How GSK Built a Skills-Based Organisation in 18 Months

Speaker A: This episode of the Digital HR Leaders podcast is brought to you by techwolf. For many companies, the skills transformation conversation starts with a platform. A vendor is chosen, a system is launched, and the hope is that capability will follow. Zakha Farha, Global SVP for Talent Learning, Organization and Capability Development, and her people team at GSK started somewhere else entirely. When Zaka and her team examined why GSK was relying heavily on external hires, seeing limited internal mobility and struggling to close capability gaps, the answer pointed back to a fundamental business question about capability. And that starting point shaped everything that followed. In just 18 months, GSK retired more than 20 legacy systems, built a single skills and learning infrastructure from the ground up, and did it in a way that was self funding. I actually got a preview of this work at a Tech Wolf skills workshop in Ghent recently where Zaka's colleague Tanya Jain walked the room through what JSK has been building. And it was definitely one of those moments where you think this is what good actually looks like. So I was really keen to get Zaka on the show to go deeper, uh, on the story. Today we get into the five factors JSK focused on to build organizational readiness and capability. How they approach skills, taxonomy and inference, how, how they have transformed learning, what they chose to stop to make room for, what was new and where Zaka and her team are heading next. Whether you're at the start of a skills transformation or already mid journey, there's a real blueprint here. So hit save, make sure your earphones are fully charged and let's get the conversation started. Today. I'm absolutely delighted to welcome my guest for this episode, Zaka Farhat, uh, Global SDP for Talent Learning, Organization and Capability Development at gsk. Zaka, uh, welcome to the Digital HR Leaders podcast. Really looking forward to this conversation Today, let's start with an introduction to you. What was your career journey that brought you to your current role? Ah, as we said earlier, Global SVP for talent Learning, Organization and Capability Development at gsk. You've had quite an impressive career.

Speaker B: Thanks, David. It's actually a real pleasure to be on this podcast. I've been a long time listener. Honestly, my journey wasn't linear. I started actually out in finance and smoothly into HR in different industries and the last 15 years I was in healthcare. What's pulled me through? Every transition has been really the same underlying question. What is it that makes people genuinely perform at their best? And what gets in the way? So that sounds simple, but it's the question that sits underneath every part of what HR does throughout My career I learned that strategy without behavior change is just a deck. In the most recent role that I have now with GSK in my talent role I saw what genuinely AI enabled people solution and intelligence could look like at scale. But however, I would probably share throughout my career three things that um, stayed consistent with me. First, I genuinely believe HR best work sits at uh, the intersection of business strategy data and deep respect to people experience. The second one is which I've always been drawn to, to the messy transformation roles rather than the steady state ones. That's a particular, there is actually a particular kind of energy in building something rather than maintaining it. And thirdly, I've come to believe deeply in purpose led organization and that's why I probably was stuck in healthcare for the last 15 years.

Speaker A: That's very good. And I saw that you, you actually early on in your career you spent some time at the UN as well. So that kind of purpose driven career has obviously been a common thread throughout your career, hasn't it?

Speaker B: Yeah and it's really interesting because definitely shifting from finance to HR wasn't uh, by purpose but you know finding a purpose in hr, ah and also finding a purpose in a company that has a big mandate, it's really double the, the excitement and also, also gives, gives me a motivation to even give my best every day in the job I love.

Speaker A: And I think Zaka, what you said, you know you were talking about those three, three threads that, that, that are important when I was looking at ah, your you know you lead the global coe of talent, learning, leadership and skills enablement at GSK and I saw that you have like talent, intelligence, people, analytics, culture, performance, AI transformation, organizational effectiveness and development, inclusion there. That's a, that's a great collection of things to have to, to to as you said help understand what makes people perform and get the things out the way that prevent them from doing that. That's, that's, that's a great, great position for you to have in terms of having all those sort of things together that you can and levers that you can pull.

Speaker B: Yes, actually when I, I took my current role in gsk, it, it this actually brought all of those together as Anna mentioned. In a company that actually whose purpose I genuinely believe in, my mandate was actually in JSK is to modernize the HR offering in our um, AI era and to do this in a function that touches every leader, every employee, every workforce decision we make. That's a hard job and a deeply interesting one. Uh, I haven't been bored to be honest. A single day since I started this, this job.

