WILLIS TOWERS WATSON
1. It could help increase HR’s value Machine learning can be used to boost efficiency/save costs or drive more strategic ROI for the business. When it comes to efficiency applications of machine learning, employing tools such as chatbots and personal AI assistants to substitute various administrative and transactional tasks in areas such as benefits administration and recruitment can save those in HR a lot of effort and improve the experience of those who use HR services (i.e., employees, job candidates, contractors, etc.).
When it comes to driving strategic value for the business, machine learning can be deployed to predict turnover more effectively, assess who will be a good hire, build better profiles for what makes great managers and senior leaders (including the CEO), map career paths for employees and create customized learning and development experiences. These are often highly strategic and complex core HR accountabilities which can be augmented by machine learning but ultimately, should be driven by skilled HR practitioners and functional leaders.
In addition, the use of automation will create demand for new work and skills in HR. For example, the use of machine learning to generate greater insights will require talent with advanced analytical skills to review and update algorithms and tell the “story” from the new insights being generated.
WILLIS TOWERS WATSON is a leading global advisory, broking and solutions company that helps clients around the world turn risk into a path for growth. With roots dating to 1828, Willis Towers Watson has over 40,000 employees in more than 140 countries. For more information, please write to Leah Denoga at leah.denoga@willistowerswatson.com, call 877-5148 or visit www.willistowerswatson.com.
2. Spur the development of an HR data strategy One of the reasons machine learning has made such significant progress over the past few years is because of the amount of data that humans now produce. In HR specifically, it’s common to have many different systems that house employee data with varying levels of accuracy, currency and completeness.
Since machine learning tools are only as useful as the data that goes into them, HR needs to start thinking critically about how it manages and organizes the vast amount of often disparate employee data that’s available. An HR data strategy involves thinking about where HR data will be stored, how it will be categorized/ organized, who will have access to it and in what situations, who will provide governance and importantly, how can it be used alongside business or operational data.
On the back of increasing concern around data privacy, the use of personal information and new legislation such as the General Data Protection Regulation, it’s important for HR leaders to be aware and vigilant around the level of transparency they’re providing to their people in terms of how employee data is being used, where it’s stored, for how long and under what level of security.
The reality is that while this kind of data is highly sensitive, it’s most useful when paired with business data to draw tangible insights. So, if you’re thinking about ways to leverage machine learning within your HR function, or any of the AI tools now available, you’ll need to develop a strategy on how your people data is managed.
3. Provide an opportunity to transform human performance, not just automate work
When we think about AI, we often think purely about the automation of jobs. The reality is that automation presents interesting opportunities for substituting mundane work, augmenting important work or creating other work to be performed by humans. HR has the opportunity to lead the way in helping organizations deconstruct jobs, reorganize tasks and reconstruct new, more human jobs that add more value to the business and to the people who are doing them. Willis Towers Watson’s recent Future of Work survey found that 57% of employers were planning on leveraging technology to augment human performance rather than using it purely to reduce costs, and 70% of employers already use AI and robotics somewhat or to a great extent to support humans in completing business processes.