There’s a growing conversation in talent acquisition about the role of artificial intelligence (AI) in hiring. Increasingly, industry leaders are exploring the idea that organisations could build AI agents to conduct candidate interviews, potentially reducing the need for recruiters to handle initial screenings.
At first glance, this feels aligned with where the industry is headed. Teams are under pressure to move faster and do more with fewer resources. AI is already delivering meaningful efficiencies in areas such as scheduling and communication. As these capabilities expand, they raise an important question: what role is AI actually playing in the hiring process?
When AI is used to streamline logistics or reduce administrative burden, the value is straightforward. It helps recruiters focus on higher-value work and improves the overall flow of hiring. However, when AI moves into candidate evaluation—such as asking interview questions or interpreting responses—it crosses the line from supporting the hiring process to influencing the selection decision itself.
In traditional hiring practices, any method used to evaluate candidates is expected to be job-related, consistent and defensible. For example, structured interviews were introduced to reduce variability and improve fairness in how candidates are assessed. Over time, we’ve learned that unstructured approaches can introduce risk through inconsistency and bias.
What’s interesting is that some of today’s conversations around AI seem to reintroduce similar challenges, just in a different form. The idea that “anyone can build an agent” is technically true. The tools are more accessible than ever, and it’s possible to create an AI-driven interviewer with relatively little effort. But hiring has never been a space where accessibility alone determines effectiveness. Without a clear framework, the process can quickly become inconsistent and hard to defend. This is where an industrial-organisational (I/O) psychology perspective becomes especially important.
Behavioural evaluation uses methods such as interviews and assessments and is about more than just collecting information. It’s about understanding how that information relates to success in a role, which inherently requires structure and a clear definition of what is being measured. Without that foundation, even the most advanced technology can produce outputs that feel useful but lack real predictive value.
One of the strongest considerations for thoughtful AI adoption is that AI acts as an amplifier. It doesn’t fix underlying issues in a process, it scales them. If a hiring process is already structured and aligned to the role, AI can enhance it. If it is unclear or inconsistent, AI will simply make those gaps more visible and, in some cases, harder to identify.
This is why simply layering AI onto existing workflows often falls short. It improves speed but not necessarily decision quality. If your hiring process is headed in the wrong direction, AI simply helps you reach the wrong decision faster.
There is also a practical consideration that often gets overlooked. As hiring processes become more automated, organisations still need to be able to explain how decisions are made. If a candidate is screened out or not moved forward, there should be a clear rationale tied to the requirements of the role. This is no longer just a theoretical concern.
Recent litigation in the HR technology space already demonstrates that when AI influences candidate outcomes, organisations may be expected to explain exactly how those outcomes were reached and why the process was fair, job-related and legally defensible. Transparency and defensibility remain essential regardless of how technology is used.
None of this is to suggest that AI cannot bring value to hiring. In fact, its greatest potential may be in strengthening the process rather than replacing human judgement. For example, AI can help deliver a more consistent candidate experience while giving hiring teams better tools to support structured, defensible decision-making.
As organisations continue to explore what is possible, the conversation should shift from what AI can do to what hiring is designed to accomplish. The goal is not simply to move candidates through the hiring process faster, but to identify those most likely to succeed in the role. The most effective approaches will likely balance efficiency with rigor, using AI to enhance decision-making while maintaining the structure that supports sound hiring decisions.
At first glance, this feels aligned with where the industry is headed. Teams are under pressure to move faster and do more with fewer resources. AI is already delivering meaningful efficiencies in areas such as scheduling and communication. As these capabilities expand, they raise an important question: what role is AI actually playing in the hiring process?
When AI is used to streamline logistics or reduce administrative burden, the value is straightforward. It helps recruiters focus on higher-value work and improves the overall flow of hiring. However, when AI moves into candidate evaluation—such as asking interview questions or interpreting responses—it crosses the line from supporting the hiring process to influencing the selection decision itself.
In traditional hiring practices, any method used to evaluate candidates is expected to be job-related, consistent and defensible. For example, structured interviews were introduced to reduce variability and improve fairness in how candidates are assessed. Over time, we’ve learned that unstructured approaches can introduce risk through inconsistency and bias.
What’s interesting is that some of today’s conversations around AI seem to reintroduce similar challenges, just in a different form. The idea that “anyone can build an agent” is technically true. The tools are more accessible than ever, and it’s possible to create an AI-driven interviewer with relatively little effort. But hiring has never been a space where accessibility alone determines effectiveness. Without a clear framework, the process can quickly become inconsistent and hard to defend. This is where an industrial-organisational (I/O) psychology perspective becomes especially important.
Behavioural evaluation uses methods such as interviews and assessments and is about more than just collecting information. It’s about understanding how that information relates to success in a role, which inherently requires structure and a clear definition of what is being measured. Without that foundation, even the most advanced technology can produce outputs that feel useful but lack real predictive value.
One of the strongest considerations for thoughtful AI adoption is that AI acts as an amplifier. It doesn’t fix underlying issues in a process, it scales them. If a hiring process is already structured and aligned to the role, AI can enhance it. If it is unclear or inconsistent, AI will simply make those gaps more visible and, in some cases, harder to identify.
This is why simply layering AI onto existing workflows often falls short. It improves speed but not necessarily decision quality. If your hiring process is headed in the wrong direction, AI simply helps you reach the wrong decision faster.
There is also a practical consideration that often gets overlooked. As hiring processes become more automated, organisations still need to be able to explain how decisions are made. If a candidate is screened out or not moved forward, there should be a clear rationale tied to the requirements of the role. This is no longer just a theoretical concern.
Recent litigation in the HR technology space already demonstrates that when AI influences candidate outcomes, organisations may be expected to explain exactly how those outcomes were reached and why the process was fair, job-related and legally defensible. Transparency and defensibility remain essential regardless of how technology is used.
None of this is to suggest that AI cannot bring value to hiring. In fact, its greatest potential may be in strengthening the process rather than replacing human judgement. For example, AI can help deliver a more consistent candidate experience while giving hiring teams better tools to support structured, defensible decision-making.
As organisations continue to explore what is possible, the conversation should shift from what AI can do to what hiring is designed to accomplish. The goal is not simply to move candidates through the hiring process faster, but to identify those most likely to succeed in the role. The most effective approaches will likely balance efficiency with rigor, using AI to enhance decision-making while maintaining the structure that supports sound hiring decisions.
Michelle Flynn
Sr. Behavioral Scientist, Talent Science, Infor
Michelle Flynn, Ph.D., has 5 years of experience helping organizations design and employ talent-related programs. As a Behavioral Scientist, Michelle works with organizations to leverage data-driven insights examining critical business outcomes to facilitate the alignment of talent strategy. She has advised clients from many different industries, including healthcare, retail, property management, and hospitality in the selection, development, and growth of top talent.