Application volume is at an all-time high, and AI has made resumes nearly impossible to tell apart. Here’s what leaders are doing about it.
Talent acquisition teams are dealing with a problem that has gotten much worse over the past two years. Application volume has climbed to record levels, and the resumes filling those queues increasingly read the same. In our 2026 Talent Acquisition Trends Study, a survey of nearly 1,000 talent acquisition leaders about how hiring is changing, we found some troubling statistics:
- Roughly seven in ten employers reported that application volume rose over the past year.
- More than nine in ten said AI-generated or AI-enhanced resumes are now at least somewhat common in their applicant pool, and about half called them very common.
- The number one way employers filter candidates is resume screening.
However, when most resumes are created and polished by the same tools, they stop telling recruiters anything useful about who is actually qualified.
My colleague Ben Eubanks, Chief Research Officer at Lighthouse Research & Advisory, recently sat down with two leaders from Indeed to work through that problem: Danielle Papermaster, Senior Product Manager, and Hannah Lindsley, Senior Director of Data Science. Both conversations kept circling the same culprit — the resume has stopped doing the job we rely on it to do.
This shows up in the day-to-day work of recruiting. A recruiter used to read through a stack of resumes and the strong ones stood out. Now the stack is bigger, and the resumes look alike, so they read far more applications before finding anyone worth following up on. As Hannah described it, AI hasn’t produced more qualified candidates. It has produced more resumes that look qualified, and recruiters have to sort through all of them to select the right candidate(s).
A blind spot in how we screen
The resume has become less reliable, but hiring teams haven’t adjusted how much they rely on it.
In our study, about three in four leaders said they are confident the resume reflects a candidate’s true skills, and more than eight in ten still use it as a primary or key factor when deciding who advances (2026 Talent Acquisition Trends Study). The catch is that a resume is the easiest part of an application for AI to write and inflate, yet it is still what teams trust most.
Hannah’s view is that the resume was never a full account of what someone can do, and AI has made it even less revealing. The first job of any screening process, then, is to help genuinely qualified people prove it — by gathering the signals a resume leaves out, such as verified credentials, examples of real work, and answers to a few specific questions. We’ll come to how that works. First, the cost of getting it wrong.
The cost of getting it wrong: the black hole
Candidates feel that cost directly. They apply and never hear back — no reply, no rejection, no sign anyone read their application. The industry has called this the black hole for years and often frames it as a follow-up failure, as if better reminders or etiquette would fix it. The real cause is capacity. When we asked the leaders in our study which activities they had cut in the past year to manage the workload, the human touches topped the list — following up with candidates, calibrating with hiring managers, and sending personalized messages (2026 Talent Acquisition Trends Study). In other words, those are the first tasks to go when a team has far more applications than it can read.
When candidates hear nothing back, the damage runs far deeper than hurt feelings. Our candidate research shows that the clearest way a company signals respect is simply by responding. A candidate who never hears back assumes the company isn’t interested and moves on, even when they were a strong fit. Speed matters as much as the reply itself. Hannah pointed to Indeed’s data showing that reaching a candidate within 15 minutes keeps the odds of connecting near 85 percent, while waiting longer drops that to about 35 percent. No human team can hit that window on its own, because applications arrive around the clock. When a frustrated third-shift worker applies at 11 pm on a Friday, no recruiter is at the desk to respond, and by the time someone follows up on Monday, that person has often already taken another job.
Hannah Lindsley on screening for signal in a world of AI resumes.
What better screening looks like
Speeding up resume review doesn’t help, because the resume itself is the weak link. What works is gathering better information earlier, so recruiters spend their time on people who actually fit the role. Danielle and Hannah described three changes that do this.
- Check credentials when the candidate applies.
Many roles require a specific license or certification, and most teams confirm those near the end of the process. Hannah, for example, described an employer with a whole team dedicated to phoning every applicant who claimed a license, just to check it was current and valid in the right state. Asking for that proof at the moment of application clears eligible candidates instantly and spares the team from chasing people who never qualified.
- Let candidates show what they can do.
A resume favors people who are good at writing resumes, which has little to do with many jobs. A skilled electrician or plumber may have a thin, plain resume and still be excellent at the work. Give that person a chance to describe how they handled a real situation on the job, and their ability becomes obvious. A short, structured conversation early in the process brings those people forward instead of screening them out.
- Ask every candidate the same questions.
A consistent set of screening questions means everyone is judged against the same criteria. Danielle pointed to research showing that the same recruiter will rate the same candidate differently from one week to the next, depending on timing, mood, or the meeting they just left. Standard questions remove that randomness, which makes the process fairer for candidates and more reliable for the employer.
These three changes are starting to appear in screening products. Indeed’s smart screening, for example, captures license and credential details during the application, runs a short AI-led conversation that draws out context the resume omits, and scores each candidate against the criteria the employer sets- three ways of replacing resume guesswork with actual evidence.
Danielle Papermaster on the candidate experience and the black hole of hiring
Keeping humans in the lead
Used this way, automation takes over the sorting so recruiters can spend their time on judgment and relationships. “Human in the loop” is the phrase people reach for, but it sets the bar too low; the implication is a person passively watching while the software runs the show. The better aim is to keep humans in the lead: let automation clear the work that doesn’t need a person, so recruiters make the decisions that do, such as who advances, who gets a call, and who will fit the team.
Leaders say this is exactly what they want from automation. When we asked where saved time should go, they chose improving candidate experience and care over sourcing or strategic projects (2026 Talent Acquisition Trends Study). That maps onto the earlier finding: the personalized communication and hiring-manager calibration that teams have been cutting under pressure are the very tasks recruiters want back. Automation that removes busywork is how they reclaim the time for it.
Two things determine whether teams actually get that benefit. The first is how much work the tool takes off recruiters versus how much it adds. Implemented poorly, AI creates work instead of removing it; the most common complaint in our study was the extra time recruiters spent checking and validating the output the tools produced (2026 Talent Acquisition Trends Study). A tool that can’t explain why it scored a candidate a certain way forces recruiters to second-guess every result, which defeats the purpose.
The second is candidate trust. Job seekers worry about being rejected by a ‘rigged’ system, so being open about where AI is used, and what it does, matters as much as getting the decision right. As Danielle pointed out, even a quick, clear rejection beats silence, because it tells someone a real decision was made about them.
Solving the right problem
The problem to solve is the one the resume created in the first place; it was never a great reflection of who is or isn’t qualified. Solving it means building a screening process that surfaces the evidence a resume leaves out, gets candidates a real response faster, and frees recruiters for the parts of hiring that need a person. The process stays visible to candidates, and the decision that matters still rests with a human.
Screening done well takes the manual sorting off people’s plates, while teams hire right and hire faster, and treat candidates better in the process.
All figures cited are drawn from Lighthouse Research & Advisory’s 2026 Talent Acquisition Trends Study, a survey of nearly 1,000 talent acquisition leaders.
Roberta Gogos is an HR Industry Go-to-market Leader and Analyst. She has been behind the scenes at market-leading companies to help them shift market narrative, influence buyer behavior and expand into new markets. She is known for her ability to turn strategic vision into measurable execution through positioning, storytelling, and operational rigor.
Since 2024, Roberta has been focused on industry research used by investors, corporate leaders, and vendors to assess technologies and inform M&A decisions, improve GTM, enable sales, inform product development, and develop thought leadership.
She has 20 years of experience in marketing, positioning, and strategy, with 10+ years of that being directly related to talent and the workforce.
