When Artificial Intelligence Goes Headhunting

When Artificial Intelligence Goes Headhunting

Over the past few years, recruiting leaders have been struggling to find the best ways to apply a plethora of emerging technologies to their function. The challenge of keeping up with the pace of technological change, along with the potential headache of implementing new tools and the risk of making an expensive mistake, can make recruiting leaders understandably hesitant to take the plunge. Woo, a recruiting platform that matches employers with potential job candidates, is hoping to make it as easy as possible with the launch of Helena, an AI-powered headhunter that automates candidate sourcing and also communicates to the company on behalf of the candidate.

Woo, which has received over $11 million in funding, claims that 52 percent of candidates sourced by Helena advance to the interview stage, compared to about 20 percent of human-sourced candidates. It automatically finds the best candidates, matching them to the company and role description. Helena also makes the first outreach and then works on behalf of both the candidate and the company. In addition, the product includes data about how similar companies’ listings for similar roles are performing and how and why job seekers choose not to pursue an opportunity.

While Woo is currently trying to automate just the start of the recruiting process, its founder and CEO Liran Kotzer tells Forbes he believes they can automate the whole thing:

“The recruitment market is broken,” Kotzer says. “It’s a 200bn market in the US alone – and the problem we have is that 95 per cent of the effort and money spent in that market is wasted. When you have talent and employers trying to find each other – 95 per cent of both of their efforts are going on filtering each other. Even if they go to interview, most interviews end without a hire, so that’s another point where both parties filer each other out…

“If you think about an interview it’s an outcome of a lack of information on both sides. They [candidate and employer] have to talk with each other in order to understand what you know and what you don’t know. But if there’s a machine that knows everything – like a god – knows about your past experiences, about your projects, your culture – the machine is going to tell you that there’s a perfect fit and both parties won’t question it.”

Technology such as this has potential to help companies improve both the quality and the diversity of their candidate pools by leading them toward candidates traditional approaches could miss, there are always inherent challenges in depending on AI in this way. We don’t know exactly what kind of data the company or Woo are feeding into the machine-learning program to optimize candidate fit. As we have discussed before, the typical organization’s workforce data is of relatively poor quality, which can lead to “algorithmic bias” when automated systems end up replicating the implicit biases embedded in that data. If an organization’s data is not reliable, automated processes that depend on that data may not help the organization reach its goals.

At CEB, now Gartner, our diversity and inclusion research recommends making sure HR, IT, and recruiters are involved in the development and continuous re-evaluation of recruiting technology systems in order to ensure that these systems are working as intended and not “learning” the organization’s pre-existing biases. CEB Diversity and Inclusion Leadership Council members can read more of our insights on algorithmic bias here.