HRE Moderator 0:03
Thanks for joining “AI advantage: Strategies from high-performing Pacesetter organizations.” I will pass the stage to Kathi Enderes, PhD, Senior Vice President, Research and Global Industry Analyst, at The Josh Bersin Company.
Kathi Enderes 0:54
I’m super excited for this webinar, and we have so much to cover. But before we jump in, let’s do just like a one second introduction, Stella, you want to go first, and then I’ll go and then we’ll, we’ll kick it off.
Stella Ioannidou 1:12
Hello, everyone. This is Stella Ioannidou. I have partnered closely with Kathi on this groundbreaking research that we’re going to be presenting to you today. So looking forward to sharing with you what are the six secrets of high performing organizations.
Kathi Enderes 1:38
Love it. And I’m Kathi Enderes, SVP, Research and Global Industry Analyst, and I live in sunny California, so that’s where I’m calling from. So we’re very international presenters today around the world. And yeah, I’ve had the pleasure of working together with Stella on doing this research, which we’ll share all about but before we do that, let me just set very quickly the context on why we’re looking at these Pacesetter organizations. Then explain to you what these pay set organizations are, and then we’re going to dive into these, what these Pacesetters are doing differently in the AI agent. So, so what we’re seeing in the market, just taking a little step back, won’t take too much time on that. What we’re seeing in the labor market, in the business environment, and why these pay set is so important, so labor market very challenging around the world, many companies experience kind of an imbalanced or dislocated labor market, where they see really big shortages in some areas and some jobs, healthcare, transportation, generally, frontline workers, hospitality, retail workers, manufacturing, but then also uncertainty and kind of an imbalance in kind of the white collar jobs, maybe the marketing jobs, HR, finance, sometimes technology as well. So we see this kind of very challenging labor market for us in HR and in talent. The second area has to do with businesses. So every business is transforming and reshaping and merging with other industries, and Stella is going to actually share more of what we see in our research there. But every business is also becoming much more agile, much more multifaceted and much more interconnected as well. The third area has to do with AI. And I know we’re going to talk about the AI take off Pacesetters of these most successful organizations, specifically. But every organization, of course, is thinking about how they integrate AI, not just into HR, but then into every job, into every work process, and reshape organizational structures, reshape their models in order to create better solutions and better value for the organization. And the last area has to do with the employees themselves. Every employee is very stressed, and we see that employee engagement is at an all time low around the world. Employees are thinking about AI. They’re worried about AI taking their job. They’re worried about not being ready for AI. They’re thinking about how to build their career. So for us in HR and in talent, it means we need to support employees to build these stronger careers, these more enduring careers, and help them foster and build their skills for the future as well. So that’s all going on at the same time, CEOs are actually looking for creating not just cost savings from AI, but they are investing really heavily into creating value and new revenue sources with AI, and this comes from the PwC CEO survey, over 4,000 CEOs around the world, so 92% of investing really heavily on that, but only 7% actually see real value from AI. So that’s really big, this big imbalance of investment versus value. And we nature can actually help CEOs and our organization to not just get cost savings from AI, but really get to that value creation, to that higher revenue, that to that higher organizational performance. And the way to do this is how we phrase this as having helped every employee to become what we call a superworker. And a superworker, very powerful concept where we are saying, AI transformation is not really a tech transformation. It’s really a people and culture transformation. It’s all about people. It’s all about building that capability in every employee to use AI for more meaningful work, better performance, better contribution, better careers, and really empowering every employee. And that means as organizations, we have to build superworker organizations, those really dynamic, change agile organizations that help every employee around all job categories to utilize AI to create a kind of better performance and also to experiment with AI. Very agile, very dynamic, very change forward way of thinking about organization, and we’ll share more about how these pacesetter organizations do that from a skills perspective. Actually, the AI transformation means that skills are changing really, really rapidly. So you see here that, on average, 1/3 of skills have changed in the last three years, but the most changing jobs, that top changing jobs, actually have seen three quarters of their skills change in the last three years. So it means that skills change rapidly over time, and also the pace of skill change is accelerating, because if you think about, for example, AI skills. It used to be when generating API came first on board. Everybody was talking about prompt engineering, right? Everybody was talking about that skill. Well, this is a skill that’s actually not so important anymore, because prompt, the AI systems, the generative AI, the AI chat bots, for example, they are now asking you the questions. So it’s no longer so important how you prompt them, but it’s more important just to answer the questions and get really clear on the problem you’re trying to solve. And that happens in every area, of course, AI readiness, but then also in every functional skills and technical skills and industry skills. So that means actually, we need to, as an organization, be much more agile in terms of the skills that we build as well. Stella, you want to talk a little bit about the AI transformation that we’re seeing here and how we define the Pacesetters.
