AI in Recruiting is Solving the Wrong Problems

AI in recruiting fails most often because companies apply it to the wrong problem. In this episode of The Missing Manual, Julia Levy and Jenny Cotie Kangas bring back what they heard at RecFest Nashville. Their main observation is that many organizations are using AI to get to the wrong answers faster. The clearest example of AI used well came from a team that pointed AI agents at its recruiting funnel conversion data instead of adding more applicants at the top. The analysis showed that Indeed was not producing quality candidates for those roles. The team turned it off and ended up with stronger candidates. The counterexample was a large company that fixed its internal process but never brought its MSP along. Candidates got a worse experience, and that experience reflected on the employer's brand, not on the MSP. Julia Levy explains why AI screening breaks down when job postings are never fixed. Candidates use AI to match their resumes to the posting, and screening tools grade them against that same posting. If the requirements are wrong, both sides are optimizing toward the wrong target. She also shows how a weak needs analysis and a rushed intake conversation lead to scope creep three months into a search. Jenny Cotie Kangas calls this the same problem wearing different clothes: misalignment that never gets captured in the process. Her fix is to treat AI change as a program rather than a project, and to use AI to build a runbook that helps a first-time hiring manager pre-mortem the search before the role opens. The Missing Manual is a podcast for working professionals about the unwritten rules of work, careers, leadership, and AI strategy, with every episode under 20 minutes. Find The Missing Manual wherever you listen to podcasts.