How To Build An HR Strategy With AI
In the latest HR Leaders Panel, experts from ChenMed, Bayer, Eightfold, and Pearson explored the transformative role of AI and GenAI in HR strategies. They discussed successful AI integration, measuring its effectiveness, engaging employees on AI benefits, and presenting a compelling business case for AI investments in HR to executive leadership.
Join us to delve into the transformative role of Artificial Intelligence (AI) and generative AI (GenAI) in HR strategies.
Top experts detail how integrating AI technology is pivotal for enhancing how talent is managed, developed, and retained, ushering in an era of data-driven decision-making and personalized employee development.
🎓 What you will learn:
What successful AI integration into HR looks like
How to measure AI's effectiveness in improving HR processes and outcomes
Best practices for informing and engaging employees about the role and benefits of AI in HR processes
Presenting a compelling business case for AI investments in HR to executive leadership
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Chris Rainey 0:07
Hey everyone. Good morning. Good afternoon. Good evening, depending where you're tuning in from. My name is Chris Rainey, co founder here at HR leaders and I'll be your host for today's live panel discussion where we'll be talking about how to build a HR strategy with AI. Let me introduce you to our amazing panelists. We're joined by Beatrice Rodriguez, who's a group talent or true Group chief talent and diversity, equity and inclusion officer at Bayer. Christopher Lind, Chief Learning Officer at Chen med. Tim Young, Senior Vice President of HR operations at Pearson. And last but not least, Jason Serrato, who's vice president of market strategy at eight fold. Ai. Nice to see everyone Christopher psycho tradition of you being on the shows that I pick on you always, first, for it. At least you had a sip of your water, you knew it was coming? You're like, let me I did I
Christopher Lind 0:57
did make a drink, get ready for this. Yeah,
Chris Rainey 1:00
no pressure. So, so sorry. And you know, what are your thoughts on the potential of of AI in HR, and how this is going to really shape the future of the function? So
Christopher Lind 1:11
what's funny about this question is, it's a complicated one in that, ah, AI will change literally everything, and also nothing at the same time, which sounds like a paradox. But it's actually true in that I see where AI is going right now. It's very much like the internet, like maybe in the early days of the internet, you went, how's the internet going to change HR. And now we look at an oval, literally, everything we do has something to do with the Internet. And the same is true. With AI, I think the challenge we have is that figuring out where when and how we're going to focus that attention is the big part. Because I on my podcast, I had the gentleman who led the team that created Siri, and we were talking about one of the challenges we have with AI right now is that in the past, you used to have to target AI, and it would only do what you targeted it to do. Now with these large language models and advanced AI algorithms, they kind of can do anything. And so it's more about telling it what you don't want it to do. And so really, this is a shift in our thinking of going, Okay, where do we want to apply it? What do we not want to going into, but at the same time, the core of what we do in terms of thinking about people, the operations of things, that's, that's going to stand the test of time, we're just going to do it radically different if
Beatriz Rodriguez 2:29
I can actually to pain if I think about ourselves, as an HR organization is actually today. And it will continue to make us really uncomfortable with our capabilities and our skill set. And this is because it's it's also in many aspects new to many of our HR population and many of our HR teams. So it's something that we will have kind of two hats off to you think about bringing this into the organization, one is upskilling ourselves into a world that potentially we have not been in touch much for the for the past 20 years. But also at the same time, we sort of carry this partner in sense of helping the organization to understand what does it mean for for them. So we're actually going to be challenged on many different fronts. And I think it's a good thing, because just being uncomfortable with AI in our own skin, as HR is just going to make us better in the future. And it's going to bring us to a level that, you know, we need to be so I think it's it's just something that will help us tremendously as professionals. To
Christopher Lind 3:35
your point about the discomfort. I think what's throwing people for a big loop is there's so many things that we've historically been like, this is what I do. Yeah. And it's like, well, actually, no, it's not anymore, because quite frankly, the machines can do it better than you. And if you're gonna stand on that podium, you're gonna get kicked off the stage. And so figuring out, oh, wait a minute, like, I've got to rethink how I show up. And what that looks like, is extremely uncomfortable for a lot of people.
Chris Rainey 4:00
Yeah, definitely. Yeah,
Christopher Lind 4:02
Tim, answer all your questions, Chris.
Chris Rainey 4:05
We're just getting started. I know, I know. I know, Jason and Tim have a lot of thoughts on this, as well as being quiet. We like Tim, we spoke about this only a few days ago. So I'd love to hear your thoughts.
