Today, we’re thrilled to sit down with Ling-Yi Tsai, a renowned HRTech expert with decades of experience helping organizations transform their talent acquisition and management processes through technology. With a deep focus on HR analytics and the integration of cutting-edge tools in recruitment and onboarding, Ling-Yi offers invaluable insights into the evolving role of AI in hiring. In this conversation, we explore how AI is reshaping the hiring landscape, the growing mistrust it has sparked among recruiters and candidates alike, and the steps needed to foster transparency and authenticity in the process.
How has AI transformed the day-to-day work of recruiters and hiring managers in the hiring process?
AI has really revolutionized the hiring process by automating a lot of the repetitive, time-consuming tasks. For instance, screening resumes, scheduling interviews, and even initial candidate outreach can now be handled by AI tools, saving recruiters hours of manual work. I’ve seen teams cut down screening time by half, allowing them to focus on building relationships with top candidates. It’s also helped hiring managers by providing data-driven insights, like predictive analytics on candidate fit, which can guide tougher decisions. But it’s not all smooth sailing—there’s a learning curve, and not every team is equipped to interpret AI outputs effectively.
What specific aspects of hiring have become more challenging with the introduction of AI?
While AI speeds up certain tasks, it can complicate others. One big issue is the over-reliance on automated systems for screening, which sometimes means qualified candidates slip through the cracks due to rigid algorithms. I’ve also noticed that hiring managers often spend more time now verifying candidate authenticity because of AI-driven misrepresentation, like scripted responses or fake backgrounds. This has led to a paradoxical situation where technology saves time on one end but demands more effort on the other, especially when trust is at stake.
How do you view the idea that AI enables better hiring decisions with fewer resources?
I think there’s truth to it, but it’s not the full picture. AI can analyze vast amounts of data quickly, helping identify patterns or red flags that humans might miss, and it does reduce the need for large recruiting teams in early stages. I’ve worked with companies that have made stronger hires using AI to match skills and cultural fit. However, the quality of decisions depends on the quality of the AI model and the data it’s trained on. If there’s bias in the system or a lack of human oversight, those “better” decisions can actually backfire, leading to unfair outcomes or missed talent.
What are some of the key concerns you’ve observed among hiring managers regarding AI in hiring?
Hiring managers are increasingly worried about authenticity. Many are skeptical about whether they’re interacting with the real candidate or an AI-generated persona, especially with things like fake voices or pre-written scripts becoming more common. There’s also concern about the sheer volume of applications—AI makes it easier for candidates to apply en masse, but it overwhelms hiring teams who then struggle to sift through what’s genuine. This lack of trust is pushing a lot of managers to rely more on their gut during in-person interactions to confirm who they’re really dealing with.
Why do you think there’s a noticeable shift toward conducting more in-person interviews lately?
It’s largely a reaction to the rise of AI-driven deception. In-person interviews offer a layer of verification that virtual interactions or automated tools can’t match. I’ve heard from hiring managers who feel they can better gauge a candidate’s personality, body language, and spontaneity face-to-face. It’s almost like a defense mechanism against the polished, potentially fake personas that AI can help create. While it takes more time, many see it as a necessary step to ensure they’re not being misled by technology on the candidate’s end.
How significant is the issue of candidate misrepresentation through AI in your experience?
It’s becoming a real problem. I’ve encountered situations where candidates use AI to craft perfect responses or even alter their appearance or voice during virtual interviews. It’s not just about resumes anymore; it’s about entire interactions being staged. While not every candidate does this, the fact that it’s happening at all erodes trust. I’ve advised teams to incorporate more unscripted, situational questions or practical assessments to catch discrepancies, but it’s a constant game of cat and mouse as technology on both sides evolves.
How confident are recruiters in the ability of AI systems to accurately screen candidates?
There’s a mixed bag of feelings here. Many recruiters appreciate how AI streamlines screening, especially with high application volumes, but confidence in the accuracy isn’t universal. I’ve spoken with recruiters who worry that their systems might reject strong candidates due to keyword mismatches or overly strict criteria. The lack of transparency in how some AI tools make decisions doesn’t help. It’s why I often push for regular audits of these systems to understand what’s being filtered out and why, though not every organization prioritizes this.
What steps do you recommend to ensure AI screening doesn’t overlook qualified candidates?
First, it’s critical to have human oversight at key stages. AI should be a tool, not the final decision-maker. I encourage teams to periodically review a sample of rejected applications to spot patterns or errors in the system. Adjusting the criteria or weighting of certain factors in the AI model can also help. Additionally, training staff to understand the technology they’re using builds confidence and helps them challenge AI outputs when something seems off. Transparency with candidates about how their applications are evaluated can also reduce frustration on their end.
What are some of the biggest hurdles job seekers face with AI in the hiring process?
Job seekers often feel like they’re up against a black box. Many don’t know if or how AI is evaluating them, which creates a sense of unfairness. I’ve heard stories of candidates applying to dozens more jobs than they would have, just to game the automated filters. There’s also frustration when they tailor resumes to match job descriptions, only to be rejected without feedback. It’s a dehumanizing experience for a lot of people, and it fuels the perception that the process prioritizes efficiency over individual merit.
How can companies make AI-driven hiring feel more equitable and transparent to job seekers?
Companies need to be upfront about how AI is used in their hiring process. This means clearly communicating whether applications are screened by algorithms, what criteria are prioritized, and how much human input is involved. Providing feedback, even if it’s brief, can also make a huge difference—I’ve seen candidates appreciate knowing why they were passed over. Beyond that, incorporating identity verification or other trust-building measures can show candidates that the process values authenticity on both sides, not just efficiency.
What do you think is driving the growing mistrust around AI in hiring between candidates and hiring teams?
It’s a combination of factors. Candidates feel misled when they’re not told AI is part of the process, and hiring teams are wary because they’ve seen or suspect AI being used to fake credentials or responses. It’s a vicious cycle—both sides are using technology to gain an edge, but it undermines genuine connection. I’ve seen this mistrust lead to longer hiring timelines as teams double-check everything. At its core, the lack of transparency and the fear of being deceived are what’s fueling this divide.
What’s your forecast for the future of AI in hiring over the next few years?
I believe AI will continue to play a central role in hiring, becoming even more sophisticated in areas like predictive analytics and candidate matching. But I also foresee a stronger push for balance—more emphasis on human judgment alongside technology. We’re likely to see regulations or industry standards emerge to address transparency and fairness, as mistrust can’t be ignored. My hope is that companies will invest in educating both their teams and candidates about AI’s role, turning it from a source of suspicion into a tool for mutual benefit. The next few years will be about finding that equilibrium.