How Artificial Intelligence Will Shape the Future of Talent Acquisition

By Kyle Hager,
April 26, 2018

In February, I touched on some of the current buzzwords floating around the talent acquisition space, and focused on programmatic and RTB space as it stands and where I think it’s going. After attending TAtech’s AI and machine learning focused conference, I’d like to share what was presented and how AI and machine learning will likely shape the talent acquisition industry in the very near future.

First, what is AI?

In few words, AI is the ability to amplify cognitive capacity. Machine learning is simply the current means in which we are deploying AI methodologies. You may notice that this is similar to relationship between programmatic and RTB, with one being the broad concept and the other an option of applying the concept. The main problems we’re trying to solve with AI are complexity, scale, endurance and velocity.

One of the general themes of the conference was trying to break down what’s realistic and key hurdles, especially as it relates to talent acquisition.

What kind of artificial intelligence can be used right now?

There are several immediate use cases where you can use machine learning to automate menial tasks. Chatbots can help answer candidates’ questions when they visit a career site. Scheduling tools can remove the rigamarole of going back and forth on times that work to schedule interviews. Resume-matching tools work to parse through resumes and help identify relevant candidates. Finally, bidding algorithms can take previous applicant data and place job postings on channels where they’re predictively most likely to succeed. All of these can be leveraged in some way, shape or form right now, but will also undergo vast improvements in the near future.

What are the roadblocks for AI in HR?

In general, the main challenges from AI will come in three different buckets: Hoping it can do more (or everything), difficulty merging multiple uses of AI together, and a high price point.

You may purchase an automated resume review service that cuts the time spent vetting applicants, but you’ll also want an automated scheduling tool to remove the back and forth email scheduling your company currently uses for interviews. However, if these separate tools can’t both successfully integrate with your applicant tracking system, you may spend the same amount of time trying to make a connection work as you were spending before the artificial intelligence came into play.

On the other hand, consolidating into a platform that claims to do all of this for you will end up being a much more expensive endeavor for the time being due to all of the integrations the platform would have. And don’t rule out the fact that candidates will want to make some human interactions with a company before deciding where to take the next step in their career-that’s not likely to ever change.

The verdict

In general, the TAtech AI and Machine Learning conference was incredibly thought provoking on how AI is currently utilized and feasible paths for how it might evolve. If you’re looking for a fully conscious machine to be making all of the hiring decisions for your company, you might want to set the bar a little lower. However, I would take the advice of keynote speaker Michael Stewart, the CEO of Luicd.ai – that the key traits of tomorrow’s intelligent age workers will be adaptability and flexibility.  AI and machine learning will continue to improve the way we make business decisions, and having a workforce than can be flexible and adapt to changes will keep your business at the forefront of your industry.

About the Author

Kyle is the Programmatic Manager at Hireology for Applicant Engine. He is responsible for improving sourcing for Hireology’s clients. Before joining, Kyle was a programmatic manager at the digital media agency Spark Foundry. Before beginning his career, Kyle attended the University of Michigan, where he studied Actuarial Mathematics.