POST WRITTEN BY
Rudina Seseri is founder and managing partner of Glasswing Ventures.
Artificial intelligence can change how recruiters do their jobs. But robots like SoftBank’s Pepper aren’t a threat yet. (Credit: Kiyoshi Ota/Bloomberg)
While it might seem ironic, artificial intelligence and machine learning are well on the way to lending a helping hand when it comes to solving the problems of human capital management. Recruiting is one of the toughest issues businesses face today. Finding the right talent is critical for business success, but it’s challenging to say the least, and hiring the wrong person is expensive. Recent estimates put the cost to find and hire a new employee at a quarter of a million dollars — a cost that can quickly become astronomical if that person turns out to be wrong for the role. With the ever increasing data available about candidates and employees, some innovative AI companies are taking on the challenge of helping improve talent acquisition efficiency and effectiveness.
Recruiting efficiency: the low hanging fruit
Recruiting is a high-touch activity that involves stakeholders across the organization. AI startups are significantly reducing the operational burden by automating low level tasks and providing better information for decision makers. An example of this can be seen with X.ai a solution that can help tackle the administrative nightmare of scheduling interviews. ClearFit saves recruiters sourcing time by automatically finding and ranking candidates and Filtered can help assess technical candidates through auto-generated coding challenges (also improving their effectiveness). While these might not be killer apps for HR, they can deliver immediate value while helping AI companies gather data to expand into new areas.
Recruiting effectiveness: the challenging holy grail
The big prize for enterprises comes with improved effectiveness. To tackle this, companies need better data and intelligence that allows them to find the right people for the job and focus on the right indicators when screening.
With each recruiting process, there is a substantial amount of data created, but it isn’t being captured for future reference. When companies need new talent, they post a job, source candidates, screen them across different interviews and eventually pick one to fill the position. Every single time either companies or candidates go through this process, they have to start from scratch, losing not only time but also valuable information across recruiting events. Wade & Wendy is trying to address this problem with a virtual assistant that is the first point of contact with applicants that goes on to build a trail of each interaction the applicants have with the company.
When it comes to sourcing, companies still struggle to get their message through and target the right candidates at the right time. AI startups are leveraging existing data to tackle this very aspect. Textio aims to helps companies create better job postings that will help differentiate them, while Engage Talent allows them to discover passive job seekers, and target them with personalized messages at the right time.
Screening tends to rely on resumes which are both an indirect indicator of a person’s skills and an incomplete picture of their achievements and capabilities. Harver is creating a new type of screening by generating engaging tests that assess candidates on tasks they’ll have to do on the job, while Ansaro is unifying all the data companies have about their employees to build predictive models that will help them hire in smarter way.
The business model challenge
AI startups can provide significant value to both companies and applicants, but they need to understand how this value is created when they define their business model. Recruiting is a two-sided market where high value is created in very discrete time intervals (i.e. when a person is recruited). This creates a challenge for tech companies. While they are used to recurring revenue streams, their customers are likely to prefer to pay on a per job/position rather than a typical SaaS model. There are ways to overcome this, either by targeting segments that have high recruiting needs (e.g. high turnover businesses/temporary workers) or creating a product that can deliver recurring value, but startups and investors will have to consider a commission-based model in the early days and to think about how to make the transition to a recurring model if at all.
AI can make recruiting smarter
While companies have increasing amounts and diversity of data they can leverage to try to identify the best fit for each position, the recruiting process is still stuck in the past, based on standardized resumes and (potentially biased) interviewers’ opinions, and companies are suffering because of that. Even though we are talking about people, machines seem to be able to help not only source but also screen applicants. In fact, studies have shown that humans are notoriously bad at picking the right applicant and a meta-analysis illustrated that algorithms can outperform human experts in hiring.
Even though AI is far from being able to autonomously decide who a company should hire when it comes to figuring out who the best person is for the job, you might want to check out how AI is make recruiting smarter.