Stella Ioannidou 8:10
Yeah, I know that everyone in their uncle is talking about AI nowadays, right? And we’re talking about, how are we bringing that into the business? What is the impact on the business? AI helps us solve, say, hairy, messy problems in the world of work.
Speaker 1 8:32
Or is it just another high that you’re seeing? There is a big impact of artificial intelligence throughout the global economy.
Kathi Enderes 9:45
Basically the agentic AI is really a big kind of evolution where we used to have the generative AI, which was, of course, very helpful and is still very helpful for us on an individual basis. Now these AI agents actually can connect and do transactions and connect with each other as well, almost serving like a person, but I think we need to be careful also not to personalize that.
Stella Ioannidou 10:52
So overall, I would say, what pretty much sums up the impact of AI in the world of work right now is that it’s not the linear thing that you believe that it’s going to impact the business in one specific way. It’s impacting the whole array of business operations. And this is a quite interesting angle to, let’s say, acknowledge and understand when we’re talking about high performing organizations, or pacesetters, as we call them, because and by the way, we’ve been studying this, and I’m surprised, I mean, we were chatting backstage with Kathi. It’s almost four years that we’ve been actively studying this area of trying to understand what really sets apart those organizations who are achieving not just their their business goals, but they’re achieving in multiple different areas, and they’re over performing, and they’re able to not just be financially successful and operationally, let’s call it sane, but also talent leaders and innovative organizations. And it comes to our work at the global workforce intelligence project that we have been lucky enough to be partnering with the eight fold team and double click and dive into their talent intelligence platform, where they have billions of data points from worker profiles and skills and roles, and we’re able to see how these tie back to successful organizations. And if there’s, let’s say two things that we’re seeing by studying industry per industry. And we’ve done quite a few of those industry deep studies already, and Kathi is going to briefly present some of them in a bit. But what we are seeing is that it becomes harder and harder for us analysts and people in the business to differentiate industries and verticals. If you used to believe that, hey, I’m part of this industry, let’s say a part of healthcare. And it’s very specific. What I’m doing. We’re doing nursing. We’re caring about the physicians, about our facilities, about, you know, research, but more and more new considerations are coming. So healthcare, for example, is more into telemedicine. They’re worried about cybersecurity constraints, they are worried about their business operations and so on and so forth. And we’re seeing this industry conversions across the board. We’ve studied consumer packaged goods, biotech, banking, insurance, and what really sets apart organizations, who we identify as pacesetters, is not just their financial leadership, like they’re growing immensely, their revenue is growing. They’re very profitable and all that, but it’s also in conjunction, in combination, with their talent leadership, understanding that they are amazing places to work, and that employees have great experiences, that they acknowledge their experience there, but also they’re recognized as leaders in their area, in their industry, in their vertical and across the board, and they’re achieving high maturity in at least one of our systemic HR practices, which means that they ident there. They understand how not just to work in silo HR, let’s say modes, but understand what the underlying issue and value of HR is. And work has all areas of HR work closely with one another. But if there’s one thing that Pacesetters are great at identifying is that this landscape that we once considered is static, that it’s changing, it’s converging, and they need to move fast to adopt new ways. Of working and adapting to these conditions. Kathi, would you like to guide us through some of the pace that organizations and what we’ve what we’ve seen per industry, and what are the the main themes that we’ve seen emerge per area?
