AI is the buzzword on every industry’s lips. It has somehow become the silver bullet for making everything faster, easier, and more cost-efficient—including recruitment.
From resume screening to the use of AI-assisted messaging for conducting preliminary interviews, AI’s role in recruitment is growing rapidly. Yet amid the excitement, there are mounting concerns regarding bias, transparency, and poor candidate experience when AI is used in hiring processes.
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 critical key to successfully implementing AI in recruitment isn’t just about identifying opportunities—it’s also about mitigating potential challenges early so you can avoid the pitfalls and reap maximum benefits from the technology.
How is AI in recruitment changing the hiring process?
AI has multiple beneficial use cases 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—the areas where volume management are most significant.
According to LinkedIn’s The Future of Recruiting 2025 report, over 37 per cent of recruitment teams are actively integrating AI into their hiring processes, with the top expected benefits being improved hiring efficiency and job post effectiveness. Gartner further inflates this expectation, citing that by 2025, 60 per cent 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. So have the demand for phone manner and analytical reasoning skills.
This seems to signal the undeniable importance, more than ever, of putting the ‘human’ first when it comes to human resources, especially in the age of AI.
Does AI in recruitment really improve candidate quality?
HR teams and talent acquisition specialists strongly believe that AI in recruitment can improve how they measure the quality of a hire. According to LinkedIn, 61 per cent of talent acquisition professionals believe that AI will improve how they measure candidate quality by helping them develop metrics and analyse employee performance data, allowing them to identify trends and predict long-term success.
Gartner also cites AI in talent acquisition as being able to improve candidate quality by automating the recruitment process, personalizing candidate experiences, and using data-driven models to help HR teams prioritise top candidates.
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 actually 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—were they then truly the best quality candidate?
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 AI 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 effectively implement AI in recruitment without losing top talent
Remember that AI is only a tool. How you wield it determines its effectiveness in delivering outcomes for your organisation.
The key to successful use of AI in hiring is in how you define your frameworks. When it comes to sourcing and screening candidates, prioritizing skill-based searches have been found to increasing the likeliness of a quality hire by 12 per cent, according to LinkedIn. This means focusing on skills-based or task-based assessments to gauge adaptability, problem-solving, and technical competencies, rather than relying on “traditional” markers such as demographics, degree requirements, or previous job titles that may not accurately reflect a candidate’s potential or fit for the role.
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 AI 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.
Measuring and improving quality of hires also requires a strategy that is skills-based and tailored to the needs of your organisation. For example, Uber created a three-part framework for defining quality of hire that is based on:
Building a success profile of the common attributes of the best performers in their organisation;
Creating an assessment process that is based off the above, allowing them to screen for these attributes and create a benchmark for evaluating candidates; and
Validating the quality of their hires by following up with hiring managers using a post-hire survey.
The first step is undeniably the most important as it builds the foundation for your organisation’s talent acquisition strategy. Beyond skills, be sure to look at job performance ratings, new hire retention, and hiring manager satisfaction when evaluating the quality of a hire.
What’s next for the future of AI 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 AI 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 per cent of organisations using AI in HR say they lack the necessary confidence or ability to ensure their AI systems are managed and governed ethically.
To close this gap, HR leaders should prioritise AI literacy so that their 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.
