AI and talent mobility: Practical progress for HR leaders 

Based on a conversation with Michali Henig, Head of Global Mobility, HelloFresh, and Michael Nash, Commercial Leader for Career Europe, Mercer

AI is already helping global mobility teams solve practical, everyday problems. The most effective efforts are not always the biggest or most complex. More often, they start with a clear need, a simple solution, and a willingness to improve over time.

That’s the path HelloFresh has taken. Michali Henig, head of global mobility at HelloFresh, and her team saw that global mobility topics were often difficult for people to understand: administrative requirements, limited understanding of how tax works, and concern about legal issues often added to employees’ anxiety. As a result, mobile employees did not always understand the processes affecting them. At the same time, HR teams and business leaders were focused on their daily responsibilities and often found it difficult to build enough familiarity with global mobility issues to explain them clearly to employees. 

To address this, HelloFresh’s Global Mobility team first created an intranet portal with resources to help employees better understand their situation. But the written materials did not fully meet employees’ needs, as many found them difficult to access and absorb. The team then leveraged technology to create resources that were easier to understand. They began with infographics that made complex mobility processes easier to follow. They then introduced interactive self-assessment forms, followed by vibe-coded apps co-created by the HelloFresh team and AI-assisted apps connected to live data. The result was practical and immediate: clearer guidance for employees, fewer escalations for the mobility team, and no added external spend. 

As Michali put it, “Additional costs beyond internal team capacity for building this was zero.” That’s an important point. Useful AI does not always require a major investment. In many cases, the best place to start is with the tools, data, and talent already in front of us.

The strongest AI use cases usually begin with familiar problems: repeated questions, slow response time, missing information, and employee anxiety.

HelloFresh focused on those issues first. That made the solution easier to build, use, and trust. It also helped the team demonstrate value quickly. That matters, because new technology gains traction faster when people can see how it improves their day-to-day work.

This is where many AI efforts stall. Teams start with the tool, rather than the problem they need to solve. A better approach is to ask where friction exists, what employees need most, and how simple automation could reduce avoidable work. 

Digitizing a manual process doesn’t automatically improve it. Real value comes when teams rethink how work should happen. 

Michali’s team did not simply move the old process online. They redesigned the workflow so HR, leadership, and employees could self-serve earlier, get answers faster, and come to meetings better prepared. That improved the experience for employees and gave the mobility team more time for work that requires human expertise and judgment.

That distinction matters. AI should not just make existing processes digital. It should help make them better.

Mobility work often involves immigration, tax, and legal obligations. In those areas, speed matters — but accuracy matters more.

Michali was direct about AI’s limits, noting, “AI is lazy and is searching for the simplest answer.” That’s exactly why human oversight must stay in place. AI can support routine questions, first drafts, and early guidance. But it should not be left to make decisions on its own when the consequences are significant.

The right approach is clear: use AI to support the work, but keep human judgment at the center whenever regulatory risk is involved. Build clear escalation paths. Verify important outputs. And make sure people know when to stop and check. 

Teams build confidence with AI by using it. 

As Michali observed, “The best teacher for AI is AI.” That’s a useful lesson for leaders focused on adoption. Hands-on experimentation helps people learn more quickly than theory alone. It also helps them understand both the value of the tools and the limits.

Leader sponsorship matters here, too. Where senior leaders encourage responsible AI use and provide enterprise tools, teams are more willing to experiment, learn quickly, and scale what works.

When work involves company data, teams need enterprise-grade AI tools and clear governance. 

HelloFresh’s approach shows why that matters. Using corporate tools within a broader policy framework helps protect sensitive information and keeps teams aligned with company expectations. It also gives people more confidence to use AI responsibly.   

The standard here should be clear. Never use free public tools or non-enterprise subscriptions for work-related data. That creates real risk, including data leakage, identity theft, and greater exposure to fraud and scams. 

Good governance does not slow progress. It makes progress possible.

AI can be helpful, but it’s not perfect. It can hallucinate, miss context, and struggle with large or complex datasets.

Michali’s experience makes that clear: “My biggest learning is not to trust it fully.” That’s not a reason to avoid AI. It’s a reason to use it with care.

Teams should verify calculations, document sources for legal matters, and build fallback checks into the process. The goal is not to slow work down unnecessarily. It’s to make sure faster answers are also safer answers. 

The best AI projects are not judged by novelty. They’re judged by business impact. 

At HelloFresh, a practical calculator and follow-up interactive tools helped reduce escalations and clarify timelines. That gave employees faster answers and freed the mobility team to spend less time on firefighting and more time on strategic work. 

That’s the real measure of success: not whether a tool looks impressive, but whether it reduces risk, improves the employee experience, and creates more capacity for value-added work.

A practical checklist for mobility leaders

• Define your problem statement: start with one recurring pain point.
• Build a simple solution quickly with low-cost tools, then improve it over time.
• Redesign the workflow, not just the format.
• Use enterprise AI tools for work-related data.
• Train people through hands-on use.
• Keep human review in place, especially for legal, tax, and immigration outcomes.
• Track time saved, reduced escalations, and employee satisfaction.

AI will not replace the judgment, care, and relationship work that mobility professionals provide. But it can strengthen those capabilities by removing repetitive tasks and giving employees timely information during major life transitions. 

As Michali put it, the goal is to “thrive and not to fear.” That’s the right mindset for mobility leaders today: start small, protect the business, and scale the tools that clearly improve both the employee experience and the mobility team’s ability to operate strategically.