AI is increasingly embedded in global work and how organizations manage people working across borders. Advanced technologies are helping them to triage immigration cases, monitor compliance, and coordinate payroll workflows.
This is accelerating traditionally manual, fragmented processes. And the gains are tangible, from faster turnaround times to less administrative work for mobility teams. These advanced technologies are reducing costs and increasing consistency, and for many mobility teams these will be the first signs of AI’s value.
But organizations that fixate on these productivity improvements could be setting the bar too low. The bigger shift from AI is not in how work is done, but in how organizations decide where talent can go, taking into account potential risks and greatest value.
If that is the case, why are so many organizations limiting AI’s use to productivity? Let’s explore what’s holding them back and how AI can help them unlock more value than they realize.
Most organizations are using AI only at the task level, capturing efficiency but missing the bigger shift. Tools are helping teams in day-to-day tasks, including writing emails, building presentations, and summarizing documents. The benefits are real but confined to individual tasks rather than processes or decisions.
We call this the chat zone, a stage of AI adoption where the technology is used mainly as a prompt-and-response tool for individual tasks, rather than embedded in systems or decision-making. In global work, teams may be working faster, but AI is not yet improving the way they decide where talent can go, at what cost—and with what risk.
This reflects a wider enterprise pattern. McKinsey’s 2025 research on the state of AI found that adoption is now widespread across organizations, with 88% of survey respondents reporting regular AI use in at least one business function. However, only 7% have fully scaled AI across the organization.1 This suggests many companies remain in experimentation mode rather than transforming decision-making.
When it comes to global work, one reason for this sort of pattern is structural. Mobility functions often sit at the edge of the business and are not tightly integrated into core systems. As a result, AI tends to be adopted in isolated pockets instead of being part of a coordinated approach. Another reason is practical. AI depends on connected data, but global work data is often fragmented across systems and manual workflows.
This lack of connected infrastructure is visible in how organizations currently measure mobility itself. According to research by Vialto2, 44% of organizations do not currently track core success metrics for their mobility programs.
The gap becomes even clearer when looking at longer-term workforce impact. More than three-quarters of organizations (76%) do not track what happens to employees after an assignment, despite the role mobility can play in leadership development, retention, and workforce planning.
Without connected data and measurement frameworks, AI is likely to remain confined to isolated productivity use cases rather than broader strategic decision-making.
Governance adds another layer of complexity. Global work involves sensitive personal and regulatory data that is spread across multiple jurisdictions, which makes it difficult to define how AI can be used safely and consistently at scale.
Mobility runs on experiential knowledge: insights, skills, and judgment held by individual specialists rather than embedded in systems. This is the second constraint on AI in global work. Delivering successful global work and global mobility programs depends on specialist expertise across key areas, from tax to relocation. But much of that knowledge is not fully systemized.
This makes AI adoption harder because the technology works best with structured and connected data. Mobility has relied instead on knowledge embedded in workflows and at the desktop level. But this challenge also creates an opportunity. If that knowledge can be captured and connected, it can be applied more consistently and at greater scale.
Together, these constraints point to a deeper issue: Global work is not yet set up to take full advantage of AI. Addressing that requires a shift in how global work is structured and managed.
The most important shift AI enables is not automation, but more connected decision-making across functions that have traditionally worked in silos. Decisions about global work rarely sit in one place. A tax choice might affect immigration, or immigration constraints might limit where people can work. Likewise, workforce planning decisions can create compliance risks or unexpected costs.
Traditionally, organizations have managed these links through manual coordination between specialists working in separate areas and using distinct systems. Now, AI is making it possible for them to connect their decisions more systematically. Instead of working in silos, organizations can take an end-to-end view of global work across tax, immigration, payroll, workforce planning, and regulation. The aim is to gain continuous insight that informs decisions across the global work life cycle. This allows mobility to move from executing decisions to influencing how and where talent is deployed.
The urgency of this shift is increasing as the wider operating environment becomes more volatile. The World Economic Forum’s Global Risks Report 2026 describes an emerging “age of competition,” with company leaders primarily concerned about geo-economic confrontation and state-based armed conflict.3
In this environment, decisions about where talent can move, and under what conditions, are becoming harder to manage through fragmented systems and reactive processes alone.
Connected data will define which organizations capture the AI dividend. To realize this potential, organizations need data that is connected and usable across functions, and in global work, that data is rarely located in one place. It spans sensitive client information, internal workforce and HR data, and external regulatory requirements that vary widely across jurisdictions. Some countries offer advanced digital infrastructure and real-time access to information. However, others still depend on fragmented or manual processes.
In many organizations, the issue is not the absence of data but the difficulty of bringing it together. Relevant information often sits across disconnected systems and functions, which makes it difficult to standardize and use consistently. AI doesn’t just depend on access to information; it depends on the ability to connect and contextualize it. Organizations that can do this effectively will gain an advantage. Connected data allows mobility teams to move beyond isolated workflows and generate broader insights about talent deployment and compliance exposure.