Speaker A: So, Zaka, I had the privilege, um, recently of attending a, uh, skills workshop hosted by Tech Warf in Ghent where your colleague, I think one of your team, actually Tanya Jane, was, was walking through what GSK has been doing around building around uh, your L and D and skills transformation. And it was super impressive and everyone in the room was, was really impressed with the work that you're doing. So I was particularly delighted when the opportunity, um, came to, to speak to you on, on this show. So let's go back to the beginning. What was the trigger that made GSK stop and say we need to fundamentally rethink how we approach learning and skills?

Speaker B: Yeah, thank you. And yes, I mean Tanya is brilliant, uh, and also Carlo and the team and the wider team have been doing exceptional work, uh, and I'm glad it actually landed very well in that conference for us. The trigger wasn't really a skill problem and was it? Actually, it even wasn't a learning problem, to be honest. It was a commercial one. Over a relatively short period of, uh, time, our R and D engine had really scaled dramatically. Our investment had doubled since 2016. We've been really growing also, uh, double digit as a company for the last few years. And more than half of our pipeline, uh, has now been shaped by business development, strategic partnership. So we actually been scaling externally into areas that demand new capability and those capabilities exceeded our ability to build them at scale internally as a company. So most of the time we've been really hiring a lot of those skills externally. So we're buying those skills. And uh, we know buying, um, external skills comes with a cost and a higher cost. And, and we've seen our internal mobility was actually very limited. And in a regulated and complex business like us, um, external hires takes a lot of time to productivity. So the result is basically was higher workforce cost and slowered time to value. So that was really the big issue we're trying to, uh, address. And when we interrogated why, the answers were actually uncomfortable, but they were clear. We didn't have the enterprise visibility first on the skills. We didn't have the common language on what GERT looks like across all roles. We have a lot of developmental offering programs at every level in the organization and all of the countries very decentralized, uh, but was very generic, wasn't targeted. And our learning technology, which was the biggest mess, was very fragmented. So this is costly. And the bigger one actually we didn't have, we have a gap in our skill needs. So once we name that as a business capability and mobility problem, basically the imperative was very obvious. It wasn't about really patching a learning system, it was about moving quickly to close that gap. Um, uh, that was costing us money, time and also losing a strategic advantage that we have.

Speaker A: And what really struck me uh, again about the gsk, uh, approach when Tanya was walking through it and the conversation that we had as well Zaka, a week or so ago, is that you didn't just launch uh, a new learning platform and call it a transformation. You actually first of all had to build the conditions for it to work first. Can you walk listeners through that please? What did organizational readiness actually look like in practice?

