Machines are 25% more efficient than humans in hiring right talent: TeamLease

TeamLease report says 45% of the surveyed SMEs will be faster to adopt machine learning-based hiring as against 13% of all large businesses surveyed


Machine learning-based hiring is a process in which recruiters can use algorithms powered by machine learning to hire candidates. Photo: AFP
Machine learning-based hiring is a process in which recruiters can use algorithms powered by machine learning to hire candidates. Photo: AFP

New Delhi: Hiring based on machine learning is 20-25% more efficient than manual hiring, a survey by recruitment and staffing company TeamLease showed. The report ‘The New Landscape of Hiring’, shared exclusively with Mint, says the time, cost and attrition rates in machine-based hiring are lower than in manual hiring.

Machine learning-based hiring is a process in which recruiters can use algorithms powered by machine learning to hire candidates.

45% of the surveyed small and medium enterprises (SMEs) will be faster to adopt machine learning based hiring and social and mobile technologies as against 13% of all large businesses surveyed, the report said.

“With machine hiring, one could estimate attrition (early/premature as well as long-term) likelihoods and therefore choose candidate types that are associated with lower likelihoods. This is a rare (or not a) possibility in case of manual hiring unless data is manually captured and analysed,” said Rituparna Chakraborty, co-founder and executive vice-president, TeamLease.

The time taken in machine-based hiring ranges from two to four weeks against 15 days to three months in case of manual hiring, depending on the job role and geography. Also, the cost involved in machine-based hiring is a maximum of Rs200 per candidate based on volumes, while the cost of manual hiring ranges from Rs2,000 to Rs10,000 per candidate based on sourcing complexity, said Chakraborty.

As per estimates of ministry of micro, small and medium enterprises, the MSME sector contributes 8% to India’s GDP (gross domestic product), 45% of manufactured output, 40% exports and employment to 80 million persons through 36 million enterprises with more than 6,000 products.

Government initiatives fostering innovation and entrepreneurship, such as Start-up India and Skill India, also provide the much-needed impetus to SMEs, the survey said.

“Hiring the 80 million candidates would be possible through machine learning given the turn-around-time one needs to meet. Churning heavy data to resource the right candidate would be efficient, and also avoid every human bias,” said Nabomita Mazumdar, who has the title of evangelist at Sheroes, a career community for women.

Mera Hunar, a cloud-based human resource information systems (HRIS) solution provider, which has designed a software for hiring and payroll management has seen a larger demand from SMEs. “Larger organisations may have legacy enterprise resource planning (ERP) softwares and historical data. A conversion to disruptive technology would take time to charter this new course,” said Vinay Dalal, chief executive officer (CEO) and director, Mera Hunar.

The TeamLease study revealed that machine hiring allows companies to avoid judgment errors caused by short attention spans. Gig work—a formalised version of freelancing—is upending hiring models across the world. Choice, flexibility and work-life balance top the considerations for 65% independent workers while lower costs and operational efficiencies are top considerations for 75% employers surveyed by TeamLease.

The survey also revealed that over the past three years, recruiters using LinkedIn and social networks for hiring have gone up from 42% to 50%. Facebook’s popularity as a melting pot of sourcing talent has gone up to 45% from 32% three years back. Businesses using mobile for hiring is up from 56% to 85%.

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