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How global mobility can build a strategic AI foundation

Photographic portrait of Kushan Shah
Kushan Shah

EY Global People Advisory Services Tax Technology Leader

9 minute read 16 Mar 2026
Related topics
Workforce
Tax
AI

Organizations require a holistic view of GenAI and agentic AI. Mobility functions can see short-term wins and long-term evolution.

In brief

  • Most mobility functions face data and operational constraints preventing short-term wins and long-term planning.
  • Horizon-scanning and hyper-personalized employee experiences are high-impact areas for mobility to explore.
  • Functions should avoid a tentative approach to AI waiting for quick wins without building the foundations needed for more robust tools in the future.

From every angle, generative AI (GenAI) and agentic tools are disrupting how organizations consider operations, strategy and investment. Despite a rapid increase in AI adoption levels, most organizations are still seeking a “killer app” for functions that have high strategic importance like mobility, global payroll and human resources (HR) more generally. Many organizations are stuck experimenting with isolated tools, waiting for near-term benefits, while also failing to lay the foundations for long-term impact. Other organizations are filling their operations with semi-autonomous AI agents, only to find that these agents simply turn broken processes into ones that fail faster.

Functional leaders have already heard about AI’s potential for helping to close talent gaps, and about the promise of agents ushering in a new wave of efficiency and cost savings. But leaders need more than promise and potential for the phase to come. They need roadmaps toward real value that keep the concerns of people from getting lost during the journey.

Hype around GenAI can’t sustain interest and investment for the long term. Now is the moment for workforce mobility functions to chart the path toward practical AI use cases, with strong opportunities in three key areas:

  1. Strategic purpose with deeper horizon-scanning
  2. Elevating employee experiences with integrated personalization
  3. Bringing ground-level examples of agentic AI’s value today

As the initial shockwaves of GenAI and agentic AI continue to subside, mobility functions that move proactively, with purpose, will be best positioned to realize value from an AI-enabled workforce.

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1

Chapter 1

Why strategic AI matters to mobility

Early focus on operational gains from AI tools misses the bigger picture. Risk forecasting helps plot a future course, while agents can help improve user experience today.

Increased adoption of AI tools by individual and enterprise users is finally moving the conversation from broad awareness to strategic action. An overwhelming 88% of employees say they are using AI tools at work to some degree, with 37% using them daily, according to the EY Work Reimagined Survey. But the survey shows only 28% of organizations are positioning employees to realize transformational impact from AI.

Functional leaders will recognize the roots of this disconnect: teams need to balance operational needs and routine processes with a desire to focus on more strategic, higher-value activities. With tight budgets and market volatility, executive sponsorship for technology investments in functions like mobility require clear returns on investment (ROI). As a result, some mobility functions delay action altogether, expecting those clearer returns, while others adopt tools without a roadmap to connect early experimentation with longer-term transformation.

Sustainable value, which can elevate the internal use case for AI transformation, is built through enhanced strategic foresight and risk management, improved employee experiences and functions reshaped for an agentic future.

There is also still confusion over the skills and experiences needed to get the best from AI, which is slowing down progress as teams scramble to upskill. However, the distinction between the “business side” and the “technology side” has vanished, replaced by a singular requirement for integrated intelligence across teams that breaks down the technology vs. business barriers that once existed.

Benefitting from an AI foundation

AI is transforming the nature of work carried out by humans, calling for new skills, operating models and processes. In short, it is challenging the way we get work done. For some organizations, this disruption is forcing a shift in how functions operate and how they are organized, especially functions like mobility and global payroll, which are vital to fulfilling executive talent strategy.

Many functions remain constrained by fragmented data, legacy workflows and operating models designed for manual execution. Mobility teams, in particular, need to find ways to deploy AI to enhance high-touch processes such as coordinating immigration steps, collecting assignment-related compensation data across multiple systems, and managing year-end tax reconciliation cycles that still rely heavily on manual inputs.

But technology is only as useful as the people who are trained to use it. Adoption and adaptability are key.

There are already examples of functions laying the groundwork for a future where human creativity and agility are prized, as AI augments such technical capabilities. The EY Tax and Finance Operations Survey shows that almost all respondents agree that strategic thinking, problem-solving and critical thinking are essential for future tax professionals, with communication and collaboration skills cited by 78%. A premium is put on human connection and authenticity.

Greater than individual skills, functional leaders can see this as the foundation of teams that can see long-term and short-term benefits from new tools without falling prey to tabloid messaging around AI.

Horizon-scanning for the long term

Global organizations should already have capabilities for horizon-scanning and scenario planning to help assess and evaluate potential market events and risks. GenAI systems allow for a supercharged analysis of scenarios based on large and sometimes disparate data sets. The use of digital agents can break this distillation of historical data and predictive analysis into semi- or entirely autonomous steps.

More specifically for mobility functions, this could mean continuously monitoring immigration reform proposals, upcoming tax treaty negotiations, shifts in cost-of-living indices, or geopolitical developments that may disrupt current or planned assignments.

In reality, few mobility teams have the capacity to monitor and synthesize these signals today, leaving them reactive rather than prepared when change arrives. But this isn’t sustainable. The risks of not enhancing horizon-scanning, and missing regulatory updates, can create cascading effects, from assignment delays to unexpected payroll reporting obligations. Early detection becomes a tangible strategic advantage.

