AI in Recruiting Is Mostly Hype. Two Use Cases Are Real.

There is an enormous amount of noise in the 'AI for recruiting' category right now. Most of it is repackaged email automation with a language model writing the first draft. That's a productivity gain, not a strategy. It's also already commoditized.

Two applications are doing real work. The first is signal extraction — pulling movement signals out of public data faster and more reliably than a human team ever could. This is where the actual time savings live. A recruiter with a clean signal feed each morning is operating in a different category than one starting from a list.

The second is conversation prep. Models that can read an agent's three years of public production, MLS notes, and social activity and generate a one-page brief before a meeting are genuinely changing how prepared the recruiter shows up. The agent feels the difference within thirty seconds of the conversation.

Everything else — generic outreach generation, scoring of who 'might' move, AI 'assistants' that schedule meetings — is fine, but it isn't the edge. The edge is signal in, prep out. Build for those two use cases first.