By Sinead Kinney, PHR and Rebecca Norberg, SHRM-CP
It seems like everyone is talking about AI in hiring. You’ve seen the stats: nearly 70% of companies are expected to use AI in recruitment by the end of 2025. Why the massive AI adoption? Simple: AI solves fundamental human limitations in recruiting, especially when dealing with high-volume applications.
But here’s the secret: merely using AI won’t guarantee you’ll get the right candidates. Are you even sure you’re sourcing the right candidates? In 2023, iTutor was on the hook for hundreds of thousands of dollars for age discrimination because their AI tool automatically rejected over 200 qualified women over 55 and men over 60.
To truly nail talent acquisition, you need a smart strategy that pairs AI’s muscle with disciplined human oversight and proven methods for checking our own unconscious biases.
Think of it as a dynamic duo: AI handles the heavy lifting, streamlining and automating tasks at speed, while acting as a thought partner for the human who knows how to wield its power and verify its accuracy. In turn this frees up precious time and energy for tasks requiring human thought, creativity, diligence and experience. The pair? Unstoppable. Separate? Flawed and fallible.
Talent acquisition on AI autopilot
Right now, most organizations are using AI to automate and sort through applications, cover letters, emails, and job boards all while thinking they are finding qualified candidates, making data-driven decisions, and managing massive applicant pools. AI does an amazing job during these first levels of the funnel, but still requires a human to review and make decisions.
- Zeroing in on top talent
AI tools are fantastic at cutting through the noise. AI-driven sourcing and matching systems can instantly scan hundreds of millions of professional profiles, analyzing job requirements against skills, qualifications, and experience. Your recruiting team doesn’t have to waste time manually scouring job boards; instead, they get a ranked list of top candidates prioritized by how well they match the must-have and nice-to-have criteria. This frees them up to focus on what truly drives success: communicating and building relationships with those candidates.
- Boosting quality and catching misrepresentation
AI doesn’t just speed things up; it improves the quality of the candidates who move forward. Research shows that the use of an AI-assisted pipeline resulted in a 20% increase in the pass rate for final-stage human interviews, proving AI identifies higher-quality talent.
Even better, AI helps detect the widespread problem of skill inflation (or lying on a resume). In 2025, BetterHealth found that 78% of job seekers have considered misrepresenting their resumes, and Business News Daily reported that 60% have admitted to actually lying about their proficiency in skills they did not have. By implementing conversational AI assessments organizations can identify the roughly one in five candidates who claim technical skills they cannot actually demonstrate.
- Data-driven decisions
Forget guessing. Predictive analytics is the ultimate tool for evidence-based hiring. It analyzes patterns from your most successful current employees to forecast future needs and pinpoint characteristics correlated with high performance. This strategic approach provides objective data points that complement human evaluation, moving beyond intuition or gut feelings. Plus, by focusing on skills and objective indicators, AI helps mitigate unconscious bias and aids in increasing diversity by widening the talent pool.
With 70% of candidates using AI to apply for jobs (for everything from matching their resumes to skill requirements on job postings, completing applications, to even answering questions during interviews), organizations need to be smarter, faster in order to stay ahead.
Breaking the cycle of bias, the first step is awareness
In AI, bias is defined as a systematic error in judgements, which can develop in AI systems due to the humans inputting and training the algorithm. Then as humans and AI continue to interact without this understanding, they create an echo chamber, which can magnify their biases.
Our brains are hardwired with unconscious biases, the NeuroLeadership Institute (NLI)’s SEEDS Model® refers to these as Similarity, Expedience, Experience, Distance, and Safety biases.
- Similarity Bias – our brain’s preference for people who are similar to us or share common goals, like opting for a candidate that graduated from our alma mater.
- Expedience Bias – our brain’s way to conserve energy by relying on mental shortcuts based on intuition or limited information, like discarding resumes missing a college degree when it may not be a bona fide occupational requirement.
- Experience Bias – our brain’s assumption that our perceptions are an accurate, objective representation of reality, like restricting your talent pool to those who have worked within a particular industry.
- Distance Bias – our brain’s natural tendency to overvalue things that are near in physical proximity, time, or responsibility, like preference for the most recent candidate you interviewed even if they weren’t the most qualified.
- Safety Bias – our brain’s way of protecting against the unknown which is viewed as inherently riskier than the known, like not choosing to make an offer to a candidate because they “aren’t a culture fit.”
Now that we can recognize the cycle of bias, we can partner with AI leveraging each other’s strengths, while mitigating our weaknesses.
Let’s explore what happens when we combine this awareness, The Neuroscience of Better Hiring® (NLI’s SELECT product), and AI’s processing power.
The human touch—mastering bias mitigation to flip the script on AI in talent acquisition
AI may handle the data, but ultimately humans make the final decision. That means we must actively guard against the biases hardwired into our brains. This is where NLI’s SELECT core habits come into play: Embrace the evidence, Follow a process, Challenge your thinking. Anchoring on the SEEDS model, they help recruiters and hiring managers interrupt and redirect thinking by breaking bias mitigation into habits to make better decisions.
These can be super charged with the help of NILES, our Neuro Intelligent Enhancing System.
NILES isn’t your standard GenAI. With expertise from over 200 PhD scientists and subject matter experts, and nearly three decades of business practice with Fortune 100 companies and NeuroLeadership Institute’s own bias mitigation strategies, NILES is built to bring you to insight, not to tell you what to think. This paired with human experience embedded in recruitment, allows organizations to leverage the power of AI while mitigating bias – flipping the script on the typical AI-Human dynamic.
In practice this might look like having a conversation with NILES to determine if:
- Your job requirements truly fit the role.
- Your interview style is consistent across candidates.
- You’re dismissing a candidate that may need a little development but has a lot of potential.
- Bias is impacting your decision between two candidates.
- You’re making assumptions about their ability to integrate with the team or company culture.
AI is here to stay. Candidates are already using it, not always with the best intentions, to get the job, whether they’re qualified or not. Organizations can choose to adapt their strategies and incorporate the ethical use of AI to recruit with intention, or they can be left behind, make bad hiring decisions, and be outright fooled – all of which can cost time, money and competitive edge.
But when you see the power that partnering with the right AI tools can provide, the possibilities are endless.
Want to learn more about how NILES can super charge your Talent Acquisition strategy, and more? –> Learn More <–