Tim Young 4:15
As already been noted, fundamentally, the way we go about our days will change. And we've already seen that we're already at all adopting it. And it's such at such a fast pace. But we can't also forget that there's just a whole host of people out there that just can't quite see how it's gonna affect them. And that's just incredibly scary. And I think a big part of leadership in the space right now is just simply helping people understand how they themselves get from here to there. And providing them those opportunities is going to be how you know how we evolved because ultimately, while we want AI to now take over some of these tasks and enable us in other ways. We need to now reinvent what humans do to add value And that's going to be a learning process all of its own. Something
Christopher Lind 5:03
you hit on Tim there that I think we especially as HR professionals really need to be mindful of is that potential divide, that's going to show up in our organizations where the people who adopt sooner, are accelerating faster, and the people who are lagging behind are falling further behind. And we actually run the risk of we just have to care for those folks and actually help bridge them, because there's some people who if we don't, and it's really our responsibility to be the guardians of that, if we don't, we're actually leaving a lot of people in a really rough spot. Yeah.
Jason Cerrato 5:34
And I think it's that it's that shift of, you know, leading versus lagging data, and being more predictive and proactive. You know, I love the video that shows all the things that used to be on your desk that are now on your phone. Right, and that was the story of how software ate hardware. And now what's happening is AI is eating software. And as a result, what it's doing is it's eating the transaction. And we've talked about how HR has had this habit of being framed as transactional. Well, as a result of this, that transaction is going away. And we get out of the habit of being, you know, the spreadsheet heroes and being burdened by the system. But it allows us to have the focus on people and being more strategic. But part of that is, you know, we've been burdened by systems sometimes. But also we've had people that have been building careers on their ability to manage systems, and their ability to manage transactions. And some of this is a shift. And some of this is changing the way they're going to get their job done. And their, the way they're going to interact with the organization. So this is a little bit of discomfort. But it also is for a lot of people, what they've always been trying to do, and have had difficulty accomplishing because of the administration that they've had to fight through. Well, a lot of that is disappearing as a result of this capability. The
Christopher Lind 7:00
crazy part about that, though, is is now in some ways, we've been kind of PTSD into that because we are to the point where like it I actually encountered this regularly were the things that we said I hate doing this, I wish we had more time for this, as that's being taken away. People are going wait, why are you taking my stuff? Yeah, like, No, we will. This is the stuff you wanted to get rid of before. But we actually have to make that shift. Because otherwise the question of well, couldn't we just get AI to do what you do? The answer that will be well, actually, yes, you could, if we don't make that shift.
Chris Rainey 7:31
I've seen it with recruiters at the moment. Like all the things that they were complaining about in the past or the admin stuff or stuff like that. Now it's all being taken away. They're kind of having this Oh, my God moment, but it is actually freeing them up to have meaningful conversations of candidates and do the things they always wanted to do. And AI is allowing them to do that. But you're right, like this is PTSD of like, no, don't take it away. Because the fear factor, or no, we're taking this away. So you can actually have be more strategic and have more, all those things you said you never had time for. You now have time for those things to do. One of the big challenges. And so when it comes to you, Tim is for our audience when I'm speaking in day to to them day to day is Chris, I don't understand how to make a compelling business case for AI, in HR, just conveying that. So I'd love to understand how you've approached us at Pearson.
