Economic Times flags hiring automation failures

Hiring software is supposed to make recruiting cleaner and faster. But the weird thing happening now is almost the opposite — the process looks more organized from the company side while feeling more chaotic from the candidate side. That gap is the story here. A person can get a rejection email from a company portal and, hours later, get a recruiter message from that same company for what looks like the same kind of role. Economic Times put a spotlight on that contradiction this week, and turns out it lands because it feels instantly believable. ### Why does this happen at all? Because “the hiring system” is usually not one system. It is a stack. An applicant tracking system filters inbound resumes. A screening model scores keywords, titles, dates, or location. A recruiter may separately search LinkedIn, a talent database, referrals, or a sourcing tool. Those lanes do not always talk to each other in real time. So one part of the machine says no while another part is still shopping for the same person. (economictimes.indiatimes.com) That is not necessarily one human being contradicting themselves — it is a workflow problem. ### How common is the automation piece? Pretty common. The ET story says more than 75% of large companies now use automated hiring systems and AI resume screeners. A broader SHRM snapshot points the same way — 43% of organizations now use AI in HR tasks, up from 26% in 2024, and resume screening is one of the most common use cases. Basically, companies adopted these tools because application volume got too big for fully manual review. (economictimes.indiatimes.com) ### So is the software just broken? Not exactly. The catch is that these tools are usually optimized for speed, consistency, and recruiter workload — not for giving candidates one coherent experience. A screener might reject someone for missing one field, a location mismatch, or a formatting issue. A recruiter doing outbound search may care about none of that and just see a plausible fit. The result feels absurd to the candidate, but from inside the stack each step can still look “logical.” (economictimes.indiatimes.com) ### Why does this matter beyond annoyance? Because hiring decisions are not just operational. They are legal and reputational. The EEOC has said federal employment discrimination law still applies when AI is used in hiring. If an automated tool screens people out in ways that create bias or block disabled applicants, the employer does not get to blame the software and walk away. Even apart from lawsuits, a process that feels arbitrary pushes good candidates out fast. (economictimes.indiatimes.com) ### Is oversight getting stronger? In one sense, yes — the legal risk is clearer. In another sense, no — the policy picture got messier. Some federal AI-in-workplace guidance was removed from agency websites in early 2025, even though the underlying anti-discrimination laws still apply. So companies are in this awkward middle zone: more automation, lots of vendor promises, but less obvious public guidance on how to run the systems cleanly. (eeoc.gov) ### What should candidates take from this? Do not treat one automated rejection as a final verdict on your fit. That sounds unfair, but it is the practical lesson. If a role really matches, it still helps to be visible in more than one channel — application portal, recruiter outreach, referrals, and a profile that shows concrete work. The point is not to “beat the AI.” It is to avoid being trapped by one brittle filter in a messy system. (natlawreview.com) ### What is the bottom line? This is not a story about robots fully taking over hiring. It is a story about fragmented software quietly making hiring feel incoherent. More companies are using AI to sort, rank, and reject. But unless those tools are connected to actual human judgment — and audited like they matter — candidates will keep getting the same surreal message: rejected by the machine, wanted by the recruiter. (economictimes.indiatimes.com)