Kathi Enderes 15:17
Yeah, for sure. So one of the themes. And we have lots of logos here, and we’ll actually share specific case studies and examples throughout, when we talk about how these Pacesetters approached what we call the Super worker area, how they approach AI transformation. But I think the common theme is that around the world, they are not. They’re well known organizations. But there are also organizations that you might not think about, and they are across different industries as well, and they’re solving different business problems. And when we started this, this global workforce intelligence project, as Stella said, we actually didn’t think about AI transformation all that much because it was before even generating an API was a thing, because it wasn’t available then. But what we identified they have actually a lot in common as well. So you see all of these organizations, and again, we’ll share some of the details on how they perform, how they exceed the expectations of all others, and how they lead the industry. But we wanted to know basically, what are they, what are they doing, and how are they performing differently, and how are they solving problems in this AI transformation age. So that’s kind of the big question that we asked. So this was really kind of the first of its kind study, because we looked longitudinally over four years, billions of data points six deep dive industries, and what we found was actually not just applicable for these six industries, but it was really applicable for any industry, for any leader in any organization, as you think about AI transformation. So let me talk a little bit about what we’re seeing here. And these are the six studies that we have done based on the eight fold data where we’re saying they each have different different kinds of problems in each of these industries, and how they approach the key of business problems in a pace set away is different too. So as we go through that, just give you a little glimpse into that, and you can read much more about it. But the automotive industry, automotive manufacturing, which is actually a study that we just completed about, kind of maybe last fall, maybe half a year ago or so, maybe, well, almost a year ago, I guess, where they their big business problem was to transition to electric vehicles and create more integrated offerings for their clients. And how they do this is they moved away from just using manufacturing roles and manufacturing skills. They automated all of that, and they really focus much more on software robotics, AI, roles and skills. And so they pivoted really quickly from these kinds of legacy roles, of manufacturing roles, to these kinds of software engineering AI robotics kind of rules. So a pivot to different different skill sets, and really quick, quick pivot to these different skill sets consumer banking was a different story. But actually the skills transformation was a similar one. So they said, especially after the pandemic, when we all did our banking online, their customers said, well, we need to have more digital products because our customers are looking for that. Our consumers are looking for that. And so they had to pivot really quickly to technology skills, because they still have these kinds of old fashioned technology skills like COBOL or something like that, to much more agile methodologies, much more technology forward, like basically next generation technology skills and roles. So again, a pivot from what consumer banking used to have pretty quickly to emerging roles and changing all the time. So you see this kind of changing and pivoting really quickly and adjusting to what you need for your company, consumer packaged goods. Another story here, where they are seeing new market entrants, smaller companies and changing consumer preferences. So they had to focus not just on producing these consumer packaged goods, but kind of pivot on the skill side for R&D skills, product innovation skills, and also kind of sometimes cut out the retailer and go direct to consumer so very different consumer experience. Skills as well that they needed here. So that’s what these pacesetter consumer packaged goods. Can you state healthcare has this huge clinical shortage clinical talent roles, and every time we look at kind of open jobs in the US, for example, nurses or RNs is always the top of the list, like 800,000 open roles or something like that all the time, and it’s not getting better, it’s getting worse and worse. So they have to think about how they redesign the work of the nurses so they actually need less clinical people, how they use AI integrated into the work processes in order to reduce the workload and help them to perform what we call top of license. But then they also had to reskill and upskill and hire for new skills, kind of digital skills, health innovation skills, patient care and patient satisfaction skills, patient experience skills, AI skills. So as you see again, this pivot, this moving to different skills insurance, and that’s a study that Stella just completed, actually a couple of weeks ago, a few weeks ago, where they are seeing more natural disasters, more need to kind of predict what’s coming down the pike. So they have to have much better risk prediction skills and also help their clients with much more digital offerings, digital products. So again, from a skills perspective, you see less of these kinds of old fashioned insurance skills that they may need before the next generation, maybe analytical skills, maybe AI skills, maybe digital skills. And last but not least, pharmaceuticals and pharma and biotech, they are always at the race, of course, to develop drugs and treatments really quickly and differently, and they see that they have to do R&D and innovation, not just in their R&D group, but across the entire organization, to also have different skills in their R&D group. So they’re not just kind of drug development people, but they’re really also thinking about new treatments, for example, for things like ADHD, where they’re maybe using video games to treat that. So thinking less about just pharmaceutical treatment to overall treatment, and that approach to health, not just kind of giving people drugs and developing those drugs. So all of these had, of course, different problems, and it always takes a while to distill these problems, and these are different for each of these industries, but what they all had in common for their kind of their approach to skills and to approach these problems is what we call skills velocity. So they’re really quickly, as I talked through that, really quickly, pivoting identifying what skills they need, what new skills they need, letting go of the old skills that they maybe have too much of. And then they, of course, recruit and retain people and develop these new skills at speed, at scale across the organization, but then they also redesign the work to leverage these new skills and use AI, of course, and that’s in the AI transformation that kind of skills velocity is