Tim Young 8:26
I mean, first, the good news is we don't have to pitch the fact that AI is a good idea, right? I mean, I think all executives at every company, if they're worth their salt, or right now trying to figure out the right ways to invest in AI in order to make a difference in their business. I think what the problem then is that we face in HR and I certainly have had this is that we are in a very crowded field of people with great ideas of how to use AI. And there's always limited resources, both from a monetary standpoint, but then also the skills in order to apply AI in a company. You know, we're coming up to speed, I think from a skill set standpoint, from our IT partners, for them to be able to manage the demand the appetite, and then the maintenance over the long term. So I when I think about the business case, we first have to acknowledge that this this creates a massive pressure on it in order for them to be ready for what we're bringing at them for them to be able to consume it all because guess what, we still need HCM, we still need learning management systems, right? We still need all of those underlying systems for now AI is not ticking over those. And now we need to add an additional layer adding to tech debt and other things. So the reality of the crowded field and it is readiness are too early factors. But what I would say from my experience, and what has been successful is there's really three main strategies to employ they're really easy, but if you think about them, they need to be employed together. The first one is you've got to start with your home run case right Not the little ones, not the little incremental ads, but your home run case, that's going to state this is why it matters for the company, right? This is why you should really pay attention to what I'm doing. I think the second one is to bring champions with you, that are not in HR, right. So if I go and I say, This is great for HR, this is great for employees, I'm going to be one of many voices. So bringing along in my case, finance it for shared services and saying, if we go and invest in AI in this space, it's going to help me Yes, but guess what it's going to help them as well. And all at the same time, we can, we can create huge value if we're doing it together. And so they're they're at the table with mean, it's not seen as just an HR play. And then I think the third one that we just can't ignore is that we don't start with employee experience on this case, because there's going to be so many revenue, adding sorts of initiatives and options with AI, that we have to come with money, right beat, meaning that we have to prove how AI can save money, and it's not hard AI can definitely save a lot of money. But you've got to do the groundwork to bring that into the equation. So it is about employee about value proposition it is about the experience is about creating new abilities, but we're also saving money. So I think if you can employ those three, then you have a much better chance in the crowded field. To
Christopher Lind 11:26
your point, Tim, one of the things I'd add to that is the shift from employee experience, to actually we can incorporate the idea of customer experience through our employees with a lot of this stuff. Because again, as we talked about, like as AI starts to help us think about how can our people be focused on the higher order activities? Well, those are the activities that actually differentiate and give us a competitive advantage in the market. And so when we come to the table with, Hey, here's how we can leverage this technology to have people spending more of their time on this value add activity that differentiates us as a company, it's a it's improving the employee experience, because that's really what employees want to spend their time doing. It's improving the customer experience, and it just makes good financial sense. So I think there's a lot of ways we can position this, and not be like, Hey, here's the AI thing that we need budget for
Beatriz Rodriguez 12:17
this right? For me, there is a that is that is something which is really simplifying the language in which we are bringing this to life onto the organization. Because over the last few years, we've been bringing in HCM systems, and we've been using employee experience. And we've been sort of like trying to put together the efficiency case, the all of this and salaries to sort of, you know, the HR strategy on the employee experience. In reality, the way for me to bring AI into HR is truly appealing to one simple thing, which is a talent that we all need to be able to compete in the marketplace. And I think that is really putting talent at the center. I think we've come to a realization in the past few years, both from people leader perspective, business perspective, an HR perspective that we are simply not able to articulate a skills, we're unable to articulate, you know, what are the songwriting skills, what are the sunset skills that a particular organization needs, we are not able to articulate manually the learning opportunities, the upskilling opportunities, because data has gone to the next level of actually what we've been doing it in sort of, like STL sheets. So are they really using AI to say, look, at the end of the day, what were what is this is all about is to be able to bring talent closer to you as an organization that you really need to be able to deliver on your business priorities. And it's as simple as that. Because when you start the business case, talking about efficiency and technology and all of that, it kind of like we've already been talking about this in many different ways. So it's truly about about really putting talent at the front of AI and sort of as well breaking some of the myths regarding what AI does to Tallinn. And you know, and sort of helping people understand what what that talent ecosystem could could look like for you as an organization and must have a business to be able to deliver your business results. At the end of the day, you need people at the center,
Jason Cerrato 14:22
and it's beyond HR. So again, echoing everything everyone said, it's telling the story in business outcomes. So when we say business case, the key word is business. So talking about not just optimizing processes in HR, it's how is this delivering to the business in a new way. So, you know, unrealized capability that we are now uncovering through new visibility to talent in our organization or you know, untapped capacity in our organization that we are now uncovering through adjacent skills and learn ability or the ability to deliver to the business through faster speed to productivity and, you know, tying skills to to training courses and the Win Win of showing people career pathing in the organization, but telling this story through, you know, business outcomes, and not just HR metrics, one
Christopher Lind 15:17
of the things you hit on right there that I think is so important for us whether I mean, HR leaders, yes, I think we can play a key role in influencing this. But I think everybody right now needs to focus on this is that cross functional, one company mindset and activity that has to happen, because I actually think one of the greatest risks we have, as AI continues to increase in its usage in organizations is a rugged individualism that will destroy the organizational performance of the company, because well, AI may be great for a solopreneur that can now do the job of 10 people, to an organization that requires cross functional partnership, and teaming and all this other stuff when you can go, you know, I really hate working with that cross functional team. They're really annoying. And so I'm just going to ask my AI bot here to do all that, because they've got all the wisdom of that function anyway. That kind of behavior will destroy organizational performance. And as HR leaders, we have an opportunity to go, Hey, how do we help guard against that, because that's a real risk coming for organizations.
