put together some seven standards for kind of responsible AI deployment, which includes some of that education. So something to take a look at as well, in future. Well, all looks like we are not live anymore. So at the at the hour mark, it cuts, but this will be live for the on demand recording. So thank you, to the BD team and, obviously, the Cielo team for your partnership. And and, Adam, thanks for hopping on too, especially early on the West Coast this morning. We really appreciate it all. Great to. see you guys. Thank you. so much, team. Appreciate you. Thank. you. Thank, you, too. we're both