The skills-based hiring report: what it is and how it will reshape work in 2026

I dream with a labor market where people are evaluated for what they can do today, not just where they studied or where they happened to work years ago. As someone who thrives on curiosity and career change, the rise of skills-based hiring feels like overdue progress.

Companies are shifting their talent strategies toward skills-first hiring. Not because it’s a trend, not because LinkedIn says so, but because the traditional model is breaking down and the cost of a wrong hire is too high.

In a labor market where tasks evolve quickly and technology reshapes every job family, education and past titles are losing their value as predictors of performance.

This report examines, critically and with data, how skills-based hiring is reshaping the world of work and what it means for both employers and professionals. It covers:

• What skills-based hiring is and why it outperforms traditional screening.
• Three market forces pushing companies toward skills-first models.
• Adoption trends across the United States, Europe and Latin America.
• Industries where skills-first evaluation is accelerating, and regulated sectors where progress is slower.
• How formal education and credentialing are changing: microcredentials, alternative pathways, and public policy.
• The role of OpenAI Jobs, AI certifications, and recent changes in LinkedIn Recruiter.
• Benefits and risks: bias, low-quality assessments, and unprepared companies.
• Employment and skills predictions for 2026 - 2030.
• What companies and candidates should do to adopt a skills-first strategy.

What skills-based hiring means today

Skills-based hiring is, at its core, a shift in the criteria used to evaluate a candidate.

Instead of assuming someone is a good fit because they “have a degree,” “spent X years in a role,” or “come from a well-known company,” this approach forces a more useful question:

This model moves focus away from traditional proxies (degree, job title, seniority) and toward recent, observable evidence of capability: how someone works, how they solve problems, and which skills they can transfer into a new context.

In just two years, this model has moved from niche to mainstream. According to the State of Skills-Based Hiring 2024reportreport, 81% of companies now use skills assessments, up from 56% in 2022.

Even so, adoption is uneven. Sectors like technology, data, digital operations, and logistics adapt quickly. Regulated industries that depend on mandatory credentials move more cautiously.

Benefits of skills-based hiring for employers

• Better role fit from day one.
• Lower operational risk. In fast-moving environments, hiring someone without the right capabilities slows teams down and creates avoidable mistakes.
• Shorter ramp-up times and faster execution.
• Larger and more diverse talent pools, uncovering candidates who would otherwise be overlooked.
• More transparent and less biased processes.
• Stronger alignment with dynamic markets. In stable markets, past experience matters. In volatile ones, the critical question is what a person can learn and execute today.

Benefits of a skills-first model for professionals

• Greater access: opens opportunities for people without traditional degrees or with nonlinear careers.
• More mobility: changing careers or industries becomes more feasible.
• Value can be demonstrated through projects, hands-on experience, or well-designed certifications. You don’t need long, expensive credentials.
• Recognition of self-taught learning, something traditional systems ignore.
• Clearer learning paths, reducing confusion and avoiding low-ROI educational choices.

The three market forces accelerating skills-based hiring

1. AI, automation, and the shift in core capabilities

Generative AI, automation, and software-driven workflows are reshaping tasks at unprecedented speed. When tasks change, the skills that drive performance change with them. In this landscape, relying on degrees or legacy job titles is no longer enough.

Data from Lightcast and LinkedIn Economic Graph show that one-third of in-demand skills today either didn’t exist or weren’t relevant three years ago.

2. Democratized learning and mass access to knowledge

The cost of learning has crashed. A decade ago you needed to enroll in formal programs (long, expensive ones). Today, anyone can learn programming, design, video editing, data analysis, generative AI, or corporate finance without entering a university. All you need is a computer and time with YouTube, tutorials, free courses, bootcamps, and online communities.

Starting in 2026, this trend accelerates due to two dynamics:

1. Generative AI as a personalized tutor.

   Learning becomes faster, more practical, and individualized through real-time feedback, adaptive exercises, and ongoing guidance.

2. AI-driven work requires micro-skills.

   Digital roles will rely on smaller, more specific, frequently updated skill units.

The result is a labor ecosystem where skill becomes the currency of the job market.

