Whether you are fan of Artificial Intelligence or being skeptical, one thing is sure! That today, AI is present everywhere. From Finance and Marketing, up to Bio-engineering and Cybersecurity, organisations seem to rapidly embrace Artificial Intelligence in their work streams.

But what about Human resources? Is it coming to terms with the new reality? Although progressing more slowly, AI appears to be a game changer on HR too!

Much has been said about various technologies and the impact they can and should be having in the HR space. Here are some examples of where AI is working in HR:

1. Chatbots

Chatbots provide valuable assistance in answering repetitive FAQs that emerge on a daily basis. They are capable of answering simultaneously to numerous questions, resulting in freeing up valuable time for the HR staff. As a result, they have time to focus on more complex questions.

Further, they can provide assistance in the interview process as well. By asking the relevant questions to the applicant, they can schedule an interview with the “right” people.

Apart from answering questions, chatbots can also play an important role in scheduling. For example, scheduling your holidays. Instead of logging into the company’s HR software, send email to managers and so forth, you can simply ask your chatbot about all relevant information regarding your vacation and ask to arrange accordingly.

It can even warn you how likely/unlikely is the event of your vacation to be approved, according to the number of other employees requesting holidays on the same dates. Or, imagine being sick and not be able to come to work that day. You can simply tell a chatbot and it will contact your managers to inform them.

2. Predict turnover/Burnout

It is no news that employee turnover (attrition) concerns every company. It has a great cost to the organization, thus predicting it before it happens implies a huge added value. The Society for Human Resource Management (SHRM) reports that the cost of recruiting and training and new employ can reach more than 60% of his/her annual salary.

Machine Learning algorithms can be very effective in predicting possible attrition by taking into account relevant criteria (e.g. Age, Overtime, Income, distance from home etc.). In fact, not only predicting it, but also gain insight into the factors that are linked with this bleeding of talent. Analysis of satisfaction surveys over the employees can really serve as an early predictor before the incident happens.

Similarly with attrition, employee burnout is also a huge issue that companies have to face. By employing a predictive model with strong predictors, AI can spot potential cases of employees that have higher changes of experiencing a burnout.

3. Recruitment

A strong weapon in the arsenal of recruiters is video interviews. Advanced programs that use Machine Learning algorithms (eg. MyInterview software), can analyze the recorded interview of an applicant and detect “unusual” behavior, such as lying, avoiding questions or even if he is secretly assisted by a friend on the phone during the video interview.

But even before make it to the interview part, recruiters have to face a huge pool of applications. Software like HiringSolved can screen and analyze the applications of the candidates and automatically suggest if he is a good fit for a job. In fact, there are cases where AI was more successful in choosing the right applicant compared to recruiters.

Going one step further, there are examples of software that scan the web social life of an applicant, by gathering data from his/her social media presence, opinions on forums etc. This way, it can construct his personality profile and predict how possible is to accept a job, or for instance which positions would be suitable for him.

To go even further, AI offers valuable help even after someone is hired, and more specifically in the process of onboarding. Computers, by taking into account the characteristics of the new employee, such as education or interests, can schedule an optimal and unique training program for the newcomer that is tailored to his needs.

4. Reduce Bias

Even though the fact that AI tries to mimic human intelligence and it is not an actual person is usually seen as a drawback, surprisingly enough it can prove beneficial in some cases.

For example in reduction of bias. A Human can rarely be 100% objective, hence bias over a variety of factors (gender, ethnicity etc.) is usually present, even if we don’t want to. Machines don’t fall that easily into the trap of discrimination (although it can happen) and can achieve a more unbiased decision compared to a human.

Conclusion

Of course, all these are just examples and the potential opportunities where AI can be applied are countless. Artificial Intelligence is a speeding train and can’t be easily stopped. The real question is if we are ready to take part to that journey?

About the authors

Michael has a genuine love for probability theory. This led him to work with statistics and data science. He enjoys incorporating engineering methodology to data analytics problems!

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