Pulse Session: Building A Smarter Workforce Mobility Program

Foundational steps for advancing to a more modern model

Key takeaways

• AI adoption in HR is accelerating fast, but a relatively small percentage of global mobility managers currently use AI to enhance relocation programs specifically. That gap reflects structural challenges, not a lack of readiness.
• As mobility leaders look to advance their programs into more modern, adaptable approaches, a five-stage maturity model can help assess where your program stands today and identify your next investment priority.
• Data fragmentation, not technology, is the real barrier to effective AI use in mobility. Connected systems through APIs are the foundation that makes the most meaningful and valuable use of AI possible.
• Mobility data can become a genuine strategic asset once integration reaches a certain maturity level, shifting the conversation from “what happened” to “what’s next.”
• Technology should own the transaction. People should own the relationship. Human expertise and empathy remain essential even as automation expands.

In a September 2026 Pulse Session webinar, Sterling Lexicon’s Managing Director, EMEA & APAC, Peter Sewell, and Director of Thought Leadership, Kristin White, discussed how HR professionals can take their workforce mobility models to the next level, using a blend of technology, automation and human expertise.  Here we explore some of the key elements of that conversation.

Why the AI gap exists in global mobility

AI adoption across HR functions is moving quickly. Salesforce projects a 327% increase in AI agent adoption by the end of 2027, and Gartner estimates that 60% of HR tasks will be completed through intelligent agents or large language models by 2030.

And yet, Sterling Lexicon’s Global Mobility Blueprint found at the beginning of 2026 that only 27% of corporate global mobility managers were currently using AI to enhance relocation programs. Even if this gap is quickly closing, it’s important to note that it doesn’t signal that mobility professionals are behind. Instead, it points to some structural realities like dependency on RMC or partner platforms and their adoption of AI, competing operational priorities and resources, wide variability across programs and genuine definitional ambiguity about what “AI” truly means in this context.

A maturity model for assessing your program

Rather than treating AI adoption and an evolving program as a single milestone, organizations can benefit from thinking of progress along five stages:

1. Reactive administration: Manual, email- and spreadsheet-driven processes with limited reporting. The goal is standardizing the basics.
2. Operational excellence: Defined workflows and consistent measurement of cost and cycle times. The goal is repeatability at scale.
3. Integrated ecosystem: HRIS, payroll and vendor systems connected through APIs, eliminating data silos.
4. Data-driven program: Executive dashboards and predictive forecasting replace static reporting.
5. Intelligent, employee-centric: AI-enabled personalization paired with human support for the moments that matter most.

Each stage builds on the last. Organizations that try to skip ahead to AI without first strengthening the foundation typically find that the technology underdelivers, not because it’s flawed but because it lacks the data it needs to function well.

Data fragmentation: the real obstacle

Relocating a single employee involves multiple touchpoints and areas of expertise.  Crucial considerations like visa eligibility and obtainment, tax and payroll compliance, housing, destination services and moving logistics are often spread across different providers, systems and timelines. When these pieces aren’t connected, mobility teams aren’t working with data. They’re working with assumptions, and no amount of AI can fix that.

APIs solve this by acting as translators between systems, enabling real-time data exchange, streamlined document workflows and the full visibility that AI needs to perform reliably. For more on breaking down data silos, see Peter Sewell’s article, ‘AI can’t fix what global mobility programs haven’t connected.’

Where AI can add real value

Once the foundation is solid, AI and automation can deliver genuine benefits in reducing friction: answering status questions instantly, flagging exception trends, automating document workflows and triggering timely employee communications. Predictive insight also allows organizations to anticipate destination-specific challenges and inform smarter policy design based on real patterns rather than guesswork.

Turning data into a strategic asset

Mobility teams have access to extremely valuable data about talent deployment, risk mitigation, cost patterns and employee experiences and outcomes. However, it rarely gets synthesized into insights that leadership teams can act on. Shifting from reporting on what happened to informing what is most likely going to happen next transforms mobility from a processing function into a strategic partner in workforce planning.  To get there, it’s critical that mobility teams define what success looks like, not just in terms of moving volumes and satisfaction scores, but in terms that business leaders really care about. That might include telling the story about its contributions to leadership development, for example, or the time it takes to fill critical roles or how it boosts retention rates.

The human element remains essential

Even as AI capabilities expand, they cannot replace the judgment, empathy and cultural intelligence that mobility requires. Behind every relocation is a person, and often a family, navigating significant change. As noted in the conversation: technology should own the transaction, and people should own the relationship.

Ready to assess where your program stands? Walk through the five diagnostic questions in the maturity model provided at the link below and identify the first one you can’t confidently answer “yes” to. That’s where your next investment belongs.