claudewerder-f6dukvgks_raw-audio_claude-werder-pedro-malburg-jenny--4-mo_2023-nov-30-0107pm_excellen

Rwy'n gobeithio y byddwn yn gweithio'n fawr iawn, ond rwy'n gobeithio y byddwn yn gweithio'n fawr iawn. Rwy'n gobeithio y byddwn yn gweithio'n fawr iawn, ond rwy'n gobeithio y byddwn yn gweithio'n fawr iawn. Rwy'n gobeithio y byddwn yn gweithio'n fawr iawn, ond rwy'n gobeithio y byddwn yn gweithio'n fawr iawn. Welcome everyone to Brandon Hall Group's Excellence at Work podcast. I'm your host today, Claude Werder. I'm Senior VP for Research Operations and Principal Analyst for Brandon Hall Group. My special guests today are two exceptional executives from Kindrel, Mary Jo Charbonniad, the Chief Human Resources Officer, and Yvonne Dauplay, the Leader of Digital Workplace Services. A warm welcome to you both. Thank you. Great to be here, Claude. Thank you very much, Claude. Great to be here too. I'm really looking forward to our conversation today. The overarching topic we'll discuss with Yvonne and Mary Jo is employee experience and the role and impact that digital workplace technologies, ranging from simple work management tools to Gen AI applications, can have on eX, or employee experience. Kindrel has unique insight here, I believe. It's the world's largest IT infrastructure services provider, serving thousands of enterprise customers in more than 60 countries. The company designs, builds, manages, and modernizes the complex, mission-critical information system that the world depends on. Before we start our conversation, Mary Jo and Yvonne, tell us a little bit more about the company, if you like, your role, and what brought you to Kindrel. Mary Jo? Sure. As you said, I'm the Chief Human Resource Officer. I've been with Kindrel just over two years. I joined after two other CHRO assignments. Really, I love big, transformational assignments. That's what drew me to Kindrel and the chance to work with a great set of colleagues to do something really significant. Yvonne? Yeah, thank you. As I said, I have the privilege, actually. I call it really a privilege to lead the digital workplace practice. As you said, while it's called a practice, what it's all about is creating the best employee and customer experience for our clients. That is at my heart because I believe, as a service company, it's wonderful to be absolutely focused on the human interaction with the infrastructure environment. I came over from a former company and I was leading, in the last 30 years, worldwide customer units and markets. I'm excited to be here. Great. Well, we've been excited at Brandon Hall Group to be working with Kindrel. We've had the privilege of working with the company on a research project this year about employee experience and the impact of digital workplace technologies on employee experience, or EX, as you'll hear me refer to it sometimes, and, by extension, also the impact on customer experience. It's been eye-opening in many ways. We've learned that HR leaders don't always account for enterprise technology as much as maybe they should as a driver of employee experience. Improving the digital workplace to improve the work experience really ranks toward the bottom of a list of employee experience drivers that HR leaders prioritize, even though that very technology impacts most of those drivers, such as skills development and improved candidate experience and onboarding and the like. We also learned that only 47%, a little less than half of HR leaders, believe they have strong or excellent collaboration with IT related to technologies aimed at improving employee experience. On the flip side, or the good side of that, our research interviews with HR leaders revealed that there is a level of collaboration that's beginning to improve in many organizations, and we're also seeing signs that HR organizations are striving to re-engineer their approach to employee experience and increase their input into technology strategies and decisions that improve the productivity and experience of the workforce. So Mary Jo, Ivan, and I are going to delve into several topics related to employee experience and the technologies that help define it. So, Mary Jo, let's start first by looking internally at how Kindrel's HR team is re-engineering the employee experience. Sure. Well, it was really a once-in-a-lifetime opportunity, one, as a new company, and two, to think through as we became Kindrel, we had to rethink many of our technologies that were on a transfer services agreement. So you don't often get the chance to rethink all your financial systems, all your procurement systems, all your HR systems, and all your payroll systems at once. And that's what we got to do. And I think we could have just thought of it as a systems transformation, but what we really wanted to think of it as is a culture and experience transformation. And really, when we think about what does Kindrel do for a living, you know, we really sell skills. So the skills of our people are at the heart of our business. And what we wanted to think through was, as we eliminated 58 systems, five data lakes, and created a different set of HR platforms and processes and experiences, how could we put the employee at the center of those processes? How could we put their skills at the center of that? And how