“The great news is that machine learning can help you connect with applicants a lot earlier in the hiring process. Candidates don’t want computers; they want people.”
By Shana Lynde , Co-founder & CMO , Janeous
I’m just going to say it: No. But there’s more to the story.
Today’s hiring process is not what it was a couple of decades ago, pre-internet. There is more competition than ever for top talent; sourcing is arduous and rife with flaws; hiring timelines are way too long as a result; and candidates feel the brunt of these issues. Companies have begun to realize the need for revolutionary products to overhaul our stale recruiting methods.
Enter machine learning, algorithms, AI, and other automated HR tools. ML-driven HR Tech has a lot of promise to pull us out of this endless, flawed hiring cycle. But do these promising tools have what it takes to be the ultimate recruiting gamechanger we so desperately need?
Machine learning and AI address every stage of the recruitment process: sourcing, filtering, screening, matching, scheduling, interviewing, follow up, feedback, and more. Startups like Janeous, who are at the vanguard of the HRTech industry, are using ML-driven algorithms as the foundation of their platform, as a given. It is no longer a question of whether we use computer algorithms, but in what way are they best implemented?
ML increase efficiency, big time. Take, for example, interview scheduling. How much time does all scheduling and re-scheduling of candidates take you as a manager or a recruiter? Hours, maybe days? What if you could use that time for something, anything else, by replacing it with ML-driven dynamic scheduling? I don’t know a company out there who would turn down the opportunity to streamline your hiring while expediting the process.
The question is not whether should your company enlist HR Tech built on machine learning, but why would you not? So why are these shiny automated tools not changing the face of hiring in the way we had hoped?
The hiring process is not broken because it’s too slow, although this is one piece of the giant, complicated puzzle. Sure, recruiters never have enough time in the day. Faster, more efficient, automated is the best solution you could ask for. So why doesn’t’ efficiency solve the problem?
Hiring professionals’ only experience one side of the coin. The candidate experience is where the hiring process breaks down to the point of no return. When recruiters and managers are inundated with the wrong resumes, too busy to coordinate scheduling effectively, and lack time to reach out to candidates effectively, it is the candidates that suffer. Many reach for automated tools and often ML isn’t enough. Companies and recruiters are now opting for the tools that can mimic human behavior and replace the need for human interactions (AI).
The candidate side of the story looks a lot different than your own. Candidates send out dozens of resumes into the black hole and rarely hear back. You might combat this by using an ML- driven chatbot to connect with them. Still, that is a computer. From chatbots to ML-analyzing of non-resumes to video interview selfies, candidates are more lost in the weeds than ever. Hiring professionals are saving time, but at what cost?
I believe that this entire broken cycle is driven by a lack of human interaction in the hiring process. The great news is that machine learning can help you connect with applicants a lot earlier in the hiring process. Candidates don’t want computers; they want people. There are still flaws with platforms built on machine learning, and it is worth examining each tool closely to be sure it is not only solving your needs but is fixing the candidate experience. That is why my company’s mission is to bridge the gap between HR tech and human interaction from the earliest stages of the hiring process.
Shana Lynde is the Co-Founder and CMO of Janeous & Two-time entrepreneur. She previously built a company through global partnerships, leveraged famous influencers, and tripled profits each year. A top producing executive recruiter for Fortune 500 medical device companies. She also led the content marketing strategy at a SaaS tech startup.
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