Global mobility is doubtless on the rise. In the constant war for talent, the possibility of ‘working from everywhere’, has taken remote working to the next level. ‘Digital nomads’, ‘satellite employees’, ‘hybrid cross-border workers’, ‘workcations’, and ‘virtual assignments’ are post-pandemic trends that companies need to adapt to in order to attract and retain talent. These trends signal major departures from traditional global mobility models centred on things like postings, salary splits, and transfers.
Moving from working five days per week at the office to ‘working from everywhere’ sounds like a nice idea, but it comes with a number of legal challenges. These include immigration, employment rights, social security cover and contributions, tax, compliance, and data security, with each case potentially involving a unique set of parameters and processes. Different approaches may be required depending on an employee’s nationality, home country and host country, among other considerations. Immigration, compliance, payroll, tax, and contractual requirements will vary between jurisdictions, and there can be difficulties in determining which laws apply. Companies are, at present, struggling to assess these risks and to set the right balance as they define their policies in relation to these new forms of global mobility.
And here is where AI tools may be of assistance. AI has the potential to support global mobility practitioners in their risk assessments and to make processes more efficient. Some repetitive, less interesting and administrative tasks like contract generation or the filling out of applications could be automated, allowing more time for the more strategic and complex aspects of the job.
Drawing on historical data, AI has the potential to anticipate legal and practical challenges and risks in some countries, to identify cross-border successes and failures, to map cross-border gaps, and to project costs. In the war for talent, it also has the potential to offer a more personalised offering tailored to each employee’s individual case and needs when preparing a global mobility assignment.
However, as with any other use of AI in HR, it also gives rise to some concerns. First, there may be concerns around privacy and data security, in particular when processing personal information. An employee’s work permit or visa application can include sensitive medical information and criminal record checks; special care is also required for things like social security and tax status. Further, caution should be taken to ensure that any algorithms used are free from bias and do not amplify existing inequalities.
Finally, each global mobility case is unique, with its own parameters. It may be driven by an employer, for business reasons, or sought by an employee for personal reasons. External circumstances may also play a significant role, as can be seen in times of war or economic crisis. It should not be forgotten that there is always a human aspect and assessment where global mobility is concerned, and that this requires a human assessment that AI tools cannot provide.