Speaker B: This, you're absolutely right actually. Um, and this is part of the story that doesn't get told often enough. Ah, buying a platform and switching it on is actually the easy bit. The real transformation was building the plumbing underneath and the condition that you just highlighted that have to exist before that platform even stand a chance of landing. So for us organizational readiness meant really five things. And I can, and maybe there are more than that, but maybe I would highlight the five first really the leadership alignment at the top. So really we worked very hard to get that alignment and uh, to speak the same language. Although we've done the transformation in 18 months. But the outcome takes more time and it's not like a 12 months platform launch. That distinction makes matters because skills work doesn't deliver visible results. As I mentioned, the foundation take longer than that, um, uh, which is basically the carrot there. Um, but without leadership that understood what we are uh, what we were investing in, we weren't investing in buying a learning experience system or building a skilled taxonomy and a system or buying an insights platform. We are investing in capability infrastructure that is sustainable and it's actually even less costly. So second, I think this, and you just mentioned it is that single data foundation. And I know every company is really looking into this right now. Even with Agentic, it's going to be the most important thing we need to build. Um, and in a company that has a lot of legacy of differently data sits in a lot of places, it becomes even more complex to really connect the data and make it um, really usable. Um, so this is actually the partnership that we've been working with, you know, with my team, you know I have a, also uh, a team uh, that runs people data analytics. So really building that platform that is laying on top of our people data and even bringing more data that we didn't have in that data lake, uh, that it's really important because the skill data, learning data, workforce data, recognition data and mobility data um, and much more um, so all of this actually has to talk to each other and has to tell a story. So they are really giving us a lot of signals. But it's not about really only data. It's really how you pull them together to really understand what you're doing. I think the third element is really governess and I think companies who work in a decentralized model, specifically if you have compliance training, product training, technical training sits in a different areas of the organization. We had to work on it because we built a single platform to decentralize the employee experience which is basically the go to, we called it lnd Hub. And basically to enable what's get into that aggregator we have to stand up a global learning uh council and skill council uh also to enable us to future proof our skills taxonomy and not only building it once like how we can make it agile, how we make uh our learning content more uh aligned to what we really need and also setting minimum standards for learning. So we had to introduce new tools even to get, to increase the quality of the learning um, um and generate um learning in a certain way um, ah and I think the fourth one is actually decommissioning so really how you move forward to decommission and how you move from the past to the new. And this is actually where most organizations skip but I would argue is the most actually um important. So we made a call early to retire legacy systems, um um to rationalize our vendor portfolio very aggressively cleaning up our assignments profile and by the way we unlocked millions of dollars. So with that money we reinvested in a new AI powered ecosystem um and we increased the experience tremendously and we still saved with that transformation which is actually who doesn't like this story? For a CFO and CEO it's ah probably a very compelling story. Um and I would say the fifth one which is underrated is the capability in our HR team themselves in building that and really letting go and empower because we want to empower now our people and leaders to go and use all of those tools themselves with direct access. Um so that's why actually we piloted with our HR community last year first before we scale up uh because we want them uh to understand the journey and what we and what are and what are the change we really want to, we want to make. So basically the platform launch was the visible moment but the real transformation really lived into that. 18 months of alignment, data, plumbing, governance, stopping Old things and also building new capabilities.

Speaker A: This episode of the Digital HR Leaders podcast is sponsored by techwolf. The world of work is being rewritten faster than HR systems can keep up. Um, skills age in months. Roles get redesigned quarter by quarter. Chros have quietly become AI transformation leads. And the data they need to lead it doesn't exist in any HR system. That's why the world's most forward looking enterprises have built on techwolf. Techwolf is the data layer for the AI era of work. It connects three data sets that have never lived together. The skills your workforce has, how their work is changing under AI and where their labor market is heading. Skills intelligence, work intelligence and market intelligence in one layer. HSBC, AMD T Mobile, GSK ServiceNow, Pfizer and many more rely on Techwolf to deliver measurable impact, including cutting time to a unified skills foundation from 18 months to three, servicing 800 deployable internal candidates in under 30 days and unlocking more than $8 million in projected L&D savings at one global biopharma. If skills work and labour market data is what's standing between your enterprise and its AI transformation, talk to techwolf. The data layer for the AI era of work. Visit techwolf AI. That's techwolf AI. You mentioned a number of things that you know, uh, Zacho, skills taxonomy, governance, job to skill mapping, um, also important um, in building an effective skills transformation. Um, you know, what was some of the thinking about building that foundation? I mean again dive into any of those five areas or the skills taxonomy for example. So it's hard isn't it? So you kind of, that's why you need the leadership support. But yeah, tell us a little bit, how did you think about building that foundation?

Speaker B: So the way we actually approached it is starting with really the end in mind. So the taxonomy itself wasn't the end. It actually enables what we really want. So we ask some simple questions up front. What uh, do we need our employees to know and what decisions do we want to take using this data? And that's really when we answer those questions. At the beginning we can structure the taxonomy and really connect the system in a way to enable us to um, make better decisions across hiring, development, mobility and workforce planning. And this is how we start designing it really like what do we want to get, what kind of data and insights really want in 18 months from now? And this really shifted the focus to what good look like. And um, we started actually with job hour architecture. We didn't know at that point that actually we need to revamp our job architecture. We just felt like we could infer, we can use AI to infer the skill is from the job architecture and everything will be fine. So we had to redo some rework and say, okay, let's go back now future proof our job architecture. And I think a lot of companies like us as well, we use our job architecture for just salary, benchmarking, grading, not really to define the work and really capture the work. And that was big aha moment for us and we took time. But the good thing is that we did it in a very, um, compressed period of time because we were using AI at all levels and so basically that made it faster. And um, I think working with the uh, SMEs across the company to really not starting from a blank page. We didn't tell them, just go and tell us what skills that attached to those jobs. To really using AI to do all those sickness inference, uh, both for um, the job and the job families, even getting external signals for them to really understand how they really want to look at their jobs and also look at the skills. We also did some work, um, centrally on the uh, skills that we think are important for our leadership and also the skills that are attached to our culture. I think, um, there is a misconception in that and I was guilty as well. Ah, I didn't think skills could be soft, uh, or not soft. I would say I didn't think that actually skills could be behaviors. And I think really we have actually a skill taxonomy that has different layers, could be technical, could be also related to the behaviors we want to embed in our culture. So I think that was really a good way to start priorizing what's important, um, and not debating the definition because we had to standardize the vocabulary of the skills. I think the biggest, to be honest, challenge was moving from competencies and we have those everywhere in the organization to say now you don't really need it, now you're going one level down. So you have the capabilities that you really need and you want to translate this to skills and you don't really need those big competencies model. Um, and that's, that's, I think the shift.