Chris Rainey 16:18
On that point, I want to flip the conversation, we spoke a lot about communicating risk to the business. I want to flip that now Beatrice and talk about how you're, you're communicating the advantages of AI and HR, to your employees, in terms of fostering acceptance and abusers in from fam, which links into kind of what Christopher was just trying to talk about as well.
Beatriz Rodriguez 16:38
Yeah, actually, it's like, the approach is, this is not HR, communicating anything, okay. So this is not HR, again, being the protagonist of the movie, this is not HR bringing the next shiny thing into the organization. So we adopted the strategy of being the business communicating this to their own population, it is truly a business enablement. So it's not HR having to communicate what it's in for you is truly your peers is your organization, your same, your same business, your function, the one that truly brings to life, this for you as an individual. So we adopted that, that strategy at Bayer, and we were sort of like, you know, define what are our early adopters, so what are really are the parts of our organization that are pulling us versus pushing. And I think that one of the strategies of communication is, you don't do these big bang for everybody, you approach these into MVPs, where you can actually test under, and you actually have early adopters that truly brings in the journey, and you go with what the pool is versus pushing it. And those are the ones that communicate about, you know, how you introduce yourself. And when you're profiling talent marketplace, what are the benefits of its skills? What is career path, look, what career path looks like in a skill based organization. So here constantly to what we typically think of HR communicating an HR addressing the organization, we we adopted that totally different as ever, as we are there enabling but the business is in the driver's seat, and we enable the business and we let our business and our leaders to really bring everyone else in the journey, which really made us to, you know, have an adoption and sort of a buy in into the organization that probably would have eight is much, much greater than the way that we've done that in the past and in other companies. So that's, that's one way. But I'm look, I mean, I don't know what you the all the colleagues thinks about that, too.
Tim Young 18:48
I mean, one thing that our CEO at Pearson has done really well, is to tell everyone across the company in every chance he gets that it's their job, to understand AI, right, be students of it, follow it follow technologists, no matter your role, no matter your level. And why is that important? He sees it as a strategic advantage as a business, if everyone in our company is well versed and aware of how to apply AI in whatever they're doing. Right. And I think that helps in two ways. One is the outcome I think he's going after is truly for us to reap that advantage with people who understand the technology. But I think the other advantage is it puts people in the driver's seat of their own career in unfamiliar territory. We don't know what we'll be doing in five or 10 years. And we won't know until we get there. So but if you're just an observer from the side, it's going to be a lot harder to adapt than if you are understanding the technology, what it's good for, how it's built, how to apply it, that that can be a big game changer for any organization.
Christopher Lind 19:56
On that front. I think as HR leaders, one of the things we really on this employee side that we have to be really careful of, is being the guardians of our employees, though and helping them see what that path in that future looks like. Because everyone right now is reading headlines by people like Larry Sanders, open AI founder saying AI will be the end of all human labor, and things like and so everybody's going, Oh, my gosh, this is the end of me as a professional. And that's what and when we aren't proactive in pushing forward that human centric voice of Yes, AI will play a role in our strategy. But so do you, and we are committed to finding your pathway and helping you on that pathway, Tim, to your point of saying, Yeah, you have a responsibility to get on that pathway and walk it. But we are committed to you. If we don't proactively fill in that void. People are filling it in with all the other news they're reading. And it's not good news. And that's going to crush, it's gonna crush organizational performance and engagement. And the reality is organizational trust is already at a very low level right now. And so being able to bring that up and really reinforced that, yes, AI is a competitive differentiator. Yes, we are invested, and we want you to participate in that change. But yes, we are also committed to you and helping you on this journey. Is that key part of the narrative that sometimes gets missed? We'll because everybody's so excited about what it's going to do for the business.
Chris Rainey 21:23
Yeah, Jason, you're in a quite unique position where you're helping hundreds, if not 1000s of companies go through this process of implementing and communicating AI, what have you seen as some of the best practices or things that have worked well, both from a communication point of view, but also an integration point of view, because you're seeing this every day, right? In what you do, and the customers that you serve?
