There’s an ever increasing demand of expeditious mechanisms that may limit the implications of human mind, which can vary from a person to another and put a standardized technique into use. The developments in technology in the past few decades have witnessed industries undergoing all round evolution, enabling Human Resource function faster than ever.

What is Machine Learning?

To put it in simple words Machine Learning is the digitized version of “Scientific Management”. Conventionally, data scientists were responsible for collecting and organizing data. This data management enabled them to identify trends of an organization and they shortlisted candidates for recruitments accordingly. This procedure involved a lot of human input and hence proved to be tedious and it lacked efficiency.

However, over the decades, Machine Learning has gained wide recognition. What data scientists did for us, is now what computers do. Machine Learning can be best described as allowing computers to devise complex algorithms to identify patterns in data usage and organize them into useful information. Machine Learning employs Artificial Intelligence into use that proves to be efficacious in generating successful business models. It lets computer learn and explore without actually explicitly programming it. Moreover, it can analyze data and make predictions based on its analysis.

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Where all does Machine Learning  apply?

Machine Learning is present in every industry in the newfangled world. Major of its applications include:

Industrial Recruitments: The recruitment procedure can be broadly classified into three steps. All these steps utilize Machine learning at a large scale.

• Candidate Sourcing: Industries scuffle to attract apply qualified candidates for job vacancies. Recent surveys have shown that 46% of talent acquisition leaders still suffer to attract qualified pool of candidates.  Machine learning can capacitate industries to solve this problem. It can explore the data that people post online (resumes, social media profiles) to find the desired set of candidates for a particular job. This slicks the otherwise insipid manual operations involved in outsourcing. The time saved can be brought into play by interviewing the strongest candidates.

• Candidate Screening: While looking for a job, people tend to apply for every vacancy, irrespective of their qualifications. Consequently, industries receive 75-88% unqualified resumes.
    Undoubtedly, sorting applications manually is one aggravating and time consuming task. Machine learning automates the screening process using artificial intelligence. This is done by learning information about existing employees’ skills, resume, experiences and other qualifications and then systematically screening and grading new candidates. This type of screening also augments resumes by using public data sources about existing employees.

This saves time and labour involved in a low value task which can rather be invested in other high priority tasks.

• Candidate Matching: According to 52% recruiters, the most difficult part of their jobs is to identify the right candidates from a large applicant pool. This mammoth task has been eased by Artificial Intelligence. It uses mechanisms to sort out the strongest matches for a job opening, based on data of candidates such as personality, skills and salary requirements. For example, when a job description is posted on LinkedIn, it ranks candidate by matching the required skills for job to the skills posted by candidate in his profile. 

Artificial Intelligence finds the most qualified candidate from a pool of candidates who are actively looking for new jobs. Hence, companies don’t have to waste time on passive candidates who are not actually interested in the particular job. 

Detection of Attrition:

Understanding human psychology and why employees decide to stick with a job or leave it , is one exasperating task for HR. Detecting risks of attrition and developing risk management plans demands for advanced algorithms that may handle a large array of variables efficiently.

In a webinar held for the “Human Capital Institute” (HCI), Glint’s Justin Black articulated a hypothetical situation of identifying specific risk factors based on scores to an employee survey. If a human was appointed to try and detect attrition risk among female engineers in Palo Alto with less than 2 years of tenure, the variance analyses to attain a conclusion that possibilities are countless, like finding a needle in haystack.

Machine Learning has the unparalleled ability to efficiently connect these dots in a matter of seconds, enabling HR to spend more time on improvising their teams. Machine Learning finds patterns in the interests of staff over a given period of time and hence can efficaciously predict all the attrition risks, enabling HR to prepare management and back up plans beforehand. Advances in artificial intelligence have extended the ability to produce large amount of unstructured data, and algorithms. It also has the potential to monitor emotional activities in comments and tease out actionable suggestions. Black described the “prototypicality” algorithms have the ability to sort out individual comments that sum up the things everyone else is saying, allowing companies to monitor the emotional perspectives of employees and understanding the employees’ motion towards company processes and specific issues.

Performance Development:

Machine learning is expanding its horizon in training and development. It has the potential to boost individual skill management and development. There is huge room for development in this industry, platforms that can give the calibrated training without the need of human coaches. This can save a lot of time and present opportunities for people to hone their skills in their respective careers and stay engaged. One example of a company which builds customized training programs for employees is “Workday”. It develops training programs based on company’s needs, market trends and employee specifics.

These performance development assessment programs are advantageous at individual levels. Albeit, they become onerous at organizational levels, where there’s a need of making sense from an enormous amount of raw data. This is an area where Machine Learning is incessantly evolving with prime focus on overall performance of the corporate lattice.  

Future Trends

Entreprise Management:  

Google’s People Analytics department has been the trailblazer in developing performance-management engines at enterprise level. Over the last half of a decade, the team has made immense improvements in company-wide actions, such as:

• Limited the number of interviews needed for an application (limited to four)
• Established optimal organizational size and departmental size
•  Efficient management of maternity leave
• Created on-boarding agenda for an employee’s first four days at work, along with increased productivity level up to 15% 

Internal Behaviour:

The movement of employees within an organization has been a unique issue for the HR and analytics. This issue can be easily resolved by Machine Learning by hiring best suited candidates at the entry level so that they may climb up the corporate ladder rather than shifting departments. While early systems pertaining to this issue are already in place, more scalable and robust models are still projected to be released in the coming years. 

Well-designed AI algorithms serve three major cross-functions: main expertise, data science expertise and design/user experience expertise. Presently, very few providers are able to employ all the three functions to the maximum extent. The best solutions, don’t entirely replace human, however, Machine Learning still has enormous space to grow more effective. It has the capacity to develop a more people-centric approach, paving the way for efficient and time saving problem solving models, reducing bias in programs, less administration and more development.

Machine Learning is revolutionizing the Human Resources function and upon us!