Originally published September 21, 2023 following a Connecting Competency Communities webinar featuring Elliot Robson of Eduworks and Jim Ireland of HR Open Standards. Updated to reflect the continued evolution of AI, LER standards, and Gobekli’s approach to skills-based hiring.

Skills-based hiring promises a better way to connect people with opportunity.

Instead of filtering candidates primarily by degrees, previous employers, job titles, or résumé keywords, an employer can focus on what the work requires and what each person can demonstrate.

That sounds straightforward.

In practice, both sides of the hiring process are working with incomplete information.

Employers publish job descriptions filled with inherited requirements, vague responsibilities, inflated skills lists, and language copied from previous roles. Candidates respond with compressed résumés designed to pass automated filters. Applicant tracking systems compare keywords. Recruiters interpret the results under time pressure.

Adding artificial intelligence to this process can make it faster.

It does not automatically make it better.

Data standards can make information more structured, portable, and verifiable. They do not automatically make hiring equitable.

The real opportunity emerges when AI and data standards are combined with transparent requirements, contextual evidence, individual agency, and accountable human judgment.

Skills-Based Hiring Begins With the Work

Most skills-based hiring conversations focus on the candidate.

What skills do they possess? Which credentials have they earned? How closely does their résumé match the job description?

But the first source of uncertainty is often the opportunity itself.

Before evaluating candidates, an employer must understand:

• What work actually needs to be performed
• Which outcomes the role is responsible for
• Which activities are essential
• Which capabilities are required on the first day
• Which capabilities can be developed after hiring
• What evidence would reasonably demonstrate readiness
• Which requirements are truly necessary
• What conditions shape someone’s ability to succeed
• How the role interacts with other people and workflows

A long list of generic skills does not answer these questions.

If an employer cannot clearly describe the opportunity, AI will simply automate comparisons against an unclear target.

Skills-based hiring must therefore begin by converting the job description into a more complete and reviewable Opportunity Profile.

Candidate Records Have the Same Problem

Candidates are also represented through incomplete proxies.

A résumé may show that someone held a role, but not what they actually did. A credential may verify completion of a program without showing how the learning was applied. A skills tag may identify a capability without connecting it to evidence, scale, context, or recency.

People also develop valuable capabilities outside conventional employment:

• Military service
• Volunteer leadership
• Caregiving
• Apprenticeships
• Community participation
• Entrepreneurship
• Independent projects
• Creative work
• Informal learning

These experiences often disappear when a person must translate an entire life into a two-page résumé.

Skills-based hiring cannot work if employers improve the language of job descriptions while candidates remain trapped inside records that conceal their capabilities.

Both sides need richer, more structured information.

What Data Standards Contribute

Data standards allow different systems to exchange information using more consistent structures and meanings.

Within hiring, learning, and workforce systems, standards can help make records:

• Machine-readable
• Portable between platforms
• Connected with known issuers
• Easier to validate
• More consistently structured
• Reusable across different workflows

Learning and Employment Records can help people carry information about education, credentials, skills, and employment across institutional boundaries.

Verifiable credentials can help an employer confirm who issued a record and whether it has been altered.

HR and résumé standards can help employment information move between applicant, employer, and workforce systems without requiring every platform to interpret a new proprietary format.

This infrastructure is essential.

But structured data is not automatically objective data.

A standardized requirement can still be irrelevant. A verified credential can still be an unnecessarily restrictive proxy. A machine-readable skill can still lack context. An interoperable hiring system can still reproduce biased decisions at greater scale.

Standards improve the reliability and portability of information. People and organizations must still decide whether that information is relevant and how it should be used.

What AI Can Contribute

Hiring involves constant translation.

Employers translate work into job descriptions. Candidates translate experience into résumés. Recruiters translate requirements into search criteria. Hiring managers translate interviews and evidence into judgments.

AI can help make these translations more complete and consistent.

Used responsibly, AI can help:

Structure the opportunity

AI can analyze a job description, ask clarifying questions, and help recruiters separate essential responsibilities from inherited or unnecessary requirements.

Help candidates describe their experiences

Conversational AI can ask people what they did, what responsibility they held, what challenges they encountered, and what evidence supports their claims.

This can help candidates recognize relevant capabilities without requiring them to know the employer’s preferred terminology.

Translate between talent languages

A candidate, employer, school, and credential provider may describe related capabilities differently. AI can identify possible relationships among these descriptions while preserving the original language and source.

Connect claims with evidence

AI can help organize work samples, credentials, activities, outcomes, and endorsements around the requirements they may support.

Explain possible alignment

Instead of producing only an unexplained match score, AI can show where evidence appears strong, where the relationship is uncertain, and where more information is needed.

Reduce administrative work

AI can help summarize candidate information and prepare it for review, allowing recruiters to spend more time evaluating evidence, speaking with people, and understanding the context of the role.

These uses position AI as a guide and translator—not as an invisible decision-maker.

What AI Should Not Be Allowed to Decide Alone

AI systems can make hiring appear more precise than the underlying information justifies.

A model may infer that someone possesses a skill because it appeared near a job title. It may rank candidates using patterns derived from previous hiring decisions. It may interpret unconventional experience as less relevant because it does not resemble the records on which the system was trained.

The result can be a confident answer built on incomplete assumptions.

Responsible skills-based hiring should avoid:

• Assigning permanent capability labels based only on inference
• Treating résumé language as direct proof of proficiency
• Collapsing a complex candidate into one numerical score
• Ranking people through criteria they cannot see
• Using protected or inappropriate information as a proxy
• Automatically rejecting candidates without meaningful review
• Presenting AI-generated interpretations as verified facts
• Allowing historical hiring patterns to silently define future talent

AI can surface possible connections. It should show the basis for those connections and allow people to correct them.