3. Productivity, performance, and competency-based evaluation

Productivity is the combination of contribution time, work quality, and error reduction. These are notoriously hard to measure, but all three improve when companies hire people after seeing real evidence of their abilities.

Hiring for transferable skills builds teams that can respond quickly when technology or markets shift without losing productivity. That is the environment we’re heading toward in 2026.

Global skills-based hiring trends in 2026: U.S., Europe and Latin America

Adoption of skills-based hiring varies widely depending on a country’s labor market structure, technological maturity, and cultural and regulatory context.

Globally, some regions are naturally aligned with skills-first evaluation, while others move more slowly because they rely heavily on degrees, hierarchies, or regulated credentials. Understanding these differences helps explain how the trend will evolve after 2026 and why certain countries act as early “laboratories” for what comes next.

Anglo-saxon countries: where skills-first hiring took off

The United States, the United Kingdom, and Canada embraced skills-based hiring earlier because their labor markets share three defining characteristics:

1. Highly liquid job markets where roles are organized around tasks, not rigid hierarchies.
2. Pressure to increase candidate volume per opening and reduce time-to-hire.
3. A tech and education ecosystem built around microcredentials (later we’ll cover the role of OpenAI Jobs and its emerging task-specific AI certifications).

In these environments, degrees stopped functioning as reliable signals. Roles evolve quickly, candidates upskill and switch paths rapidly, and employers need ways to measure actual capability.

Europe: the AI Act and Equal Treatment Framework are accelerating skills-based models

In Europe, a regulatory push is nudging companies toward more objective hiring criteria. The combination of the EU Equal Treatment Framework and the AI Act requires employers to justify, with greater rigor, which criteria they use to evaluate candidates and why.

The law does not explicitly mandate “skills-based hiring,” but it does push companies toward transparent, comparable, and bias-resistant criteria; conditions where a skills-first approach fits naturally.

Latin America: credential culture and SME-dominated labor markets

In much of Latin America, adoption is progressing, but it’s slower and more fragmented because the starting point is different. Culturally, formal education levels and job titles carry significant weight, and career mobility is lower. Work culture is less task-oriented and the education system is more rigid and institution-driven.

Add to that the structural reality: these economies are dominated by SMEs and microenterprises, which represent over 99% of the business fabric according to CEPAL. These firms generate between 60% and 70% of formal employment.

Most of these companies do not have structured HR teams or formal methodologies to assess skills, which makes it much harder to shift cultural norms. As a result, skills-based hiring adoption depends more on each company’s immediate pressures (digital talent shortages, productivity needs, operational modernization) than on a widespread cultural movement.

Industries leading the shift to skills-based hiring (and where it’s slower to take hold)

Adoption of skills-based hiring doesn’t depend only on employer willingness. It’s shaped by the nature of the work, the speed at which tasks evolve, and how regulated each sector is.

Technology, Data and Product

These professions have operated with a skills-first mindset for years, largely because:

1. Technical skills are easy to assess through hands-on tests.
2. Demand for talent has historically outpaced supply, forcing companies to look beyond degrees.
3. Skills expire quickly: what you learned three years ago may be irrelevant today.

This is why companies prioritize recent, verifiable evidence: repositories, project portfolios, real-time data work, AI-assisted workflows, product reasoning, experimentation, and problem-solving.

Marketing, Sales and Customer Experience

In marketing, sales, and customer experience, adoption is accelerating for one simple reason: most high-impact skills today are observable.

Companies need people who can interpret data, operate digital tools, automate workflows, understand behavioral patterns, and communicate clearly.

In sales, productivity depends more on how someone thinks and performs than on where they previously worked. Simulations, role plays, and case assessments give employers a much clearer read on actual fit.

Looking ahead to 2026, AI as a commercial copilot will redefine these roles. Companies will need people who can work with models and agents, not just traditional CRMs. This will make the gap between “experience” and technological adaptability much more visible.

Finance, Industrial Roles and Operations

These areas operate in two different worlds.

In corporate finance, credentials still matter (business, economics, CFA, MBA), but they’re no longer enough. The field now requires new capabilities: automated reporting, advanced analytics, data governance, and analytic AI.

The trend is hybrid: credentials still count, but so does demonstrated skill.

