How GenAI reimagines global mobility

In this episode of the Tax and Law in Focus podcast, EY leaders share how GenAI and managed services help workforce mobility evolve to better manage cost, risk and talent.

Key takeaways:

Define real GenAI use cases and align them with mobility and talent strategy.
Design a data blueprint including objectives, inventory, preparation and quality, ethics, human-in-the-loop.
Automate high-friction areas first – payroll and compensation, tax notices, onboarding – then iterate.
Use AI analytics for workforce planning: skills gaps, immigration timelines, attrition risk.
Create a cross-functional steering committee, set KPIs, leverage existing technology and invest in change management and broad-based mobility skills.

For your convenience, full text transcript of this podcast is also available.

Susannah Streeter

Hello and welcome to the Tax and Law in Focus podcast. I'm your host, Susannah Streeter. Global mobility leaders are increasingly under pressure to deliver talent at speed at a time of increasing complexity. Costs are increasingly under the microscope. Compliance risks are multiplying and disruptive technology is reshaping almost every process. However, help is at hand. The EY 2025 Mobility Reimagined survey shows that organizations embracing managed services and harnessing the practical power of GenAI are building a clear advantage. They can deliver efficiencies, manage risk and enhance the experience for employees. In this podcast, we're going to explore what it really takes to evolve from traditional fragmented mobility management to a strategic technology-enabled approach. And I'm very pleased to say a trio of subject matter experts will join me to show us the way forward. We'll dive into the survey data and provide plenty of real-world examples of success to learn from organizations whose mobility functions really have broken free from operational silos. As you'll find out, they've automated processes and shifted focus from transactional compliance to be more adaptable, resilient and advisory-focused.

But before I introduce my guests, I just want to tell you about our new tax podcast, Tax Threads from EY. Our quicker look at tax and law issues. You can find it anywhere you download these podcasts.

Also, remember, conversations during our podcast should not be relied on as accounting, legal, investment or other professional advice. Listeners must, of course, consult their own advisors.

Now, please welcome my guests, who are all partners at EY's People Advisory Services Tax Practice. First of all, Jason Ward. Jason, great to have you with us. Where in the US are you based, Jason?

Jason Ward

Thanks, Susannah. Great to be here today. I'm based in Dallas, where we're getting to that time of year where it's nice to be outside again. Good to be with you today and I look forward to our discussion.

Streeter

Yeah, really looking forward to it. Also, Maggie Lundervold, where are you talking to us today from, Maggie?

Maggie Lundervold

I am joining from Chicago, which like Jason, we're getting nice to be outside, although instead of coming down from the hot, we're staying away from the cold. Great to be here with everybody today.

Streeter

Gareth Paine, who is also a partner at EY's People Advisory Services Tax Practice and leads on mobility technology topics. Where are you today, Gareth?

Gareth Paine

I travel most weeks, but today I am in Italy, so weather here today is getting a bit chillier actually now, anticipation of snow and ski, so a little bit different to my presenters.

Streeter

Well, it's great to have voices from right across the world. Let's start with you, Jason. We've entered a new era of cost and risk. Do you think this is what's driving the need to rethink mobility going forward?

Ward

I think cost and risk certainly are having an impact, but I'd also look at balancing that with what I often refer to as stakeholder experience, which I'll come back and define in a second. But first and foremost, cost is always going to be a focus in this space. Cross-border mobility can be quite costly for an organization, if not carefully planned. Further to that, when it comes to risk, each organization has different thresholds as to what they find acceptable from a risk standpoint. Obviously, there's careful consultation needed with various cross-functional stakeholders to arrive at the risk positions that the company is willing to take. While I see many companies focused quite heavily on cost and risk, which they obviously should, I think there are also opportunities for one to make these processes more transparent to the stakeholders that one is engaging with. Again, when I refer to stakeholders, I'm looking at both cross-border employees, but also business leaders and making sure that they've got a better understanding of some of the levers they can pull, buttons they can push to control cost, mitigate risk and at the same time, participate in the decision-making effort. Sometimes they're a bit isolated.

Decisions are made upstream and pushed upon them. I think by involving them in that process, you can enhance that overall stakeholder experience and find them much more willing to collaborate on the implementation of those decisions, drawn down the line.