Jason Cerrato 21:48
One of the things that, that I think works well, part of it is the communication and an explanation of outcomes. So it's not enough just to say, hey, we're implementing AI, and this is what we've done. But let's explain what we're trying to accomplish. And let's explain the application of it. But also highlighting the outcomes. That's a key part of it. But a big thing that we see amongst a lot of the organizations that are doing this well, is also modeling this across the leadership. So for example, depending on the problem they're trying to solve, a lot of organizations are applying tools like AI, to try to drive internal mobility. So we see organizations that are doing things like encouraging it, not only in the organization, but also encouraging it amongst their leadership. And they're modeling it with their leadership to say, hey, you know, we just had our senior leaders rotate positions, and we're going to, we're going to talk about it, and we're going to highlight it, because we're modeling it and modeling the behavior and showing how this is happening in our organization, and then how we're moving people in the organization. And then as that cascades down, and we champion that behavior, we're going to show you how we're enabling that with these new tools that we're rolling out. And oh, yeah, by the way, this is how, you know, this cascades down in the organization, and how we encourage managers to have these conversations with employees. So part of this isn't just speaking about the tool or using the phrase AI, it's storytelling. And, you know, modeling the behavior in a way that encourages and kind of says, It's okay.
Because, to your point, I'll go for it. Because
a big part of this is it's more than just technology, it's changing the culture, right and rewarding behavior. So, you know, we can't just say it's a tool, or it's AI for AI sake, it's creating this, you know, behavior in this culture, and modeling it through leadership.
Christopher Lind 23:51
Well, and to the point of senior leaders, you better be modeling and learning this as well, because AI is just as disruptive at the senior level as it is to any other level in the organization, you know, as a senior leader, your ability to analyze all the different situations and read the reports and figure out what's going on, I can do that better than you can. So you need to be evolving with this as well. So I think your point of modeling isn't just hey, so that we inspire the rest of the organization, but even for your own viability as a senior leader, or
Jason Cerrato 24:23
if, you know, if someone comes into the organization with an adjacent skill that may have been non traditional, and they have a you know, great success with the Success Path. Let's not keep that a secret, let's tell tell a story. And do you know these testimonials and highlight some of these cases, so that it becomes more more common knowledge? And it becomes more common then unique? And, you know, it's it's part part of culture, right?
Beatriz Rodriguez 24:53
Yeah. This one, there's one acknowledgement that I think we need to do, which is that we really need to be practical about AI. Because otherwise, it looks like a really great, you know, tagline a story like the flavor of the moment. In reality, we need to acknowledge that there is a great deal of variability in the knowledge that across the organization everyone has related to HR and what truly a skills man. So how many times you get the question, what is the difference between a skill and competence, what is the difference between capabilities is now a skill. So, at the end, there is a level setting that we really need to be aware and making it so practical, relevant in terms of the language that we use, or otherwise as experts. And when you bring in your expertise, it really could the gap in understanding and resonance with the, with your employees, it is really is really big. So I do think that there is a there is a ground level work that has to be done prior to bring the entire story which is translated in a way and knowledge base every as much as you can in the organization, so that you bring people in the journey, otherwise, you run the risk of losing a great compelling story, just because it's something really new when you are not there today into AI. So
Christopher Lind 26:19
to your point, Beatrice, and I think this is something else that we need to be really mindful of and is concerning, in some ways, is you hit on something that AI can accelerate and exacerbate destruction just as quickly as it can accelerate and exacerbate really good things. And so if you're not intentional about this, yeah, you're gonna make a much bigger, more expensive mess, a heck of a lot faster than you've ever been able to do in your career. And so that thoughtfulness and that wisdom and that discernment for how and what you're hoping to achieve is critical, because it can get ugly, quick. Yeah.
