By Olivier Meier, Mercer
Mobility management is shaped by a range of factors, including shifting organizational structures, fluctuating economic conditions and evolving employee expectations. Ideally, best practices would offer a clear path to success, but the reality is much more complex, creating dilemmas for mobility professionals.
While recent years have opened new opportunities, they have also introduced significant challenges, complicating decision-making: the advent of artificial intelligence (AI) in human resources (HR), new forms of mobility and virtual working complementing or even replacing traditional assignments, and various attempts at transforming the mobility function. Mobility managers must provide actionable solutions to leadership, but straightforward answers are elusive. Instead, they navigate a landscape filled with intricate issues and are forced to find pragmatic compromises.
The way mobility managers address these dilemmas will shape the success of talent mobility initiatives going forward. In this exploration of the seven key dilemmas facing mobility managers in 2025 and beyond, we will discuss each issue and offer insights on transforming these challenges into opportunities for international HR and talent mobility professionals.
AI adoption in mobility management: Fast implementation or thorough preparation?
Observations
The integration of AI into global mobility management presents both exciting opportunities and distinct challenges. Historically, the mobility sector has lagged behind other areas of HR in technology adoption. A significant portion of mobility teams still heavily rely on Excel spreadsheets, and the transition to specialized mobility platforms is often fraught with challenges. This sluggish adoption is partly attributable to the niche aspects of mobility, which typically operate on more limited budgets and impact a smaller employee demographic compared to other HR functions.
In contrast, recruitment has seen the most significant adoption rates, particularly in using AI to match candidates’ skills with organizational needs. Moreover, AI adoption is on the rise for talent development and onboarding, where customized solutions are being deployed to enhance skill acquisition. A number of organizations are also successfully integrating AI into their reward and performance management systems, reflecting the pressing nature of these issues in today’s workforce. Strong business cases have emerged in these domains, particularly in light of increasing demands for pay transparency and equity considerations.
AI’s entry into mobility can be approached from multiple angles:
- Through generic AI tools like ChatGPT for basic user interactions, chatbots and increasingly AI agents; these generic tools can serve as building blocks for more advanced functionalities.
- Through dedicated AI solutions tailored to HR functions indirectly related to mobility, such as recruitment and talent management tools.
- Finally, for organizations that already have mobility platforms, through the progressive adoption of AI modules within these systems. These modules can facilitate cost projections, automate document generation and enhance analytics.
The dilemma
Despite the clear potential for AI, a significant dilemma persists: many organizations implement new technologies without fully assessing their specific needs. As a result, the implementation often yields only marginal benefits.
On one hand, there is mounting pressure from management to advance AI adoption fast; on the other hand, the organization-specific business cases for deploying AI within mobility management have not been thoroughly clarified.
The pressure to move quickly on AI adoption is also justified by the lack of resources in HR and mobility teams to manage the growing forms of mobility, such as frequent cross-border moves or international remote working, along with their related tracking and compliance challenges. Mobility teams are increasingly being asked to broaden their purview without an increase in headcount. Sixty-four percent report that they are under-resourced in terms of staff, and sixty-six percent in terms of tools and technology (Mercer’s 2025 Talent Mobility Outlook survey).
However, when engaging in conversations about desired technology, a concerning trend emerges: many stakeholders are focused on requests for old approaches — basic functionalities, fixed dashboards, enhanced data filtering options and improved menu designs. While these requests reflect valid concerns with legacy technologies, they overlook the transformative potential of AI-driven solutions.
AI operates fundamentally differently; it emphasizes dynamic interactions and a conversational approach, allowing users to articulate their needs and, in turn, generate customized outputs. This shift in mindset towards a more interactive dialogue with technology requires a reimagining of how we frame our technological needs.
A fundamental issue arises from the inadequate assessment of organizational needs prior to technology implementation. Many mobility teams fail to lead a comprehensive analysis of their current processes, available data and pain points, making it difficult to identify the most relevant AI applications. This oversight often results in the deployment of solutions that do not effectively resolve existing challenges, further perpetuating inefficiencies in mobility operations.
Resolving the dilemma
Reconciling speed of adoption and thorough preparation implies a need to step back and reflect on what organizations are trying to achieve and their starting point. Some organizations and teams are more advanced than others in their digitalization journey.