In industrial and operational roles, skills-based hiring has always fit naturally because productivity depends on concrete technical abilities: safety procedures, precision, protocols, equipment handling, and coordination. You either know how to do it, or you don’t.

As collaborative robotics, smart sensors, and automation spread, technical and digital skills will blend. Identifying workers who can operate in these environments becomes essential.

Hospitality, Retail, Logistics, Construction and Maintenance

These sectors have always relied on skills to make hiring decisions, whether someone can perform the task or not. Historically, this evaluation was intuitive and unstructured, which also made it biased.

What’s changing now is not the substance but the process. The skills required for these roles are finally being defined and measured more systematically, professionalizing what used to be implicit, subjective criteria.

Hiring processes now include technical assessments, and digital adoption is introducing new skill requirements. Many restaurant chains, hotels, and retailers are beginning to map technical and behavioral skills because it lowers turnover and improves service quality.

Regulated Sectors, Education, Healthcare and the Public Sector: slow and structural drag

These are the sectors where, in my view, regulation weighs more than any desire to innovate.

Regulation determines who is allowed to practice, and those requirements are built around degrees and formal exams. Education, healthcare, and public administration operate with mandatory credentials, examinations, and official registers that leave little room to rethink access criteria.

Add to that the regulated professions (law, architecture, licensed engineering fields, auditing, pharmacy) where degrees and licensure are not proxies but legal requirements. These sectors can improve how they assess skills within the licensed population, but credentials cannot be replaced.

Progress toward 2026 will be slower but still unavoidable in some areas, because digital transformation is reaching every sector, including public ones. AI will reshape workflows, and part of the current workforce lacks the skills required for new technological demands.

When that happens, even regulated sectors will need to create space for selection models less anchored to degrees and more connected to real ability. Either that, or they’ll default to the classic workaround: exams that certify “competence” on paper but don’t truly assess it.

How formal education is changing in Europe (and why)

Formal education still matters, but it’s no longer sufficient. For decades, degrees functioned as the dominant signal because they were the simplest way to certify structured learning. That worked when the labor market moved slowly and knowledge was scarce.

Today the opposite is true, and education systems are starting to adjust.

Public policies to align training with employability

Europe has launched a coordinated framework to reduce the disconnect between education and labor-market needs. The European Skills Agenda (2020–2025) and its associated initiatives (such as the Pact for Skills) aim to strengthen skills development in critical sectors, while the Digital Education Action Plan 2021–2027 modernizes digital learning and introduces verifiable credentials and common competency catalogs.

In practice, these policies translate into:

• public investment to help workers update their skills,
• fast-track routes for shortage professions,
• shared competency frameworks (such as ESCO),
• recognition of non-university training within the official system.

Governments are trying to strike a difficult balance: support employability while maintaining academic quality, and prevent labor shortages in strategic sectors like healthcare, energy, industry, and digital.

Integration of official microcredentials into formal education

The most significant shift is the integration of microcredentials into formal pathways.

The EU Council Recommendation on Microcredentials (2022) established a common standard to recognize external certifications (bootcamps, technical courses, intensive modules) as part of official vocational and university programs.

Organizations like CEDEFOP and the Joint Research Centre (JRC) are documenting how this transition allows traditional education to be combined with modules that are more current, applied, and aligned with labor-market needs.

The result is a more flexible system where students can complement degrees or vocational training with skills that match actual industry demands.

OpenAI Jobs, its AI Certifications and LinkedIn Recruiter

The major players in the tech ecosystem are increasingly shaping how we define, measure, and formalize skills. Their roles differ: some build infrastructure, others create credentials, and others supply signals that influence how the labor market prioritizes candidates.

The real impact depends on whether these signals genuinely improve evaluation or whether they create new weak proxies (the very thing skills-based hiring aims to eliminate).

OpenAI Jobs and AI Certifications: impact and risks

A few weeks ago, OpenAI announced that in 2026 it will launch official AI certifications and OpenAI Jobs, a job platform connecting employers with professionals “certified” in OpenAI technologies.

In the short term, this may help establish minimum standards for AI usage. In the long term, it raises important questions. If we want a labor market truly grounded in real ability, we cannot ignore the structural risks of this model.