really, really critical across all of these industries. So how quickly, not just what depth of skills you have, and from an individual perspective, is not just about how many skills you have, but how quickly you can pivot, how much quickly you can reinvent yourself and see what skills might be needed in the future. And we as an organization, of course, have to support our employees to do that. Reinvention is really critical in this AI transformation. So how do they do this? In this AI transformation, we identified these six secrets, and we’re going to go through each of them in more detail. So these are the six secrets that we identified, again, based on data, based on all these data sets, and based on all the skills and roles inside that we had from each of these industries. So the first one is about using AI, not just for cost control, for kind of efficiency, cost reduction, but for organizational growth and value creation, the same way that we just talked about the CEOs, what the CEOs are looking for. We need to support that within our organization to not just see, how can we replace tasks, or how do we just speed up tasks? That’s certainly part of it. But how can we create new products, new value, new revenue, with AI? So that’s the first thing. The second thing has to do with always innovating at the core of the organization, innovation, usually and a lot of organization, it’s something that you leave to an innovation team, to an R D team, to kind of a tech team, but with AI, because it impacts every single person, every single job, we need to support innovation as at the front line, at every single for every single employee. Uh. For every single team to basically see, how can we innovate as a core capability of our organizations? And Pacesetters do this really well. Third one has to do with how we think about the work itself. So we call this productivity based work redesign, where we’re basically putting AI into the team and then rethinking what the jobs, what their work looks like. We mentioned, for example, the clinical jobs in healthcare, where the nurses job now looks very different because they have, for example, maybe ambient listening to patient, patient conversations, and then they transcribe that with AI really quickly. So we need to redesign the jobs how they look, because the nurse is not doing that, and that happens in every single job and in every single pay set organization, to reinvent how the jobs look like. Fourth one has to do with how we bring in complementary skills, talent density. So basically, not saying when we want to grow the organization, we just need to add more people. But how do we kind of think about the skills of every single person as complementary to the skills that we already have in the organization? We call that talent density. Stella is going to talk about that in a minute as well. And then we see a different approach to change altogether, because change management, this old concept where you say we’re just going, like getting ready for a change, and we’re just building up to the change, and then we communicate and we train people, and we do the stakeholder management, and all of those kind of legacy approaches to change management doesn’t work anymore for AI, because change is constant, and the AI is transforming all the time. So we see this move from managing change to change agility across the organization. And the last area has to do with what we call systemic HR, how they manage the HR organization and all that talent processes, not just in silos, but connect them across all the different functions and power them with AI. So those are the six, and now we’re going to dive much deeper. And we have, I think, probably like, 25 or 30 case examples that we’re going to touch on. But you can also see the report and read the report as well and get all these examples. So Stella, take us away on secret number one.
Stella Ioannidou 27:21
It’s amazing how many companies at the pace that are level are applying each and every one of these secrets, right? And you’ll see that there is no one way to understand and apply, but there are these common threads. So starting off with AI transformation, as Kathi kicked us off, she said, hey, it’s not about optimization. It’s not about where we are, what do we automate to cut costs? It’s more about how do we strategically incorporate AI, even at the key level, even at the simplest momentum, just to make sure that we’re supporting business growth, introduce new program products, and make sure that our customers really love whatever it is we’re offering them. And we’re seeing it across the board, across industries. We’re seeing they’ve been biotech, where moderna currently is investing major amounts, almost $100 million in digital technology and AI skills through their capability Academy. But we’re also seeing other organizations make sure that AI supports work, let’s say the location of talent into work that’s meaningful for them. For example, DBS and Asia based Bank Financial Service Organization is actually creating their has actually created their own internal AI powered talent marketplace where they are making sure that everyone in your organization is mapped and matched to opportunities that they’re really passionate about whether it’s a job, whether it’s a training opportunity or a mentorship opportunity, they have a 40% or zero out of their internal or fill rate. We’re seeing other organizations like Tesla from the automotive industry, supercharging their vehicle automation pipelines. But what we’re really seeing around, let’s say the AI transformation narrative, is the focus on the skills that allow them to have that competitive advantage. AI is not about bringing external help and doing a project, about modernizing some part of the organization. It’s more about nurturing and growing internally, the skills that are going to be supporting our AI and organizational design transformation, and we’ve seen that specific, let’s say, point of view around the AI related skills and the digital skills across the board. Or across industries that are not necessarily tech driven or so well digitally related, we just see that talent and skills element to be super prevalent. What we are seeing is a big focus on not just having the right skills and bringing the right tag, but also around this go into rethinking the overall experience. It’s not only about completing actions faster with AI. It is part. It’s the first layer you see this. But pacesetters go beyond that. They say, You know what? Let’s make work smoother and easier for our people and for our customers, and then make sure that we’re accomplishing the right outcomes with AI and we’re actually impacting performance for every employee, to make sure that the AI tools or the processes or the let’s say, touch ups that we’re providing are actually empowering their productivity. We’re seeing, for example, organizations, let’s say mercy, health, who are they’ve actually created their whole platform that we call it, like Uber rising nurses, where nurses