Aarti Borkar is the vice president of Product Management and Design for IBM’s Watson Talent and Collaboration businesses. She leads a worldwide team of product managers and designers, and is responsible for vision, strategy and execution for the business. Borkar is a highly respected technologist with a Bachelor of Science in Computer Engineering from Bombay University, a master’s in Computer Science from the University of Southern California, and a master’s in Tech Commercialization from the University of Texas at Austin.
I had the opportunity recently to connect with Borkar and explore her ideas and perspectives regarding artificial intelligence, IBM Watson, and how both are playing an increasing role in talent recruitment.
Artificial intelligence is quickly changing talent recruitment for both recruiters and candidates. Can you explain the impacts for organizations today?
The recruitment process today is growing increasingly complex and skills-based. Yet, recruitment teams spend much of their day sifting through resumes, chasing managers, or searching for candidates instead of talking to top talent. Industry studies have shown that recruiters are so inundated that they spend around six seconds on a resume to determine potential fit. There’s a pretty big margin for missed opportunity in that six seconds. Artificial intelligence helps solve this. With AI tools, teams are able to refocus their energies on more value-add activities rather than administrative tasks.
How can AI be utilized to help employers and job seekers resolve some of these issues?
We’ve developed some new tools. IBM Watson Candidate Assistant helps create a better candidate experience by matching job seekers with positions that will best fit their skillset. IBM Watson Recruitment is focused on helping talent acquisition teams identify quality candidates and prioritize workload. However, both tools are designed to improve both the recruiter and job seeker experience, and help ensure the best candidates end up in the right jobs.
With Watson Candidate Assistant, job seekers can chat with a virtual assistant to get job recommendations that match their skills and interests as well as learn more about the company and its culture. For recruiters, this reduces the number of applicants that need screening and ensures a better match across positions that applicants are applying for. One perk we’ve heard repeatedly from early recruiting users is the value of the insights gleaned from questions candidates ask. This information helps guide their recruiting strategy.
Watson Recruitment uses data from applicants to automatically analyze and rank candidates that are the best match for the job, while reducing bias. Not only does this result in more focused efforts on the part of the recruiting team, but candidate screening effort is minimized, and productivity and retention is increased. The tool also offers insight into employee and candidate sentiment through social listening, resulting in better conversations with candidates.
Talk to us a little more about the natural language technology that you are deploying to match job seekers with positions that will best fit their skillset. How does it work?
We recognized some serious pain points in the candidate experience, particularly related to the onerous process of searching for jobs via keyword. Watson Candidate Assistant uses natural language understanding to gather concepts, skills, and keywords from both resumes and job descriptions to provide the best job matches for a candidate. The conversation API used enables candidates to have a conversation with Watson as if they were talking to a recruiter.
We piloted the tool within IBM and saw three times the amount of applications compared to our standard keyword search tool. Most recently, an independent digital media company piloted the tool in partnership with Uncubed, and saw that candidates using Watson during the pilot were 34 percent more likely to progress to a face-to-face interview.
How are you helping talent acquisition teams automate and improve the process of identifying quality candidates?
Watson Recruitment compares attributes found on candidate resumes against the attributes found on the job role; thereby, assigning a score. It then automatically surfaces the right candidates – and how they compare against each other – for any job requisition. The tool also helps recruiters prioritize open requisitions using AI-powered insights. By analyzing historical data on requisition complexity, skill requirements, and duration to fill certain jobs, it provides an assessment of which roles will be more difficult to fill and why. This helps recruiters allocate their time more efficiently and helps recruiting managers allocate open requisitions better across more and less experienced, or specialized, in-house and external recruiters.
According to the 2017 IBM Institute for Business Value study Extending Expertise, “Sixty-six percent of CEOs believe cognitive computing can drive significant value in HR.” In what ways will organizations see an ROI?
The IBM services team ran a pilot program for Watson Recruitment with clients, and found that early adopters have seen a 20 percent increase in average monthly hires by recruiters and a 47 percent increase in candidates submitted per day from recruiter to hiring manager. Initial results also show that we are seeing better predictions of successful candidates—on average, it was tested as predicting success with 84 percent accuracy. Watson Recruitment can also predict those who won’t succeed, which could eliminate the need to review up to 47 percent of the average applicant pool. With stats like these, it’s no wonder that talent acquisition professionals say that AI’s impact on recruiting will be significant. Between recruiter time savings, improvements in time to hire, increased productivity and retention across employees, and a higher likelihood of positive referrals from candidates, the ROI potential is huge.
To find out more, visit IBM Talent Management.
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