5 Use Cases for Machine Learning in HR
As the HR industry continues to evolve from a primarily administrative field to a strategic one, modern people teams need more advanced technologies to aid their work. Machine learning offers the potential to transform people operations to become more efficient, data-driven, and hyper-personalized.
While it may seem counterintuitive to rely more heavily on technology in the field of human resources, machine learning has the potential to enable HR departments to spend more time on the humanity of their work, while automating some processes that currently monopolize their time.
In this article, we’ll share some HR applications for machine learning that can help you save time, improve efficiency, and enable more strategic, people-centric work.
What is machine learning?
The term “machine learning” refers to self-learning algorithms that use data and statistical models to find patterns and make predictions. Unlike technologies programmed to perform specific actions, machine learning algorithms are trained on huge datasets they can “learn” from and recommend actions based on the patterns they detect. Ideally, the more data points they’re exposed to over time, the more accurate their predictions become.
Most of us interact with machine learning applications every day. For example, if you’re on Instagram, every time you scroll through your feed, the platform’s machine learning algorithm is learning more about your interests based on the content you engage with. Ever notice how you keep coming across products you like or funny memes that are oh-so-relatable? That’s no coincidence—your feed is literally serving up content curated just for you.
From customer service chatbots to predictive text on your phone to your Netflix recommendations, if you regularly use technology, you’re probably interacting with machine learning.
The difference between machine learning and artificial intelligence
Artificial Intelligence (AI) is a broad category, and machine learning is one of the technologies that drive it. AI mimics human cognition to analyze data, make decisions, and perform complex tasks.
Think of AI as the car and machine learning as the engine. As a subset of AI, machine learning algorithms help power AI to learn and become more proficient at making predictions and performing tasks.
Advantages of HR machine learning capabilities
While this may all sound a bit like science fiction (far removed from the people-centric field as we know it), HR departments have used data analytics to make decisions for years. But until recently, all that data collection and analysis was done manually, taking up significant time and resources. Machine learning has the potential to change all that in a big way.
Potential advantages of machine learning in HR include:
- Improved decision-making based on data insights
- Ability to grow and scale HR programs
- Better workforce planning
- Increased efficiency in recruiting activities
- More personalized employee experiences
- Higher employee engagement and retention
- Reduced bias in hiring and performance evaluations
According to Richard McColl, VP of Talent Technology at IBM, all HR tasks, processes, and experiences will be touched by machine learning in some way. “It’s not simply those processes that benefit from automation, speed, and efficiency,” he said. “How do we make super managers? How do we make better-informed decisions? How do we help people find opportunities that are now visible to them because we’re using machine learning to identify patterns of success in careers?”
With machine learning capabilities to augment their work, HR teams can free up more time and resources to focus on strategic priorities, planning initiatives, and having meaningful conversations with team members.
5 examples of machine learning in human resources
While we still have a way to go with AI and machine learning—and it’s still very new to the HR world—many early adopters are already using the technology to streamline people processes and improve business outcomes.
The primary focus today is to find ways to leverage machine learning capabilities for advanced data analysis and to automate routine or redundant tasks. By reducing some of the busy work, HR professionals have more time to focus on people and to hone their strategies to attract, develop, and retain talent.
1. Candidate assessment and tracking
Leveraging machine learning in candidate assessment and selection processes can streamline hiring and help teams find high-potential talent faster. Applicant tracking systems powered by machine learning enable recruiters to more accurately predict the time-to-fill open roles and more quickly identify and rank candidates with matching skills.
When used in the recruitment process, machine learning algorithms can look at data points from various relevant sources to assess candidate qualifications, experience, interests, connections, professional memberships, and more. From there, recruiters can review candidate profiles and take the next step to learn more about the ones they believe might be a good fit.
It’s important to note that when using machine learning for identifying and tracking candidates, human oversight is critical. HR teams must be able to apply context to any final decisions about who deserves to move forward in the process and who doesn’t.
2. Employee engagement and turnover prediction
When it comes to employee engagement data across an organization, there are thousands of individual data points that can be looked at and analyzed—more than most HR teams have the knowledge or capacity to take on. But what may take a person days or weeks to process and analyze can take a machine learning algorithm mere seconds.
Machine learning tools use predictive analytics and real-time monitoring to identify patterns that contribute to employee attrition and can predict the best next steps to take. The algorithms can gather data from HRIS systems, surveys, and other records, and analyze it against engagement drivers like workload, employee experience, compensation, manager relationship, time off, etc.
With the ability to predict staff turnover before it occurs, HR teams can see where breakdowns are happening and where to focus their efforts to increase employee engagement and reduce the attrition rate.
3. Candidate success prediction
While it’s still early and AI-powered solutions for employee performance management are just beginning to emerge, machine learning has been shown to successfully assess and predict candidate and employee potential.
In practice, this means predictive models can be used to better match candidates or new hires to the best-fitting roles based on their skills, qualifications, strengths, and past performance. Machine learning-enabled tools can match available employee data with the competencies and skills required to succeed in specific roles.
These capabilities could also be used to uncover skills gaps and connect employees and managers with the right learning and development opportunities to level up, opening new potential opportunities for career growth and advancement.
4. Frictionless onboarding
Onboarding processes driven by machine learning can help HR teams scale personalized experiences for new employees and help them get the best start possible.
The technology can improve HR efficiencies by automating certain repeatable processes, such as granting access to IT systems or connecting new employees with the right information and company resources.
Machine learning can also improve the training process for new employees, and recommend individualized learning paths based on experience level and role. By assessing where an employee is on their learning journey, L&D teams will be able to provide new employees and their managers with more data-driven recommendations and feedback.
5. Reducing bias in hiring decisions
When it comes to reducing bias in hiring and other talent decisions, HR leaders must strike a fine balance between using machine learning to uncover biases, while at the same time watching out for potential bias within the algorithms themselves.
Ideally, machine learning can be used to uncover human bias in job descriptions and candidate evaluations. It can catch problematic language and make recommendations to improve postings and evaluation criteria. That being said, the datasets that machine learning algorithms are trained on contain human-generated content, which can inherently contain bias.
While machine learning can provide HR teams with a gut check on potential bias, it’s up to HR leaders to talk with your technology vendors about how bias is addressed in their systems, and always review any AI-generated content or recommendations before using them.
The future of machine learning in HR
Any sci-fi buff will tell you that we need humans to remain in control of important decisions, or bad things can happen. But while human resources should always remain human, technology can enable you to do more faster and make a bigger potential impact.
While machine learning algorithms are great at making predictions, we still need empathetic humans with free will to interpret those predictions and apply context. We must think of AI technologies as a way to augment our own capabilities rather than replace them.
As advancements in AI and machine learning technology continue to shape HR practices, transparency in how these powerful tools are used will be essential to maintaining employee trust.
As Barbara Cosgrove, VP and Chief Privacy Officer at Workday, explains, “ML [Machine learning] isn’t about supplanting human decision-makers. Rather, ML-fuelled applications make predictions that, when combined with human judgment, help inform better decisions. But the success of ML, like any emerging technology, depends upon trust, and that trust will exist only if companies adhere to responsible, ethical practices.”
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