The Human Resources space has large potential for application of AI and machine learning techniques to better improve search and operational efficiencies. Performance and satisfaction management systems are able to monitor employee engagement and satisfaction, minimising churn while providing quantifiable performance metrics. Moreover, attrition models are able to forecast expected employee turnover by department and seniority. Most prominently, however, are the use of natural language programming models to conduct resume screening processes with speed and impartiality that are unattainable by human reviewers.


With regard to Current Employees: remember, this is the age of Big Data. Managing employees means gathering data on a host of areas – that span employee attitudes and feelings, qualification verification, employee approach towards policies, compensation management, and addressing relevant external developments. This means a giant reservoir of data, that’s ever-growing. Manual management is clearly not an option. Here’s where Machine Learning comes in. ML can effectively accept, store, process and manage these giant data volumes and offer smarter insights via simple analytics.


In Recruitment: as big data comes from various sources – forums and social media – companies are struggling to decipher all the data and locate the right candidates. Machine Learning can look at a variety of key criteria – qualifications, experience, interests, professional connections, and memberships, among others.
ML helps to reduce HR’s manual efforts, streamline applicant discovery, and importantly frees up teams to focus on more strategic and productive activities.