Streeter

Yeah, collaboration is absolutely key, and also is getting a real view of the landscape that we're facing in the EY Mobility Reimagined survey. Does that, doesn't it, Maggie? What does it tell us about why it's so urgent for organizations to take action now?

Lundervold

Yeah, it's interesting. When we did our survey earlier this spring, one of the key themes that came out of it is focusing on GenAI as a game changer. That was the thematic piece, one of our top three themes, in fact, that came out of the survey. So much of it is, to your point and Jason's point, is there's so much data out there in the mobility space, and whether that's within the organization, with the systems that they collaborate, what they get from employees, what they collaborate on with vendors. It's really trying to figure out how to use GenAI and other technologies to be able to harness that data, and not just use it to drive efficient processes, but use it to drive insights, to be able to make strategic decisions and not just keep doing the operational pieces. I think that's what we heard through the survey, and to everybody's point, is really what we're seeing.

Streeter

Yeah, really interesting what the survey has thrown up. What does it show about companies that are getting it right and embracing this new age of mobility? What lessons can we learn from those successes?

Lundervold

Yeah, and I think it's a fair question because I think what we're seeing a lot of companies do is ask, What are you doing with AI? How do I use it? I feel like it's one of those where people have moved or are trying to start to move from those early stages of using AI and automation and whatever you want to call it, to be able to drive efficient processes and figure out. Okay, how do I get from point A to point B in a less manual, more automated way? But as we were starting to talk about, I think part of that then is being able to take things away from just chat bots or ways to answer questions quickly or ways to drive processes quickly and be able to do some of that machine learning and thinking around, again, data-driven insights, how to help make strategic moves within the program, and be better partners to those business stakeholders that Jason was talking about, too.

Streeter

It's interesting at this current time, we are seeing some concern about whether you're really going to get return on investment into AI technology. Let me bring you in, Gareth. I mean, there is some concern that there is just too much AI hype around at the moment. But do you still believe that there are concrete opportunities that organizations really can take when it comes to harnessing that technology to reimagine mobility?

Paine

Yeah, I think there is, and there is a lot of hype about GenAI. Just on the weekend, I was reading articles around the AI bubble. The message is that there's been no other tech, really, if you look back, the last 20, 30, 40 years, that has got as much interest as GenAI in such a short space of time. But I think now, if you look also where GenAI is headed, and the hype cycle is a great way of looking at it. The Gartner Hype Cycle, it basically maps and plots where tech is. And last year, GenAI was actually viewed as being potentially transformational with some really big impacts to business. This year, it's now more in what they call the “trough of disillusionment”. So corporations are trying to figure, okay, what really is the potential here and what are the potential limits? It's almost a little bit like we've come out of that mass excitement area, and now we're into this corrective phase of figuring out what to do next. And there is, I think, if you look across the use cases, perhaps a bit of a crisis as to what the use cases are.

I don't think we've found that killer app, really, for GenAI. And some critiques are very pessimistic around the future of it. We're thinking maybe it's like a parlor trick type status. I think whilst billions are still being poured into GenAI, I think that the message is to tread optimistically, but carefully. To do it responsibly, a lot of clients are taking a few different approaches here. I think number one is ethical guidelines. So many corporations are taking a step back and already starting to establish specific ethical guidelines to govern the use of GenAI to make sure it's fair, and there's transparency on how it's used. Also, this concept of bias mitigation. So again, clients are also thinking about how do we mitigate potential bias in our AI models? How do we do that now to ensure a fairer outcome? And linked to that is about having the human in the loop. So many clients are implementing human oversight into any AI decision-making process to really make sure that there's a human in the loop at every step, particularly in these early use cases. And I think those types of steps are where clients are starting to get a benefit of AI, but also now planning ahead to minimize and mitigate your risks with responsible usage.

Streeter

Keeping that human in the loop is absolutely crucial, isn't it? So many examples are popping up of what can go wrong when you don't do that. But can you give me some examples of how you see AI agents being really successfully employed?