were able to select what shifts they want to work on, on demand that really supercharged their overall engagement, because they were able to select the shifts that work for them, rather than have them assigned on shifts and then have to make ends meet to accommodate them. We’re seeing companies like L’Oreal actually becoming transitioning in the last five to 10 years into, let’s say beauty technology companies with AI power diagnostic and digital skin tools, and me going into their website and figuring out that, let’s say dark brown is a nice color for for my hair, but what if I tried dark red? So we’re seeing so much work, not only on the client end, but also on the employee end, but also on the candidate and Nestle, for example, has an AI, let’s say assistant, that is able to address candidate questions, 24/7, real time, and there’s a lot of examples. I’m pretty sure you understand that we can cover everyone in detail, but you’re seeing the common thread. It’s not AI for AI. It’s not AI because it’s a hype. It’s AI to make sure that we’re supercharging the organization’s productivity, and we’re driving business growth now. All this is not just, let’s say, a nice thing to do, because we want to supercharge our operations and make things faster and smoother for for us as an organization, or for our employees and for our clients, we’re seeing pace at organizations becoming, I want to say, like brackets in within brackets, like obsessed with innovating at the core. Are we doing the best that we can to reinvent ourselves at any level, the frontline level, the operations level, the backhand and the technology level, to make sure that we are not just innovating in a product or in a specific operational line, but actually, are we thinking through the talent and how they’re working? Are we embedding innovation throughout all skills and all roles across the board? For example, buyers have pharmaceutical companies actually including innovation capabilities and skills throughout their job, families and for all the roles, are we prototyping faster? Are we incorporating seamlessly the feedback of our customers? Are we reorganizing continuously around the work that needs to be done, and not necessarily what the job hierarchy and the family and the job family, let’s say, tells us to do. This is what Toyota is doing, for example, and we’re seeing a whole array of innovating the business capabilities. Can we make sure that we are rethinking not just internally, but how we service our communities, how we serve our customers, how we offer complete or holistic or integrated, or whatever that means for each of the industries, right, a service or a product to our clients, for example. And we’re seeing across the board that if there is one thing that Pacesetters are, let’s say it’s top of mind for them, and they’re not letting that go. Is thinking about innovating at the. Process level, innovation at the capability and the skills level, and innovating at the technology level. So it’s a big part of the whole business transformation around Are we really rethinking and pushing the boundaries of what’s possible on on the third one, I’m personally, let’s say I love this specific one, because it is around the whole idea of making sure that we don’t just have AI digitized, whatever it is we were working on, but actually rethinking the whole array of how are we working and where are the biggest opportunities for us to integrate artificial intelligence? And artificial intelligence is one of the tools. It’s not an end in sight. We don’t bring AI for AI sake, but pacesetters, what they understand very, very well is that we need to redesign the whole approach of what people are working on, what are the roles that are? What do we think about productivity? What do we think about meaningful work? What are the processes and how do we make sure that we’re enabling new ways of working for everyone across the organization? Ing is one of the leading examples of that when they have identified them and designed their one agile way of working. They have organized the way that they’re working through cross functional squads, and they are heavily invested in, invested in supporting everyone to use advanced technology there, they have a similar operating model, such as Spotify, for example. And we’re seeing pace that are not just, let’s say, not being very fast at incorporating things and technology and redesign work that they don’t understand what the impact is. So it’s not about having artificial Intel, bringing artificial intelligence into a role and just having, like, enabling someone into, let’s say, just doing the same thing that they were doing about 5% faster. It’s about how to do it, not about incremental impact. How do we redesign whatever it is that we’re doing currently, so that, when we put and include the AI element, it supercharges their productivity. It supercharges and fundamentally enables people to work at, As Kathi said at the beginning, top of their license. How do you maximize the talent, the impact of the specialized talent, for example, that you’re bringing in. Some organizations have some amazing stories around that top of license narrative Providence, a healthcare organization renowned for their complete redesign of clinical roles, where they realize that, hey, how about that huge nursing shortage, right that the entire healthcare industry is, let’s say, working to address, and we actually run the numbers. Kathi herself ran these numbers, as she saw that, hey, currently in the United States, there is a 2.1 million nursing gap. We don’t have enough nursing talent to fill the needs of the US healthcare system. What’s the biggest opportunity to try to, let’s say, hire as many nurses as they can, or as I call it, hire their way out of the problem? Well, you could try that, but many, let’s say, people, don’t want to become nurses anymore, so they don’t go to nursing school, or they don’t complete nursing school. And even if you do manage to recruit some of the nurses, it’s only, let’s say, a small percentage of the overall gap. We could try retaining some of the nursing talent that we already have, and this also, let’s say, solves for some portion of the gap. Or we could re skill nursing aides or other talent that, let’s say adjacent, in terms of their skills and their the work that they’re performing to support that. But the biggest opportunities are, hey, how about we completely redesigned the work so that we need less nurses, or we make sure that we maximize the impact of our nursing talent so that, hey, I want to hire a nurse. I would not like to have this nurse be in charge of scheduling and putting, let’s say, time sheets for other nurses. Let’s have an admin person do that, because that’s not a top of license activity for a trained, let’s say nurse. Talent, and this is a big part of the impact that is currently being, let’s say, supercharged when it comes to AI, because AI gives that ability to rethink and redesign more quickly, not just ideate around it, but also try it, try and