Consequential hiring decisions must remain accountable to the employer and understandable to the people affected.

A Better Skills-Based Hiring Workflow

A more trustworthy process can combine standards, AI, evidence, and human judgment across five stages.

1. Define the opportunity

AI helps the recruiter turn an existing job description into a structured Opportunity Profile.

The recruiter and hiring manager confirm:

• Responsibilities and expected outcomes
• Required and developable capabilities
• Necessary credentials or licenses
• Relevant activities and experience
• Acceptable forms of evidence
• Working conditions and practical constraints
• Criteria that should not influence the decision

The employer—not the AI—owns these requirements.

2. Help the candidate build a relevant application

The candidate receives understandable information about the opportunity and creates a purpose-specific application.

Conversational guidance helps them identify experiences that may be relevant, including experiences they might not normally place on a résumé.

The candidate reviews the resulting profile and decides what to share.

3. Connect requirements with evidence

The application brings together selected information such as:

• Roles and activities
• Skills and knowledge
• Credentials
• Work samples
• Outcomes
• Endorsements
• Reflections and explanatory context

Where a claim is verified, the source should be visible. Where it is self-described or inferred, that should also be clear.

4. Support transparent human review

The system organizes information around the employer-confirmed criteria.

Rather than declaring a universal score, it can indicate where the application provides strong supporting evidence, where the relationship is partial, and where more information may be required.

Recruiters remain able to review the underlying experiences and evidence instead of relying on the system’s summary alone.

5. Learn from what happens next

Hiring should not be the end of the feedback loop.

With appropriate consent and governance, organizations can learn whether their requirements predicted success, which capabilities were developed after hiring, and where the opportunity profile failed to describe the work accurately.

Candidates and employees should also receive value from this feedback. Their records can grow as they contribute, learn, and demonstrate new capabilities.

This turns hiring from an isolated transaction into part of a longer relationship.

Where TalentPass and TalentSync Fit

Gobekli is building this approach through TalentPass and TalentSync.

TalentPass

TalentPass helps a candidate build a person-controlled source of truth about their experiences, activities, capabilities, credentials, evidence, and goals.

Pythia helps the person reflect on what they have done and create a purpose-specific TalentPass Application Profile. The individual reviews the profile and controls what is shared.

TalentSync Recruiter

TalentSync Recruiter helps an employer turn a job description into a structured Opportunity Profile and review TalentPass Application Profiles against the criteria the employer has confirmed.

The goal is not to identify a mathematically perfect candidate.

It is to replace keyword matching and incomplete résumés with a more transparent conversation between:

• What the opportunity requires
• What the candidate has experienced
• What evidence supports the connection
• What may need to be learned next
• What a human reviewer still needs to understand

TalentPass Application Profiles can supplement existing applicant tracking workflows, allowing employers to begin using richer human intelligence without replacing every system at once.

Equity Requires More Than Removing Degree Requirements

Skills-based hiring is often described as an equity strategy.

It can expand access by recognizing people whose capabilities were developed through different pathways. But simply removing degree requirements or adding skills labels does not guarantee a fair process.

A more equitable system also requires:

• Criteria directly related to the work
• Accessible application experiences
• Recognition of multiple forms of evidence
• Clear distinctions between required and preferred qualifications
• Transparency about how information is evaluated
• Opportunities for candidates to add context
• Review for biased requirements and proxies
• Accommodations for different abilities and circumstances
• Human oversight and a way to challenge errors
• Continuous evaluation of who benefits and who is excluded

Data standards can make records more portable.

AI can make them easier to translate.

Only thoughtful governance and human accountability can determine whether the resulting process is fair.

Skills-Based Hiring as Part of the Human Intelligence Layer

Hiring is one decision inside a much larger human system.

Organizations also make decisions about onboarding, team formation, development, advancement, succession, workflow design, and the use of AI. People continue learning and changing after they are hired.

A Human Intelligence Layer connects these experiences into a continuous feedback loop.

It helps people maintain a living understanding of what they have learned and done. It helps organizations understand how work and capability are changing. It gives AI more trustworthy context while keeping people involved in the creation, correction, and use of that intelligence.

Skills-based hiring becomes more valuable when it is not treated as a one-time matching exercise.

The same information that helps someone enter an organization can support their continued growth. The organization can learn from how work is actually performed. Opportunity requirements can become more accurate. Learning investments can respond to real capability gaps.

This is how AI and data standards can support better hiring—not by automating judgment, but by improving the information and dialogue on which judgment depends.

The Goal Is Better Understanding

The future of hiring should not be a faster system for rejecting people.

It should be a more trustworthy way to understand an opportunity, recognize relevant human capability, evaluate evidence, and make accountable decisions.

Data standards provide a foundation for trusted and portable information.

AI can help people and systems understand one another.

Verified credentials can strengthen particular claims.

Human Intelligence provides the context that connects these pieces into meaning.

When all four work together—and when people retain agency over how they are represented—skills-based hiring can move beyond a slogan and become a genuinely better relationship between people and organizations.

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Product availability notice: This article describes Gobekli’s broader vision for skills-based hiring and the Human Intelligence Layer. TalentSync Recruiter is currently available through Launch Partnerships in limited production use, while capabilities continue to expand. Visit the Product Availability page for the current status of TalentPass, TalentSync, Pythia, Profiles, Passport Pages, and the Talent Tree.