Info-tech

TCS rolls out Workforce Analytics to tackle talent management challenges of digital era

Our Bureau Mumbai October 20 | Updated on October 20, 2020

The AI-based system of engagement provides insights, foresight for employees, managers, CXOs

TCS has launched its Workforce Analytics, an AI-based system of engagement, insights and foresight for employees, managers and CXOs, with an aim to enhance productivity and workforce experience.

The new solution is designed to help enterprises deal with the talent management challenges of the digital era, TCS said. Digital transformation calls for new kinds of talent, with diverse skillsets and capabilities, which traditional recruitment methods are ill-suited to assess. The challenge is made worse by the large-scale shift to remote working and virtual interactions, requiring the adoption of new ways of keeping employees positively engaged, motivated and productive.

Also read: Rising tech demand post Covid has led to quick recovery: TCS CFO

TCS Workforce Analytics uses machine learning (ML), natural language processing and a patented cognitive engine developed by dotin Inc, a TCS co-innovation partner, to evaluate skills, personality traits, strengths, cultural compatibility, workplace values, and the learning style of an individual. These insights can then be used to make workforce-related decisions. “The new ways of working in today’s world are fundamentally transforming the talent management function. Role fitment, talent development and engagement at scale can only be accomplished using data, analytics and cognitive technologies,” said Dinanath Kholkar, Global Head, Analytics & Insights, TCS.

Further, the solution enables organisations to support diversity and inclusion, contextual engagement, and ensure compliance to HR policies.

Published on October 20, 2020

Follow us on Telegram, Facebook, Twitter, Instagram, YouTube and Linkedin. You can also download our Android App or IOS App.

This article is closed for comments.
Please Email the Editor

You May Also Like