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How should companies differentiate their AI recruiting approach by job type?

September 1, 2026 The Josh Bersin Company

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For entry-level or low-skilled roles, companies can automate heavily with AI, moving through a quick screening process focused on basic skills, availability, and physical requirements. For highly technical, licensed roles — like nurses, electricians, and software engineers — and especially for executives and professionals, companies should invest significantly more in human recruiting and validated assessments, because the cost of a bad hire and the difficulty of replacement are much higher.

The recruiting process carries vastly different stakes depending on the role. Hiring costs range from hundreds of dollars for high-volume, low-skilled positions to $5,000–$10,000 or more for highly-skilled professionals. For senior engineers or executives, companies may pay almost one-third to one-half of the first-year salary in recruiter fees alone — a massive investment that demands careful vetting.

Automation Works for High-Volume, Low-Skill Roles

Entry-level and low-skilled positions are ideal candidates for heavy AI automation. These roles typically prioritize speed, basic competency matching, and rapid screening over deep evaluation of culture fit or advanced capability. Resume parsing, automated phone screening, and AI-driven initial qualification work well here because the cost of a hiring mistake is contained and replacement is straightforward.

Companies in retail, customer service, logistics, or manufacturing benefit most from this approach. The volume is high, turnover is expected, and the hiring cycle can be compressed without significant risk. An AI system that flags candidates with the right availability, location, or baseline skills accomplishes the goal efficiently.

Human Expertise and Validated Assessment for High-Stakes Roles

Skilled and professional positions demand a different strategy. Human recruiters and validated assessment tools become cost-effective investments when a hiring error carries serious consequences — a misplaced nurse puts patient safety at risk, a bad software engineer compounds technical debt, and a wrong executive pick damages strategy and culture.

As discussed in this episode of The Josh Bersin Company, roles requiring technical certifications, specialized knowledge, or leadership capacity need human judgment to evaluate nuance, potential growth, and cultural contribution. AI can support this process — skills assessment tools like Galileo help, for example — but the final evaluation must involve seasoned recruiters and hiring managers.

The alternative — over-automating these roles — risks flooding your team with technically marginalized candidates or, worse, introducing fraud. Even advanced screening faces challenges when AI systems are exposed to fraudulent resume data in the first place.

"We have AI creating resumes and producing and sending them out and AI consuming them — slop talking to slop — and the recruiters constantly tell us they're getting so flooded with fraudulent resumes that they really can't sort through it all."

This friction reveals why human judgment remains irreplaceable for critical roles. A recruiter can smell inconsistencies, sense cultural alignment, and dig deeper when something feels off. An algorithm cannot — not yet.

For more detail on how AI tools themselves vary in reliability, and how the choice of AI model can bias hiring outcomes, listen to the full episode on Listenly, which also explores the geopolitical dimension of recruiting fraud and the organizational safeguards companies need in place today.

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