by Jeremy Schiff, Founder and CEO of RecruitBot
More than 30 years ago, people started posting resumes and job descriptions on the internet, and the recruiting profession has never been the same since. Once again we’re in the midst of a transformative shift, driven this time by the impact of technologies like artificial intelligence, machine learning, and automation. This is exciting news for recruiters and job seekers alike, but a great deal of uncertainty remains as we enter this new era.
Here’s what is important to know.
1. Technology won’t make recruiters obsolete.
At the end of the day, recruiting is a deeply human profession. If you work in the talent industry, you rely on your intuition and expertise to form meaningful connections with other people, understand their emotions, and cultivate lasting relationships. This is something robots simply can’t do well – and won’t be able to do for the foreseeable future.
Furthermore, while AI is a useful tool that can make recruiting more efficient, it will always require actual humans to develop, train, and determine how and when to apply it effectively and fairly. Just a few years ago, Amazon famously scrapped an AI-powered recruiting tool because it began showing bias against female candidates. It took human intelligence to know there was a problem – and take steps to remedy it.
2. Your job is going to change.
That being said, there’s no question that technology will fundamentally impact how recruiters and other talent professionals spend their workdays. Since AI, machine learning, and automation can eliminate or reduce repetitive, laborious tasks like reviewing resumes and manually personalizing emails, people have more time to spend on aspects of their jobs where they can really add value – in ways that only humans can.
For example, recruiters can devote more hours per day to reaching out to high-quality candidates, getting to know talent, and working to fully understand hiring managers’ unique needs. They can focus on crafting compelling messaging, unearthing outside-the-box talent, developing initiatives that promote diversity, and sharing best practices across their organizations. The more they dedicate their time and energy to these higher-value pursuits and projects, the more their employers and clients will fully appreciate their contributions – and their salaries and job security will continue to grow as a result.
3. There’s a difference between AI and machine learning.
Machine learning algorithms reverse-engineer ideal candidates and provide increasingly accurate suggestions over time, based on feedback from a recruiter, hiring manager, or anyone else using the platform. This is vastly different from AI, in which all human users can expect similar results based on decisions made at the outset by a developer – so everyone who searches for “software engineers in San Francisco” sees the exact same talent pool.
With machine learning, the model adapts to your needs and preferences by taking in two types of input: candidate data and the feedback you provide about each individual. Based on your previous decisions, it makes predictions about which candidates will be a good fit for the precise role you hope to fill. This means that the more candidates you rate, the better your suggestions will be – and the more tailored to your exact needs.
4. It’s essential to watch out for AI “snake oil.”
These days, technology and software providers want everyone to know that they’ve incorporated AI, machine learning, and automation into their platforms. But if you’re on the buyer’s side of the equation, it’s important to dig a bit deeper and investigate these claims. Ask vendors how the technology works behind the scenes and be wary if they won’t divulge details because their software is “proprietary.” You should be able to understand exactly how a vendor’s models assign weight to different attributes and change those weights based on your decisions – and have it be explained to you in simple terms.
It is also recommended to question vendors about their approach to understanding and mitigating biases that can make the recruiting process less fair and equitable. A vendor should take precise measures to address this problem and tell you about them – with the understanding that while data-driven approaches can’t eliminate bias entirely, they have the potential to do it better than humans.
5. An end-to-end solution can help maximize your recruiting efficiency.
An online recruitment software should solve for every pain point at every stage of the recruiting process. This includes recruiters, recruiting agencies, hiring managers, and founders being able to find ideal candidates with help from machine learning and working in close collaboration with anyone on their hiring team.
In addition, having access to outreach tools to auto-personalize messages, follow up with prospects automatically, and ensure that emails actually reach their intended destinations. And finally, leveraging in-depth analytics to ensure that their messaging and tactics evolve along with the market.
Jeremy Schiff is Founder and serves as CEO at RecruitBot. Jeremy began his career by earning a BS and Ph.D in Applied Machine Learning from UC Berkeley. While working at OpenTable, Jeremy saw first-hand the opportunity to transform the way that recruiting works through machine learning and automation, and founded RecruitBot in 2017 to spearhead the mission to revolutionize how to find, engage, and optimize engaging top-talent.