Chris Rainey 26:54
Well, that kind
of leads us on to the next question, which is, what are the challenges that we think organizations are going to face and the hurdles they're going to have to overcome? You mentioned one there, right? It's going to just as fast as it is going to help you succeed, it could go the opposite direction, just as fast like you described there. What are you? Tim actually gonna go back for a second? Tim, someone asked earlier, I just saw it in the chat. You mentioned the free areas and the areas that you've chose to invest in with AI to get significant gain. What was that sort of up was like, What did you in a team decide? This is the application, we're gonna go for a few people. I
Tim Young 27:31
mean, for me, the foundation and the way that you get the I'd say the cost down, is really around digital assistants, right? Gen AI is so well equipped for taking organizational data, maybe some external data, and being able to answer questions, it's, it's there, it can connect to other systems to transact for you. So if you can build an amazing digital assistant, that's great, you get a lot of value immediately from that. But then, for me, that's just a great starting point for even further advancement for Talent Development, for manager training, for supporting learning. I mean, I think if you can really start with that, whatever, you're that bulkhead, you're going to, you're going to land there, and you're going to expand, making the right choice for that initial point into the conversation we were just having. If you just take the approach of, you know, shiny object of the year, we're gonna go after the next thing that comes out, and everyone's talking about, I think you're gonna find yourself with a lot of tech debt, I think you're gonna find yourself with adoption problems, and soon your spending will go out of control. So anyway, I think it is just a very sound strategy is to start off with something that you know, you're going to great gain value from Sure. But
Beatriz Rodriguez 28:46
I would I will probably add on sort of before you got we put your shiny object there, right, on your shiny platform, I would say there's sort of three things that are really critical for you to be able to meet expectations. One is data quality. So if the data quality at the very beginning is not right, probably very, you're not going to get a good experience as you go through it. So, you then pass into number two, which is working on your SQL taxonomy, you working on the architecture, and is is the is the non sexy part of putting a schema or bringing a skill based organization and bring in for example, things like Italian marketplace, in, in your in your organization, when you do need to do to go through it, which requires a huge amount of work. That is probably the work that is really, really difficult for the organization to dedicate time because all they want is to kind of see the result. And I think the third thing that really was important for us is this very beginning user experience on the onboarding side of it, onboarding into a talent marketplace on boarded into platform, whatever you call it, that onboarding really drives, either you are going to engage twice or three times into the, into the system. So that onboarding is critical for you to be able to be in the success, you know, ongoing, ongoing use of, of, of AI in any any application related to HR in this case, you know, when you think about tightening marketplaces, and so on. So those are the three things that I would say, for us, we're very critical to focus on, so that the experience ultimately made sense. And it was relevant for for our employees getting into, into our system,
Christopher Lind 30:35
or just the part I would add on this is, you know, with the speed and the capability of AI, it can give this impression that we can do everything, and we can do it all so much faster. Yeah. And that's just not true. There are certain things you will gain some efficiencies, there are some things that you can squeeze out of it. But ultimately, the best practices we've always relied on for change management, and how much things can you really accomplish as an organization? Those still ring true. And I think that's one of the risks of going back to what I said at the very beginning. You can literally apply this to anything. So the temptation is, well, let's apply it to everything, and do it super fast. And that is a recipe for disaster. So the slowing down and going, where are we going to focus? How are we setting realistic expectations on this? Because it's still going to take time, if we want to see this be successful? And then saying no to really good ideas. I can't tell you how many times teams or the organization comes to me and goes, Hey, couldn't we do this? And I go, in theory, yes. But if we look at our priorities is committing time and resources to solving this problem, actually, critically important to us. Well, no, but with AI, we could just do it. And it's like, it's not a it's not a good decision. We just need to say no. And that's really hard. When these tools do offer something that does seem like I mean, it's really tempting to go well, I guess we could, and you gotta go. No, it doesn't make sense right now. We'll get there. But not right now.
Jason Cerrato 32:03
Then Chris, like, like Beatrice, I have a list of three, I'd be happy to share. Yeah, I'm sorry. So in terms of challenges and solutions around rolling things out, you know, three common things that I see occur. One is utilizing AI is different from some other tools we've used in the HR space. And a lot of people, you know, Beatrice rightly pointed out the importance of data. And you know, a lot of this conversation goes down the trail of skills. But a lot of people get stuck there. And one of the mindset that people have to wrap their head around is, when you're using a tool like AI, which becomes continuous and dynamic, you need to shift to this concept of data governance versus data management. And if you spend all of your time mapping and tagging, and kind of steering the data, you're not allowing the AI to do what it's designed to do. So you need to clean it and make sure that it's set up to start. But at a certain point, the AI needs to operate to surface insights and surface recommendations. Otherwise, if you're mapping and tagging and steering the data, you're going to drive it to a conclusion that you were kind of pre determining versus allowing the AI to surface insights based off of things that you may not have discovered otherwise. So it's this shift from what we've kind of done in the past with managing data, versus allowing the technology to surface insights and recommendations and now governing data. So that's one, I think another is as organizations are trying to roll this out very carefully, which is appropriate. They also need to think about what is the problem they're actually trying to solve? Because one of the things that I often end up discussing with organizations is this battle between managing the project and managing the problem. And sometimes they're more concerned with managing the project than solving the problem. So for example, they're trying to focus on increasing internal mobility, but they want to roll out a pilot, and they want to roll it out only within a certain department. Well, if you're going to roll it out only within a certain department, you're not going to achieve mobility. So part of this is you need to figure out what exactly is the problem you're solving, and what's the right course of action to get there in a managed way. Because another approach to do that is you can go broader with scale, but rollout functionality in a scaled way. So there's other ways to address this, but it's this balance of, you know, trying to manage the pain object versus managing the problem. And then the third one is, it's more than a tool, as we've talked about. It's a tool, its process, its practice, its culture, its leadership, it's also policy. So I've been working with a lot of organizations that say, in order to do this, right, everything has to be on the table, right? And you can't just optimize the way you've always done things, you really have to start redesigning a new way of doing things. And you can't go into the future with policies that were designed in 1990. Right. So all of it has to be up for discussion. And you know, a lot of organizations are saying, you know, if we're really going to try to do this a different way, we can kind of do it a new way, with policies that were built for how we did it before. And all of it needs to be available to be discussed. So I think those were my three that people are running into, especially in the early stages of doing this.
