Many global mobility functions are still struggling to unlock the full value of artificial intelligence because they face significant data, process, and operational constraints. While interest in generative AI (GenAI) and agentic AI tools is growing rapidly, many organisations remain stuck in a cycle of experimenting with isolated tools in search of quick wins, without building the foundations needed for long-term transformation. In some cases, organisations are deploying semi-autonomous AI tools into flawed workflows, only to discover that these systems simply make inefficient processes fail faster rather than improving them.
The article explains that mobility, global payroll, and HR functions are under pressure to show clear returns on investment before receiving executive support for new technology. Tight budgets and market uncertainty make leaders cautious, which often results in delayed action or fragmented experimentation. Although 88% of employees say they use AI tools at work to some degree and 37% use them daily, only 28% of organisations are truly positioning their workforce to achieve transformational impact from AI. This gap reflects a broader challenge: organisations want AI-driven efficiency, but many have not yet created the right strategy, skills, or infrastructure to support sustainable value.
A strong AI foundation, the article argues, should focus on three major outcomes: better strategic foresight and risk management, improved employee experiences, and functions redesigned for an agentic future. This means mobility leaders need to stop viewing AI as just a tool for short-term automation and instead treat it as part of a broader transformation in how work is done. As AI changes the nature of tasks performed by humans, organisations will need new skills, operating models, and ways of working. The distinction between “business teams” and “technology teams” is also becoming less relevant, as future success will depend on integrated intelligence across both areas.
For mobility functions specifically, many of the challenges stem from fragmented data, legacy workflows, and manual processes. Teams often still rely heavily on manual coordination for tasks such as immigration processes, assignment-related compensation data collection across multiple systems, and year-end tax reconciliation. These kinds of high-touch processes make mobility functions especially vulnerable to inefficiency, but they also create strong opportunities for AI to improve speed, accuracy, and coordination. However, the article stresses that technology alone is not enough; organisations also need people who are trained, adaptable, and capable of working effectively with AI-enabled systems.
One of the most important long-term opportunities highlighted is horizon-scanning and scenario planning. Global organisations already need to monitor changing market conditions, regulations, and geopolitical risks, but GenAI can significantly strengthen this capability by analysing large and varied datasets at speed. For mobility teams, this could mean continuously tracking immigration reforms, tax treaty negotiations, cost-of-living changes, and geopolitical developments that may affect employee assignments. Since many mobility teams currently lack the capacity to monitor and interpret these signals consistently, they often end up reacting to change instead of preparing for it. The article notes that improving horizon-scanning can create a real strategic advantage by helping organisations detect risks early and avoid costly delays, compliance issues, or payroll complications.
In the short term, the article identifies personalised employee experiences as one of the most practical and visible uses of AI. Many employees in mobility programmes must navigate multiple systems to access tax, immigration, regulatory, or HR information, which can be stressful and time-consuming. Agentic AI tools can simplify this by generating personalised policy summaries, location-specific onboarding checklists, and plain-language assignment briefings tailored to an employee’s role, family circumstances, and host-country requirements. AI can also support more personal needs, such as information on schooling options or access to local healthcare, which are often crucial to the success of an international assignment. By reducing friction and providing more relevant guidance, these tools can improve employee satisfaction while also reducing the cost and complexity of managing information.
The article also points out that mobility teams often collect valuable feedback through post-assignment surveys, vendor evaluations, and employee comments, but this information is frequently siloed and underused. AI can help analyse this data more effectively, turning scattered feedback into structured insights that show where employees struggle, where processes break down, and which factors most influence assignment success. This kind of insight can help mobility functions improve programme design, enhance service quality, and make more informed decisions over time. The article notes that even relatively small agentic tools, such as a sentiment-analysis system, can deliver meaningful value and help teams begin building momentum for wider transformation.
To become AI-ready, the article recommends that mobility leaders take deliberate and practical steps. First, they should identify specific use cases where GenAI can add value, whether by automating repetitive workflows, improving employee experience, or generating better data-driven insights. In mobility, this could include automating data collection, improving relocation vendor coordination, or predicting assignment exceptions. Second, organisations need a clear data strategy to clean, harmonise, and organise information across systems so AI tools can produce accurate and useful outputs. Third, leaders should pilot and refine AI tools in controlled environments, such as testing them on a specific assignment type or host location before scaling more broadly. This phased approach allows organisations to learn, adapt, and demonstrate value while reducing risk.
Overall, the article concludes that the greatest risk for mobility functions is no longer moving too quickly with AI, but moving too cautiously. Teams that remain overly tentative may miss both the immediate benefits already available and the deeper capabilities they will need in the near future. By building a strong AI foundation now, mobility functions can reduce operational friction, improve employee experiences, strengthen decision-making, and better anticipate future risks. Starting with focused, practical use cases can help organisations create momentum and position mobility as a more strategic, AI-enabled function over the long term.
