Canadian HR Reporter

CANADIAN HR REPORTER MAY 2019 FEATURES 25 RELOCATION/TRAVEL Using AI to improve relocation outcomes By Keni Patel W hether you call it predictive analytics, machine learning, or artificial intelligence (AI), these technologies are all about gaining actionable insights to improve outcomes — no matter what business you're in. Few employers truly under- stand the power and limitations of AI at the onset, but as long as there's a solid business objective in mind, they can work towards it through a process of discovery. AI can be defined through a lens of practicality, as a way to au- tomatically learn and codify pat- terns by looking at huge amounts of interrelated historical data. ese patterns can explain why things happened in the past, what is likely to happen in the future and what actions we can take to affect future outcomes. A good question to start with is: How can AI be leveraged to help solve a real business problem in a measurable way? In the relocation business, it's about giving clients maximum value from their mobility pro- gram and giving relocating em- ployees an experience that mini- mizes the disruption to their job, family and life. Every relocation is associated with dozens of interconnected data points related to issues such as costs, timing and locations, re- sulting in a mind-boggling com- bination of attributes and, there- fore, potential causal relationships (meaning which combination of variables was most responsible for a particular outcome). Before AI, decision support or business intelligence (BI) systems would present complex visualiza- tions about what happened in the past, and the onus was on us to make the right decision based on that complex information. Now, AI can tell us what is like- ly to happen in a very specific con- text. In relocation, it can predict things like: the total move cost, the probability that a transferee will request a policy exception, or a particular family's total move duration. The AI can start forecast- ing these issues even before the relocation process has started, and it can refine its predictions as the process evolves. But AI offers much more than just pre- dictions — in its most useful form, it can offer explanations and generate context-specific recommendations. If old-school BI is a printed road map, then AI-powered rec- ommendations are like driving using GPS, with live traffic up- dates and turn-by-turn direc- tions. In this analogy, predictions are like knowing whether or not a person will be late in reaching her destination. It's certainly useful, but not as useful as knowing how to avoid traffic so that driver can actually make it there on time. In a similar regard, while pre- dicting the total duration of a move might be useful, it's not nearly as useful as knowing how to streamline the relocation so an employee is productive at his new job sooner. A client-facing predictive prod- uct can sift through and analyze data from past relocations to pro- vide mobility stakeholders with real-time decision support for expenditures, exceptions, autho- rization volumes, move duration, and total move cost — result- ing in better planning and cost management. is tool can provide employers with a 12-month forward-looking predictive forecast of their mobil- ity program's key performance indicators (KPI): total authoriza- tions, total expenditures, and to- tal exception costs. In addition, at the employee level, it can predict employee move duration and identify those employees who are at a high risk of exceeding move cost estimates and requesting policy exceptions. The AI constantly analyzes incoming data and updates its predictions everyday as the move progresses. Big picture KPI and fine-grained employee level pre- dictions have helped mobility managers shift from a reactive to proactive posture. It's about exploring AI to look at ways to embed it into the mobil- ity process, providing intelligent and transparent advice to guide decision-making and deliver the best possible outcome for employ- ers and employees. at means, for example, build- ing analytic applications to enable the following scenarios: • A new hire moving to an unfa- miliar place receives relocation advice based on what has led to positive outcomes for previous moves that are similar. • A rising executive embarking on his first long-term expat as- signment with his young family learns up front how long things will take, what to expect, and which services and benefits are most critical to settling in quickly. • A global mobility director de- signing a new policy receives service and benefit recom- mendations to minimize ex- ceptions, optimize cost con- trol, and reduce employee churn during a strategic group move. Keni Patel is head of data science at Cartus in New York. For more infor- mation, visit www.cartus.com. AI can start predicting issues before the relocation process has even started. QUICKLY FIND WHAT YOU NEED IN YOUR LEGAL COMMUNITY ALBERTA LEGAL TELEPHONE DIRECTORY 2018-19 Includes Alberta, Northwest Territories, Nunavut and Yukon The Alberta Legal Telephone Directory 2018-19 helps you find lawyers, courts, government departments and any other legal entities you need. Published annually, it includes new and updated names, mailing addresses, email addresses, phone numbers and fax numbers for up to 10,000 listings. Order your copy today! Visit www.store.thomsonreuters.ca or call 1.800.387.5164 for a 30-day no risk evaluation. Spiralbound • September 2018 • $49.50* Order No. L7798-8403 • ISBN 978-0-7798-8403-2 Multiple copy discounts available *Plus applicable taxes and shipping & handling. Prices subject to change without notice. Credit: metamorworks/Shutterstock

• Cover