Tim Young 35:56
Can I say something that might be a little controversial here, just for fun, I actually agree with everything that's been said. And I think it's being spoken by some people who've been doing this for a while, which is fantastic, great wisdom, I think the point I'd like to make is, you've got to start somewhere. And I think just sitting on the outside watching this train moving, it's just going to pick up speed, and you just need to enter somewhere. So not saying don't be methodical, I'm not saying don't think about the bigger picture. But you're going to learn a whole lot, the minute you commit to an idea, and go after it. And I think in the spirit of agility, you can always retract it. But I would say a lot of us have been thinking about it been wondering about it been attending things like this, to learn more about it. But I think ultimately, you've got to just start somewhere. And once you're on that train, you're gonna learn a whole lot about what you should do. But you don't know that yet. So get on the train.
Christopher Lind 36:56
I don't think that's I don't think that's controversial at all, at all. I think I think what it requires is an objective analysis of where you and your organization are, because I've been in organizations who are, have a healthy appetite for risk. And they're like, let's just try everything. And as a result, they don't accomplish anything. I've been in other organizations, where, oh, this one field in this Excel sheet is off, we got to hold the whole thing up until we perfect this and you're like, well, you're never gonna get anywhere. And so you really have to do that healthy assessment of where are we as an organization? And to your point, where do we find easy, simple ways to talk about where we can go? And so I think, yeah, we got to balance that, especially because HR sometimes has a reputation for analysis paralysis, where it's like, that's great. You guys have been talking about AI for the last four years. We're on it, like, are you going to do something with it?
I didn't know we work for the same company.
Chris Rainey 37:54
Yeah, it's an interesting one, right? Because you see the people that are succeeding, and not that competitive, aren't willing to take that risk and be brave, be bold. Many HR leaders I'm speaking to are leading the way in their organizations with AI, which I never thought I'd say that out loud. As well, and their willingness to be by but I'd like to your point that just just to jump in. And there is part of that at some point, you need to make a decision you can't like someone asked me in sharing recently about our business plan for our own AI co pilot that we built. And we like we just started building it. And we and we executed on it. And we learned along the way. And then we made pivots if we just sat there around trying to build this perfect plan and, and would be two years behind where we are right now. So at some point, you just need to
Christopher Lind 38:43
take point, Chris, the thing you hit on, though, is that willingness to recognize when it's failing and throwing it in the trash instead of going, well, we've invested this is a project, we've identified this as a goal, we got to see it through and just going, don't we tried it, it's this is not working for us. We got to we got to shift and sometimes. I mean, I've been with a company that you're like this is going to tank us and they're like, Well, I know, but we put it in our goals. And it's like that kind of attitude will get you into some real trouble.
Chris Rainey 39:12
I literally spoke to a searcher today who is invested a lot into a specific tool and has told me no one's using it. And it's still gonna continue. And I was like what? Like, because it's so invested into it, right? It's and they've gone through, you know, a couple of years of integration, and God has done and I'm like, no one's using it. No one likes it. Like, I don't even use it personally. And they're like, they're just going to continue and I hear that a lot more than you'd realize, as well. So there needs to be a point where you know when to cut your losses, and pivot.