Speaker A: And you talked about skills inference as well. We've had different, um, guests on the show that have also used technologies such as techwall for doing skills inference and then others that have kind of built um, skills by going out to employees and asking for, asking employees for skills and then looking at having them validated by managers. Can you talk a little Bit about the benefits of skills inference and uh, how that's really helped you in this transformation.

Speaker B: That definitely cut the time probably by 90% of the time investment. You do it the traditional way. I think the learning is that as much as you have good understanding about the jobs you have, you would get a better skill inference. So that's why we had to go back and look at our job job architecture. Are those, are the right jobs, the right roles, um, the right focus. Uh, is it really future proof? So if we get that I think we will be at an 80% uh, we already covered that. Which means the 20% really fine tuning and really understanding where are the you know those transferable skills are, those are critical skills for the future. So, so actually I wouldn't imagine us doing it without, without AI in this world specifically in 18 months if you have, if I have five years probably I would have done it the, the old way. But I think definitely with AI things much will become much better. But you need to be clear as a company on what is your level of tolerance of, of not everything has to be perfect. You are building something agile that might be evolving every day. You will get signals externally and internally as well. And you need to future proof that all the time. So in an agile way is actually even the most important. It's not building it once.

Speaker A: So a couple of things linked to learning really now. Um, so I know the initial thing that you were really trying to achieve was that personalized learning experience. So again this is based uh, a little bit from our conversation uh a couple of weeks ago but also from watching what Tanya presented at the recent Tech Wolf roundtable. So firstly what does a personalized learning experience look like today at ah, GSK M and then how you're using that skills data to make business critical decisions about where the investment in L and D is moving forward as well.

Speaker B: Yeah, and the good thing is that we didn't find it hard to sell the idea of personalized learning. It was more of explaining it what was like hard. Um, so I would say for an individual what's the main difference is that in the old way of doing things I would go and search in a catalog, search for my training. So trainings were around the, the, the outcome of that specific training not necessarily on the personal need. And personal needs stems from two areas to be honest. As an um, as an employee, like I want to learn a new skill and develop just because I am interested or I want to get a promotion or I'm looking to go for a lateral Move or you know what, I don't like my job at all. I want to change my jobs and I want to do something else either within the company or externally. So providing that personalized opportunity based on where you are, what we know about you right now, what we know, uh, from a skill is needed for your role and skills that is actually needed for every single job at GSK made the big difference. So suddenly you have, you go to lnd Hub, you see your own skills, you can maintain that skills. And also it's based on AI inference. So you can actually, we can save a lot of hours from an employee and also you can see the skills that are required for your job. So you can select those focus skills and immediately you will get AI will basically give you personalized opportunities and learning. And not only learning, it gives you actually different type of opportunities. Um, and we have a talent marketplace that we are looking at. It could give you job opening recommendation and doing this in a way that uh, layering on top an AI coach, it's powerful because then the AI coach would um, actually help you develop the right skills in a conversational way. Help you uh, prepare for development conversation even for your, with your employee. Uh, and also you know, doing skill reviews and skill assessment. Because when we launched it, I think last year we were relying on people to rate themselves in a proficiency. We didn't have an AI to enable people to um, determine what is the right proficiency level. And that information is really important for us as a company to understand the gaps, um, and also for employees and managers to feed into their mid year conversation, year end conversation. Because we embedded the skills conversation in those moments that matter from an employee perspective. So that is the experience that people don't have. And that's basically what we call personalization. I would say the level two, which is basically something that is more strategically important is that the data and I think you ask about skill intelligence and how we can use it. So it's really giving us insights we never had before on where we need to prioritize learning, where we have a lot of content that nobody uses. So where do we really need to double down on content to build the specific capabilities where we don't want, where are the biggest priority gaps and how we can address this as a team level or a um, country level or even a business level or even across the organization. So that is a reallocating learning investment was really a big, a big moment from a skill intelligence perspective. The other one is workforce planning. And I would argue for HR and for the organization. That's the most important element that we use skill intelligence on. I know personalized learning is amazing but workforce planning is really giving us that. And not only skills insights is actually even task intelligence and really understand where um, AI is going to impact work. What are ah, the tasks that need to be augmented, automated, boosted by AI. So those really give us understanding about the evolution of the work, evolution of our employees and how we can bridge the gap as we go in a, in again in an agile way. Because in the past we used to do workforce planning once a year. It's a heavy exercise and we link it to headcount planning and it was actually only a headcount allocation and collation. Um, and we are now moving to a way um, uh, like we have a lot of data and skill intelligence. You can use it to determine what you want to not only build, buy and borrow, but also bot and where you need to automate. And it's a different skills for HR community as well. And we are actually trying to really embark on a journey like how we can upscale our community in into that.

