Is Human Interaction Obsolete in the Hiring Process?

With the rise of artificial intelligence, hiring processes are undergoing a significant transformation. Initial candidate selection which used to take days to complete, now only needs 1-2 days. A 2025 BCG survey [1] states that automating administrative tasks in the hiring process benefits up to 92 percent of the participants, where 10 percent of them sees a 30 percent increase in overall productivity. Another report by Careernet [2] states that 42 percent of organizations surveyed have identified AI as a tool to leverage and optimize hiring processes as one of their top priorities in 2025.

This raises a question which, ten years ago, would sound hypothetical, but nevertheless a very real issue now: Should hiring processes be an all-AI process? Should companies move all hiring workforce to other human resource management posts?

 Utilizing AI during the hiring process could improve efficiency and scalability. Major companies receive hundreds and thousands of job applications each month, all with non-standardized CV formats. Recruiters spend a lot of time sifting through and selecting qualified candidates from those CVs. A Spark Hire article [3] reports that, during a manual recruitment process, average recruiters spend about 23 hours a week screening resumes, which is more than half of their available working time. With AI, the initial screening stage could be automated, even with non-standardized CV formats. Natural Language Processing (NLP) models can read, summarise, and conclude on whether an applicant is qualified or not based on a pre-determined set of qualifications. This automation could cut repetitive and administrative tasks, saving recruiters for more strategic roles in the hiring process.

Integrating AI in the initial screening process could also eliminate subjective biases often present in recruiters. Chen (2023) explains that, although biases have reduced significantly since the 1990s, they are still present. For example, “white sounding” names receive 50 percent higher callback rate from the recruiters, indicating a favouritism towards applicants from a certain racial background [3]. Aside from that, men receive more job offers and higher starting salaries than women from the same educational background [3]. Moreover, recruiters often fail to recognize their own biases, leading them to believe they are less susceptible to biases compared to their peers [4]. AI models could eliminate these biases completely as they are robust algorithms that do not have any preconceived notions of human beings, regardless of their race or gender. It is why companies have started using AI models during screening stages.

One might ask that with such clear advantages, why then recruiters still need to be present during the hiring processes. Even though fully automated hiring processes have a very strong case against conventional ones, experts argue that human presence in AI-augmented hiring processes is the way to go forward, instead of giving AI full authority [5].

For one, not all recruiters understand the inner workings of artificial intelligence algorithms. Time reports that among the surveyed participants less than half have ever heard that there are AI-assisted hiring processes [6]. This means that there are tendencies for recruiters to treat AI as a black box. This greatly reduces transparency and accountability as both recruiters and candidates could not explain why some decisions are taken. For example, a sufficiently qualified candidate might be declined for further hiring processes due to the AI algorithm seeing something both the candidate and recruiter do not. When asked to give justifications, the recruiter cannot provide them. More so, the recruiter might also need to provide justifications for the selected candidate, even though they are not qualified enough. In this case, the recruiter’s knowledge and understanding of the algorithm and how it works become critical.

Second, AI doesn’t necessarily eliminate human bias altogether. To start using an AI model, it first needs to be fed a set of training data to be able to understand which decisions are correct and which are not. The problem is that these decisions are pre-determined by humans, whom those aforementioned biases are from. These biases, when given to the algorithm and then used as examples in a real situation, could be replicated and amplified over and over again, leading to more biases. A 2022 study [7] suggests that bias repetition and amplification happen from data variation, model capacity, training set size, model’s overconfidence, and during the training itself. Based on this study alone, there are at least five factors that could shift a model’s ability to receive and process information. Given this risk, the usage of AI models which was intended to reduce biases and give more candidates the same opportunity could potentially worsen the situation.

Finally, human interaction is still critical in understanding one’s emotions. If the pandemic has taught us anything, it is that emotional cues and important context cannot be relayed effectively without direct communications. Humans have evolved as a social creature to read others’ facial expressions and body gestures to determine how they feel. A 2025 article [8] states that 75 percent of candidates surveyed prefer to have human interaction when applying, interviewing, and discussing job opportunities. This statistic shows that candidates value personal contact during key decision points and deeper conversations.

While artificial intelligence has revolutionized hiring by streamlining administrative tasks, accelerating resume screening, and reducing certain bias, it is not without limitations. The ability of AI to process vast amounts of data with speed and precision offers organizations a compelling way to optimize recruitment and increase efficiency. However, the risks of bias amplification, lack of transparency, and overreliance on algorithms highlight that fully automated hiring is far from a perfect solution. Companies must recognize that technology is a tool to enhance strategic decision-making in human resources, not replace it.

Equally important, the human element in recruitment remains irreplaceable. Candidates consistently demonstrate a preference for personal interactions during interviews and evaluations, emphasizing the role of empathy, context, and judgment in shaping fair and meaningful hiring decisions. Recruiters bring accountability, emotional intelligence, and ethical oversight that AI cannot replicate. Thus, the future of hiring lies not in choosing between humans or machines, but in fostering a balanced, AI-augmented approach where technology handles repetitive tasks while human recruiters provide the insight, fairness, and personal connection that define successful talent acquisition.

In the end, technology alone cannot build trust or belonging — people do.

By partnering with FED Insight, organizations can confidently navigate this transformation, designing recruitment systems where data and empathy work hand in hand. With the right balance between automation and human connection, HR can drive smarter, fairer, and more human-centered hiring in the AI era.

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