A foundational step in leveraging AI effectively is cultivating awareness and a minimum level of experience among mobility team members. This awareness phase is critical and necessitates hands-on experience with AI tools — even generic ones like ChatGPT. Utilizing such tools for basic, day-to-day tasks not only enhances familiarity with AI but also empowers users to propose innovative use cases for global mobility. As mobility teams become more comfortable with AI, they can begin to harness its capabilities in meaningful ways and suggest new applications for mobility management.
Practical examples of the use of generic AI tools include reviewing assignment policies and documents for consistency (but AI tools might be reliable enough for legal compliance), introducing chatbots to answer queries from mobility stakeholders and HR teams and preparing basic metrics (e.g., using AI agents to retrieve and analyze data in Excel).
Far too many organizations leap past the essential initial assessment of their requirements. This assessment must encompass an understanding of the desired interactions — identifying which tasks and processes can be enhanced by machine learning versus those that require human oversight. Moreover, organizations must clearly define their objectives: What specific outcomes are expected following the implementation of AI? Each anticipated outcome should be tied to explicit metrics, providing a framework for evaluating success.
Desired outcomes
| Examples of outcomes |
Metrics |
| Reduced administrative burden |
Percentage reduction in time spent on administrative tasks, number of manual data entries eliminated, reduction in processing time for visa and immigration applications. |
| Enhanced compliance |
Percentage increase in compliance rates, reduction in compliance-related penalties or fines, number of compliance violations detected and resolved. |
| Streamlined assignment process |
Reduction in time to manage the entire relocation process. |
| Enhanced employee experience |
Employee satisfaction scores related to mobility support, and reduction in employee complaints or issues related to mobility support. |
| Improved cost efficiency |
Percentage reduction in overall mobility costs, cost savings achieved through optimized relocation arrangements and reduction in duplicate expenses. |
| Improved talent management outcome |
Percentage increase in the number of employees participating in international assignments (talent pool diversification), employee retention rates and career progression post-assignment. |
Many organizations face challenges related to the integration of AI solutions with existing systems. HR teams often utilize disparate platforms that may not communicate effectively with one another. This siloed approach can hinder the seamless flow of information and limit the potential benefits of AI, as data from one system may not be easily accessible to another. Ensuring that any AI tools implemented can integrate smoothly with current systems is crucial for maximizing their impact. Additionally, robust data preparation — including sorting and labeling data — is essential for AI to function effectively.
Implementation — Data management
| Data issues |
Examples |
| Data labelling |
Efficiency and compatibility issues.
Possibility to use to combine data from different sources/systems and produce meaningful comparisons. |
| Output consistency |
Respecting the logic and terminology used in talent mobility policies.
Avoiding blurring the terms and conditions offered by the companies nor create undue expectations. If not properly managed.
Avoiding confusing vocabulary and suggest solutions based on their data models that are not allowed by the companies. |
| Privacy and security |
Avoiding input of company and employee data into publicly accessible AI tools.
Checking data inputs (copyrights and privacy issues)
Avoiding data privacy issues resulting from crossing sources of personal information. |
Another significant hurdle is the cultural resistance to adopting new technologies. Mobility teams may be resistant to change and skeptical about the reliability and effectiveness of AI. Resistance can stem from concerns about job displacement, apprehension about relying on technology for complex decision-making or simple inertia within established workflows. Overcoming this resistance requires strong leadership and communication, emphasizing the value of AI as a tool for enhancement rather than replacement.
Organizations frequently adopt a short-term perspective when implementing AI, focusing on immediate gains rather than developing a long-term strategy for technology deployment. This short-sightedness can lead to the prioritization of quick fixes over more comprehensive, sustainable solutions. The adoption of AI is a multi-year project and not a one-time exercise yielding instant added value to the business. This required long-term vision may clash with the business's short-term objectives and HR managers' annual performance indicators.
Organizations often struggle to define success in the context of AI adoption. Without established metrics tied to business objectives, it becomes challenging to measure the impact of AI investments, making it difficult to justify further expenditures or adjustments in strategy.
Find out more: The AI advantage: Practical applications for talent mobility