Paine

I think the obvious ones that jump out and that we see most clients experimenting with, and we're all probably familiar with, are chatbots, digital assistants, whether they be recruitment-type chatbots within HR. So chatbots are being used to engage with potential candidates, answer questions, schedule interviews, even screen CVs, and even go through an onboarding process, virtual agents to maybe help signees and employees navigate those onboarding steps. Again, cutting time, making it a little bit more efficient, even using agents to personalize the experience. I think the other way I see more and more AI agents being potentially deployed or thought about is in learning and development. AI agents actually helping us create or personalize training programs based on our skills, our career goals, our own performance data to help us learn. They're the three key areas I see, really, some more recent examples of clients talking about the use of GenAI.

Lundervold

I was going to jump in and add a piece that I think is pretty specific to global mobility, but that whole idea around mobility compensation and payroll, which I know is near and dear to many mobility practitioners' hearts because it's complicated and it's tended to be a pretty manual process. But I think we are seeing organizations look at how to use agents to do some of the, not only just the general checking of the number that I wanted, get into the number where I needed it to be in payroll, but also how correct is that? Based on what we know the person should be paid, is that's actually what's coming from the balance sheet, which is coming from their letter of understanding, getting into shadow payroll, getting into actual payroll, and being able to do those types of checks, and then start to have the machine learn when those things might be different. Also doing it within that framework of managing privacy, managing risk that Gareth referred to that I think is so key. But I think that's a huge place for mobility that we're going to see GenAI continue to drive value is in that whole payroll space.

I think we're also starting to see a little bit when it comes to organizations as they're trying to respond to notices and inquiries from tax authorities, particularly from a domestic standpoint, they may need to reach out within their system and figure out, okay, what does my wage statement say or what was reported to the government? Then I can respond to the notice much quicker with a human in the loop to check to make sure those things are right and what they actually want to send back. But I think just being able to, again, harness some of the data that's out there, taking some of the manual intervention out of it, but having manual review is going to be really key in addressing some of the mobility payroll compliance as well as just regulatory compliance.

Streeter

Yes, and really interesting, those developments, especially the troubleshooting bots. Gareth, do you think that the AI-driven analytics can also help spot talent gaps and perhaps just more generally inform workforce planning?

Paine

I think it could. I mean, there's a few applications of it. I think one is using it to help predict. Again, using the power of GenAI to help you predict a future workforce need, looking at things like your business growth, your turnover rates, allowing a corporation to address perhaps talent shortages up front, rather than being on the backfoot. I think also tying to that using AI to think about maybe where you've got some skills gaps, and not just today, but again, using it to understand where there might be some skills gaps in the future that you're going to fall short on. I think also around perhaps benchmarking. So benchmarking maybe external labor data in a new way to compare one corporation's talent pool with perhaps what is industry standard and to help them understand where are they ahead or perhaps where they're lagging in certain areas of talent. Maybe last but not least, turnover and attrition. Big challenge for corporations I speak to. How do you predict, prevent, mitigate attrition? Perhaps impossible to prevent fully, of course. But AI, again, now, with the power of that, imagine using that to maybe spot patterns in turnover, identify specific factors through attrition and understanding, again, maybe where you can step in a little bit earlier if the agent is saying there's a potential high turnover rate or a particular individual or pool of individuals.
Again, just a few examples there, I think, where AI could really help with workforce planning.

Lundervold

I was going to add one more piece on that workforce planning piece, again, very specific to global mobility. But another place that we've seen more of this data-driven insight help is particularly around immigration, knowing that that kicks off a lot of mobility processes and part of what allows you to determine how quickly you can get someone in country and doing their job, that whole speed to land concept is driven by immigration timelines. I think in terms of that workforce planning, that element of, again, mobility being able to say, tying with talent, here's the skill that we need to fill, here's the skill that our people have, and then here's who we can actually get and how long that's going to take. I think, again, it drives some of that really powerful decision-making that allows mobility to bring forth that idea of, all right, here's what our immigration timelines look like, here's where our gaps are, here's who we can use to fit it. Tying that with talent profiles, it becomes a very strategic way to do workforce planning that I think mobility hasn't been able to do as much in the past that can bolster that for organizations.

Streeter

Really exciting potential that you've been outlining there. Jason, clearly the technology exists, but how much of this is actually being used now to make decisions within mobility? What barriers are there that need to be overcome before we can fully realize this potential?