Jason Cerrato 39:46
But to think about the thing about these processes in these tools is they become continuous and the tools are dynamic. So even if you did the pre work to get things perfect. As soon as you started they will change and evolve. Like anything And yeah, yeah. So the secret is to get started. Yeah.
Chris Rainey 40:02
100%? Yeah. All right. Before I feel like we could keep talking to so much more to discover, but I have to let you go at some point. So, before I let each of you go, planning piece of advice was spoken about a lot. And then also, where can people connect with each of you if they want to reach out and say, hi, Christopher, put 21st. So there we go. The best parting piece of advice for the audience? Yeah,
Christopher Lind 40:27
you know, I guess my parting piece of advice would be, look for those put some thought into looking for these opportunities where you can go and experiment. And if it fails, it's a low risk, but it has easy transferable applications. So I talked about this on my podcast this week that Google did it brilliantly, when they were looking at their large language models with using it to identify cats and YouTube videos. Sounds stupid, but it was easy to do. And nobody really thought about and if it failed, so what nobody got hurt in the mix. But when they got it, right, the transferability of that application was massive. And so once they perfected it, they could scale and expand it like nothing else. And I think those are the really, those are the diamonds in the rough, we need to be watching for not that you don't do anything but that but if you can find those, that's where put it put, you know, throw some nice in the tank and go after those. Not
Chris Rainey 41:23
that I love the fact that someone a whole team dedicated to trying to find cats and videos is just funny, it's up to him.
Tim Young 41:33
Find cats right. Now, my My piece of advice is actually around partnerships, I think that within HR, we, you know, will do a great job we will in finding the right applications. I don't think it's too soon, though, to start to engage across your ecosystem and the company to find other organizations who are trying to solve similar problems, certainly with it, making sure that they're aware of what you're trying to do, because sometimes they can sink it because of lack of understanding, or they're not ready from a skill standpoint to support your your vision. And just, you know, like start early with talking to people across the enterprise, to make sure that they're ready for you. And that, like I said earlier, you may be able to partner in order to you know, to get the funding to get the buy in whatever you need. So just be very, you know, partner oriented, I'd say in your in your quest, not
Beatriz Rodriguez 42:37
that next. Yeah, I would say as you as we're bringing this to life, be super intentional, what it is and what is not. Because the spectrum is really big. The interpretation of what AI can solve either in a talent management space or in any other application or process of HR that you want to implement AI is very easy to assume that these will solve for everything, and anything, and everyone has actually a very different view of what is that everything and anything. So one of the things that we did, it was very intentional to say this is this is what it is, and this is what it's not. So that so that then you you are able to bring people in the journey and meet expectations in that journey. You also picture this is what is not today. And this is what it will be is not today, but it will be in the next three months and in the next six months. So that is a little bit of the the success for us to make sure that we were meeting expectations and that under that this was no another, you know, another sort of HR HR initiative that we were launching without really thinking through the business outcome. And what this is for the business
Chris Rainey 43:50
didn't leave much for you, Jason, I'll say this.
Jason Cerrato 43:55
Not all tools are the same. And not all AI works the same way. So as you're considering deploying tools, especially in your first year, understand what it is you're using and understand how it works and reevaluate your metrics. Because in some cases, your new process may actually flip your metrics on their head. And sometimes what you're looking at in terms of was this successful, may actually be a success, but they're not playing out in your metrics the same way if you're using your existing metrics. So it's really important to understand how the technology works to understand how to properly evaluate it. Because in many cases, it's designed to achieve things in a very different way. So just understand that in many you can't necessarily take the measures you used in the previous world forward into the new way of work, because sometimes success looks different. Oh,
Chris Rainey 44:57
very interesting one and I'm seeing The companies do exactly what you just described is taking the old metrics and trying to measure it. Yeah. It's very interesting. Well, I think that's a wrap everyone. Thanks so much for the amazing discussion, to all of the panelists for joining us from all over the world. And to all of you that joined us as well. I know you had a million questions in the chat that we never got sick. Thanks so much, everyone, for joining. Obviously, a special thank you to our friends at eight fold for helping us bring this panel together. We're back again on May the 30th for the next panel. So make sure you stay tuned for that we're talking about how AI is shaping the future of skills based talent management. good segue from this one, as well. Apart from that, hit the green follow button if you want to get access on demand recording and we'll see you again soon. But thanks so much, everyone. Appreciate it. We'll see you again soon. Bye for now.
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Victoria Klug, HR Director Eastern Europe at Beiersdorf.