Let’s face it, AI is here to stay — even in talent acquisition. From resume screening to the use of AI-assisted messaging for conducting preliminary interviews, AI’s role in recruitment is growing quickly. Yet, while these tools promise faster, easier, and more efficient hiring, concerns around bias, transparency, and poor candidate experience are growing.
As HR leaders race to automate parts of the hiring process, a critical question is emerging: Is AI in recruitment truly helping us find better candidates? Or is it unintentionally filtering out the very talent we’re trying to attract?
The key to successfully implementing AI in recruitment isn’t just about identifying opportunities — it’s also about mitigating potential challenges early to avoid the pitfalls and reap maximum benefits from technology.
How AI is transforming the hiring process
AI has multiple benefits for HR, including candidate sourcing, employee onboarding, talent development, and workforce planning. Specifically for recruitment and talent acquisition, AI is most commonly deployed in the areas of writing job descriptions, sourcing and screening applicants, and scheduling interviews — areas where volume management is most significant.
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According to LinkedIn’s The Future of Recruiting 2025 report, over 37% of recruitment teams are actively integrating AI into their hiring processes. The most anticipated gains reported include improved hiring efficiency and job post effectiveness.
Gartner further inflates this expectation, citing that by 2025, 60% of enterprise organisations will adopt AI-driven talent acquisition technologies to improve hiring outcomes.
On the flip side, AI in recruitment isn’t just changing the hiring process for candidates — it’s also changing the skills required for talent acquisition teams. According to the same LinkedIn report, the demand for relationship development skills in HR leaders and recruitment specialists has increased 54-fold in the wake of AI, alongside demand for phone manner and analytical reasoning skills.
Does AI improve candidate quality or just speed up hiring?
HR teams and talent acquisition specialists strongly believe that AI in recruitment can improve how they measure the quality of a hire. The LinkedIn report also revealed that 61% of talent acquisition professionals believe that AI will improve how they measure candidate quality. How? By helping them develop metrics and analyse employee performance data, allowing them to identify trends and predict long-term success.
Complementing these findings, Gartner notes that AI can further enhance candidate quality by automating recruitment processes, personalising candidate experiences, and leveraging data-driven models to help HR teams prioritise top talent.
But there are also pitfalls to watch out for. AI models trained on biased data can replicate and amplify discrimination, and overly rigid criteria or flawed algorithms can result in false negatives that filter out your top candidates.
This is exacerbated if job descriptions were generated using AI rather than tailored to the actual position. According to Greenhouse, more than half of employees reported that advertised job responsibilities differed significantly from reality once they started their role.
Multiple real-world situations have also shown how unchecked or poorly implemented AI can lead to poor candidate experiences, discriminatory hiring, and mistrust in companies that use such technology for hiring. When AI is deployed without proper oversight, it not only undermines fairness and transparency, but can also cause lasting damage to an organisation’s reputation, eroding trust, diminishing candidate engagement, and weakening employer brand over time.
How to use AI without losing top talent
AI is only a tool. How you wield it determines its effectiveness in delivering outcomes for your organisation.
The key to successful use of this tool in hiring is in how you define your frameworks. The same LinkedIn report suggests that when it comes to sourcing and screening candidates, prioritising skill-based searches has been found to increase the likeliness of a quality hire by 12%.
Skills- or task-based assessments are increasingly used to gauge adaptability, problem-solving, and technical competencies. In contrast, relying on “traditional” markers like demographics, degrees, or previous job titles can overlook a candidate’s true potential and suitability.
For organisations that have already adopted AI in their recruitment and hiring processes, it is important to regularly audit your algorithm for inherent bias. Partner with vendors who offer transparency and fairness features, and regularly test systems for disparate impact across race, gender, age and more. Inform candidates when AI is being used in decision-making and give applicants an option to request human review if screened out.
What the future of AI looks like in recruitment
As AI becomes more embedded in recruitment, the mindset that “AI is solely owned by the tech team or vendor” needs to be rethought. The vendor may execute the vision, but that vision must come from the organisation.
Building a fairer, more inclusive and effective recruitment strategy in the face of tech requires greater — not less — HR oversight. HR teams must be able to define what inputs go into their AI frameworks, interpret outputs, apply recruiter judgment, and preserve a personal touch during pivotal stages such as interviews and final selections.
Yet, according to Deloitte’s 2023 Human Capital Trends report, 61% of organisations using AI in HR say they lack the necessary confidence or ability to ensure their systems are managed and governed ethically.
To close this gap, HR leaders should prioritise AI literacy so that teams are equipped with the necessary skills to interpret insights and prevent misuse, ensuring that technology enhances, rather than replaces, the human side of hiring.
This article is contributed by RMI.