Ward

Well, indeed, a lot of nice technology resources exist. I think Maggie and Gareth have done a good job articulating some of those and the associated functionality that one can draw upon. But also state in my experience that I do not always see companies utilizing the technology that they have to its maximum capability. To your point, Susannah, about overcoming certain barriers, I think there's a small handful of elements that we should look to consider to aid us in achieving our goals in terms of how we utilize and maximize the benefit associated with the technology. The first is taking some steps to really inventory what your data needs are. Obviously, all of us are tasked with certain objectives in our roles, be it in be it in finance, legal, whatever the aspect of the organization. With that, there are certain data components that one needs to harness to better understand how their program is performing, and then use that to inform future decisions. Once you've taken the opportunity to better understand your data needs, the next step is to start to look at your overall system requirements. What do you need the technology to do to best harness the data that you have, to flag insights for you, maybe to identify some of the anomalies that Maggie was referring to a little bit earlier in a payroll context.

But it's really having a set of clearly defined system requirements so you now understand what you need a technology to perform, and perhaps are now in a better position to evaluate technologies available to you, which then takes me into my next point. I think oftentimes in an HR and mobile talent context, I see professionals automatically start to scour the market for a technology that can support their data needs and system requirements. But oftentimes, they haven't taken a step to really look at their current technology stack. What enterprise technology do you have available already in your organization that can perhaps perform some of these functions and do so at a quicker startup rate and for that matter, lower cost of ownership, given that resource already sits within your environment and has been appropriately tested from a security standpoint. So I think when you take the time to understand your data needs, understand what your system requirements are, and look at your current technology stack to see what it can perform, you put yourself in a much better position to make use of what you have to drive better business decisions as you walk forward.

Streeter

Yeah, really interesting. That focus, and it's so crucial on ensuring you understand the data. Gareth, I'm going to move swiftly to you. As we've been explaining, the essential ingredient underpinning all these tools is data. So how should companies be designing a data blueprint for GenAI? And just why is it so important?

Paine

Yeah, that data blueprint, I mean, it's crucial for companies looking to leverage any type of tech, but particularly AI. And I think if you get this blueprint structured in the right way from the start, again, it will then really help you, I guess, get the most out of AI, and it will address things like the ethical, the legal, the operational considerations. And there are a number of steps that you would go through to get that data blueprint up and running. I think number one, I'll pick out some of the important ones. Number one is define the objectives. What's the use case? What are the goals of using GenAI? Is it going to be a customer service outcome? Is it automating some specific creation of content? Clearly outline that use case and make sure the blueprint, of course, aligns then with the objective of the business. I think then steps like looking at your data, as Jason mentioned, also a data inventory. Conduct that inventory of your data upfront, the data sources that you have, structured and unstructured. Think about data prep and cleaning a few steps forward, but think about what the process is going to be for cleaning your data and transforming it into something that GenAI is able to read and to use.

Last but not least, there's a few other steps in between, but just to call out the ethical side, really incorporate those considerations into the blueprint up front, including things like bias detection. One option, one way of maybe helping the data build out, and again, a phrase you might have heard of it is synthetic data – basically generated synthetically, which again will also help perhaps with things like data privacy because it can remove things like PII in real data. It might also give you better bias mitigation by giving you perhaps a larger representation of data that perhaps might be normally underrepresented. Again, you need to trade carefully with synthetic data, but it's another thing to consider as part of your blueprint. Why is it important to have this GenAI blueprint? One, of course, aligning with the business. Two, I think it comes down to making sure you've got high data quality and integrity. Without that, the GenAI model will be as useless as the data going in, essentially. But also it will give you, I think, many clients are talking about some advantage. So the ones that can actually use that blueprint will then start to see the edge come in by proving your efficiency, using GenAI and hopefully improving the experience for the end user. But it really does lay the foundation for what's possible in a responsible way.

Streeter

So if you've got the foundations right and you've really optimized the use of their technology, Maggie, how can this increase transparency and just how valuable is that for organizations?

