A practitioner-led community exploring what human-first AI looks like in global mobility — through practical learning, experimentation and shared experience.
Practitioner-led · Human-first · Globally minded
Mobility professionals are already using AI across research, policy, communication and day-to-day casework. The harder questions are where it genuinely helps, where human judgement needs to remain, and how we use it responsibly in work that directly affects people.
We explore AI through practical mobility use cases, shared experiments and open discussion. We look at what works, what doesn't, and what changes when AI becomes part of the process — without assuming every task needs an AI solution.
AIGMH brings together people working across global mobility to compare experiences, test ideas, share useful resources and learn from each other. Different levels of AI experience are welcome; the value comes from what practitioners are actually seeing and trying.
Shaped by the people doing the work, not by whoever is selling the tools.
AI should create space for more meaningful work.
We consider different countries, cultures, regulatory environments and levels of technological access.
Interactive practitioner-led sessions where participants experiment with AI tools, test real workflows and learn together — live. Participatory, not lecture-based.
Explore HubLabsAIGMH-created guides, checklists and prompt libraries — plus a curated external AI learning library, filtered for quality and relevance.
Explore the HubPractitioner experience, useful resources and emerging questions at the intersection of AI and global mobility — published on LinkedIn.
Read Not Another Newsletter on LinkedInJoin the wider practitioner community to compare experiments, ask questions and share what is changing across global mobility.
Join the LinkedIn community groupEvery 60-minute session offers something useful at every experience level. Bring a mobility task and work out the right relationship between AI and human judgement.
Four practical Tuesdays help you move from a safe first use to a workflow AI can perform within rules you define. You may be at Start for one task and Direct for another.
What can AI actually do in mobility work, and what must never go into it?
For a task where AI is new, informal or still untested.
Can you trust what AI just gave you?
For a task you already own and want AI to support.
Where should AI sit in your mobility workflow?
For a workflow where you need to decide where AI belongs.
What would it take to hand this to someone else?
For a workflow AI can perform within rules you define.
A curated library of courses, frameworks and reports — organised around what a task needs, not a person's level. You might Start with one task and Direct another.
Free to join. No gatekeeping. Practitioners only.
Join on LinkedInMcKinsey's latest analysis identifies the specific task categories most exposed to AI capability. Administrative coordination and document processing rank highest.
Core mobility admin tasks sit directly in the highest-automation category.
Enterprise HR functions are moving from AI experimentation to scaled deployment. Mobility is consistently identified as an under-served function with high automation potential.
For mobility functions still finding their way into AI, this represents a practical window to experiment — understanding what genuinely useful AI adoption looks like before broader organisational expectations arrive.
Claude Projects, Copilot Agents and similar tools are moving AI from chat to persistent, task-delegating agents. Early adopters are already using these tools to coordinate multi-step workflows rather than single tasks.
Assignee lifecycle management, vendor coordination and compliance tracking are exactly the kind of multi-step processes these tools are being built to handle.
EY's analysis examines the governance gap emerging as AI systems shift from advisory tools to autonomous decision-makers. As AI moves from recommendation to action — scheduling, approving, flagging — accountability frameworks haven't kept pace. The question of who is responsible when AI gets it wrong is becoming urgent.
Mobility teams are already using AI to flag tax exposure, assess assignee eligibility and surface compliance risk. When those outputs inform real decisions, the question of accountability is live — not theoretical. Understanding where authority sits is now a professional competency.
Manual review across multiple documents — 4 to 6 hours
AI-assisted gap analysis and summary, leaving more time for interpretation and policy judgement
Writing individual assignee emails from scratch — 45 min each
Structured prompts create a first draft, leaving more time for empathy, context and relationships
Turning spreadsheet data into exec presentations — half a day
AI supports the first narrative, leaving more time to interpret the story and advise leaders
Manual task sequencing and handoff documentation
AI maps the workflow and handoffs, leaving more time for exceptions, decisions and improvement
AIGMH brings together global mobility practitioners from across the world — different countries, cultures, regulatory environments and levels of AI access. That breadth of perspective is part of what makes the community worth being in.
Share real AI experiments from their mobility work
Attend and contribute to live HubLab sessions
Access prompts, outputs and session recordings
Time back from admin-heavy tasks
Confidence applying AI to real mobility work
A peer network that's actually doing this
Try AI safely on a task that is new, uncertain or low-risk, with clear human review.
Explore Start resources →Apply AI to a defined task, give it useful context and verify the result.
Explore Use resources →Design where AI belongs in the workflow, including judgement, people and data.
Explore Shape resources →Set rules, review points, escalation and handover for delegated AI work.
Explore Direct resources →Practitioners sharing what works, what fails and what they're still figuring out — at the intersection of AI and global mobility.