When companies are recruiting, they are, in fact, selling themselves to potential employees. This means that attracting the best talent is a matter of presenting your offer most competitively and customizing your message so that it resonates with the target audience.
However, HR departments in most organizations use the same decades-old recruitment methods. The problem with this approach is that people are becoming more and more used to personalized approach that they see in retail. In a world where Amazon knows exactly what kind of socks you would like, a potential employer that has no idea what package of benefits they should offer you, falls short.
The first thing they need to let go of is the idea that one size fits all. Currently, there can be up to four different generations in a single workplace, including baby boomers who are almost retiring, Generation X members who fill in the most senior positions, millennials working their way up the corporate ladder, and Generation Z representatives who are just hired on junior positions.
A potential solution to this problem is borrowing the right tools from the retail sector and analyzing employees’ wishes and behavior much as you do with regular customers. AI can help because there are already algorithms performing these tasks at a high level, they just need to be trained on data sets related to employee wishes and needs.
Data Mining and Privacy
Every time AI and machine learning are in focus, there is the problem of data privacy involved. In the case of recruitment strategies, this becomes even more important, as most HR managers have access to very personal data about candidates.
Using such information to generate models, to train them, and then to offer people the best employment benefits, as suggested by the algorithm, could be regarded as an ethical problem.
However, it is not the first time this is discussed. The same issue has been under scrutiny when retailers decided to customize the experience they offer to customers. Even if some people are still scared by AI’s capacities, most of us just enjoy the convenience of being offered what is just right for us. Using AI for employment would have the same effect.
For example, if the algorithm identifies that you are most interested in life-work balance, it can suggest a work from home arrangement instead of just more money or a gym membership.
The Right Vacancy Promotion Tools
Machine learning can be used for more than just designing the best benefits package for each employee. This technology has the ability to find the best channels to advertise an open position for maximum impact.
Not only can it detect the right profiles to target social media ads at, or the users to be sent a newsletter rather than any other materials, it can also suggest the right picture or copy to include for better results.
New Employee Training Tools
AI can replace countless hours of training sessions by providing new employees with complete learning experiences. Through AI, companies can design training programs that are tailor-made for each employee. This means a shorter learning curve, less stress when starting a new job, and a perk of following a personal growth pace.
AR and VR tools powered by AI can offer realistic training sessions in almost any industry. Leaving a bulk of the training process to machines gives managers and employees in senior positions more time to do the jobs instead of looking for mistakes in the work of recruits. Overall, this means more productivity and less stress for everybody.
New Assessment Tools
Until now, managers just relied on quarterly or annual assessment to analyze the performance of their employees. Yet, the process usually only allowed looking at employees’ final results, not their intermediary steps that could better explain their professional dynamics.
With the help of AI and big data, organizations can analyze their employees’ engagement in real time. You won’t have to wait for the monthly or quarterly assessment session anymore. By looking into work patterns, an algorithm can detect if an employee is less productive and trigger a warning sign for the manager.
AI firm InData Labs points out that even something as mundane as typing speed can indicate stress levels or lack of interest in the task. Of course, these tools shouldn’t be used against the employee, but as a way to understand them better.
If the tool identifies a drop in employee engagement, it can warn the manager about this problem and suggest possible actions.
Instant Engagement Evaluation
As AI becomes more sophisticated in recognizing human emotions, from their faces to their voices, these new capacities can be used by HR departments.
For example, we can soon expect to have automatic office listeners which monitor employees’ well-being by looking at their facial expressions, tone of voice, and other indicators.
In the case of extreme stress levels, such a system could recommend the employee to take a break or offer alternative tasks until they feel better.
Although it sounds like an invasion of privacy, this is more of a way to prevent burnout and is very similar to smart fitness trackers that fulfill similar functions in our everyday life.
Are HR Departments Ready for AI?
The short answer is no. So far, AI for HR has been just in the experimental phase. However, as we’ve learned from retail, once there are real benefits to be drawn from these new ways of working, the adoption of new practices accelerates.
HR professionals are not ready to be replaced by machines, and most employees wouldn’t feel comfortable to be evaluated by an AI system similar to Alexa or Siri either. In this situation, it is better to see AI more in the role of an assistant.
Once employees begin to trust such innovations, more companies can make a step further and delegate more tasks to machine learning, while HR managers will stay more in the evaluation and control role. On the other hand, some employees will be thrilled about the new system because it eliminates the subjectivity of human evaluators.
How to Boost Employee Engagement with AI: A Guide to HR Managers was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.