Answer extracted from the The Josh Bersin Company podcast — listen to the full episode below.
Why are AI video interviewing tools limited in their current form?
Even the most sophisticated AI video interviewing tools from companies like Eightfold and HireVue are designed primarily for screening, not final assessment. While they offer candidates the convenience of applying without scheduling constraints, they fail to capture the subtle environmental and interpersonal signals that emerge in face-to-face interactions, and rejected candidates often have no one to contact for meaningful feedback.
Screening versus assessment: the real limitation
AI video interviewing tools excel at volume processing—they can handle thousands of applications in parallel without the bottleneck of scheduling human time. Yet as Josh Bersin explains in The Josh Bersin Company podcast, this efficiency comes at the cost of depth. These platforms remain effective primarily for initial screening phases, where the goal is simply to filter candidates by basic qualifications or communication readiness.
The moment a hiring decision requires nuance—assessing how a candidate collaborates, handles pressure, or fits a team's culture—the AI tool's limitations become apparent. A video interview cannot replicate the organic flow of conversation, the unscripted moments where real competencies and personality emerge, or the mutual evaluation that happens when recruiter and candidate truly engage.
The feedback void: rejection without dialogue
Perhaps the most damaging gap in current AI video interviewing systems is their lack of feedback mechanisms for rejected candidates. When a human recruiter declines a candidate, that person can at least request a conversation, ask what went wrong, or understand where to improve. With an AI tool, rejection is often a silent door.
Candidates who invest time in recording responses to algorithmic questions receive no explanation—no insight into which skills were flagged as insufficient, whether it was their communication style, tone recognition, or something else entirely. This absence of dialogue creates frustration and erodes trust in the hiring process itself, especially when candidates have no recourse or clarity.
The broader challenge, as discussed in this episode of The Josh Bersin Company, is that hiring costs money—sometimes $5,000 to $10,000 or more for a skilled role, with executive placements reaching up to half of the first-year salary in recruiter fees. When AI tools remove human judgment and dialogue from the equation, they may accelerate screening but ultimately create a less robust and less humane hiring experience.
Josh Bersin — Global Industry Analyst & CEO of The Josh Bersin Company, a research and advisory firm focused on talent, learning, and organizational effectiveness in the enterprise. Bersin has spent decades analyzing the talent acquisition industry, benchmarking hiring costs, and tracking how technology reshapes recruitment workflows. His analysis spans the full recruiting lifecycle, from job board evolution to modern AI-driven screening and assessment tools.
One concrete detail worth exploring further: the episode digs into how AI-generated resumes are now flooding the system, creating a cascade effect where AI consumes AI-written content, making the screening phase even noisier and less reliable.
Key takeaways
- AI video interviewing tools are optimized for high-volume screening, not nuanced final assessments or leadership evaluation.
- These systems fail to capture the interpersonal and environmental cues that human interviewers naturally pick up in real conversations.
- Rejected candidates typically receive no feedback or dialogue from AI-driven tools, creating a frustrating and opaque experience.
- The cost of hiring a skilled worker ($5,000–$10,000+) demands human judgment and clear communication—elements AI screening alone cannot replace.
See also
What role does industrial-organizational psychology play in the future of AI recruiting?
Josh Bersin identifies smarter IO (industrial-organizational psychology) assessments built into AI as the future direction of recruiting technology, moving beyond simple screening toward predictive hiring outcomes.
How should companies differentiate their AI recruiting approach by job type?
For entry-level or low-skilled roles, companies can automate heavily and move through a quick process focused on basic skills, while executive and specialized roles demand far more human involvement and nuanced assessment.
How does AI perform on skills assessment compared to holistic candidate evaluation?
AI tools, including Galileo which is loaded with a massive skills library from Lightcast, perform well at skills-based assessment and technical screening, but struggle with broader competencies like cultural fit and interpersonal dynamics.