Machine. Artificial. Automated. These can seem counterintuitive words for a profession with “human” at its core. But central to the shift toward machine learning is a very human need: data on our side and on our team to back up our instincts, to help us win the talent wars and to create efficiencies that free up our team’s time to focus on high-touch candidate interactions. Let me add this: one of the key factors that drew me to the talent space is the current role and future potential of using technology to transform the way we attract, vet, hire, onboard and engage talent—and to humanize the process along the way.
And our category is beginning to embrace data as part of our mix. Deloitte’s 2017 Human Capital Trends report indicated that one-third—33 percent—of surveyed HR teams are using some form of artificial intelligence (AI) technology to deliver HR solutions today.
Early studies have shown that algorithms may be more consistent at reading X-rays than even oncologists. This doesn’t replace the oncologist, but allows her to do her job more precisely and more efficiently. The same goes for HR.
So if you’re part of the 67 percent that have yet to tap into AI, here are four ways making data a part of your team can put your results into overdrive today.
• Challenge No. 1: You have a widely mixed bag of candidates at the top of the funnel. In HR, we need to be everywhere our talent is, which can seemingly be exactly that: everywhere: on Facebook, on Glassdoor, on LinkedIn, and so forth. A byproduct of this omnichannel approach is a need to be hyper-targeted in how and where we find talent, even as we expand our channels. The missing link here is data. Data on who the best talent for us is, where that best talent lives, what appeals to them, and what about our company speaks to them most. Without this, our open casting calls for talent have no accurate filters, leaving us with potentially lots of inbound—yet off the mark—talent in our inboxes.
The Solution: Establish filters at the top of the funnel. Data-driven technologies exist today to help pinpoint recruitment marketing efforts. For instance, using recruitment advertising technology, leading health care provider Northwell Health improved the number of “best fit” candidates that came through their talent doors—while also reducing talent scouting costs by nearly a quarter (23 percent).
• Challenge No. 2: You don’t have enough time to review every resume that comes in, losing top talent to more nimble competitors. Consider this: Recent reports show that 85 percent of resumes aren’t quality ones, but if they are the first ones that come through your email, you can be considering the wrong talent pool.
The Solution: Going from “first in” to “best in.” We leverage deep machine learning so organizations can consider all qualified candidates in a pool, rather than the 2.5 percent typical in a manual screening process. Technology allows us to progress from “first in” talent—where we only have time to review the first handful of resumes that come in the door—to a “best in” approach—where we’re able to effectively yet efficiently screen all talent to rapidly identify high potentials, even if they are at the bottom of the so-called stack.
• Challenge No. 3: Your company’s interview approach is inconsistent, and so are your results. What’s your company’s batting average when it comes to identifying the absolute rock star at offer time? If you’re not consistently hitting home runs, or if results vary depending on the hiring team, interview time of day, or where in the business cycle you are that month, it’s time to lean on data to improve your high-potential talent batting average and standardize your interviewing and deliver a consistently high-quality candidate experience.
The Solution: More consistent hiring quality. Early studies have shown that algorithms may be more consistent at reading X-rays than even oncologists. This doesn’t replace the oncologist, but allows her to do her job more precisely and more efficiently. The same goes for HR. We’re only human, so it’s natural that hiring teams bring a level of inconsistency and innate bias to the process. Harvard University is tackling this via its Project Implicit initiative. And technology is already at work in companies big and small to add a non-biased data layer to our hiring expertise and instinct.
• Challenge No. 4: You’re hungry for a more diverse talent pool. Deloitte’s 2017 Human Capital Trends report revealed that 69 percent of executives rate diversity and inclusion as an “important” issue, while only 12 percent of organizations have a mature level of inclusivity or the ability to achieve their well-intended diversity objectives.
The Solution: More innovation via more diverse candidates. Increasing diversity in the workplace isn’t just the right thing to do: it has the ability to increase productivity and revenue across the board. Yet somehow, with organizations doing everything they can to eke out the smallest bit of innovation, it has fallen under the radar. Casting a significantly “wider net” with a scalable on-demand technology platform insures you’re drawing from as broad a talent pool as possible. To get around our “like hires like” approach, AI and deep learning helps top performers we may not spot otherwise, quickly rise to the top.
AI will be not an automation of a high-touch function or a replacement for stellar HR organizations, but a critical tool in the arsenal of excellence for HR teams from here on out. In fact, having data on our side is making the work we do on our best days a reality every day.