Speaker A: I want to take a short break from this episode to introduce the insight 222 people analytics program. Designed for senior leaders to connect, grow and lead in the evolving world of people analytics. The program brings together top HR professionals with extensive experience from global companies offering a unique platform to expand your influence, gain invaluable industry insight and tackle real world business challenges. As a member, you'll gain access to over 40 in person and virtual events a year, advisory sessions with seasoned practitioners as well as insights, ideas and learning to stay up to date with best practices and new thinking. Every connection made brings new possibilities to elevate your impact and drive meaning meaningful change. To learn more, head over to insight222.com program and join our group of global leaders. You talked a little bit about the decommissioning part, Zaka. So but I'm also interested. What else did you have to let go of to kind of make this work? And I kind of asked that because, you know, a transformation of this scale usually means stopping some things as much as starting them.

Speaker B: Yeah, and genuinely this is the question most transformations get wrong. The conversation. Why? Because the conversation is almost always about what's new, what's the new shining tool you want to bring, what's the new platform you want to launch. Um, very rarely is about what you want to stop. And we stopped a lot and I think we spoke a little bit about this. But um, let me maybe put really the Big ones, which I think I eluded before, which is really retiring those legacy system. And a lot of people actually had an emotional attachment to them because they are embedded in the organization for a long time. Uh, so we really want to rationalize this and with the support definitely of the leadership team with a big business case, we were able to rationalize that down to a one single hub experience. And I would say the one of the toughest actually call was about also coaching. And we are really um, moving to a digitally first coaching environment. We are now in a pilot mode with a big, big groups and leaders. We are moving in June to basically making it available to everybody in the company. So it will be a combination between human led coaching when it makes sense to a digitally first coaching. And that's a different approach. So when people are not used to have an always on coaching assistant available to them and it's not an AI coaching in a traditional form of sense, it's really a companion to help you even assess your skill, look at your skills and develop, recommend your development plan, help you in every single conversation, help you role play if you want. So none of those decisions I would say were easy, but every single one of them has definitely a champion, a stakeholder, a passionate, I would say advocate. Um, I think the biggest principle that we follow is that you cannot fund the future from the leftovers of the past. And if you try layer a new world on top of the old world, you end up with both and your employees will even more confused. Uh, and your leaders. And we cannot from a budget perspective sustain both as well. Um, so my recommendation, I mean to the listeners, if it's worth it, is that um, to start naming what they need to stop, really write it down, communicate it. Um, take uh, the discomfort because in my experience naming what stops is a strategic as naming what starts. Um, and it's the test whether you're actually running a transformation or just buying new things.

