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AINews-AIMachine learning in human resources: how it works & its real-world applications

Machine learning in human resources: how it works & its real-world applications

According to research conducted by Glassdoor, on average, the entire interview process conducted by companies in the United Stated usually takes about 22.9 days and the same in Germany, France and the UK takes 4-9 days longer [1]. Another research by the Society for Human Resources that studied data from more than 275,000 members in 160 countries found that the average time taken to fill a position is 42 days [2]. Clearly, hiring is a time-consuming and tedious process. Groundbreaking technologies like cloud computing, big data, augmented reality, virtual reality, blockchain technology and the Internet of Things can play a key role in making this process move faster. Machine learning in human resources is one such technology that has made the recruitment process not just faster but more effective.

Machine learning (ML) is treated as a subset of artificial intelligence (AI). AI is a branch of computer science which deals with building smart machines that are capable of performing certain tasks that typically require human intelligence. Machine learning, by definition, is the study of algorithms that enhance itself automatically over time with more data and experience. It is the science of getting machines (computers) to learn how to think and act like humans. To improve the learnings of a machine learning algorithm, data is fed into it over time in the form of observations and real-world interactions. The algorithms of ML are built on models based on sample or training data to make predictions and decisions without being explicitly programmed to do so.

Machine learning in itself is not a new technology but its integration with the HR function of organizations has been gradual and only recently started to have an impact. In this blog, we talk about how machine learning has contributed in making HR processes easier, how it works and what are its real-world applications. Let us begin by learning about this concept in brief.

Machine learning in HR

 ai and machine learning in hr | iTMunch

The HR department’s responsibilities with regards to recruitment used to be gathering and screening resumes, reaching out to candidates that fit the job description, lining up interviews and sending offer letters. It also includes managing a new employee’s on-boarding process and taking care of the exit process of an employee that decides to leave. Today, the human resource department is about all of this and much more. The department is now also expected to be able to predict employee attrition and candidate success, and this is possible through AI and machine learning in HR.

The objective behind integrating machine learning in human resource processes is the identification and automation of repetitive, time consuming tasks to free up the HR staff. By automating these processes, they can devote more time and resources to other imperative strategic projects and actual human interactions with prospective employees. ML is capable of efficiently handling the following HR roles, tasks and functions:

  • Systematic scheduling of HR functions including interviews, group meetings and performance appraisals
  • Automation and streamlining of workflows
  • Personalizing employee trainings Reporting relevant 
  • Gaining insights through data analytics
  • Measuring and managing employee engagement 
  • Tracking employee growth and performance 

SEE ALSO: The Role of AI and Machine Learning in Affiliate Marketing

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Riddhi Jain
Riddhi Jain
Riddhi Jain is a technology content writer. She is based in India and has been working as a content writer since 2018. Riddhi has been writing content in the tech domain since May 2020 and can’t get enough of it. Riddhi has pursued most of her education from her hometown, Indore. She has graduated as a Bachelor of Business Administration and discovered her love for writing blogs while pursuing an internship during college. Once she discovered her love for writing, she went on to improve this skill set (and hasn’t stopped since).
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