test it. What is the impact? Does that work? Let’s try it again, and let’s say, bring that impact faster than we ever anticipated. It could happen, and pacesetters know that very, very well. The fourth one, I’m a little bit obsessed about it. We just finished a big study on introducing what talent density is. Me and my colleague, Nehal Nanya, did this report, and we studied organizations who understand that it’s not about bringing in as many people as you can and growing by over hiring. It’s about bringing in and strategically hiring the right skills, right talent, and making sure that it fits not just our current state, but also our future state, for example, not just the need of the specific university degree or whatever it is we have as a role in our in our mind today, but also how the market and the industry In our role and each role is going to be changing five years from now. Because, remember, in the beginning, we talked a little bit, we touched upon the industry convergence and how everything is changing so swiftly, we kind of need to move faster than ever before. So the Pacesetters understand that it’s about strategically hiring for the right skills at the right time, making sure that they don’t over emphasize the hiring part. It’s one part of the equation. If you remember the four brackets of the nursing talent analysis that Kathi pulled, it’s a part, it’s a quadrant, but it’s a part, not the whole picture. So how about internal mobility? And we have, for example, great, a great example from Bon Secours mercy, a healthcare organization. They and there, of course, there’s a lot of organizations, but we are, we have their story in the report about how they have designed career pathways and dedicated internal mobility programs so that everyone can find whatever it is they’re looking for, not even not just today, but even in the future. We have other examples, like Scotia Bank, a Canada based financial service organization. They completely dish out resumes. They said, You know what we’re done, let’s do a skills assessment. Because we really don’t care about your university degree and your university academic accolades, we care about whether you have the skills that they want? We want technical and interpersonal skills for you to be a good skills and cultural fit in the organization and other players in the financial services industry who get this is, for example, BNY Mellon, who have identified what we also saw as the skill adjacency. So what is a skill or a role that’s very close to one another, and how do we move people into next gen, let’s say technology roles, through identifying adjacent skills, rather than trying to hire those skills externally. Kathi, I could go on for hours and hours to double clicking on this, but I think I’ll pass you the baton to summarize the final two and drive us home.
Kathi Enderes 43:26
Thank you, Stella. And yeah, I know I already see we have a lot of great questions, so we’ll get to them. Hang on. And some of them might be, might be already included, but let’s, let’s talk about the next one, which is that the move from how they manage change, so that move from change management to what we call change agility, and that’s really a different way of thinking about change, and it’s the only way to do this in the AI transformation and the pace service do this really well, because they are realizing, and they are seeing that AI transformation is not a once and done thing. Of course, it changes all the time. Every day. I think we see this all the time too. Every day a new AI capability comes aboard, a new way of using AI tools in any kind of job comes along, and so it can’t be that you can’t treat it like a regular tech implementation. We’re saying we’re preparing everybody. It’s really top down, but we need to iterate. Give people the way to kind of experiment, to try things out, much more bottom up, much more kind of employee focused and employee driven, ongoing change. So that kind of change. Agility approach is really, really key, and what that means is you get a focus on kind of the skills and the roles to drive that change. So for example, such a health care organization here in the US, they’re using analytics to kind of understand where. The new patients have the highest need, and then prioritize where they send the patients that they’re using, AI Of course, for that as well, to really direct patient flows. And that means they’re very adaptive, basically. And changing agile leadership, of course, plays a huge part in changing agility, but in a different way, because in regular change management, you have the leader usually just as what we call it, a speaking horn, right? They’re just communicating down messages that they might get from kind of the upper, upper levels of management, where they’re just kind of sending out these messages, but really much more in a kind of human centered leadership approach like ing is doing in their agile approach. So they’re really empowering teams. Have self directed teams where the leader serves much more as a coach, as a supporter, as a mentor, rather than this top down leadership approach, and they can then support this cross functional team collaboration in their agile ways of working. So different ways of thinking about leadership and different ways of thinking about data, too. If you think about changing agility, it’s really important to know what’s coming next and really quickly adjust to that. And that’s where what we call systemic business analytics comes in. And that’s actually a study that Stella and I finished. Stella was leading it late last year, where we’re seeing the way that we’re using analytics, people analytics, kind of workforce analytics, can’t just be in a silo within your people data, but it has to connect with business data as well. So for example, Toyota is using data to drive kind of improvement, continuous improvement. They’re really known for their continuous improvement and their enabling approach. But they don’t just focus on their kind of employed workforce, but they focus on the entire workforce. As Stella said before too. They have 40% of their workforce contingent, and they don’t differentiate. They have the same kind of data level on the people side that then they connect with business data to drive that kind of continuous improvement. And that’s how they can be a pacesetter in electric and hybrid vehicles. I love my Toyota Prius. I always say that when we’re talking about Toyota, and they were the first out of the gate with the hybrid vehicle. And I think the reason for that is because they’re so people focused, and so data focused, as well, integrating business data and people data is really, really important for that as well Providence Health, another healthcare organization. They’re not just looking at the typical kind of survey data, turnover data, patient but on compensation data, but they integrated with patient care data to really see what’s driving patient care, patient outcomes, patient satisfaction, patient experience, because that’s what