Speaker A: So I know you're, you've already, I mean you've already achieved a lot in 18 months. Um, and I smiled earlier when you said that, you know, if we hadn't used skills inference, if I'd had five years, I might have done it differently. I'm not sure we ever get five years to do a living in the current world, but you've achieved a lot in 18 months. What are you seeing in terms of outcomes so far that you're able to share with listeners?

Speaker B: Yeah, I mean I would be very also honest on that. Um, the main outcome is adoption is really we See a lot of adoption people like more than 80% of our people went into our hub um really rated themselves on the skills selected. Uh their focus skills start learning more than ever. We see an uptake in, in really upskilling as well and a lot of returning users because of the experience. Um uh so that's all the great things but what is really is it really driving the outcome? We're still not there yet and I want to be open about that. Um so the development conversation is definitely been equipped more with data and we've seen that in the folks groups we have so we always have really open channel that we talk um to our leaders and employees. Um so still really early. Um I uh think the biggest signal or the biggest outcome is really saving which we saved a lot last year and this year um I think which is really the good one because having a self funding transformation with the savings is really makes the conversation with finance easier because we want to add more and more. This is not our end. It's going to be really an evolving system that we're going to add more functionalities, more tool. Um as we know we've seen some uh uptake in hiring internally versus externally with the visibility more um and connecting definitely the systems together. But the deeper outcome to be honest like measuring skill growth across priority skills time to productivity for new hire, um internal mobility supported uh by really skills data at scale even retention of our top skills holder or business performance with capability investment. It will take probably longer to get this and to really measure it but we are working on it to really see how we measure it and what's the best way to bring this. Uh uh but again I would probably push back on everybody including me who claims that short term transformation outcome really um provided business impact level in a short period of time.

Speaker A: What's next on your skills journey?

Speaker B: So I think I will we look me and my team are looking at three areas basically Go deep, go broad, broad go agentic. As simple as that. So so the go deep I think we are on the right track. Uh really trying to um go deep, rationalize more content, expanding our learning offering. Uh really rolling out the AI supported skill assessment and and really even unlocking the AI coach to support specific business capabilities not necessarily enterprise capabilities. Uh uh like how AI can be actually a tutor can be your insights assistant. So all of those areas. Um so that's the go deep basically it's more about optimization, enriching data um and so on the go broad is really going beyond L and D and we Started doing that last year as we went to Skill Insights Workforce Planning. Now we are doing it internal mobility, hiring ah talent uh, for sure. So we, we, our aim is to connect more and more and embed more and more into that to to because we have the data layer we really want and we have the connectivity systems that work together. So basically when a business leader ask uh what happens if we grow this capability by 20% or what's the risk profile of this organization over three years? Uh we can actually answer. So it's not a quarter thing but um. So the workforce planning is basically the conversation we need to have with the business as we go broader with that. So the go agentic is the one I'm really excited about. And we started actually testing an AI agent. Few agents definitely. But on that side specifically we're testing an agent. We call it Task Intelligence and Workforce Planning Insights agent um, which is we are really experimenting with um in a conversational way how we can support our HR business partner and leaders to really redesign their organization. Looking at their work evolution, looking at their skill gap and um, uh and even uh getting data not only from within our system but also externally to see what actually companies are doing. What are the emerging skills like how actually um, uh you uh, can transform the work and the workforce. So that's. We are really exciting about that. We are doing definitely other, other areas. Uh, like the AI coaching is also um, uh agents. Uh we are also exploring expanding this to talent agents and other agents. We are now exporting and testing. Yeah so it's really um, very exciting and if all I think goes well. So HR actually is becoming more and more science and with the science and tech function uh, uh in its own. I was saying it's on own rights so it's not a back office anymore. Um, and not a compliance function. I think that also comes with a challenge because we upskilling our HR business partner becomes really a challenge on how we can fill that gap quickly.

Speaker A: If you had to give one piece of advice to an HR leader, uh, um, who's either in the early stages of their skills transformation or about to embark on or thinking about embarking on a similar journey, what would be your kind of key recommendation? I'll let you have more than one if you want to.

