Demystifying Artificial Intelligence & Machine Learning In HR
By Dan Harrison, PhD - February 26, 2019
Artificial intelligence (AI) is changing the workplace in ways large and small, and the changes aren’t limited to automation of production tasks. Over half of the 6,000 executives questioned in a recent survey believe that artificial intelligence can provide significant value to HR in multiple areas. Though most organizations have not yet fully transitioned to machine learning (ML) for HR processes, many have adopted AI for some repetitive tasks, freeing up HR professionals’ time for higher-value work.
This article will focus on the role of AI in HR analytics and processes—those that are already commonly used and those that are expected to have significant future impact on the way HR professionals do their jobs.
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5 Ways Organizations Are Using Artificial Intelligence In HR
1: Using Artificial Intelligence In HR For Recruitment
AI is already in widespread use for recruitment, as any recent job applicant can attest. Where HR professionals once spent hours reviewing job applications and weeding out the majority of candidates who were not a good fit for the position, artificial intelligence allows applicants to be quickly sorted and evaluated. Computer algorithms can easily sort applicants on the basis of skills, experience, career trajectory, and education, leaving HR to sort through only those most qualified for the job.
The speed at which AI can narrow the field of applicants becomes especially important when the labor market is tight. Rapid winnowing of applications allows HR to connect with the most qualified applicants before they are hired elsewhere.
Sorting through applications is just the beginning of the recruitment process; a number of other tasks such as correspondence and scheduling interviews can be automated. Using AI to handle these administrative tasks saves time and hastens the hiring process.
Machine learning confers an additional benefit on the recruitment process in terms of diversity. Research shows that all people are subject to subconscious biases that impact assessments. The machine has no social cue biases; it looks only at how well the applicant fits the position.
2: AI Support For HR In Onboarding
A range of tasks that need to be accomplished during the onboarding process can be automated. New employees may find the organization foreign; automated systems can deliver information to help newcomers get the lay of the land. New hire information such as management hierarchy, team members, and tasks assigned for the first week on the job can be delivered electronically to help orient new employees.
Applications such as Chatbox and other advanced messaging tools can help direct new hires to information and resources, and free HR from fielding routine questions. These tools also improve the early employee experience by empowering new hires to easily find what they need to do their job, without having to rely on another person for information.
3: Machine Learning Mechanizes Repetitive HR Tasks
HR professionals often spend a large amount of time and effort performing repetitive, low-value tasks, such as fielding questions about policy, benefits, and leave. AI systems can generate automated responses to answer questions or direct employees to a source for the requested information. Leave requests can also be automated; the system tracks scheduled leave for all employees and responds with the best dates for scheduling time off.
By automating these and other routine but time-consuming tasks, ML in HR frees personnel to spend more time on strategic work, where HR can really impact business success.
4: Using AI In HR Analytics To Improve The Employee Experience
Coupled to a rich data set, artificial intelligence systems can deliver personalized messages to employees via email or messaging apps like Slack to enhance the employee experience and engagement. These nudges can be based on employee preferences, interests, or professional development opportunities. Employee preferences expressed in surveys or collected during the onboarding process guide the system in generating personalized messages.
AI nudges are particularly useful for managers. The system can send information on employee preferences for recognition and outside interests, so recognition and rewards can be personalized to the employee’s preference, and also provide managers with reminders about work anniversaries and other information. AI can also be useful in retention strategy—if data has shown that employees in a specific job start leaving after three years, the system can alert the manager that it is time to have a career conversation with those employees and discuss development opportunities.
Artificial intelligence can itself offer learning and development. Digital tutorials and micro-learning courses allow employees to learn from online modules without the need for a trainer or scheduled training time, empowering employees to guide their own development.
5: Using Organizational Network Analysis (ONA) To Inform HR Analytics
Though HR manages a lot of important functions, its focus tends to be on HR tasks and concerns. As a result, HR personnel are not always aware of the informal structure of the organization, which determines how work gets done. Organizational Network Analysis (ONA) can provide a clear illustration of this informal structure.
ONA visualizes formal and informal relationships in organizations by analyzing who is communicating with whom, and how communications, information, and decisions flow through the company. By analyzing metadata about email, phone, social media, and other communications, nodes of influence and knowledge can be detected.
As a new employee enters the organization and begins to grow in their job role, ONA can deliver insights about others they might want to connect with in the organization. The system can suggest fellow employees with similar or complementary skills and interests, and identify who has influence and who is a knowledge broker.
The Future Of AI In HR: Proceed With Caution
New technologies have already been introduced that allow AI tracking of employee engagement through monitoring emails, browsing history, phone calls, and even tone of voice. There is a school of thought that, through this type of passive social monitoring, it’s possible to understand engagement and identify which groups are engaged and which are not. The argument in favor of this approach is that it gives management and leadership a good idea of which employees are disengaged and need intervention to improve retention.
That’s a worthy goal, but it’s important to keep practical reality in mind. Just because it’s possible to do this type of monitoring doesn’t necessarily mean it’s a good idea. There is a Big Brother aspect to some of the technology that’s discomforting for many employees. With the boundary of work and personal life less well-defined in today’s workplace, it’s also inevitable that some of this monitoring is listening in on personal information, which might pose legal risk for the organization.
It’s impossible to observe a behavior without altering it, and this is as true for AI social monitoring as it is for any other means of observation. People who don’t like being continually monitored will either change their behavior to avoid it or go to work for another organization. The entire premise for monitoring is to try to improve the experience and identify disengaged employees for intervention; the risk is that use of these technologies may have a net opposite effect. While employees who are disengaged might be identified, the use of the technology itself may damage the experience for all other employees and increase the overall risk to retention.
Ethical Considerations For The Use of Artificial Intelligence In HR
Emerging AI technology challenges HR to formulate a code of ethics for the use of AI in the workplace. Although machine learning can generate a wealth of data, not all of it is useful, and some of it cannot be used ethically. For example, data might indicate that recently-divorced males are the most productive workers. That’s interesting from a sociological standpoint, but there’s no ethical way to use the information. Hiring based on this metric or attempting to destroy employees’ marriages to boost productivity are both well out of bounds. Developing guidelines about the type of information that’s ethical to use and how to collect that data will keep HR aligned with company strategies, goals, and values when evaluating emerging AI technologies.
Artificial intelligence has the potential to radically transform HR by bringing together large amounts of data that is more predictive and less descriptive. (Tweet this!) Traditional survey dashboards can show data about attrition, employee reasons for leaving, engagement hotspots, and more, but the information is somewhat restrictive.
AI offers predictive capability; mechanized data analysis can make predictions based on how an employee responds to questions and give the organization an opportunity to get in front of attrition risk. The data can predict when that risk is most likely to occur, why, and what should be done to try to prevent it. Based on what’s known about the organization’s workforce, AI can predict who is likely to leave and when, as well as employee preferences about how they work, and give insight into how to shape the employee experience so the company doesn’t lose the talent they’ve worked to find, train, and develop. By using a cautionary approach and taking ethical considerations into account when adopting new AI technologies, HR can realize all of the benefits of artificial intelligence while avoiding potential pitfalls.
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