The job landscape has shifted significantly in recent years as artificial intelligence reshapes both the nature of work and the ways people apply for jobs. A recent Stanford study found that young workers (ages 22–25) in the most AI-exposed occupations have experienced a 13% relative decline in employment. On the other side of the equation, job seekers are turning to AI tools to mass-generate applications. Many now apply to hundreds—sometimes thousands—of jobs with a single click, often without any manual effort.

For employers, this has meant a dramatic increase in application volume—but not necessarily an increase in qualified applicants. Applicant tracking systems have responded with AI filters designed to catch AI-generated applications, but industry experts report these tools are largely ineffective. Recruiters still find themselves skimming endless PDF résumés, giving each only seconds of attention before moving on. And as résumés become more homogenized, it gets harder and harder to separate the wheat from the chaff.

At the same time, HR leaders are under pressure from CFOs to do more with less. Many are asked whether AI can cut headcount or whether expensive vendors can be eliminated. To reduce risk, employers have leaned on third-party skill assessments, but these drive up time-to-hire and cost-per-hire, putting further strain on leaner recruiting teams.

The shift to remote work adds another layer of complexity. Today, 22.8% of the U.S. workforce is remote, and with that comes new challenges: foreign applicants using false identities, candidates inflating credentials or hacking interviews, and even legitimate U.S. workers secretly holding down multiple full-time remote jobs while collecting multiple paychecks. Employers are being asked to trust less and verify more—but the traditional résumé simply isn’t built for that.

But the problems don’t stop with remote hiring. The other 77.2% of jobs that are in person come with their own set of headaches. Employers often report that when they reach out to an applicant they believe is a good fit, they get no response. Or, they schedule an interview—only for the candidate not to show up. These gaps in reliability and commitment increase both time-to-hire and cost-to-hire, leaving employers with unfilled shifts and frustrated teams.

What Can the LER Community Do?

Learning and Employment Records (LERs) hold immense potential to bring hiring data directly into the system, but so far the community has missed the opportunity to issue credentials at scale that actually solve the problems employers are facing.

Today, digital credentials are most often used to verify course completions, microcredentials, skill validations, or certifications. These are useful signals, but they don’t address the biggest pain points in hiring right now. Employers aren’t losing sleep because they can’t verify a course badge. They’re struggling with identity fraud, noisy applicant pools, unreliable candidates, and a lack of trustworthy, job-relevant signals.

A New Mandate for LERs

The real opportunity is for the LER community to move beyond academic validation and focus on employability signals that restore trust in hiring:

• Trust & Identity
  LERs can anchor identity verification to credentials, giving employers confidence that the person behind an application is real, unique, and who they claim to be.
  
  
• Context & Relevance
  Employers don’t just want to know what someone learned; they want to know how it translates into readiness for their job. LERs can carry contextual evidence—projects, portfolios, performance metrics—that demonstrate applied skills.
  
  
• Efficiency & Differentiation
  LERs can deliver structured, machine-readable data that plugs into hiring systems, allowing recruiters to filter for qualified candidates instantly instead of drowning in look-alike résumés.
  
  
• Signal at Scale
  LERs won’t matter if they trickle into the market one learner at a time. Employers need whole programs, institutions, and credentialing bodies issuing them consistently so they become a standard, trusted currency.
  
  
• Basic Reliability Signals
  Employers also need help with fundamentals: Who shows up? Who responds? Who follows through? Institutions are uniquely positioned to measure consistency, attendance, persistence, and coachability across their entire student body. If LERs could carry these kinds of signals—reliability, growth mindset, and capacity to learn—they would provide immense value for the in-person workforce, where these traits often matter more than technical skills.

From Proof of Learning to Proof of Employability

If the LER community wants to truly help employers, it must shift its focus: from documenting education to enabling hiring. Digital credentials are not ends in themselves—they are building blocks of a system that employers can use to make faster, fairer, and more confident hiring decisions.

When LERs move from proof of learning to proof of employability, we’ll start solving the problems that matter most to employers: filtering noise, reducing fraud, restoring trust, and ultimately connecting more people to meaningful work.

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