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Can robotics really ease hiring pain?

November 28, 2017 08:29 IST

HR solutions powered by robotics and machine learning are enabling recruiters to make better hiring decisions. Sangeeta Tanwar reports.

AI ML HR hiring jobs

Illustration: Dominic Xavier/

The most important people challenges that hiring managers today face feature tapping into inactive or passive job seekers for the top job; getting the dropout rate of selected candidates right (particularly in case of large-scale hiring) and preempting the exit of high-performing employees.

However, it appears that help is round the corner as recruiters can make better hiring decisions by leveraging HR solutions powered by robotics and machine learning (ML).

To begin with, number crunching and ML softwares can throw up patterns and scenarios that help hiring managers' match the right people with the right jobs.

Tata Motors, for example, has a database of employees hired through leading institutions, tracking success rates from each, the rate of retention and performance highlights of the individuals recruited. Leveraging data analytics, the auto company has managed to sieve through 30-35 of the top 50 institutes which it used to visit earlier.

"A detailed analysis of the database gives us a clear indication, and helps us ascertain which of the institutes are the best choice for us and which ones can be dropped, if at all, from our hiring list," says Gajendra Chandel, chief human resources officer, Tata Motors.


Robotics and artificial intelligence (AI) go a long way in discovering best fit jobs.

Data analytics and AI's natural language processing can extract critical information that paints a personal picture of what a candidate desires or can make the requirements of a job description available as a checklist that the system can process.

These AI techniques give contextual clues to provide personalised search results that match the job seeker's goals and experience, says Raj Mukherjee, senior vice-president, product, Indeed. 

Having spotted and approached the right candidate, companies -- particularly in information technology (IT) -- face the risk of unannounced dropout in large numbers right before the joining date.

Shaakun Khanna, senior director, HCM strategy and transformation, Asia Pacific, at Oracle, points out a lot of junior employees accept joining offers at IT firms but about 50 per cent don't turn up on the appointed date leaving HR managers in the dark.

To tackle this, Oracle has built an ML algorithm which takes into consideration multiple historical factors that could influence and impact candidates' joining decisions.

Accessing a huge database of future employees, the algorithm came up with an observation that candidates staying 5 km away or more from their workplace exhibited a higher tendency of not joining the organisation. Building on these results, Oracle matched the pin codes of future hires with the company's offices across India.

This was done with a view to offer available positions to people living in the vicinity of Oracle offices, thus increasing the probability of selected candidates joining the firm. 

A growing number of companies are taking to robotics and ML to cut down on the steps in their hiring cycle and processes such as on-boarding.

This helps them save time and achieve cost efficiency, since they are able to put hired employees on the job from the word go.

For instance, consultancy firm EY counts itself among the biggest users of bots. With 1,500 bots at work, it has deployed them at scale.

Repetitive HR tasks such as data management (leave calendar) as well as provisioning (providing new hires with work station etc.) have been delegated to bots.

Earlier, EY had a team that handled these tasks, but now 90 per cent of these jobs are done by bots, freeing up considerable time for HR managers to interact closely with the employees.

EY is working with a number of firms that are looking at solving hiring problems using robotics, AI and cognitive thinking.

One of the IT services companies (with 180,000 people on its rolls) that EY worked with faced issues in matching the best person with the right job for specific projects.

For example, in the BFSI industry, SAP is a key skill. The traditional HR tool is capable of reading a particular field to identify this skill.

Using ML, cognitive piece and robotics, EY has developed a solution that trawls the firm's in-house database and identifies the skill most relevant to the project in candidates' profiles even if that skill is not explicitly mentioned on the résumé. 

Milan Sheth, partner and national leader, technology sector, EY India, sees the services industry leading with the adoption of robotics and cognitive HR solutions.

Telecom, BFSI and retail are next in line.

"Robotics solutions will find takers in industries where talent is scarce or difficult to find. Example, the shipping industry where one has to hire globally. Other areas where firms are looking to deploy technology relate to specialised roles such as actuarial personnel and fraud analysts," says Sheth.

"While it is apparent that technology is a great tool to drive an agile organisation, we continue to be in the business of people -- it is important not to lose sight of that," says Richard Lobo, executive vice-president and HR head, Infosys.

"While we can speed up the process and ensure better hiring results, it remains the task of the human recruiter to connect and engage with prospective hires, give them the assurance that they are making the right career choice and help them transition from being a great prospect to a valued employee," he adds.

Cases in point
  • Leveraging data analytics, Tata Motors has managed to sieve through 30-35 of the top 50 institutes which it used to visit for recruitment
  • Oracle has built an ML algorithm that takes into consideration multiple historical factors that could influence and impact candidates' joining decisions
  • EY counts itself among the biggest users of bots. With 1,500 bots at work, repetitive HR tasks such as data management as well as provisioning have been delegated to bots
Sangeeta Tanwar
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