they’re really ultimately driving towards. It’s not just on the HR side of things, and that’s how they get it. So change ready and change agile. So just like being really data focused and thinking about data holistically and integrating when that gets us to our last area where we’re seeing the HR role, and I know we had some questions there, also, when does HR get involved in the AI transformation? We see that the Pacesetters get HR actually on board and being an integral part of the AI transformation right out of the gate, because you need to be there for AI upskilling, but then also for the bias mitigation, all of these kinds of ethical considerations as well. HR plays a really important role, as well, as with the change agility and, quite frankly, also experimenting and incorporating AI into the HR processes strategically as well. Some great examples here were the Pacesetters. First focus on building HR capabilities around the around, kind of all of these areas, but also HR capabilities in AI, in kind of technology, in workforce planning, in organization design, in change, agility, Bond, secure, Mercy is a great example of that another healthcare organization where they have career pathways and HR kind of capability building across all of these domains, all of these HR domains to really support kind of this problem oriented way of solving business problems with AI and HR needs to Do the same thing, of course, as well breaking down HR silos. So rather than thinking about each of the silos of learning and talent acquisition and employee experience and rewards and organization design, think about the problems holistically, and we call that systemic HR approach. And Unilever is a great example. They are breaking down these COE silos and barriers and working really all with the purpose of creating a great employee experience. And last but not least, on this side, using AI in HR, strategically, another auto manufacturer, actually an Indian auto manufacturer, Mahindra. And Mahindra, they’re using AI across their entire HR function to not just decrease cost and and increase efficiencies with the HR processes, but to drive better candidate experience, better employee experience. Get better candidates, get better performance of all of their people, a much different way of looking at EI and HR as well, like a much more integrated way, much bigger way. And the last area that they are actually that also factors into these, the systemic HR area is the expanding, bigger role of the CHRO and that goes to the question too, how does HR get involved in this? In the AI area, moderna, for example, has the CHRO now leading Tracy Franklin, she’s now leading the AI and digital team as well, in addition to the HR function. So we sometimes see this combination of HR now with the digital and the AI team. So of course, she’s leading the AI governance, because she has both the HR side as well as the IT side. We see this broadening of the CHRO, the chief HR officer role in other areas, to Standard Chartered Bank, for example, one of the Pace Setter banks have their their CHRO tanush, and not just leading HR, and she’s leading HR as well, but she leads strategy, business transformation, a technical transformation, and many of the other corporate functions, like communication and and and marketing. So like, she really has this end to end accountability, integrating HR into the business as well, and with AI kind of integrating all the different functions all together, this kind of expanding role of the HR CHRO becomes more and more important. Business capabilities in the CHRO become also much more important, the CHRO of L’Oreal. Stephanie Kramer, for example, she used to be the president of one of their major beauty lines at L’Oreal, and now she’s the CHRO, and she brings this business acumen that kind of really thinking about the business problems that we’re trying to solve with AI to the to the HR function as well. So some great examples and some great stories on how to do this. So I’ll wrap us up, and then we’ll get to the question, so what does it all mean for you? Well, first, if you download the report, and I know we have the link in the chat, and I think we also have it maybe as the related resources that you’ll get as well with the recording of this, of this webinar. The first thing that we started off with is focusing on this skill’s velocity, not just skill steps. So how quickly can your organization, how quickly can you enable your employees to change skills, to pivot to new skills, to create new skills, to learn new skills, and to understand what skills will be more important for the organization and for their career. So that kind of skills velocity really being key criteria. Then look at all these six strategies and see where you stand. And I’d be curious to see where you stand against these six strategies and use them to prioritize your AI transformation, and then always be rethinking your organizational strategy or talent strategies with AI, because AI is always changing, and so your organization needs to be changing all the time too. What this means for you, if you’re in HR, and I think most of you are probably in HR or talent or learning and development, talent acquisition, our role actually becomes more important than ever in the AI transformation, because AI is not just technical it’s not just a technical transformation. It’s really a people and culture transformation, and that’s what these pacesetters do. So your role becomes expanding beyond your functional area to think about becoming an economist, always looking outside becoming, of course, a transformation expert, a coach, a consultant, a psychologist, constantly learning, constantly advising the business as well. So it’s really important and really, I think, the most exciting time to be in HR to support all the AI transformation that every organization is going about. And with the pace center secret, we can, we can help you understand how to do this. So with that, let’s. Go to a few of these questions. I know we have five minutes, and I know some of you have put some of these questions in. So maybe we’ll start with Chris’s question. And Chris is asking, “have you seen any trends in where to push to fully utilize AI across the organization?” I can start with Stella. I’d love to get your thoughts on that too, but I see this as, yes, sometimes it’s a CEO push, but more often than not, it’s not just the CEO or the CTO that’s pushing it, but the role of HR, I think, is critically important too. And we hear from many of these pace organizations that HR is actually more in the driver’s seat than they ever have been, and they are kind of moving the needle forward. Some of them say we are going to start basically showing the organization how we can use AI in HR for value and kind of moving the organization around CEO support, of course, helps where the CEO is asking for that value creation, not just for the cost cutting. And that’s what we see in the first kind of pace cut, PACE set a secret, but Stella, anything you’d add?
