HR is arguably one of the organizational functions that has been most impacted by rapid advancements in artificial intelligence. In fact, I’ve been writing about these impacts since 2017, well before generative AI made its appearance.
HR has been quick to embrace AI technology. That simply makes sense as it is an organizational function that is highly transactional in nature. That’s particularly true in the talent acquisition process, where paperwork burdens can be high, especially for very large organizations. Wading through hundreds, if not thousands, of applications for a single open position, for instance, can be daunting.
But it’s a double-edged sword. While AI has undoubtedly had a positive impact on the hiring process in many ways, it has also created new challenges. Despite potential benefits, there are equally compelling reasons to be cautious about the use of AI.
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Navigating AI biases in hiring
While 67% of hiring decision-makers cite saving time as a key reason to implement AI, 66% of U.S. adults won’t apply for a job if the company uses AI to make hiring decisions.
Bias is a key concern. But despite widespread reports of AI’s potential for bias, these tools have been shown to have a 48% increase in diversity hiring effectiveness.
Yes, there is the potential for bias in the hiring process. But there always has been! Humans are innately biased in ways that are both conscious and unconscious. AI is trained from data and information that came from humans at some point. To eliminate that bias, you need to understand it, identify it, and take steps to minimize its potential.
Both AI adoption and abandonment are up
In 2024, a Resume Builder survey indicated that 7 out of 10 companies would be using AI in hiring in 2025. Chances are good this number is even higher today. After all, nearly all major recruiting and ATS platforms have some form of AI capability.
Still, many fail to live up to the hype. For instance, basic keyword matching and résumé parsing, which are the most common AI applications, provide limited value beyond administrative efficiency.
But even while AI use in the hiring process is rising, so is the percentage of companies abandoning their use of AI, according to CIO DIVE, citing research from S&P Global Market Intelligence. This year, 42% of businesses reported scrapping their AI initiatives—that’s up from 17% last year.
Job seekers are using AI too, but are worried
Employers aren’t the only players in the hiring mix to introduce AI into the process. Job seekers are also using AI to find and apply for positions. Data from Capterra indicates that:
- 40% use it to write or refine their résumés
- 33% use it to improve their cover letters
- 31% use it for mock interviews
- 28% use it to respond to interview questions
That was last year. Those numbers are also likely even higher now.
Despite employees’ use of AI, Pew Research data shows that they are largely concerned about AI in the workplace (52% are worried, and 33% are overwhelmed).
The candidate experience is still an afterthought
Candidate experience with AI in hiring is also largely overlooked. Employers don’t have a full grasp of how they are using AI or how they feel about potential employers using it. Companies also aren’t transparent about their own use of AI in the hiring process. That too is both conscious and unconscious.
Some employers fail to disclose how they use AI in hiring; others may not even be aware of its application or extent. Some vendors also claim AI capabilities that really aren’t part of their products, while others may fail to fully share how they actually use AI.
Still, despite potential concerns, the genie is out of the bottle at this point, and there is little likelihood that it will ever return.
AI is here to stay. Our Artificial Intelligence Ethics Policy helps you use it clearly, ethically, and responsibly.
AI legislations are rolling in
The EU’s AI regulations have only been in place for six months, and the impact of the Act is still largely limited. A lot could change when they review the legislation in October (and over the next couple of years).
In addition, most perceived risks are still largely theoretical—we haven’t had enough time to really see them play out on a larger scale, especially when considering the long-term impact of biased training data.
So, how can employers navigate this tricky terrain and address the many dilemmas they face?
What employers need to do
In this shifting environment, there are some important steps employers should take to maximize AI benefits while minimizing risk.
- Understand how your HR systems are incorporating AI into their processes and outputs. Carefully consider the issues of cost, data privacy, and security risks when auditing or vetting products.
- Audit and verify your own AI data inputs to determine how and where bias may be introduced.
- Stay on top of regulatory changes and their impact. Keep a close eye on the EU’s AI Act for early indications of potential shifts.
- Maintain a focus on the overall candidate experience, encompassing both AI and human considerations.
- Don’t overlook or ignore the need for human intervention and oversight in the hiring process. Tech alone can’t do the job.
- Recognize and even embrace the ambiguity of the cloudy AI hiring environment. Ensure ongoing communication with and education of those involved in the process.
I’ve found AI, and specifically GenAI, to be a great aid in reviewing and synthesizing information, responding to well-worded queries or prompts, and offering best practice suggestions. However, just as I wouldn’t leave an intern or new employee to their own devices without my oversight, I wouldn’t give AI free rein either. Not in the hiring process, and not in any other application.
The expertise of human HR professionals, recruiters, and hiring managers must remain a part of the hiring process. Use AI as a collaborator in your hiring process, not a replacement.
As Wharton professor Ethan Mollick encourages in his 2024 book, Co-Intelligence: Living and Working with AI, viewing AI as a “co-worker, co-teacher, and coach” is the best way to leverage its value.
