Our Take
Employers are optimizing for speed over quality because AI-polished applications arrive in minutes, but the data shows the slowest, most deliberate candidates are the ones worth waiting for.
Why it matters
Class of 2026 graduates are more adaptable to AI workflows than previous cohorts, but traditional screening methods can't distinguish them. If you close requisitions early to manage volume, you're filtering out your highest performers.
Do this week
Talent leaders: audit your time-to-hire rules and screening thresholds before next cycle so you capture applications from deliberate candidates who arrive after the first 48 hours.
Graduates outperform on AI-readiness, but hiring pipelines miss them
Matt Kirk, head of market insight and solutions at SHL, analyzed more than a million assessments across roles and geographies and found that recent graduates consistently outperform non-graduate peers on behavioral skills tied to AI readiness. Yet many employers aren't structured to identify or hire them.
The screening problem is clear. Applications jumped 26% in 2025 (per the National Association of Colleges and Employers, sponsored by Indeed), driven largely by AI tools that let candidates submit polished applications at scale. The National Association of Colleges and Employers data shows 70% of candidates now use AI at some stage of hiring, producing applications that are increasingly indistinguishable.
To manage volume, many employers are closing requisitions early. This tactic has an unintended consequence: it rewards speed over quality. AI-assisted applicants move within minutes of a role going live. More deliberate candidates apply later and miss the window. The result is a talent funnel that filters out precisely the people most worth hiring.
Graduates are digitally native and learning-oriented, but you can't see it on a polished CV
Graduates tend to be more digitally native, more oriented toward continuous learning, and better positioned to extract value from AI tools. Kirk argues that adaptability is invaluable in an AI-adjacent workplace. Organizations serious about building a workforce that works with AI rather than simply alongside it need to measure the right things.
The catch is methodological. AI-optimized CVs reveal little about the candidate behind them. A perfectly formatted resume tells you effort was expended, not competence. Traditional screening was designed to filter credentials on paper. It was never built to assess behavior or learning agility in an environment where applications are machine-assisted and volume is unmanageable.
Kirk's conclusion is direct: many organizations are actively increasing their graduate intake because they've learned closing pipelines early risks eliminating the strongest candidates. The class of 2026 may be the most AI-ready workforce cohort employers have seen, yet the systems hiring teams use don't reflect that.
Invest in behavioral assessment, not credential filtering
The path forward requires a structural shift in what you measure. Rather than screening for credentials that signal effort (a polished cover letter, a well-formatted resume), assess the behaviors that predict performance in an AI-fluent role. That means moving away from time-based filtering and toward competency-based assessment.
For talent teams, the evidence points in one direction: invest in graduates and measure the behaviors that prove they can work with AI. Leave requisitions open longer than feels comfortable. The deliberate applicants who arrive in week two are more likely to succeed than the batch that applied in the first hour.