Creating an AI Strategy in Hiring

It rests on three essential priorities.

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May 18, 2023

The AI genie is out of the bottle. Career-site chatbots, resume reviewing, interview scheduling, and skills assessments rely on machine learning or natural language processing to accelerate nearly every part of talent acquisition. And similar technologies take analytics and workforce intelligence to a new level.

But there is doubt and hesitation around the technology. Some of the world’s foremost innovators seek a pause on AI development. A recent TA technology report by Aptitude Research found that “the number one reason some companies hesitate to invest in AI is that they do not know enough about it.” Few talent decision-makers would consider uprooting their current AI-based technologies, but many are asking questions about the future.

For organizations seeking a path forward in their talent technology investment, AI offers opportunities that outweigh most risks. Still, a thoughtful approach to the AI conversation will be more critical than ever in gaining buy-in, reducing doubt and making smart technology decisions.

An effective way to move that conversation forward is to touch on three essential priorities leading to a sound AI strategy.

1. Identifying AI’s Influence

We often hear of anxiety about algorithms determining who gets a job and who does not, but in most cases, AI still does not make final hiring decisions. Instead, we see a growing role in how technology influences hiring by helping determine who is on that final shortlist.

A modern talent function uses AI-driven applications to automate many aspects of the candidate journey, from the recruiter’s search for the candidate to the actual application process. To start, it helps refine the initial talent pool through automated candidate care at the outset of the process.

Career site chatbots were an early face of conversational AI at the beginning of the hiring process. They answered questions (and helped filter our candidates by steering them away from opportunities that did not match).

The conversational AI that drives such chatbots continues to evolve, and more companies are looking at the technology as a complement to, not a substitute for, the human recruiter in the process. The interactions enabled by the technology are more sophisticated than in the past. They can be more candidate-driven and provide deeper detail and answers that specifically address the questions candidates ask.

Of course, the role of AI extends further than ever in assessing job seekers in the early stage of the process. Intelligent applications automate initial resume reviews by stack ranking candidates best fit for a role, and they support screening through online testing and basic early-stage interviewing.

Notably, the progress in technology over the past few years has also been felt in that initial screening interview. No longer do companies need to focus on the proverbial knockout questions. Instead, we are seeing AI point candidates more directly to the opportunities that fit or better prepare them to succeed in their job hunt.

Finally, and perhaps overriding other aspects of AI, there is the rapid rise of ChatGTP in the candidate-employer equation. In fact, smart job seekers now use ChatGTP or similar applications to create answers or profiles that take advantage of the algorithm matching typical in today’s ATS. In essence, they can arrive at an input specifically inclined to be selected or ranked highly.

Many would say this flips the technology advantage from the employer to the candidate. However, a more appropriate view is that it positively evens the playing field.

Understanding exactly where AI influences the candidate’s journey is the first step in better controlling the impact of the technology. AI technology that answers candidates’ questions or coordinates interviews also frees up overworked recruiters and hiring managers to better serve the candidates who need attention.

2. Tracking Performance and Impact

The risk that technologies pose throughout the recruiting process is that they can build bias into the journey. Automated resume analysis is the most obvious case, as a solution can stack rank potential candidates in a way that keeps them from gaining the recruiter’s or hiring manager’s attention.

How a search is written, how a job is defined, and where the job is promoted all determine who gets in the door at the outset of the hiring process.

In every case, AI technology builds on the data fed into it, which can reflect the human biases of the past. Addressing the risk of bias is less a matter of controlling the technical aspects of any application and more a case of detecting where bias occurs. Access to data and awareness on the part of the recruiter are the best tools to fight bias.

Using detailed data, companies can pinpoint precisely where bias influences the process. That means knowing exactly where, when, and how large an imbalance occurs. It could be that diversity in candidate slates is disproportionately low for one hiring manager or location. Or, it could be that a high percentage of hires quit before their first 90 days on the job in one location or for one specific role (an indication of a poor job definition and requirements).

Ironically, the technology that enables such detailed reporting is likely to be powered by, you guessed it, AI.

3. Accounting for the Human Element

People remain integral to the hiring process, but expectations of them are changing. For example, in an AI-enabled environment, recruiters do not have to spend large amounts of time sifting resumes or answering simple questions from potential candidates. Instead, the technology can automate resume review and care for first-time career site visitors so recruiters can focus on higher-value activity.

But what does higher-value activity mean? In most cases, it boils down to building relationships. The recruiter should have the capacity to learn about individual candidates, guide them toward the right opportunity, and coach them for successful interviews.

Or, they may take the time to work with hiring managers to define job roles and skills requirements based on the specific current need, desired outcomes and market conditions.

When building the hiring manager and the candidate relationship, the advantage of AI is lost if the recruiting organization responds to available bandwidth by simply raising the recruiter’s requisition load. Everyone involved — the recruiter, hiring manager, and the candidate — should benefit from the time freed up by automation and the intelligence it provides to support sound decisions.

Ultimately, companies are taking a pragmatic approach to the technology, and that is encouraging to see. They have a healthy respect for the risks and the means to monitor performance. Most of all, they recognize that people will always be part of the process. Rather than ask people to do more of the same things they do today, ask them to do the things they need to do smarter and better.

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