Human resources, or HR, powered by artificial intelligence (AI) is the new driver of business growth today. It is not surprising, then, that both HR leaders and managers are leveraging AI to augment decision-making and interactions with employees. “The age of the intuitive HR leader is gone. Today, you have to use data while making a selection. HR was earlier very process-oriented. Now, we are moving out of that phase, and using decision science and AI to enable productivity,” says Diane Gherson, chief HR officer (CHRO) at International Business Machines Corp. (IBM).
Gherson is a huge advocate of technology and is responsible for digitally transforming the HR function, incorporating AI and automation across all HR offerings, resulting in more than $100 million in net benefits in 2017. The company has automated its services around recruitment, employee engagement and learning development solutions for HR with the creation of chatbots, and Gherson claims that 40% of the company’s traffic is now via chatbots.
Technology is also used to curate highly personalized learning experiences that are customized to individual needs. This has resulted in an increase in average learning hours for India by nearly 40% year-on-year on IBM’s learning platform. Other applications such as Blue Matching use predictive analytics to produce a list of jobs currently available based on an employee’s geographical location, grade, job role, experience and other factors. This has resulted in about 1,500 internal placements globally, through this app alone. The company also has a conversational chatbot for new hires and a career advisory app, both of which use analytics, AI and cognitive technologies to respond to individual needs.
According to a report by HR.com, The State of Artificial Intelligence, AI has the greatest potential to enhance HR in five functional areas: analytics and metrics, time and attendance, talent acquisition, training and development, and compensation and payroll. Gherson agrees, saying that AI helps managers, for instance, see things they may otherwise miss.
The data picks up patterns using multiple sources that helps managers manage large teams that may not be geographically together. For instance, it can help identify employees who are at risk to leave or make recommendations on promotions solely based on data.
Gherson acknowledges that privacy can be a challenge, given the amount of employee data at their disposal. But she is quick to clarify that as a company, IBM does not snoop on emails. “Sentiment analysis is dangerous…we capture the tone and volume of what is said, rather than the exact words and who said it, and that helps us take corrective action.”