could we drive that change in a way that was emblematic of the new culture we were trying to create about being restless, empathetic, devoted, flat-faced, and focused? And that, you know, back to one of your earlier points, a real collaborative effort with leaders like Yvonne, but also with our CIO teams, our finance teams, our procurement teams, and it meant almost a weekly governance model where all of us were talking together to make sure we were thinking jointly about how these things were going to work, what was the employee or the people manager experience going to be? Thank you. Thank you very much. And I think, actually, it is very unique that what just Mary Jo said, where we came from and what we do with and can do with our customer, together with our partners, actually, this, what we call a customer zero experience of ourselves, what we experience ourselves, have done for ourselves, is kind of doubling down what we can and want to do with our customers and our partners. Now, when we look at the employee experience, we really look at this from a holistic point of view, meaning end-to-end. We still, and also I would say we came from a very pillar-thinking environment, and what is needed today is the integration. And the integration really starts at how do you leverage data, how do you bring this integration of technologies, and then we talk about integration also like generative AI and automation, into a fuel of innovation and really elevate it at the level of employee. But I also would like to say level of employee and customer experience. What we really learn and what we see out there, this goes together. But, you know, there's more to it than just an approach and an ambition. It's about how also do you measure it? How do you make something really touchable, tangible, and, you know, see how it progresses? And there we talk about experience-level agreements, which is a measure for the employee experience, and it's baked into all our digital workplace solutions. You know, you need to kind of look at this like a level of agreement. How do you quantify the experience from a service provider which is delivered to an employee, rather than technical aspects of a service? I always kind of quote, you want to rather focus on a good experience than on how you actually are, you know, beating up a service individually. And actually, service-level agreement measures the impact of the service on the employee's productivity, satisfaction, and the business outcome. And, you know, the research has also shown that there is an absolute correlation between employee experience, customer experience, and improved business performance. So it's not about just a sentiment. It's about how to really bring it into your business objectives, bring you into measurements, and how you can see on progression. Just a little example, which very aligned and very close up with the HR processes, onboarding, offboarding processes. And don't think only just, you know, the one-day event where you have a new employee. Think of companies which need to be flexible, having service people, maintenance people, people which are just stepping in for a certain time, stepping out. Others are staying for you with 30 years, and you have a life cycle. But the life cycle can be from a day, a week, to 30 years. This process often is pillared, meaning you have a process of HR, you have a process of procurement, you have a logistic. How do you bring this together and make it one experience? And we have had the privilege just to mention like Dow or Singapore Airlines, kind of really big different companies in the world where we work with it and where we create together, co-create, so to speak, this employee experience and their measures. Great. Thank you for that. So let's change gears a little bit now, move to generative AI. The research we've been working on together indicates that many HR organizations have been taking kind of a wait-and-see attitude about gen AI, and they're concerned about potential repercussions such as job loss or privacy and ethics issues. And they're not necessarily always taking a lead role in participating in the development of gen AI strategies. So I'm curious, both of you, what your advice would be for HR leaders as they consider the future of gen AI. Mary Jo, let's start with you this time. Sure. I think it's really, we ought to be talking about all emerging technology. Gen AI is the latest example. Technology change tends to speed up on us. So I always counsel HR leaders, really think of it in kind of three roles, your hats you have to wear. The first hat is, how is any new technology like gen AI going to impact your business commercially? We want to be strategic business partners. That means knowing the business and being able to talk with your business leaders about what they're seeing, how it's changing their work, their talent needs, how they need to organize to pursue those business and commercial opportunities. The second hat we often wear is around governance. And that takes a couple of sizes, shapes, and flavors in our area, but all really important to think about, especially with gen AI. And that's around regulation. It's around code of conduct issues. Think of it as you probably work maybe with your CIO on acceptable use policies. I like to