However, technology alone is not enough. Organizations also need operating models that empower teams to access, share, and act on information across traditional functional boundaries. The greatest value is often realized when mobility, tax, immigration, payroll, HR, and workforce planning teams are working from the same data and insights, rather than making decisions in isolation.
Together, these data and operational challenges may help explain why many organizations are still experimenting with isolated AI tools rather than deploying enterprise-wide mobility intelligence. In Vialto’s Mobility Matters survey2, respondents most commonly described narrow use cases such as automated cost estimates, workflow automation, and communication capture between teams.
But the overall picture from that research suggests experimentation rather than transformation. Few respondents described mature analytics capabilities or enterprise-level AI integration across mobility workflows.
When connected data, codified expertise, and end-to-end visibility come together, AI’s role shifts from completing tasks faster to improving how decisions are made across global organizations.
Many mobility decisions have historically relied on disparate systems and siloed expertise. Specialists often had to assess large numbers of variables under time pressure and with incomplete information. AI can help by connecting data and modeling scenarios across multiple areas at once.
This makes it easier not only to generate insights consistently and at scale, but also to model scenarios and support forward-looking planning across multiple variables simultaneously. For example:
The result is not simply faster execution, but more connected and better-informed decisions about how talent is deployed globally.
For organizations, the next challenge is to show where AI creates value beyond traditional productivity measures.
The AI dividend is the value organizations capture when AI moves beyond task automation to shape decisions about talent, risk, and growth.
Traditional ways of measuring AI’s impact, such as time saved or costs lowered, are still important as the starting point for investment. But they will not capture the technology’s full value.
Productivity gains tend to change the nature of work instead of simply reducing it. Teams use their freed-up capacity to explore more options and provide better advice. There is also value in what does not happen, such as errors that are avoided and costs that are not incurred. In global work, fixing mistakes after the fact is particularly costly. Errors can span multiple jurisdictions, creating compliance risks and requiring complex and expensive remediation.
So organizations may need to look at a wider set of indicators:
In many cases, the real value lies in supporting decisions before risks or constraints appear elsewhere.
These changes will reshape the mobility function and allow it to play a more central role. As organizations expand into new markets or respond to shifting demand, their ability to move people becomes critical. If mobility capability is constrained, it will be a brake on growth.
AI strengthens capability by improving the mobility team’s visibility into where talent can go, and at what cost or risk. This allows the team to contribute to business decisions earlier.
In practice, this means being embedded in strategic planning rather than reacting to it. Mobility insights can shape where operations are based and how teams are structured.
While AI might be reshaping the mobility function, it goes without saying that human judgment remains critical. Decisions about global work affect individuals as well as organizations, and errors carry legal and financial consequences, as well as personal ones. Trust and compliance are essential.
Intelligent technologies will generate a great deal of insight, but the organization will need experts who can interpret and use it. This will include validating outputs and shaping how systems are trained.
And automation has its limits. Removing human oversight entirely creates risk, particularly in complex, compliance-driven areas such as immigration status or tax exposure. Here decisions must be interpreted and ultimately owned by experts.
Global work is still at an early stage with AI, with many organizations only beginning to move beyond basic tools. Data challenges are proving difficult to overcome, and governance frameworks are still evolving.
But the full value of AI will not be defined by efficiency alone. Instead, it will depend on whether organizations can connect their knowledge and integrate global work into decision-making. The real dividend will come from mobility playing a direct role in decisions about where to deploy talent, how to manage risk, and how to support the leadership team’s growth agenda.
What is the AI dividend in global mobility?
The AI dividend is the value organizations capture when AI moves beyond task automation to shape decisions about where talent is deployed, how risk is managed, and how mobility supports business growth. It goes beyond traditional productivity measures such as time saved or costs lowered.
What is the chat zone in enterprise AI adoption?
The chat zone is a stage of AI adoption where the technology is used mainly as a prompt-and-response tool for individual tasks—drafting emails, summarizing documents—rather than being embedded in systems or decision-making. Most organizations are still operating in this zone.
Why is AI adoption harder in global mobility than in other functions?
Mobility functions often sit at the edge of the business, are not tightly integrated into core systems, and rely on experiential and internalized knowledge held by individual specialists. AI depends on connected, structured data, so fragmented systems and undocumented expertise both constrain its impact.
How should organizations measure the impact of AI in global mobility?
Beyond time saved and costs reduced, organizations should track how quickly decisions are made, how early risks are identified, how effectively talent is deployed, and how closely mobility supports business priorities. Errors avoided and costs not incurred also matter, particularly in cross-jurisdictional work.
What role does human expertise play as AI expands in global work?
Human judgment remains essential. Experts are needed to interpret AI outputs, validate them, and own decisions in compliance-driven areas such as immigration status and tax exposure. AI scales expertise; it does not replace it.
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