Machine learning for HR

Machine learning has been making a splash on Human resource management for a long time now. Perhaps the most significant way in which it has arrived to the scene is through talent discovery platforms, which are designed to provide a company with the best possible people for the job openings. Traditional approaches rely on formal definitions of criteria as minimum work experience, education level, courses and exams taken, to filter the available candidates and then find the best one for the job among them in various ways – interviews, tests, etc. AI algorithms on the other hand look at a multitude of data sources and analyze them through specially designed algorithms to offer the best candidates for the job. Since an abundance of data related to people is now available, utilizing this data gives a completely new prospective on finding the optimal candidate and a competitive edge compared to the traditional approach. Importantly, machine learning algorithms allow to train the platform continuously, so that it becomes better and better at finding the best person for the job.

Another benefit of the machine learning algorithms for HR is through also taking one step back and changing the very process we reach talent in the first place. Traditionally reaching for new recruits was done through ad postings or perhaps by requesting the resources of one of many HR agencies, and then applying techniques to collect data and find optimal candidates. ML tools on the other hand replace this human-centric approach with targeted automated campaigns through linkedin, SMS, phone marketing and e-mail. With their large and expanding databases of people, containing much data, including personal details and contacts, some machine learning HR platforms allow you to advertise to literally millions of people directly. This changes completely the talent reaching dynamics and the value added prospects of the HR professionals.

Machine learning does not stop at job hunting and hiring though. AI techniques are also used to analyze workforce CVs and skill sets, to match tasks with available skills. Such tools can be used for both analyzing company tasks in view of available talent, and also for employees to look for task opportunities within the company. This is a double benefit for the company. On one side it does not always need to look for new hires for every new job position, job rotation becomes a very real possibility. On the other hand, employees do not necessarily need to quit if they feel their job position is not adequate and they can look for suitable tasks inside the company. This increases retention rates, team satisfaction and reduces the cost for acquiring new talent.

With all these opportunities already available as platforms and continuing their development, here are some important areas that we believe will be further developed with machine learning algorithms in the near future.

  • Analyzing employee at risk of quitting. Especially in businesses with high people turnover, analyzing people at risk will become an increasingly important factor. Using past company data of employees, who quit and matching it with all available data sources – employee CVs, payment history, working hours, training, location, performance reviews, etc., ML models can be built to try and assess employees at risk of quitting. Such models can be used to analyze potential improvements, so that retention can be increased. Keeping people in the company is becoming increasingly important so being able to keep your talent is key for business, especially in the current challenging conditions where training and trust need to be establish through an online environment.
  • Employee performance analysis. While regular reviews – yearly, twice per year or sometimes quarterly, are a must and most large companies have such established practices, performance review may in the future turn out to be continuous. Machine learning algorithms can assess performance continuously. More importantly predictive analytics can be used to try and assess potential performance given available data. While this sounds as something far-fetched, the reality is in today’s fast-moving world having a clear grasp of performance cannot wait for once per year process and AI technology is here to help.
  • Another area of potential improvement with respect to performance is removing any bias with regards to people. AI can help look beyond the obvious and see the results more clearly than before. While we as humans have a tendency to like or dislike someone, that usually affects our judgment, even involuntary. Having machine learning algorithms to go through actual data and create a clear set of statistics cuts through any of our biases and distills the essence of how a person is performing.