say, do I care that somebody maybe used chat GPT to write their performance appraisal? Maybe not. But if they put confidential company or customer information in there, yeah, I care a lot. So we have to be thinking about really kind of day-to-day practical applications in the governance space. And I think we're going to see regulation, like on privacy and on data for us, increase in this area. So it's important we stay networked. We are talking with our general counsels, our CIOs. And then the third one, and there's a lot of press out there on this in the HR space, but it's how are we using it ourselves to do our own part. And I heard someone say, first, fall in love with the problem you're trying to solve. Try to figure out what's the right technology used for it. And I think what we're trying to do and what we all should be thinking about, what's the big business problem or what's the big HR service delivery problem that I'm trying to solve, and how can technology help me do that? And then how do I know that technology is safe, consistent with our policies, and it's going to create a good experience for employees? So, again, those are the three things I like to talk about and I think about, and I'm involved. And I think that shows the importance, too, right, of IT and HR working closer together. It takes a collaboration to do all three of the things that you just talked about. Yvonne, what's your take on this? Yeah, first of all, I think, I mean, after so many years of being in the business and actually always in the field and talking to customers, I think it's one of those times where I haven't seen a comparable interaction, actually, between the different organizations, CIO, HR, legal, as I said, mentioned by this demand of you need to understand what you're doing, but it has legal impact and it has human impact and it is technology-driven. So, but before we go out again, I just want to make a bit of a step back because we are already using terms maybe which, you know, deserve just a kind of, what is it when we talk about AI, when we talk about machine learning, when we talk about alternative AI? Just to ground ourselves, first of all, artificial intelligence is where machines and systems are really performing tasks that normally require human intelligence. So, things like understanding languages, recognizing images, and so on. We are working since so many years embedded in what we do as Kindrel and FOMR. So, AI is in the foundation of what we are doing. Now, machine learning is the next level where actually it is a subset of AI, which focuses on training systems to learn and make predictions from data, but without really being explicit programmed for it. It's always, you know, a moment maybe of confusion here, but when it comes to alternative AI, this is really another subset of machine learning, which uses more advanced algorithms and models. And this is the moment where it creates output or the focus is creating new realistic content, which is overspending everything, text, images, music. And that makes it like so, you know, compelling where the end user comes in immediately. I would say AI and machine learning has been a bit in the background, in the back offices. Generative AI now just kind of has moved it overnight because everybody expects this. But what are we actually bringing to the front, right? It is often just thrown as a problem statement. But I think generative AI needs to really also be based on what is the use case? What is the outcome you want to attend? I mean, what is the opportunity you're grabbing? What is the measures that you want to have with it? What is the return on investment? So, the customers need to go through this, and I think we have an opportunity to help them. And we improve here definitely the employee's experience with this personalization it gives. And, of course, HR can benefit a ton, as Mary Jo alluded to. But just be clear, we are on a journey, and we can help this journey. And I think we need to bring the things together. Yeah, great points. So, following up on that, if I may, based on everything you said, how should organizations assess their readiness for Gen-AI for business opportunities while also addressing the employee concerns or the concerns the organization may have? Yeah, thanks for this question. I think that most, you know, the first question, once you have kind of stepped over, we need to have generative AI. This is how. And I think nobody needs to kind of feel bad about it, because it's really a journey which came at high speed. I mean, who had discussed generative AI 12 months ago? It wasn't even on the radar. By the way. So, what are we going to discuss in eight months? We don't know. But what it is for sure, you know, let's be clear, generative AI is building on data. And, you know, I like the sentence, if you create garbage in, you create garbage out. So, the first step really an organization needs to take is, what is your data strategy? Where are you with your data strategy? And how do you make sure that you're taking a measured and responsible approach to it? And just, you know, flashing it out, it's not the data only you have. How do you use your internal data? How do you use the external? How do you allow to have this mix? All these kind of things are now in play. And