But this strategic value is dependent on having the highest quality data and the “humans in the loop” who have the skills and abilities to plan, troubleshoot and adapt the process as needed.

These are central factors in the long term, just as they are in the short term.

How EY can help

Personalized experiences in the short term

One of the more straightforward and high-visibility uses of agents in the near-term is for personalization of common tasks. EY data show AI use today is still concentrated in certain key areas, including customer or user experience, with 31% using AI to access customer support, and in personal applications like content translation (29%).

 

These are immediate, tactical benefits that functions can deploy but often don’t. Many organizations struggle to move beyond pilots or point solutions, leaving tangible improvements to employee experience unrealized.

 

In mobility especially, users need to access multiple systems to find tax, immigration, regulatory or HR information. Agentic tools can streamline this by producing personalized policy summaries, location-specific onboarding checklists, or plain-language assignment briefings that reflect an employee’s family situation, role and host country requirements. Any friction between employees and these systems can create stress on themselves and their families, and disruptions in their work. Personalized guidance using the latest natural language processing capabilities can also extend to family-specific needs, such as schooling options or access to local medical care, which are often make-or-break factors for assignment success.

 

With relatively little effort, AI tools can customize access to data and provide formats that are most likely to help the employee, even nudging employees with additional insights that may be of help based on personal circumstances. This ultimately can help improve employee experience and sentiment, while helping to control costs of data management and access.

 

Metrics and benchmarking can be built into these systems, providing a feedback loop that helps iterate in real time. This is particularly powerful in mobility, where post-assignment surveys, vendor evaluations and free-text employee comments often remain siloed and under-analyzed despite containing rich insight into assignment success factors.

 

Efficiency gains from AI deployment are positive, but that is only part of the puzzle. Poor customer or employee experience will almost always lead to worse outcomes.

 

Taken together, these shifts make it clear that the next phase for mobility isn’t simply about understanding AI’s potential, but about putting it to work in ways that solve the function’s everyday challenges.

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Chapter 2

What mobility functions can do with AI now

As mobility teams confront rising complexity and tighter expectations, the most practical gains from AI often begin in unexpected places.

How EY can help

Because mobility sits at the center of tax, immigration, payroll and employee experience, even small agentic tools can deliver outsized returns. They help teams interpret the signals already hidden in their own processes, clarifying where people struggle, where operations stall and where expectations fall short. The heavy data lift and analysis can be accelerated with AI agents to help teams move faster in their interpretations.

 

One reason progress stalls is that mobility teams lack clear, actionable insight into where experience breaks down today and how those signals connect to future program design. The example that follows illustrates how mobility can begin experimenting with these tools today, starting with a simple sentiment-analysis agent that turns scattered feedback into structured insight.

Three steps to prepare mobility functions to be AI-ready

Without deliberate action, mobility functions risk falling into a familiar pattern: limited experimentation that delivers little return.

To bring advanced AI tools into organizations and realize real-world impact, mobility leaders have an opportunity to level-up themselves and their functions. To foster human success, leaders can work to upskill themselves and their teams, invest time to learn about tools and use cases, and create an environment for experimentation and iteration.

More broadly, functions should lay the groundwork for a strategic AI foundation:

  1. Identify and assess use cases where GenAI can add value to roles and processes, enhance employee experience, or provide data-driven insights for decision making. Seek to identify the problem you are trying to solve for. For mobility teams, early candidates include automating data collection workflows, improving relocation vendor coordination through agent-to-agent task handoffs, and predicting likely drivers of assignment exceptions.

  2. Design a data strategy to gather and organize clean data needed for GenAI to function effectively with accurate and relevant outputs. This may require harmonizing disparate assignment-related datasets — from tax equalization inputs to policy exceptions across a variety of platforms — to create more reliable longitudinal insights into assignment success.

  3. Pilot and iterate GenAI in controlled environments to refine its application and demonstrate its value before scaling it across the organization. For example, functions might begin with a single assignment type (such as short term assignments or commuters) or a single host location to test the impact before rolling out broader AI augmentation.

For mobility functions, the risk is no longer adopting AI too quickly, but adopting it too tentatively. Those that fail to act now may miss both the near-term gains within reach and the longer term capabilities they will soon need.

Special thanks to Gareth Paine, Partner, People Advisory Services Tax, EY Advisory S.p.A. who significantly contributed to this article.

Summary

Many global mobility functions are approaching AI with caution, experimenting at the margins while postponing the deeper work required to see real returns. Such tentativeness risks leaving both short‑term gains and long‑term readiness on the table. Mobility‑specific examples show how a strategic AI foundation, paired with practical agentic tools, can reduce friction, improve employee experience and surface clearer insight today while better anticipating and adapting to risks tomorrow. A sentiment‑analysis agent illustrates how starting small can help mobility teams build momentum for sustained transformation.

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    About this article

    Photographic portrait of Kushan Shah
    Kushan Shah
    EY Global People Advisory Services Tax Technology Leader
    Technology leader. Champion of innovation. Passionate about efficiency and user-centric design.
    Related topics
    Workforce
    Tax
    AI

    EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.