Lundervold

I think, as we've talked a lot about today, mobility is such a rich landscape of data. Again, so many different sources feeding in about so many different aspects of an individual corporate and regulatory environment. I think that being able to have the data mean something is huge. Mobility has been a place where I think that whole definition around ROI, and really what it is, and how you quantify it, has been a struggle for mobility. We've been able to do that, usually on an individual level, to say, okay, it costs this much for this person. They stayed this long, and we got this specific value. If you spend all the time pulling that data together. But being able to do that on a program-wide standpoint, I think mobility professionals are being pressured every day to say, justify the cost of what you're doing. I think being able to harness that data and drive some strategic insights to say, here's actually what we're doing and the benefit it's providing or what it's costing us, is a game changer for mobility professionals to be able to have a strategic conversation and not just an operational one.

Streeter

I think you've certainly made the argument, but what are the practical steps companies can take to introduce GenAI responsibly as well? I mean, is there a roadmap that they should be following? Gareth, if you start off.

Paine

Yeah, I think there's a couple of things they can do. One is set that data blueprint, right? That is the start point, I think, for any journey with the GenAI. And really outlining clearly out front the objectives and the use case. What is the end goal in mind here? Again, lots of clients, I think you can get quite fascinated with the technology. And as we saw, that idea of it being maybe a bit of a bubble. There's lots of investment, billions being put in. But what is the real use case for it? I think that's a good place to start.

Streeter

What is the real use case for it? It's absolutely crucial, isn't it? I suppose, Jason, then it's finding the right partnerships. I mean, how do you ensure that any platform you use aligns with business goals and risks as well? There's an awful lot to consider.

Ward

There are indeed quite a few variables that one needs to think their way through, and I'll mention a few of them here just based upon some past experiences that I think we've had across Gareth, Maggie and myself. First and foremost, I wanted to define platform in this context as the various resources needed to execute a mobile talent function. That doesn't necessarily just mean a technology, even though I'll touch on that a bit here. What I'll start with, though, is that mobile talent functions tend to lean on a variety of internal and external people resources to aid their overall execution. One needs to really focus on really driving partnerships with other business functions within the organization that are going to provide inputs that are going to create dependencies for overall execution. That could be resources like HR business partners, it could be corporate tax colleagues, it could be the legal function. In some cases, it may even be the payroll function. But there are a variety of individuals that one needs to be properly connected to as a mobile talent professional to make sure that the dots are connecting and all functions are operating in harmony as it relates to cost and risk-related decisions for the organization like those mentioned earlier.

But even with that, certain organizations are still going to have gaps where they don't provide a certain component that's necessary to execute on mobility. And oftentimes in those gaps, a mobile talent function will engage a provider to help fill them. That could be a relocation-related function, an immigration-related function like Maggie was talking about earlier, or perhaps even in some cases, a technology resource that is designed to bring together multiple applications and tie together data points that I mentioned earlier. But I think at the end of the day, when you look at this, all of these stakeholders have varying business needs, and they're not going to come together to solve those oftentimes in a mobile talent context on their own. This is an opportunity for one to take charge, bring those different business needs together, and then harmonize the data that they are driving through a technology resource that allows all of them to have transparency to how the program is functioning, and then will in churn help drive, as I was articulating earlier, future business decisions to align with the organization's talent objectives.

Streeter

Have there been any specific examples that you've seen of companies really leading the way?

Ward

I would say in terms of leading the way, and I'm going to play off of my comment around, I'll call it being a catalyst around these areas, where I've seen organizations have real success, are those that have really pushed heavily on the development of a cross-functional steering committee or Steerco. It's bringing together those various perspectives and insights that exist across multiple functions that sometimes have invisible walls between them. Now, putting together this Steerco and objectives and purpose around mobility is a lot easier said than done. It takes some depth of knowledge in terms of how all these pieces fit together. It takes discipline and making sure that we can keep everyone on track to helping to align on our core objectives. It takes some ongoing commitment and participation to really drive and share inputs that lead to overall success. But when I've seen this multi-stakeholder buy-in, and this will be a subject matter I touch on in a little while, but it really sets up the organization a lot better in terms of management of change. The Steerco will make decisions, agree to a path forward and a timeline around them, and then having that multi-stakeholder buy-in really sets the organization up better to make sure that the changes that have been agreed actually stick.

Streeter

How soon do you think, Maggie, that if you use the right technology, follow the right roadmap, that you will really see leadership from this micro to more macro level of oversight?