Stella Ioannidou 56:23
I think one of the most important things we’re seeing with Pacesetter organizations is that there is no one specific, let’s say, driver for AI, because they have such a good echoing and sensing of the market. They understand that this is what we remember in the beginning, right? We spoke about the impact of AI in the market, like a 17, 16 trillion business. So they’re seeing this. They’re watching us at the executive level. They say, okay, what are we doing about it? How do we incorporate it? How do we bring as many voices as we can into the discovery and the evaluation of what that means for our organization. And in many times, As Kathi said, we see HR leading the chair the charge of understanding the impact and the implications, going hand by hand with legal and hand by hand with technology, and by, you know, hand in hand with risk, because you really can’t expect, let’s say, one area or one functional area, to to be leading it, I would say that especially in pace at our organizations, that was this would be more than an executive level, an executive, let’s say, level decision, rather than a project that an innovation unit is pushing throughout the organization.
Kathi Enderes 57:38
That’s innovation at all levels, right? I mean, we need to innovate, not just in the innovation team, but the second pace secret as well. Totally, totally agree with you, Stella. Another great question is, can organizations be successful in AI transformation if HR hasn’t evolved up that tier one consultant level. So like that tier, tier one. I know we had the maturity model. We didn’t talk too much about this, but kind of the transactional level of HR being more about transactional compliance. I feel I haven’t seen any of these examples where organizations have been successful in their AI transformation, because HR plays such a critical role. And if you’re just thinking about kind of transactional, transaction processes, payroll, compensation, kind of all of these paperwork, which, of course, you have to do, and have to do well to keep the organization out of trouble, otherwise, you can’t play in the higher levels. But if that’s all you’re working on in HR, who’s going to take care of the people, the culture, the skill development, that skills velocity that you have? So yeah, I think it’s critical. And we have actually seen in our systemic HR study that these level one organizations, these transactional compliance organizations, only 1% of them actually even have a strategy of AI and HR, for example. But 85% of a level four organization, like the most systemic, the most problem, or that most consultative organizations have that so certainly also for the AI and HR area, really important consideration to have that Stella another. I know we had lots of questions about which which other ones are we taking one minute, but I get this question a lot about the concerns about privacy and security when it comes to AI, but I do want to make sure that we’re flagging this for everyone to understand that AI is not a panacea.
Stella Ioannidou 59:28
We’re not all of us, and we’re not like suddenly AI gurus, and that’s why I said that, especially when it comes to HR professionals, we need to go hand in hand with our legal team and our technology team and kind of understand what it is that we’re is actually at stake, and how. We design the governance framework and align the processes and make sure that we understand what type of risks we are taking before we start implementing things. And the market, there’s a lot of, you know, vibes and discussions in the market right now, even at the vendor level. So when in doubt, you know, go to the tech people, if the tech people don’t know include the legal and the risk people in and CO, create a solution that works for your organization. There is no one easy solution to fit everyone.
Kathi Enderes 1:00:33
I totally agree with that. Thank you very much. I know we had tons of other questions. We’ll try our best to respond to them all as well. But thank you everybody for the great engagement. Thank you Stella for doing this together. I’m really excited for this work. Thank you to the eight fold team, also for your collaboration and for all the data that we have as well. So with that, I’ll hand it over back to Emily.
HRE Moderator 1:00:59
Thank you all so much for attending today’s webinar. You may disconnect and have a wonderful rest of your day.
