SHL Data: Most Hiring Processes Are Not Built to Find the Class of 2026's Most AI-Ready Candidates
The News
More than a million assessments conducted by global talent assessment firm SHL point to a counterintuitive conclusion: the class of 2026 may be the most AI-ready workforce cohort ever, but most hiring processes are not built to find them. Matt Kirk, SHL's head of market insight and solutions, analyzed the data and found that graduates consistently outperform non-graduate peers on the behavioral skills most tied to AI readiness — including adaptability, continuous learning, and digital fluency.
The problem, Kirk argues, is that traditional hiring workflows actively work against identifying these candidates. AI-powered application tools allow candidates to apply at scale with minimal effort, producing polished but increasingly uniform CVs that reveal little about the candidate behind them. Some employers close requisitions early to manage volume, meaning deliberate candidates — the very ones most likely to exhibit the behaviors SHL's data associates with AI readiness — miss the application window entirely. As Kirk put it: 'That adaptability is invaluable.'
The data arrives alongside other indicators of early-career market strain. iCIMS reported last month that entry-level openings are up 18% year over year, but applications from the 18-24 segment dropped 9%. Among entry-level job seekers surveyed by iCIMS, 54% believe employers now expect mid-level experience for entry-level roles, and 78% believe AI and automation are reducing the number of entry-level positions available.
Why It Matters
This is the story that connects several dots from recent weeks. More openings, fewer applications from young workers, rising AI anxiety among early-career candidates, and now evidence that the hiring process itself is filtering out the candidates most likely to succeed in an AI-enabled workplace. The combination is a structural problem that won't fix itself.
For TA leaders, the SHL data should prompt a specific kind of introspection. If your early-career hiring process rewards speed — first to apply, first to be evaluated — you may be systematically hiring the candidates who are best at rapid application submission rather than the ones best suited to the role. The risk is particularly acute as AI-generated applications flood the funnel. Kirk's recommendation is deceptively simple: assess behaviors that predict performance rather than credentials. But most ATS workflows aren't built for that. They're built to filter out noise, not to identify signal.