3 Ways Machine Learning could optimise Global Mobility

Whilst on holiday last weekend, I relied exclusively on the knowledge of machines and people that I’d never met to make decisions on where I should eat and drink. Google Maps’ relatively new feature suggests top-rated places dependent on the time of day. So, at 1pm when I’m looking for somewhere to eat lunch, inexpensively and in the immediate vicinity, I am presented with a list of suitable eateries; all I have to do is select one. Google Maps then helpfully highlights this on my map for future reference and provides me with directions. Smart, useful and made possible with the power of Machine Learning.

So what is Machine Learning?

Machine learning (ML) - the ability for a machine to learn without explicit programming - is a long established concept that is seeing a surge in popularity and sophistication thanks to the development of new technologies (Forbes, 2016). Back in the 90s Amazon pioneered the ‘recommended for you’ feature and since then the ability to deliver key information at exactly the right moment has become extremely effective in providing better experiences and responses. ML, like in the examples above, could eventually help Global Mobility functions deliver more personalised experiences to those employees being selected for, or that are currently on, assignment.

3 future uses for ML in Global Mobility:

1. Identifying prospective assignees with similar attributes

Global Mobility software that communicates effectively with HR software could be key to supplying recommendations to the Global Mobility team. A comprehensive employee history could enable an intelligent system to suggest employees for assignment by comparing their data to existing and successful assignees or comparing a new opportunity with a former project and making candidate suggestions based on past successes.

For example, ML could identify candidate with the correct skills and documentation requirements. This could be particularly useful for organisations with Short Term Business Travellers or Commuter assignments where time is of the essence. Reducing the number of manual tasks for a Global Mobility team could alleviate pressure in these time bound situations and ensure that an employee is already fully compliant.

A significant advantage of ML is that these decisions are based on factual information rather than personal opinion. Without emotional involvement, machines and software can objectively view the suitability of each candidate without personal bias or preference. Although it makes sense for a human to make the ultimate decision, ML could help eliminate candidates that are immediately unsuitable, leaving the decision-maker with a list of only qualified candidates.

2. Recommendations from other employees who have been on a similar assignment (ratings etc.)

Like my experience with Google Maps at the weekend, providing information at the exact moment it’s needed could help Global Mobility teams deliver better experiences for employees on assignment. Collating relevant reviews and useful information from current and previous assignees on-demand could make the entire relocation process much easier. For example, ML could come to understand at which point in the process particular documentation is required and then provide an employee with the document in question, useful insights into how it should be completed and who it should be sent to.

3. Intelligent defaults

The auto-complete functionality that my devices use for addresses and payment details makes my online shopping experience much more efficient. In the future, the same could be said for Global Mobility technology. With a system that could identify patterns in behaviour, similar intelligent defaults could make putting a new case together much easier. Perhaps your organisation regularly sends employees to the same host location on the same policy type? A system which can auto-populate other fields based on this information could mean a case can be built in seconds.

In addition, intelligent defaults could help gather data in a meaningful and immediate way, which could help Global Mobility teams assess the productivity of an assignment and provide strategic business insights. Speedy access to easily digestible data could provide quantifiable and valid reports at board level, justifying costs and resources. Eventually ML could assist with demonstrating a clearer ROI by automatically interpreting this data into an easy-to-read format based on the most requested parameters, enabling teams to take corrective action or build on past successes.

There is no doubt that ML can be harnessed by the Global Mobility industry in a multitude of helpful and efficient ways. However, ML requires detailed data to perform effectively and, as we would expect, this data is often personal. Therefore, the product of the data privacy intrusion must be transparent and worth the sacrifice (Deloitte, 2017). Yet, if this exchange is fair and enhances our experiences we will (almost certainly) be willing to do so - exactly like I was happy to provide my location to Google.

Here are just three ways that ML could impact Global Mobility; how could it assist your team?