Lundervold

I think in the way that we are seeing GenAI come in at lightning speed. I think we will see those changes come through as well. But it takes this prep to get there. It's, I think, that once we see organizations really embrace it, really be able to quantify the impact of having the data insights, of having these cross-stakeholder points of view and being able to pull that into one place and being able to pull the systems into one place. I think we will see an acceleration of moving from micro of just managing on the individual level or business unit level or country level, and to the macro of really being able to manage across a strategy level. I think it is accelerated by GenAI, provided all of those foundational pieces are there that Gareth touched on with the blueprint, and Jason touched on with the network and stakeholder ecosystem.

Streeter

It certainly is clear we've really moved up again. We're accelerating along in the fast lane, Jason. Are we? It's clear. So teams need to rapidly develop new skills to stay ahead of the curve. What should they be investing in and how urgent is it?

Ward

I'd say, Susannah, from my viewpoint, I'd say two core areas, and I'd say there's a high level of urgency with both. I do them both in parallel is what I'm getting at, as opposed to sequentially. So playing off of some of my last remarks, GenAI represents some level of change for an organization, in some cases more change than others. And I have found that in a mobile talent context, really starting to invest in a manage of change resources and capabilities really helps better set not only the mobile talent function, but its corresponding stakeholders for different ways in which the technology can best be harnessed to really help detect anonymities, predict perhaps for future outcomes, and overall lead into some of the business decisions that need to be made to align, as I was suggesting earlier, with broader organizational talent goals. Again, the first is that investment in management of change resources. But then second to that, and I'm going to say it's probably more talent pipeline related, but really looking at the development of broad-based mobile talent professionals. I have seen across a lot of different companies very specialized resources that are good at certain things, but sometimes have trouble playing across multiple areas.

If one is to truly design use cases for GenAI, implement them, and then harness to their maximum capability, it does need that individual with a broad viewpoint across the mobile talent life cycle and understanding its various touch points to really set one up for success. Again, I think the two key ones, depth of management of change functionality to make sure that these changes can be adopted and stick, but then second to that, that really broad-based vision across your mobile talent team so they can play across multiple areas, not just specific aspects of the mobility life cycle.

Streeter

I suppose as well, given the depth and the breadth of change needed, it's super important that this is all tracked, the progress is tracked. So Gareth, how do you think teams can best track, monitor, and also shout about their progress?

Paine

I think actually it's very similar to any type of technology or project. It's essential that you start that from the beginning to make sure you stay in line with your initial goals, you measure success, and you can, as you said, start to play back success stories. I mean, there's a few steps. Number one, I would say is, as I said already, but establish the objectives and the KPIs. What is the objective of the AI initiative? What are the KPIs that you're going to measure yourselves against? Again, they'll be with you for the duration of the implementation and beyond. I think, again, like any project, some project management framework to allow for some flexibility. It's an iterative process. I think the use of GenAI is not something you ... like a software project that you switch on and you go live per se. It's iterative, and there'll be various sprints in that journey. I think at the same time, In terms of keeping the teams together, this is quite a new area for us, but the same rules apply, I think, to any new tech. So regular team meetings, regular check-ins, discuss challenges, share insights.
I think that will help foster collaboration amongst the teams that are working on these projects and also encourage feedback loops. So open comms around what's working well, what roadblocks you have, and maybe what needs to be reworked. I think in terms of stakeholder engagement, making it clear how we're doing regular engagement with stakeholder stakeholders. So make sure that they can see how you're getting on, successes, challenges, next steps. I think transparency with GenAI is going to be perhaps even more important than maybe other tech, again, given what we said earlier around perhaps the bubble, billions being brought in, but actually, what's the use case? Again, I think this will, that transparency, it will help build trust. And again, encourage collaboration from the C-suite. And of course, keep learning. So evaluate the effect of these models that you're starting to implement. Keep up with the latest trends. It's moving very fast every day. You get blasted with a new announcement of the GenAI, but try to keep up with the latest trends and the best practices, and also encourage the teams working with you to do the same. Invest time in training, workshops, conferences, to keep their skills where they need to be.

Streeter

Absolutely. As you said, keep the C-suite on side. Maggie, what other insights does the EY survey provide in such a fast-changing world that the C-suite really does need to be aware of?

