One technological advancement causing disruption in many industries is the integration of artificial intelligence (AI) into various business practices.

It is tempting to think that human resources is the one area that will be exempt from radical change of this sort. After all, wouldn’t it be wrong to remove the “human” from human resources? Right or wrong, AI has found its way into the field, as several large organizations now use machines to scan work samples, evaluate social media posts and even analyze facial expressions of applicants.

In his Forbes article, Five Hiring Problems AI Could Solve But Probably Won’t, Tomas Chamorro-Premuzic acknowledges that AI could, in fact, augment progress in the area of pre-hire selection practices within organizations today. But he also identifies potential limitations in scope unless some basic hiring problems are first addressed.

According to Chamorro-Premuzic, many organizations struggle to translate their understanding of human potential into effective hiring practices. He suggests that AI could help in this area by revealing connections between a person’s background and his or her potential that would be missed by human observers or traditional talent tools. This would not only improve the quality of hiring decisions, but could also make job markets more efficient, he concludes.

While AI has the potential to improve the human resource profession in areas such as innovative hiring practices, there are five hiring problems (summarized below) that, according to Chamorro-Premuzic, we should not expect AI to solve:

  1. Predicting performance: “You can predict only what you can measure. Since most organizations have limited data on employees’ actual job performance, there isn’t much that AI can do to improve the accuracy of their predictions.”

He further notes that training AI to determine if someone will be rated positively by his or her boss and/or get promoted – which could be riddled with bias and unfairness – is not the same thing as predicting actual performance or contribution to a team or organization.

  1. Assessing potential: “Even if AI improved our ability to predict performance, this would generally be limited to situations where the future is fairly consistent with the past.”

For example, training AI to look at a person’s past performance as an individual contributor may not be that helpful in determining the person’s success in an entirely different managerial role. Rather, organizations should focus on “known ingredients of managerial leadership potential…with or without AI” Chamorro-Premuzic concludes.

This problem, as written, begs for consideration of learning agility from E•A•S•I-Consult’s® perspective. Identifying those individuals who can quickly and flexibly figure out what to do when faced with new and challenging situations by measuring their learning agility is a key component of measuring potential that must not be overlooked.

  1. Understanding potential: “Just because we can assess potential doesn’t mean we understand it. Without a verifiable and refutable theory, data alone has fairly limited value. Science is data + theory. It is only when we truly understand the causes of future performance that we will be able to improve our hiring practices – prediction alone is not enough.”

We may be able to train AI to collect behavioral signals of speech or non-verbal communication during an interview that may even link to future job performance, but there must be defensible explanations behind such linkages (e.g., they represent emotional intelligence or intellectual curiosity, etc.).

  1. Breaking our love affair with intuition: “There is a gap between the methods that work and the ones hiring managers love to rely on. The problem is not lack of evidence on what works and what doesn’t, or a shortage of predictive tools or methods, but that people prefer to play it by ear, assuming they are a great judge of character when in fact they are not.”

So, while the utility of AI could rest in making predictions that are discrepant with human predictions, in those instances we are likely to abandon AI and go with our instincts instead, Chamorro-Premuzic notes.

  1. Killing the politics of selection: “…Our subjective and data-free decisions are not entirely random – they are influenced, if not co-opted, by our personal agendas and the wider politics that contaminate the vast majority of hiring decisions… Even the best tools can be deployed detrimentally in the absence of strong ethics or the presence of harmful interests.”

At E•A•S•I-Consult®, our tagline is “Business Driven by Science.” We practice robust science to help you meet your business objectives. So, while we aren’t likely to sell you AI technologies anytime soon, we can help you ensure that none of the five problems outlined above inhibit or undermine your efforts to move your organization in the direction of AI.

And, in doing so, we can ensure that sound science underlies your technology. Our “human” expertise in the area of selection has allowed E•A•S•I-Consult to thrive for the past two decades and will no doubt continue no matter where AI surfaces next. We see AI as a strategic business partner rather than a competitor to fear, and we encourage our clients to do the same.

Rebekah Cardenas, Ph.D., is vice president of business development and assessment solutions at E•A•S•I-Consult®. E•A•S•I-Consult works with Fortune 500 companies, government agencies, and mid-sized corporations to provide customized Talent Management solutions. E•A•S•I-Consult’s specialties include leadership assessment, online pre-employment testing, survey research, competency modeling, leadership development, executive coaching, 360-degree feedback, online structured interviews, and EEO hiring compliance. The company is a leader in the field of providing accurate information about people through professional assessment. To learn more about E•A•S•I-Consult, visit, email or call (800) 922-EASI.