again, I think then there is this really, once you have this, and of course some steps will, you know, kind of take it in parallel, you want to understand what are your use cases? What is the benefit? Now, we are used in terms of return of investment. I'd like to add or introduce another term, return of experience, because you generate the new experience if you do it well. And doing so, for example, just make it very practical. We have, for example, a generative AI envisioning workshop where we really are helping our customers to understand the benefits and the use cases, which we are doing. So, how they can prioritize the deploy of generative AI, like co-pilots. By the way, we are doing the same. We are a co-pilot pilot, and we are now going into co-pilot deployment and production. We have to have these discussions and have continuously discussions internally. Rachel, any thoughts? Yeah, and forgive me, my camera has decided to do fun things today. So, you see me from a new angle now. So, listen, I think I'll just build on the really great points that Yvonne made. One is, you know, you do want, I think, to use new technologies like AI on good data, right? You don't want it to be going out there to answer questions on the Internet. You want it to be scanning your own employee data and your own knowledge base. First, let's talk about data in HR. That one means the accuracy of it, how does it get structured. If you're using, let's say, multiple job architectures, then it's hard for something like AI maybe to work well to know what people do. So, it's not the quality of the data, but it's the structure of it. Second, knowledge base. To get the most out of it, actually things have to be simpler to use more complicated things, right? So, you're going to want to be thinking about how consistent are my policies? How easily searchable are they? You know, as I like to say, I have 7,100 people managers who interpret policy today who use Gen AI. The question is, do I want more help interpreting more random policy or do I want to be able to use Gen AI to get better, faster answers because it's working off of a good knowledge base, another way to think about data. And then lastly, I would just say, put yourself to school. You know, with every emerging technology that comes out, we as HR leaders have to bring in our teams our own experience and our skills up to speed. So, if you don't know what Gen AI means or machine learning or any of the terms we've thrown around, go ask your IT folks to help you understand that. And if you hear people using it differently, question that to say, wait, I thought I knew what it meant. You're using it in a different way. Do we all understand? Because part of this is a learning journey everyone's on. And HR has a key role to play not only in helping build skills for ourselves and our HR teams, but across the organization. So, something else we're working on is a curriculum on these things that we can deploy efficiently out to all employees as well. Yeah, I think that's really important. And some of the clients we work with have created, you know, discussion groups and learning groups to really understand before they wade in, and I think that's good practice. So, moving on, from the CHR point of view then, Mary Jo, how can HR teams derive the most immediate value from AI and machine learning as they're on this journey? Yeah, sure. Well, I think, you know, again, what is the challenge you're trying to solve? And let's think about a common one many of us in HR are working on. How do I develop skills? How do I ensure I have the talent ready for the next business opportunity that comes along? And so what we're doing and what I would encourage all HR folks to do is really talk to your partners, whether it's the partner running your core HCM platform or some of your other partners. And they may have ways you can use it that's built in versus trying to, you know, generate generative AI and all the code and complexity that goes with that. I think we want to be looking for networking and partnering with different types of providers who have solutions for us. And one way we're using it, I'll give you a real tangible example, is because we sell skills for a living, we really needed to know all of our kindred skills because the repository of that was not something we could take with us on our journey. So we had to rebuild that repository. And so we asked all employees to put that into a piece of our core HCM platform. So now we have all skills structured in a consistent way and in a central repository. That's an example of getting the data right. And then we're using AI to, as the HCM knows when someone now changes a job, it suggests new skills because it knows our job architecture. When we have Kindrills complete a customer assignment, they get a nudge within our human capital system to say, ah, we see you just completed a customer assignment. Do you want to go back and update your skills? So that we're keeping that repository fresh so that we can, you know, make sure that when our customers call us and need a skill, we know it, we can find it and find the right Kindrill to support them. So that's some examples I would give you. Great. Okay. Go ahead. Yeah, just as a, you know, what Mary Jo and team has done here, I just need