Most Employers Are Using AI for Hiring and Firing Decisions. How Reliable Is It?

Editor’s Note: This story originally appeared on MyPerfectResume.com.

Artificial intelligence is no longer just a behind-the-scenes hiring tool. It’s now shaping who gets seen, who gets filtered out, and even who stays employed.

A new MyPerfectResume AI Hiring and Layoffs survey of 1,000 U.S. hiring managers reveals that AI is embedded across hiring and workforce decision-making. From screening resumes to influencing layoffs and restructuring, these systems are playing a growing role in high-stakes decisions, often with uneven confidence in their fairness and accuracy.

The findings highlight a shift in how organizations operate: faster and more automated, but not always more reliable.

This report examines how employers are using AI across hiring, candidate evaluation, and broader workforce planning decisions. It explores adoption rates, candidate filtering practices, employer confidence in AI fairness, and where these systems may be falling short.

Key Findings at a Glance

AI is widely used in hiring. 73% of employers say they use AI in hiring decisions.
AI filters candidates before human review. 65% say AI automatically rejects applicants before a person sees them.
High rejection rates are common. 14% say AI rejects more than half of applicants.
Potentially qualified candidates are being missed. 47% say AI may have filtered out candidates they would have advanced.
AI is expanding into workforce planning. 52% use it for decisions like restructuring and role planning.
Confidence in fairness is split. 51% say AI is fair in layoffs, while 23% express doubts.
AI is making subjective judgments. 51% use it to flag “risky” candidates.

AI Has Become the First Gatekeeper in Hiring

For most job seekers today, the first “decision-maker” isn’t a recruiter; it’s an algorithm.

Nearly two-thirds of employers (65%) say AI automatically rejects candidates before any human review. That means a significant portion of applicants never reach a hiring manager at all.

Rejection levels vary:

26% of employers say AI rejects 1%–25% of applicants.
25% say it rejects 26%–50%.
11% say it rejects 51%–75%.
3% say it rejects over 75%.

The AI resume screening statistics for 2026 reveal that only 5% of employers report that AI does not reject candidates at all.

Why this matters: AI is designed to increase efficiency, but it’s also narrowing the funnel earlier than ever. When filtering happens before human oversight, even strong candidates can be excluded based on incomplete or overly rigid criteria.

Employers Know AI Doesn’t Always Get It Right

Despite widespread adoption, many employers acknowledge that AI systems aren’t consistently accurate.

Nearly half (47%) say AI may have filtered out candidates they would have moved forward in the hiring process. While 17% say this happens rarely and 7% say it never happens, the overall trend points to a clear concern: Automation can come at the cost of missing qualified talent.

Why this matters: The trade-off between speed and accuracy is becoming more visible. Employers gain efficiency but risk overlooking candidates who don’t perfectly match algorithmic criteria.

AI Is Expanding Beyond Hiring Into Workforce Decisions

AI is no longer limited to recruitment; it’s now influencing broader organizational strategy.

More than half of employers (52%) say they use AI for workforce planning decisions, including restructuring and role evaluation. Another 28% are considering adopting AI for these purposes.

Meanwhile, 20% say they don’t plan to use AI in workforce planning at all.

Why this matters: As AI moves into areas like restructuring and layoffs, its impact extends beyond hiring into job security itself. These are high-stakes decisions, where errors or bias can have long-term consequences for workers.

AI Is Being Used to Make Subjective Judgments

Beyond qualifications, AI is increasingly evaluating behavior and career patterns.

More than half of employers (51%) use AI to flag “risky” candidates, such as job-hoppers or those with employment gaps. Another 12% are considering implementing this capability.

Why this matters: These judgments introduce a level of subjectivity into automated systems. Instead of simply matching skills to roles, AI is now making assumptions about candidate reliability and fit—areas that are often nuanced and context-dependent.

Confidence in AI Fairness Is Divided

As AI takes on a larger role in workforce decisions, confidence in its fairness remains uneven:

51% of employers say they’re confident AI is used fairly in layoffs.
23% express doubts.
26% don’t use AI in layoff decisions.

Why this matters: The divide highlights a lack of consensus around how trustworthy these systems are, especially in decisions that directly impact livelihoods.

The Bigger Picture: Speed vs Accuracy in the Hiring Process

Taken together, the findings point to a clear shift in how hiring and workforce decisions are being made.

AI is accelerating processes and reducing manual workload, but it’s also introducing new risks:

Qualified candidates may be filtered out before being seen.
Hiring decisions are increasingly shaped by automated systems.
Workforce planning is becoming more data-driven—but not always more transparent.

What This Means for Workers

Key AI layoff statistics in 2026 reveal that job seekers must now navigate a system where visibility depends on how well they align with algorithmic criteria, not just human judgment.

What This Means for Employers

Organizations face a balancing act: leveraging AI for efficiency while ensuring that accuracy, fairness, and human oversight aren’t lost in the process.

Methodology

The findings presented in this report are based on a nationally representative survey conducted by MyPerfectResume using Pollfish in March 2026.

The survey collected responses from 1,000 U.S. human resources employees involved in hiring and workforce decision-making.

Respondents answered a mix of yes/no, single-selection, and multiple-choice questions about their organization’s use of artificial intelligence in hiring, candidate evaluation, diversity outcomes, and workforce planning decisions, including layoffs and restructuring.