Speaker B: No, it's actually one um, is don't start with the platform, don't go with your vendor scouting first. Answer the business question first and start with the data foundation till you get that right. So don't even also wait for the right things. To happen because I know things takes time in a company. Start building your data foundation now and then as you answer the business question, um, and really articulate why you want to do a skill transformation. Why this matters for your CEO and cfo. Because if you don't, you lose the funding in less than 18 months. I would say. So that's, I would say the biggest one. And then I think how you can do it. Uh, I would say any question has to be framed as ah, business capability question should be a capability risk question, productivity. It has to be around the economics of building versus buying versus borrowing versus botting. I would say for your future workforce. So, so that's the cost of, I would say missed succession.

Speaker A: Yeah, that's a good, great piece of advice. And um, now we kind of transition to the, the question of the series and I think we might, you might draw on some, some, some of the conversation. Um, already on this one. Zaka. Where should HR leaders start if they want to turn AI into real impact at work?

Speaker B: So I love this question and I ask myself this question probably every single day because every day seems like there's another problem, a new challenge that we need always to keep up and sometimes we pivot quickly and it's very important to even not to stick to a single way of doing things and do what's important, um, depending uh, on where you are in your journey, which company you're working on. So there's a lot of valuables into that. But I would say the most honest question is that uh, most organizations are doing this in the wrong order. And that's what I think, um, uh, could potentially uh, put a risk in a transformation. Um, and they also realizing um, put wasting a lot of money before they even realize this. So maybe let's, let me try to be useful rather than, I would say diplomatic. So let's start with two questions. Not a tool, right? So the first one, what do my leaders genuinely struggle with? Ah, the second one, uh, uh, which, which of those struggles is a pre work problem versus a judgment problem? Because sometimes it could be actually a judgment problem. So that's frame it framing. I would say, um, the most important to ask. So AI is actually brilliant pre work. I mean that gives a lot of pre work and having those signals and um, before any decision we make in the company, it's important and then generating all those uh, coaching prompts ahead of difficult conversation. Uh, but I would probably also have being cautious about not jumping to an AI just because it's fashionable. But really it's because about, you know, is it going to be really boost and amplify human capabilities and judgment. But the most important is not, not wait too much as well to think things to be perfect to bring it because people are already on their phones doing chatgpt Claude. So before we know it, uh, people will come and demand that we need to really accelerate that agenda. So I think as an HR community we should not worry so much about things to be perfect. Uh, definitely with the ethical framework, with the right due diligence, with the right reason why it has to be because we need to build the trust and we need to have that um, compliance question. Right. But also understanding what the culture we want to build. I would say pre work is definitely important, but augment the prep with AI and I think would be good to protect the judgment. Um, so treat trust really as a cultural question and start building hybrid leadership capability now, not after the technology been landed. So this is where I would start actually.

Speaker A: I think that's a really good place to end our conversation. Ah. I mean a. The skills transformation journey you're on is super, super impressive. And as you said, that thing around trust and building that leadership judgment, really, really important, really fascinating conversation. Um, just as we end Zaka, where can people find you and what's the best way to follow your thinking and everything you're doing at GSK so people

Speaker B: can definitely find me on LinkedIn. I really enjoyed the conversation. Um, David, thank you so much for this, um, and hope the questions and the answers were really, um, helpful for our HR community. I'm actively, I'm reasonably active, um, uh, in LinkedIn and if anyone has any working similar challenges, please reach out.

Speaker A: Great. Well, Zaka, thank you very much.

Speaker B: Thank you.

Speaker A: A huge thank you again to Zaka for joining me today. It was one of the most impressive skills transformation stories we had on the show yet. I'm sure this will have helped a lot of our listeners either embarking on or at the midpoint of their skills and workforce transformation journey. For those of you listening, I'm curious what stood out for you the most from today's episode? Is there anything you would add to the conversation? Look me up on LinkedIn, find my post about this episode and let me know in the comments. I read every single one. And honestly, the conversations that happen there invariably build on the conversation with the guest in the episode itself. And if, if you think a colleague or friend would get something out of this episode, please do share it with them. It really does help us bring more of these conversations to hr. Professionals across the world. And one last thing before we go for those who would like to keep up with what we're working on@ah, insight222. Follow us on LinkedIn or head to insight222.com you can also sign up for our bi weekly newsletter, um, @myhrfuture.com to get the latest thinking on HR, people, analytics and everything shaping our field. Right. That's us for the day. Thanks for listening and we'll be back next week with another episode of the Digital HR Leaders Podcast. Until then, take care and stay well.