Applicant Tracking Systems became essential by helping employers manage applications at scale. They collect résumés, organize candidates, coordinate workflows, document decisions, and move applicants through the hiring process.

But the information flowing through most ATS platforms remains remarkably limited.

A résumé is a candidate-authored summary created for a particular moment. Job titles vary across organizations. Skills may be implied rather than explained. Credentials are usually entered as plain text. Meaningful projects, work products, community contributions, and learning experiences may never appear at all.

Artificial intelligence can parse these documents faster, but it cannot recover context that was never captured.

The next opportunity for applicant tracking systems is therefore not simply adding more AI. It is giving people and machines access to better, more structured, contextual, and trustworthy information about human capability.

Learning and Employment Records, verifiable credentials, talent passports, and related standards can help make that possible.

What an LER can—and cannot—tell an employer

A Learning and Employment Record, or LER, is not one universal document or database. It is a broad category of digital records that can represent learning, credentials, employment, skills, achievements, and other experiences across a person’s life.

Depending on its source and structure, an LER may include:

• Degrees, licenses, certifications, and microcredentials
• Courses, training, apprenticeships, and learning outcomes
• Employment history and workplace experiences
• Skills or competencies associated with a program or activity
• Projects, portfolios, work products, and other evidence
• Issuer information and verification status
• Relationships among learning, experience, and demonstrated capability

These elements do not all carry the same meaning or level of trust.

A verified credential can confirm that an issuer awarded something to a particular person. It does not automatically prove that the individual can perform every skill associated with the credential in every workplace context. A program-level skills map describes what a learning experience was designed to teach, but it is not necessarily an individually verified assessment of every learner.

That distinction matters.

LER-enabled hiring should not replace simplistic résumé matching with equally simplistic credential matching. Its value comes from helping employers consider multiple forms of evidence, understand where information came from, and interpret it within the requirements of a specific opportunity.

Better AI begins with better context

Many hiring platforms are introducing AI for résumé parsing, candidate search, recommendations, screening support, and recruiter assistance.

These systems often begin with incomplete inputs:

• Inconsistent job titles
• Keyword-heavy job descriptions
• Unstructured résumés
• Self-reported skills without supporting context
• Credentials disconnected from their issuers or learning outcomes
• Limited evidence of how a person has applied a capability
• Little visibility into transferable experience

AI may find patterns within this information, but confidence should not be confused with understanding.

Structured talent data can give an AI system more useful material to work with. It can connect a credential to its issuer, distinguish a claimed skill from documented evidence, relate an activity to the capabilities it may demonstrate, and preserve more of the context surrounding a person’s experience.

That does not make an algorithm neutral or guarantee a good hiring decision. It can, however, make recommendations more inspectable and give recruiters a stronger foundation for human judgment.

The objective should not be to let AI decide who deserves an opportunity. It should be to help recruiters understand candidates more fully, ask better questions, and make more informed decisions.

From job descriptions to Opportunity Profiles

Improving candidate data solves only half the problem. Hiring systems also need a clearer representation of the opportunity itself.

Traditional job descriptions frequently combine several different things:

• Essential requirements
• Preferred qualifications
• Responsibilities inherited from an earlier version of the role
• Cultural expectations
• Credentials used as proxies for capability
• Broad lists of skills that no single person is likely to possess

When these elements are treated as equally important keywords, matching becomes unreliable. Qualified people may be excluded because they use different language, followed a nontraditional pathway, or demonstrate relevant capabilities in an unfamiliar context.

An Opportunity Profile can provide a more structured expression of what an organization actually needs. It can distinguish required qualifications from preferences, connect responsibilities with relevant capabilities, identify acceptable forms of evidence, and clarify where a person may be able to learn after entering the role.

That creates a better basis for comparison—not because candidates can be reduced to scores, but because both sides of the relationship are described with greater clarity.

A better application experience

Candidates regularly upload a résumé and then re-enter the same information into a form. They repeat this process for each employer while losing much of the context that makes their experience meaningful.

A person-controlled talent passport can change that relationship.

Instead of rebuilding their professional history for every application, an individual could organize reusable information about their experiences, credentials, projects, capabilities, and evidence. For each opportunity, they could then create a purpose-specific application profile that shares the information they consider relevant.

This approach can provide candidates with:

• Greater control over what they share
• More room to explain nonlinear or nontraditional experience
• A way to include evidence beyond the résumé
• Less repetitive data entry
• Clearer visibility into how their information relates to an opportunity
• A reusable record that remains valuable beyond one application

It can also give employers richer application information without requiring ownership of the individual’s complete lifelong record.