to call out for us. I mean, I felt like in the previous kind of decades, I was administrated by skills. And now it feels like I'm empowered with the team. And, you know, the things are able that we are creating our own destiny as an individual as well as an organization. So it has a huge relevance here. And I think, and I just, you know, Mary Jo said at the beginning, she is a fan or a driven by transformation. It's a fantastic transformation, which we have done ourselves. But I think we can work with our customers to actually help them here too. Thank you. Thank you. Okay. So final topic for today, and that is what opportunities do each of you believe AI and machine learning hold to improve employee experience specifically across the wider organization? Ivan, let's start with you. Yeah, look, thanks for that. And I need to kind of keep me here a bit focused because it's so broad. But AI and machine learning are powerful technologies that can enhance really employee experience wider in the organization. And there are many ways. So I think, you know, most often in this environment, people are starting to thinking about, you know, the support they get 724 with all the chatbots and the virtual assistants, the self-service portals. Don't underestimate the power it builds and capabilities in. So a chatbot is not equal a chatbot. But then there's more like real-time performance of monitoring and feedback using the data which we were talking about and also sentiment analysis, meaning that the people which are working in your organization for your customers are already hinted to what is coming up and how things are working or not. Just an example, we had wonderful examples on learning and development through really personalized recommendations, you know, the adaptive learning platforms and gamification things. There's a ton of areas where we just optimize, right, and bring the repetitive tasks out being really moved to shift from what doesn't need human intervention is going to machine and making human intervention, human service, human value actually more valuable. And I think we will see more of that, what is next, but enhancing just the creativity and innovation. I think that makes also different to everybody's customer's customer. And there is things to play with the generative AI and augmented reality and digital twins. But for me, at the end of the day, it's a huge opportunity. I'm convinced we make us more flexible, more diverse, more well-being, and it gives it to a broader organization. As I said, I'm, you know, leading this practice, makes me really kind of passionate about the human-centric approach, which AI and machine learning and generative AI needs to support. And I guess Mary Jo was at the beginning very clear about the impacts, ethical, transparent, and aligned with the values is absolutely critical. Mary Jo, we'll give you the final word today. Yeah, I mean, I think the future is super exciting. And I think we're going to be on an incredible learning curve as a function over the next probably five years. And I think we have to challenge ourselves to think differently with each new technology that comes online, like generative AI. How can I use this? And what can I bring about that helps make the business stronger, the employee experience better, whether that's, you know, from the learning and development end or through, you know, core things that happen to people in life. You know, one of our core values is empathy. So some things we're thinking about on our next journey is how can we use generative AI and all types of machine learning? What if our HCM platform knew you just went on leave and then could send you all of the leave-related policies, the things that said, did you go? Have you thought about, you might want to read this? And then cue the manager to make sure four weeks later they checked in on that employee. So these are the kinds of things I think we have the ability, you know, there's a phrase in this space I love, which is white glove digital service. And how do we as a service organization, as HR leaders, partner with our CIO organization to really bring, you know, as any of us may feel the pressure on budgets still, you know, really, really use the technology to think about how might we provide white glove service digitally to our employees and, you know, the other folks who may work on premises like contractors or guests and other people who cross through our doors physically and our doors virtually. So, again, I would encourage all HR leaders to stay on their own learning curve and think about what are the business problems you can help be relevant to both commercially and in your own HR organization. Well, thank you. Thanks to both of you. We could probably talk a lot longer about all of this, but we'll leave it there today. And so, again, Mary Jo, Yvonne, thank you for joining us today, sharing your insights and the work that Kendra was doing. We really appreciate it. And I think HR leaders and practitioners can get a lot of what you said. So, we're recording this just at the start of the holiday season. So, until our next podcast that hopefully you'll come to us to view, we wish you all happy holidays. Thank you for tuning in and goodbye for now. Thank you, everyone. Thank you. Thank you, everybody.