One of the biggest challenges facing HR professionals today is finding the best talent to hire. This task has proven burdensome in the past, in part due to inefficient manual tasks that plague the recruiting process and the lack of access to the right data to make informed decisions. In fact, per research conducted by LinkedIn, 46% of recruiters and hiring managers have identified ''finding the right candidate" as the biggest hurdle in hiring today.

To tackle these challenges, new technology companies are rapidly emerging in the HR tech ecosystem with robust solutions that use Big Data, predictive analytics, and AI to automate and improve everything in your recruitment process from job advertising and resume screening to applicant engagement, scheduling, and recruiting by text. These new tools offer us ways to help overcome the limitations and biases inherent in recruiting with automated processes that are hyper-responsive to market data, complex metrics, and even budget constraints.

To truly understand what AI recruiting is all about you need to first decipher the buzzwords that come along with the hype. Let's start with the basics and work our way up to AI.

Automation:

Automation mimics human rules. It saves time and eliminates the manual effort required for time consuming tasks, but it doesn't necessarily make the recruiting process any better, nor can it adapt on its own. In some cases, it causes more harm than good. Just think about all those qualified applicants the ATS accidentally kicked out due to a mis-match of words. In addition, the pre-defined rules that control the basic automation do not change until the human goes in and manually modifies the code so improvements come few and far in between.

Big Data:

Big Data is not AI-but without it, AI would not exist. Big Data is the process of aggregating, analyzing, and correlating vast amounts of data from disparate sources with the goal of finding insights that may not be so obvious on the surface. These powerful insights translate into better decisions made by humans and machines.

Predictive Analytics:

Predictive Analytics is all about finding patterns in Big Data that may have impacted past outcomes such as job ad performance or applicant engagement, so we can better predict future outcomes. Once the patterns or attributes are determined, predictive models can be built that enhance the decision-making process and can transform hard-coded rules into "adaptive logic" that produce better results. Think of it like an "educated guess" we humans make, but much more accurate and dependable.

Machine Learning:

Machine Learning is not AI, but it is a subset of AI that trains machines to learn by automatically applying complex mathematical calculations to Big Data over and over again to find those patterns without human intervention. From there, predictive models can be built and refined automatically over time as the machine learns with each iteration's findings and as new data is introduced over time.

Artificial Intelligence:

In a broad definition, AI enables computers to do things that, without it, would require human intervention like complex decision-making, problem solving and learning. This technology not only significantly improves the process and outcomes, it also takes into account things that are not planned for or known by humans, using data to make the best decisions when it comes to carrying out a task. Self-learning algorithms are required when the scope of data and the scale of the problem are just too big for human interaction. Individually, any one of these technologies is useful, but when they are all combined together, they become very powerful. This is what AI recruiting is all about. It's about helping companies improve the way they recruit talent!

What AI recruiting is not: a magic program that eliminates the need for human decision-making or knowledge. Rather, the AI programs that help maximize outreach and candidate evaluations put the human gatekeepers in a better, more informed position to hire the right people for the available jobs.

Think of the old-school consumer advertising model. How do companies know how to target ads and messaging to particular demographics? How do they even know which demographics to target to begin with? The answer is simple-research. Since the dawn of marketing time (even back before the Don Drapers roamed Madison Avenue), companies have conducted market research to understand who is buying their products and why. Without sufficient data, they could never determine who their ideal audience is and the best way to reach them. The approach in online recruiting isn't much different, but it first requires access to tons of data, a.k.a. Big Data. The good news for recruitment when it comes to analyzing all this data is that compared to the days of Don Draper, technology has come a long way. We now have access to tools like Machine Learning and AI that can mine billions of data points in supersonic speeds to identify key trends and attributes that impact the outcome such as ad performance. With access to Big Data, predictive models and algorithms can be created that are capable of automating complex processes and making informed decisions that used to require human thought. We all know things change over time, and so does Big Data. The Big Data model is continuously updated and refreshed with new data from many different sources, which enables self-learning algorithms to constantly monitor changes and automatically adapt over time.

Corralling the various data points you have from candidates and combining it with historical data from employees, the company, the industry, etc., yields a very powerful picture.

The whole concept of Predictive Analytics in recruitment would also not be possible without Big Data. A prime example of this is the NAVi algorithm in pandoIQ, which takes a massive database of past job ad performance information and analyzes more than 199 billion data points across 5.4 million historical job ads using Machine Learning to identify the key attributes that can be used to predict job ad performance. From there, automated algorithms and recruiters alike can use predictive models built off Big Data Analytics to make more informed decisions about what to do next.

There's a chance that AI will replace all of our jobs some day, regardless of industry or job function. But if you're worried about AI phasing out the 'human' part of human resources, that day is not nigh. According to Deloitte, 38% of companies are thinking of a digital HR world, but only about 9% of them are remotely ready for that possibility.
Instead of worrying about the replacement possibility, it's more productive to focus on how AI can make things better today. Tasks that would have taken up time or been delegated to junior staff, can be handed off and streamlined by AI, freeing up resources to do other things. There's also a level of discretion that AI just doesn't have. A software program can crunch numbers and come up with predictive models, but it doesn't necessarily have the ability to gauge soft skills or context.

There's also the applicant experience to consider as well. An automated test or form is not going to be able to serve as an ambassador to the company or answer more qualitative questions about the job or company. There's still something to be said for the personal touch. Ultimately, you're people hiring people, and there needs to be a human interaction to help ensure that all is as it seems-that "good on paper" translates to just plain "good.''