That boundary is important. An employer needs appropriate information for a hiring decision—not unrestricted access to everything a person has learned, done, considered, or recorded.

Trust should be visible, not assumed

LERs can improve trust when hiring systems preserve provenance.

For any credential, claim, or piece of evidence, recruiters may need to understand:

• Who created or issued it?
• What exactly was awarded, assessed, or documented?
• When was it issued?
• Is it still valid?
• Can its authenticity be checked?
• What evidence supports it?
• Was it shared directly by the candidate?
• What inferences were added by a platform or AI system?

A trustworthy ATS should distinguish among issuer-verified information, candidate-provided information, third-party endorsements, supporting artifacts, and machine-generated interpretations.

All of these may be useful. They are not interchangeable.

Making those distinctions visible allows recruiters to evaluate evidence rather than receiving a single opaque match score.

Finding capability outside conventional pathways

One of the most meaningful benefits of richer talent data is the possibility of recognizing people whose capabilities are poorly represented by conventional résumés.

Relevant experience may come from:

• Military service
• Apprenticeships and technical education
• Community leadership
• Volunteer work
• Caregiving
• Freelance and entrepreneurial projects
• Informal or self-directed learning
• Cross-functional responsibilities hidden behind an unrelated job title
• International education or employment
• Work products that demonstrate capability more clearly than a degree

LERs and talent passports do not automatically eliminate bias or create equitable hiring. Employers must still examine their requirements, decision processes, accessibility, and use of technology.

But richer evidence can give recruiters more ways to recognize potential—and reduce dependence on familiar institutional names, job titles, and career patterns as proxies for ability.

LER integration should complement the ATS

The goal is not necessarily to turn every ATS into a lifelong talent wallet.

Applicant tracking systems are designed to manage organizational hiring workflows. Person-controlled talent systems serve a different function: helping individuals organize, understand, and selectively share information across education, work, and life.

These systems can complement one another.

A human-centered hiring infrastructure could allow:

1. An employer to translate a job description into a more structured Opportunity Profile.
2. A candidate to build a purpose-specific application using information from their own talent record.
3. The candidate to choose what information and evidence to share.
4. The hiring platform to preserve provenance and distinguish verified data from claims and inferences.
5. AI to help organize and explain alignment without becoming the final decision-maker.
6. Recruiters to review the fuller context and engage the candidate in a more informed conversation.
7. Relevant information to move into the employer’s existing ATS workflow.

In this model, the ATS continues to do what it does well. The surrounding ecosystem improves the quality, portability, and usefulness of the information entering it.

What ATS providers should build toward

ATS and HR technology providers do not need to solve the entire LER ecosystem at once. They can begin by developing several foundational capabilities.

Support portable, standards-aligned records

Hiring platforms should be able to receive structured credentials and talent data without forcing every source into a proprietary format. Open standards can reduce integration friction and help information move across education, workforce, and employment systems.

Preserve provenance and verification

Systems should retain information about issuers, verification status, supporting evidence, and machine-generated inferences rather than flattening everything into indistinguishable keywords.

Give candidates meaningful control

Sharing should be understandable, purposeful, and limited to the information needed for a particular application. Candidates should know what an employer will receive and how the information may be used.

Make AI recommendations explainable

Recruiters and candidates should be able to see which requirements, experiences, credentials, or evidence contributed to a recommendation. Systems should also acknowledge uncertainty and missing context.

Keep human judgment accountable

AI can support discovery and review, but organizations remain responsible for their hiring criteria and decisions. Technology should help document judgment and make it more transparent—not obscure responsibility behind an algorithm.

Design for interoperability

LER integration will involve credential issuers, schools, workforce organizations, talent passports, assessment providers, employers, and existing HR systems. Platforms should anticipate an ecosystem of trusted connections rather than attempting to own every part of the relationship.

The competitive advantage is understanding—not more filtering

The next generation of hiring technology should not simply filter applicants faster.

Employers need to understand what a role requires, what a person has experienced, what evidence supports their capabilities, where uncertainty remains, and what they may be able to learn next. Candidates need a fair opportunity to communicate who they are without being reduced to the wording of a résumé or the output of an opaque model.

LERs, verifiable credentials, talent passports, and AI can help create that richer understanding—but only when they are connected through systems designed around human agency, transparency, and trust.

The ATS will remain an important part of hiring infrastructure. Its future value, however, may depend less on how many applications it can process and more on how well it helps employers and candidates understand one another.

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