Lundervold

Well, and I think just that C-suite point is one, is that having leadership buy-in is something that we saw in our survey when we had our conference and we had a lot of courses that looked at the survey data of what the evolved mobility programs are successful and how is that success supported? That leadership buy-in is key. But I think when you think about building up to that, the things from the survey and that we've talked about today, those programs that are proving the most successful and feel like they're really further on their mobility journey are having that lack of silo or not having silos, rather, being interconnected, not just as a mobility team between operations and strategy, but across their internal stakeholder network as across their entire mobility ecosystem. That ability to have your deep subject matter expertise but work across is really key. We saw that coming out of the survey. Also aligning to broader talent strategy was another really key part of the survey. I think we've talked about that a little bit today in terms of having your GenAI strategy, but tying that to mobility strategy, which ties to your talent strategy, which ties to your overall business purpose.

I think we saw that strong in the survey. As we're seeing companies continue down their GenAI journey, that alignment is key because you can say, well, here's what I'm going to do with GenAI, but if it doesn't tie to anything else, it's not going to get very far. Then I think that last piece that we saw from the survey, which is supported by our conversation today, is mobility, being able to be a strategic advisor, drawing on data insights, drawing on knowing that alignment to strategy, and drawing across what's important to stakeholders. I think we're just seeing all of those things out of the survey become even more foundational to evolving mobility programs.

Streeter

Certainly. We've talked about so much today. There is such a lot to take on board, isn't there, in this really fast-moving world. But I'm actually going to ask you to distil it down to just one takeaway from each of you that the audience can go away with. If you could give the audience a really simple piece of food for thought, what would it be? I'm going to ask you first, Gareth.

Paine

Yeah, for me, I would say, invest the time and dig deep into understanding GenAI, and also not just for yourself to understand the nuances of it and what does it really mean? Really peep behind the curtain and figure out the use case applicability. But also, if you're in a position of controlling projects and making decisions on it, give your teams the breathing space to learn. I think like any new bit of tech itself, it takes time to learn. Again, lots of people will shy away saying, I'm just too busy to learn. So give your teams the ability and the freedom to learn about GenAI, and then you'll start to see better returns rather than just giving access to thousands as a copilot last in season, hoping for the best. Give your teams time and invest time in yourself and your own education.

Streeter

Invest in time and talent. Okay, thank you. Jason?

Ward

Playing off of some of Gareth's remarks, I would say, continue to focus on the strengthening of cross-functional relationships and associated business understandings. The reason for this is some of the best ideas for GenAI future use cases or even enhancements to existing ones come from engagements with diverse mindsets like these. By the way, that also includes your corporate IT function. I think making time to really engage and draw upon these thoughts are the foundation of one's future of success across their mobile talent function.

Streeter

Absolutely. Maggie, final thought from you.

Lundervold

I guess mine would be keep the purpose in mind. I think sometimes it's very easy with everything that comes at us and comes at us as mobility professionals, and that changes all the time, is making sure that the purpose of your mobility program is clear and using that to align to and your broader business, but really make sure that that purpose of the program is what helps drive your GenAI strategy and your overall engagement. Know why you're doing this, why you come in every day and support your global workforce. But that purpose piece, I think, is what I'd encourage people to stay close to.

Streeter

Well, thank you so much. Those simple changes to reimagine mobility. Great takeaways from all of you. It's been great to have you on the podcast. Thank you very much for your time.

Lundervold

Thanks so much. It was great to spend time with you guys today.

Ward

Thanks. Appreciate it. Great to see everyone.

Paine

Yeah, great session. It was helpful for everyone and look forward to the next.

Streeter

Thank you very much. Just before I go, a quick note from the EY team. The views of third parties set out in this podcast are not necessarily the views of the global EY organization or its member firms. Moreover, they should be seen in the context of the time in which they were made. I'm Susannah Streeter. I hope you'll join me again for the next edition of the Tax and Law in Focus podcast, EY: Shaping the Future with Confidence.

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Susannah Streeter

Senior Investments & Markets Analyst, Hargreaves Lansdown, UK

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Episode 24

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37m 37s

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How GenAI reimagines global mobility

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Susannah Streeter

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