The digitalization is working not only for a scientific purpose aligned with the corporate culture, but over the years, various technological developments have increased its role in the growth of organized businesses. With the introduction of machine learning (ML) in human resource management, digitalization has simplified many HR functions such as the hiring of employees, training, and development, employee engagement, retention, performance appraisal, conflict management, and above all, helping an organization in meeting both short-run and long-run objectives.
Utilizing ML in recruitment and hiring process has helped HR in speedily searching the relevant information from a large database. With the correct implementation of ML, the corporate sector is enjoying tremendous benefits in solving various recruitment problems and predicting the behavior of potential candidates.
Although the HR tech has recent hype, the talent management applications and online job boards have its root back in most of the companies. However, today the machine learning has reached down to applicant tracking systems, payroll and benefits applications along with moving from paper to digital documentation.
Many reports have shown that the companies spending their 10% of annual budget in HR technologies usually register significant growth in their annual productivity. These technologies outright all the mundane and boring tasks and help in shifting the focus on more interesting tasks like building relationships with hiring managers over good coffee and helping them solving big problems.
Need in HR
With the help of ML, the massive amount of data can be analyzed, delivered faster and more accurately. Businesses come across more profitable opportunities and stay protected against high risks. The synergy developed with the synchronization of MI, Artificial Intelligence (AI) and cognitive technologies is ensuring even more effective operations in the field of HR. The continuous development in ML boosts the desirable results, reducing the need for human interaction, and the new algorithms can be used to calculate various complex patterns and data with high accuracy. Advanced ML technology is very efficient in the different stages of recruitment and selection.
Predicting hiring needs
Presently, many companies provide a smart talent platform that prepares the information of the candidates who are in the immediate pipeline or even about the current employees. It enables the estimated time and cost of hiring speed.
Machine Learning helps in analyzing the language patterns in job adverts such as searching the posts that work best, preparing the whole advert that attracts many candidates from a diverse group. Based on the given information, the software automatically buys, places and optimizes job ads on different channels.
Today many HR departments do not follow manual CV screening, as it is a time consuming as well as tedious task. The CV screeners read CVs and identify keywords that correlate with the experience, traits and skills that are necessary for the job, helping in screening and short-listing the candidates.
Candidate assessment and pre-selection
Pre-employing tools can be a good way to identify top candidates. The software measures a candidate’s aptitude, ability, culture fit, and soft skills to succeed in the role and the organization, they are applying. This data-driven tool also helps the recruiters predicting the quality of hire.
Candidate relationship management (CRM)
To work efficiently in the overheated job market, many companies try to do everything they can to keep their candidates happy and prevent them from abandoning the company, before they are hired. CRM is a way to improves relationships with current and future job candidates. It also helps the company to engage and manage their candidates on one platform. The use of chatbot for timely messages to the candidates helps them keep engaged and informed during the recruitment process. A chatbot also answers candidate questions literally at any time.
Abhishek Agarwal, Senior Vice President, Judge Group
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