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Both Sides of the Table: How AI Can Fix Hiring for Candidates and Recruiters
Everyone agrees AI broke hiring. Almost no one is talking about how it fixes it.
Applications per recruiter have spiked, useful signal has collapsed, and trust is eroding as fake postings, proxy candidates, and deepfakes spread. Fewer than 7% of applicants ever reach a human. The instinct is to retreat from AI to referrals, in-person interviews, and pedigree, but that is what buried good people for decades, long before any algorithm showed up.
This session makes the opposite case from both sides of the market. It opens with a job seeker on what hiring feels like from the outside: applying into a void, judged by scores they cannot see, and how they’ve successfully cut through the noise. Then it brings employers, drawing on what Greenhouse sees across 7,500+ companies and millions of applications, in to show how AI, built with structure and transparency, restores what is broken: trust, with AI that screens out fraud so real talent gets a fair shot; signal, with a structured AI interview that finally reaches every applicant and rewards skill over pedigree; and intent, with agents that match candidates to roles they want instead of flooding the funnel.
That points to a bigger idea. The front door to a job becomes a fair, structured conversation, open to everyone who applies and in any language, with a human owning every call. AI didn't break hiring. Bad AI bolted onto a broken process did. Build the front door right, and the same technology becomes the best fix hiring has had in 30 years.
Applications per recruiter have spiked, useful signal has collapsed, and trust is eroding as fake postings, proxy candidates, and deepfakes spread. Fewer than 7% of applicants ever reach a human. The instinct is to retreat from AI to referrals, in-person interviews, and pedigree, but that is what buried good people for decades, long before any algorithm showed up.
This session makes the opposite case from both sides of the market. It opens with a job seeker on what hiring feels like from the outside: applying into a void, judged by scores they cannot see, and how they’ve successfully cut through the noise. Then it brings employers, drawing on what Greenhouse sees across 7,500+ companies and millions of applications, in to show how AI, built with structure and transparency, restores what is broken: trust, with AI that screens out fraud so real talent gets a fair shot; signal, with a structured AI interview that finally reaches every applicant and rewards skill over pedigree; and intent, with agents that match candidates to roles they want instead of flooding the funnel.
That points to a bigger idea. The front door to a job becomes a fair, structured conversation, open to everyone who applies and in any language, with a human owning every call. AI didn't break hiring. Bad AI bolted onto a broken process did. Build the front door right, and the same technology becomes the best fix hiring has had in 30 years.
Learning Objective 1
Understand why trust and signal have collapsed on both sides: what a surge in job applications looks like to a recruiter drowning in volume, and what applying into a void feels like for candidates.
Learning Objective 2
Learn how structured, transparent AI can restore trust and signal in the funnel, and how to evaluate any tool against the design principles Greenhouse applies to its own AI, so you're not bolting bad AI onto a broken process.
Learning Objective 3
See how AI can rebuild the front door to hiring so more applicants get a real shot instead of the resume black hole, with a human owning every call, and why that’s hiring’s best fix in 30 years.
Access Type
Conference Pass