How we use AI to amplify hiring
Initially, as many do, we thought we could leverage artificial intelligence and all the data that comes with that, to put recruitment on autopilot.
When it comes to recruitment, there’s a lot of good data that simply doesn’t show up on a candidate’s profile. As our co-founder Ben Ihle puts it, “the data doesn’t always exist”. It’s not a question of whether you can parse the data, or if your clustering is good enough.
What we’ve realised, technology can amplify human decision-making, but it can’t replace nuance or evidence-based opinion. You need humans steering the hiring ship. And if you leverage AI the right way, it frees up your team to focus on things which only humans, not technology, can.
An Amazonian lesson in algorithmic approaches
Automated sourcing has its merits, but it’s the human touch that seems to produce true talent advantage.
Take the case of Amazon. In 2014, the company decided to mechanise the search for top talent. It trained computer models to vet candidates by observing patterns in resumes submitted to the company over a 10-year period. Automation has been key to Amazon’s success within ecommerce; this wasn’t its first rodeo.
This approach produced a star-ranking system, similar to ecommerce, but perhaps lacking the ‘wisdom of the crowds’. Candidates were assigned a score from one to five stars, reduced to a single value.
Make no mistake about it: Computers process numbers – not symbols. We measure our understanding (and control) by the extent to which we can arithmetize an activity.Epigrams on Programming, Alan J. Perlis
Of course, people aren’t that one-dimensional. Figuring that out early on, the team at Attract decided to take a different approach. We realised that candidate fit, and therefore productivity, is more likely to be a function of their environment. Measuring for this is just one way you achieve exceptional results.
A unique perspective
Rather than being a hard data play, Attract is focused on letting humans do what they are great at, and leveraging computers for what they do well.
Through our hybrid model, our users gain a unique slice of candidates in any given market. From a talent acquisition standpoint, this means we provide an advantage over those using the same Linkedin search box as everybody else.
On traditional platforms, algorithms often bring up candidate profiles that have been optimised for discovery, so everyone tends to see the same profiles in a similar order. With so many recruiters fishing from the same talent pool, trying to hook the same candidates, your bait becomes less attractive. Something we’re decidedly against at Attract.
We strive to find great candidates that are more likely to respond positively, because they are not optimised for discovery and thus aren’t getting messaged regularly. Many great candidates don’t have good search presence because they are too busy excelling in their day job.
Optimising, not outsourcing, the hiring process
Internally, Attract views AI as a tool to optimise our backend systems, including performing candidate searches and writing initial outreach messages. AI helps us deliver higher-quality candidates with less effort than traditional approaches, while our in-house sourcing tool gives us a unique perspective on the candidate market.
In the early days, it took 15 minutes for us to put candidates together (largely manual), from sourcing to out the door. Nowadays we have that time under 4 minutes, and we’re expecting further drops through 2021 as we further build out our technological capabilities.
It’s a simple chart, but we think it speaks volumes about how we’ve optimised our process over a few short years. We started with AI, boosted our capabilities with humans, to then circle back and integrate the best parts of AI. From what we’re hearing, we seem to have reached a happy medium.
While we’re not done writing the new recruitment playbook just yet, we have an inkling this is how you achieve better, not just different, results.