Hiring teams are not struggling because of a lack of candidates. They are struggling because it has become harder to tell which candidates are real.
Over the past year, hiring pipelines have been flooded with applications that look polished, confident, and well-structured. In many cases, they are. Generative AI has made it easy for candidates to present strong narratives at scale. What it has not made easier is determining whether those narratives reflect real capability.
According to recent hiring data, application volume is rising while confidence in screening decisions is falling. Teams are spending more time reviewing candidates, adding steps to their processes, and still feeling less certain about their shortlists.
This shift is not theoretical. It is already changing how hiring decisions are made. CVs, once treated as a reliable starting point, are losing their predictive value in an AI-driven market.
This is not a volume problem. It is a signal quality problem.
What hiring data is showing in 2026
To understand how widespread this shift has become, Willo analyzed hiring patterns across thousands of interviews and candidate interactions as part of our 2026 Hiring Trends Report.
What emerged was not a rejection of AI, but a recalibration of confidence and trust in hiring.
Hiring teams are increasingly skeptical of surface-level signals like CVs and unstructured applications. At the same time, they are placing more weight on inputs that can be validated earlier, show evidence of work, or reveal how candidates think rather than how they present themselves.
The data reveals a clear pattern: as candidate volume increases, teams relying heavily on narrative-based screening face more downstream friction:
- More re-reviewing
- More disagreement
- More second-guessing
Teams that redesign their processes around signal quality, however, report clearer decision-making and greater confidence, even at scale.
The takeaway is not that hiring needs more automation. It is that hiring needs better inputs. AI can support this shift, but it cannot solve it on its own.
This is the lens through which the rest of this conversation needs to be viewed.
Why CVs are failing as a reliable hiring signal
CVs were designed to summarize experience and tell a professional story. They were never designed to verify evidence.
In an AI-driven hiring market, narrative has become cheap. Candidates can generate strong CVs quickly, often using similar language, structure, and framing. As a result, applications increasingly look the same. Differentiation disappears, and confidence becomes inflated.
When this happens, CVs lose their predictive value. They no longer help hiring teams understand who can do the work, how consistently they have done it, or how they might perform in a real role. Instead, CVs often reflect who knows how to present themselves well in writing or how to use the right tools.
This pattern has already been observed in high-volume hiring environments, where traditional resume screening breaks down first under scale and repetition.
The growing importance of signal quality in hiring
Signal quality refers to how useful a hiring input is in helping teams make confident, repeatable decisions.
High-quality signals reduce uncertainty. They are harder to fake, easier to validate, and more closely tied to real performance.
Low-quality signals increase noise and force hiring teams to rely on instinct, bias, or speed just to move forward.
AI has increased the amount of information flowing into hiring processes, but more information does not necessarily lead to better decisions. In many cases, it has the opposite effect. When signals are inflated or unverifiable, decision-making becomes slower and less confident.
This is why many hiring teams are re-evaluating the role of CVs and shifting toward skills-based and evidence-led approaches that surface capability earlier in the process
What signals still work in practice
The teams adapting most effectively are not removing humans from the hiring process. They are becoming more intentional about which signals deserve human judgment and which need stronger verification.
Signals that continue to hold up tend to share a few characteristics. They are harder to fake. They show evidence of work rather than claims about ability. They reveal consistency over time instead of a single polished moment.
