AI in Global Mobility: Real Progress Looks Smaller Than You Think

A new Mercer piece on AI and talent mobility makes a point worth sitting with: the most effective AI use cases in our industry are the small, practical fixes that start with a real problem and get better over time.

The HelloFresh example

The piece is built around a conversation with Michali Henig, Head of Global Mobility at HelloFresh, and Michael Nash of Mercer. Henig's team noticed a familiar problem: mobility topics like tax, legal requirements, and administrative steps confuse and worry transferees, and HR teams don't always have the bandwidth to explain them clearly. Her team's answer wasn't a big-budget platform launch. It was a steady progression. First an intranet resource hub, then infographics, then interactive self-assessment tools, then AI-assisted apps tied to live data, each step aimed at making mobility easier to understand, not just easier to digitize.

A few lessons that translate directly to our world

A handful of takeaways from the piece stood out as directly relevant to how we think about mobility programs:

Start with the friction point, not the tool. The strongest AI use cases begin with a specific, recurring problem the team already keeps running into.

Redesign the workflow itself. Simply moving a paper process online doesn't add value. The gain comes from rethinking who can self-serve, and when.

Keep humans in the loop where risk is real. Henig was candid that AI tends toward the easiest answer, which is exactly why immigration, tax, and legal decisions still need human judgment and verification.

Guardrails on data matter as much as the use case. Enterprise-grade tools and clear governance are what make broader AI adoption safe to scale.

Expect mistakes, and plan for them. AI can hallucinate or misread context. The fix is building verification and fallback checks into the process.

None of this is about AI replacing the judgment and relationship work mobility professionals provide. It's about removing repetitive friction so that judgment has room to matter.

The broader workforce is telling a similar story

New Gallup data released July 20th reinforces the same pattern from a macro level. Organizational AI adoption jumped sharply in the second quarter of 2026: 47% of U.S. employees now say their organization has integrated AI tools, up from 41% the prior quarter, and 52% of workers now use AI in their role at least occasionally.

But the more interesting finding is about where the value actually shows up. Writing and research remain the most common entry points into AI use, but they're not what drives the biggest productivity gains. Employees using AI for coding assistance or automation report positive productivity effects at notably higher rates (77%) than those using it for writing (68%) or research (65%). Gallup's data also shows that employees who apply AI across a wider range of specific tasks in their job report substantially stronger productivity gains than those using it narrowly.

In other words: broad, generic use gets people started, but real value shows up once AI is embedded in specific, well-defined tasks. That's the same lesson HelloFresh's mobility team learned by building purpose-built tools rather than a general-purpose assistant.

What our own data already told us

Henig's caution about AI, that she doesn't trust it fully, is the majority view. In our inaugural 2025 - AI in Global Mobility Survey last year, 82 mobility professionals told us much the same thing: 70% named AI "hallucinations" as their top concern, making human oversight the clear precondition for adoption rather than a nice-to-have.

At the same time, our data showed an industry moving forward anyway. 93% of respondents expected considerable increases in AI usage over the next two years, with more than a third predicting dramatic growth, and not a single respondent expected usage to decrease. The tension in that data is exactly the tension the Mercer piece describes: real caution about AI's limits, paired with real momentum toward using it more.

One gap we flagged last year was a 37-point difference between vendors and corporate teams in how each group was putting AI to use. And 63% of respondents told us they specifically wanted ROI metrics to justify further investment, which lines up neatly with the Mercer piece's point that the best AI projects are judged by business impact, not novelty.

Which brings us to our own research

This is exactly the territory we're digging into with Plus Relocation's second annual "2026 AI in Global Mobility Survey — Promise Meets Practice", which launches today, August 3rd, and closes in early September 2026. Last year's inaugural survey gave us a first real snapshot of how mobility teams were experimenting with AI. A year later, with tools like the ones HelloFresh built now commonplace across the industry, we want to know: where has adoption actually taken hold, where is it still stalled, and what separates the programs seeing real impact from the ones just experimenting?

This year we're looking across the full industry, inviting both corporate mobility programs and the service suppliers that support them.

Whether your team is doing some really interesting work with AI in mobility, or wrestling with where to start, we'd love your perspective. Then stay tuned here for results this fall.