There was a time (not long ago) when AI felt more sci-fi than business strategy; something you’d expect to find in a high-tech lab or in the plotline of a dystopian streaming hit. Something that you knew existed, but that hadn’t truly touched your world.

Today, it’s firmly on the radar for global mobility teams.

But according to Weichert’s recent research around this tech (and cultural) phenomenon, while interest is high, adoption is not keeping pace. The result is an AI adoption gap: mobility professionals increasingly recognize the value of AI, yet many teams still struggle to embed it into day-to-day operations in a meaningful, scalable way.

What the research says about AI adoption:

The signal from the data is clear: mobility teams believe AI matters, but relatively few have operationalized it. In the original survey data, 81% of mobility professionals say technology and AI skills are critical for the future, yet only 5% report fully integrating AI into their operations, while 42% say they do not use AI directly and instead rely on suppliers. In other words, the gap is not in awareness—it is in execution.

That makes the opportunity especially important. The most promising use cases are highly practical; teams see immediate value in reporting, dashboard creation, and automated communications, where AI can reduce manual effort, speed up insight generation, and help mobility professionals spend more time on employee experience, strategy, and stakeholder guidance. So, if the potential is obvious, why are so many teams still stuck in the early stages of adoption?

What’s preventing teams from optimizing AI?

1. Lack of clear use cases. Many teams know AI could help, but they have not defined where it should fit into their workflow. Without specific, repeatable use cases, adoption stays abstract and experimental instead of becoming operational.

2. Skills and confidence gaps. Teams may have access to AI tools already embedded in their platforms, but access does not equal adoption. Without training, practical guidance, and time to build confidence, capabilities remain underused.

3. Governance, privacy, and compliance concerns. In a function where accuracy, consistency, and policy adherence matter, teams are right to be cautious. When sensitive employee and cross-border data are involved, unclear guardrails can slow adoption and make AI optimization harder.

4. Change management challenges. AI adoption is both a technology shift and an operating model shift; today’s teams need leadership alignment, updated processes, and clear guardrails for when to automate and when human judgment should lead. Without that structure, adoption tends to stall after a few isolated wins.

5. Fragmented data and disconnected systems. AI performs best when the underlying data is accessible, reliable, and connected. But mobility data often lives across suppliers, platforms, spreadsheets, and regional processes, making it harder to generate consistent insights or automate work with confidence.

We intentionally addressed the challenge of fragmented data with Weichert Go—bringing critical program information from all suppliers under one roof so our AI capabilities can draw from a more complete and robust pool of data. The result is stronger recommendations grounded in a fuller picture of your program, not just isolated data points. Learn more here.

The Bottom Line

With AI dominating conversations across every corner of the mobility sphere, we know with certainty that today’s teams enthusiastically embrace AI and its potential to transform how we move people and drive business growth. The AI adoption gap is about whether they are equipped to use it well.

Closing that gap will require more than periodic experimentation. It will take focused training, stronger governance, better data practices, and a clear view of where AI can create value without replacing the human judgment that mobility depends on.

For companies and teams that get this right, AI becomes a powerful practical advantage, helping mobility functions move faster, communicate better, and operate more strategically in an increasingly complex environment.

There’s more to this story. Stay tuned for Part II of this blog post, where we’ll explore how Weichert is helping close the AI gap in mobility — and empowering our teams, clients, and partners with the tools, training, and confidence to unlock AI’s full potential.

Want a closer look at the research behind what’s driving mobility tech adoption, and what’s holding it back?

Download Designing Mobility for Success Part II.