(An excerpt from "The New Way to Hire"on O'Reilly by Esther Schindler)
Every business wants to hire the best people and create successful teams. We all aim to attract candidates with the appropriate technical skills, relevant experience or education, and attitude. As job-seekers, we want to find rewarding positions that use our knowledge, pay us fairly, and make us feel valued.
That’s been true for as long as newspapers have had a help wanted section. But in a digital era, the traditional interviewing and staffing processes have serious weaknesses. Well-meaning HR departments cannot keep up with the deluge of applicants, those candidates have a difficult time getting noticed, and job interviews are less an actual assessment of technical ability than game-show contests.
As a result, companies are thinking differently about how to hire. In a genuine desire to match open positions with stellar candidates, tech firms and experienced recruiters are trying new techniques and inventing computing solutions.
“The recruiting industry is ripe for disruption, as it must address the ever-changing global economy and related needs for a talented workforce,” says Kristen Hamilton, CEO and cofounder of Koru. “Raw smarts and technical skills aren’t enough to succeed in business.”
Companies are becoming increasingly savvy in how they recruit, assess, and retain high-performing talent. The effort to improve the hiring process takes advantage of plenty of new technologies, from big data to artificial intelligence (AI) to scientifically based skill assessments—though HR departments are trying to make the process more human, too. Although this area is still very much in development, it’s sure to have positive effects on businesses’ talent acquisition efforts. Whether you’re a hiring manager, HR professional, or lone techie looking for a better job, you should consider these options.
The Changing World of Hiring
Once, placing a help wanted ad in the local newspaper was the primary way for a business to attract applicants, and job seekers to learn about open positions. The world is far different today. The internet gave us all megaphones, letting our voices be heard over a wider area—and created a cacophony as a result.
Some changes are good, even if they also create new challenges. Many of them are better-class problems. For example, we’re all more aware of the advantages of team diversity (even if we’re not sure how best to achieve it), and our mobile lifestyles make remote work a real possibility (even when we aren’t sure how to hire or manage people who telecommute).
But some hiring-process changes present new trials that confound us all. For instance, nobody is quite sure of the legal and privacy boundaries in snooping through a would-be employee’s Facebook account.
Social issues have changed the hiring process, too. Gwen, who has worked in office jobs on and off, says it was easy for her to get a moonlighting job fifteen years ago. “All I had to do was sign on at a temping agency and get sent to a gig. If the position I was covering happened to be open, I had a job offer by the end of the week.” She knew she’d have work, short-term or otherwise, because she had marketable skills, technical knowledge, and ability. But now, Gwen says, the job search emphasizes highly subjective and arbitrary factors, such as “attitude” and “team fit” that remain mostly undefined. “It essentially comes down to whether someone likes you personally,” she says. “The pendulum has swung too far away from IQ to EQ. For an ex-staffer for whom stats talk and BS walks, I’m trying to get my head around this, and not succeeding too well.”
What factors are influencing the changes in hiring practices? Let’s consider a few.
Avrio AI, too, works in conjunction with ATS’s. Its AI talent platform uses machine learning technologies to analyze applications, open jobs, and candidates, and works to discover matches that aren’t necessarily obvious. Using an API, Avrio AI sucks all the résumés received for every job listing into its system and provides a proprietary “fit score” for suitability. And it applies those scores not just to today’s job application, but for a reasonable period of time.
You might apply to a job today for which you’d score, say, 55%, says Nachi Junankar, Avrio AI’s CEO. But tomorrow, unbeknownst to you, the same business might open up a job requisition for which you’d score 85%. You’d be the needle the recruiter wants to find in his haystack.
Using Avrio AI’s system, recruiters get a list of candidates that score well on the stated criteria, typically 10 to 20 at time. Scorecards show a detailed snapshot of each candidate’s work history, skills and experience, education, certification, and so on—highlighting both matching and missing skills and attributes.
But that assumes a recruiter knows everything he wants, and that an applicant is aware of everything she ought to mention. So Avrio AI also has an AI robot that starts an online conversation with the candidate to validate or verify skills. For instance, “Tell me more: Do you know about branding? Have you led a team of people? Will you relocate for this job?” Candidates leave off this kind of information on résumés all the time, and they can be deal-breakers or deal-makers. Essentially it customizes the résumé for this particular job or at least for this particular company.
In the course of the conversation, the bot updates the candidate’s profile, says Junankar. For instance, if the candidate did manage a team, the systems updates the profile with that data, updates her score, and transcribes the conversation into the record.
The AI system works in the other direction, as well. Applicants can ask questions such as, “Tell me about the company culture” or “How many people have applied to the job?” or “How many marketing jobs does this company have open?”
Avrio AI gives applicants some transparency about the process. If the candidate is a good match, say she scores above 70%, it says so and takes the applicant to the next phase. “If not,” Junankar says, “The system tells you, ‘You applied, but only scored 50% on this job. However, here’s some other jobs where you might be a better fit. Here’s a link to apply.’”
Note that these aren’t the only tech solutions applying data analysis to ATS databases. Another in this category is Entelo, which works from two directions. On the outbound side the company helps its customers find prospective candidates by aggregating and analyzing publicly available data, engage with candidates, and track that engagement. On the inbound side, says John McGrath, its CPO, Entelo’s data science algorithms mine ATS data to help them consider applicants for additional roles and to more efficiently sift through them.