3 key considerations for HR leaders when it comes to machine learning

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If you’ve been following the news about technology trends in the workplace, you’re probably aware of how artificial intelligence (AI) will fundamentally change the way we work. As a result, you might be thinking about how it can be leveraged to drive value for the HR function. Here, we shine a light on one segment of AI – machine learning.

What is it? It’s an algorithmic tool that uses statistical modeling to organize, classify and draw relationships across large sets of historical data to fill in missing information and predict outcomes. These algorithms learn by being “taught” by a human, on their own or through trial and error. As this technology becomes more mainstream and advances, organizations including GE, American Express, BMW and Jon Deere are using it to identify trends and draw insights across sales, marketing, design and operations.

So how can it be used within HR, and what should HR leaders think about when looking to leverage this technology within their own organizations? Here are three things to consider:

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.

When broken down, there are many ways machine learning can be leveraged in HR to benefit the business, but doing so effectively is more about asking better questions than simply having the budget to hire teams of engineers.

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.

This same research shows that few Canadian employers are prepared for talent challenges from expected surges in work automation. The survey also revealed that few companies and HR functions are prepared to address these changes and challenges. In fact, the data shows that automation is still seen as simply a way to reduce costs (45%) rather than to augment human productivity (36%) or to increase organizational performance.

Moving forward

Given alarmist headlines about workplace automation, it’s easy to feel uncertain about the impact of new technologies on work. Yes, work will change and jobs will be restructured as automation plays a larger role in our organizations, but there’s much HR leaders can do now to influence how their organizations react. Here are a few places to start:

  1. Identify a job or a group of jobs that could benefit from automation. Are there tasks within those jobs that could be augmented or substituted by technology to improve productivity or help your people be more effective within those roles? What new tasks will be created?
  2. Pick a spot within HR, Finance or the business operations and assess where machine learning could be applied more broadly. This could be within a shared services group, functional business partners, customer service organizations, talent acquisition, etc.
  3. Spur conversations with your colleagues in the operations and IT functions to understand how your business model is changing as a result of technology. Proactively address the role HR can play in reinventing jobs and engaging the workforce around change.

To keep up with an ever-changing workplace, HR needs to understand the technologies available and choose those that bring the most value to the organization. Fully understanding the potential impact of AI and machine learning on workforce operations will help organizations meet the challenges and capture the opportunities in the new world of work.


Aubrey Chapnick head shotAubrey Chapnick is a lead associate within the Willis Towers Watson Talent and Rewards practice in Toronto.

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