Here are the three I consider most critical:

1. Certifying dynamic skills with static mechanisms is a contradiction

AI evolves in near-quarterly cycles: models, APIs, usage patterns, best practices… Everything changes fast. A certification, by design, is static. It’s meant to last for years. AI skills do not. Their lifecycle is measured in months.

For an AI certification to make sense, it must be living, updated, contextual, and evaluated through real-world scenarios; not frozen curricula.

2. One vendor defining what “knowing AI” means (and the Salesforce/Microsoft/AWS Playbook)

Having OpenAI define the curriculum, the assessment, the credential and the gateway to employment concentrates too much power in a single private actor. That should give us pause.

Yes, this will accelerate AI adoption and motivate people who would never have studied technology. That’s genuinely positive. The risk lies elsewhere:

• it standardizes how people learn and what counts as “good,”
• it sidelines alternative innovation ecosystems,
• it reduces the diversity of technical approaches, and
• it creates dependency on the vendor’s stack.

I’d trust the move more if OpenAI framed it explicitly as what it is: a commercial expansion strategy. Not an educational initiative.

The clearest precedent is Salesforce. When it launched certifications, the result wasn’t “more skilled talent”: it was greater product usage, more partners, and deeper customer lock-in.

Microsoft, AWS, Google Cloud, SAP, and HubSpot replicated the model because it works as a business strategy, not as a reliable measure of competency.

The same will happen here. Certifications will drive more user adoption and more processes toward the platform, but they won’t ensure people can work with AI in real operational contexts, which is what employers actually need.

3. Certification value drops when it becomes a mass requirement

We’ve seen this repeatedly: when thousands of people obtain the same certification each month, the signal stops differentiating. That’s what happened with AWS and Azure. Companies began requiring the cert as a “filter,” even though it was not a real measure of competence. And as certifications became widespread, companies raised the bar again: they started asking for advanced (longer, more expensive) certifications to re-establish differentiation.

A certification has value only when it sends a signal the market finds credible: scarcity, relevance, and applied skill. Once it stops meeting those conditions, it becomes another weak proxy, no different from traditional degrees.

The role of LinkedIn and how LinkedIn Recruiter’s ranking models are moving toward skills-based hiring

If OpenAI is trying to define the technical standard, LinkedIn is trying to define the informational standard of the labor market. And this shift has real transformative potential.

LinkedIn has announced three major changes that accelerate skills-first hiring:

1. Job titles are no longer the primary unit of information

The LinkedIn Economic Graph has shown for years that roles cluster more by shared skills than by job titles. LinkedIn is redesigning its recommendation systems around this logic: more similarity through skill sets, less through job titles.

2. LinkedIn Recruiter’s ranking is starting to prioritize explicit and verified skills

Search results are no longer driven only by job titles or keyword matches. Rankings now incorporate:

• skill patterns,
• verifications,
• alignment with LinkedIn taxonomies,
• and fit with task-level responsibilities.

3. A strong incentive for continuous skill updates

LinkedIn knows skill signals degrade quickly. That’s why it’s embedding learning directly into the platform:

• added and verified skills,
• learning paths,
• “skills paths,”
• and educational content feeding directly into the Economic Graph.

LinkedIn’s ambition is to become a bridge between learning and employability, something traditional institutions are taking much longer to achieve.

The Risks of skills-based hiring

Skills-based hiring fixes many weaknesses in the traditional model, yet it solves nothing if it is applied partially or without rigor. The risks do not come from the concept itself but from how it is implemented.

Poorly designed assessments: bad testing is worse than no testing

One of the most common mistakes is assuming that “adding a test” is the same as evaluating skills. Poorly designed assessments often do more harm than good:

• they demand unreasonable time or effort from candidates who lack resources, are in several processes at once, or are working full time,
• they ask candidates to complete tasks that never occur in the role,
• or the opposite, they ask candidates to deliver real work the company needs (free labor),
• they measure theory instead of execution,
• they penalize different cognitive styles or work rhythms.

These issues lead companies to select the wrong people and cause strong candidates to withdraw rather than waste their time.

Insufficient training for hiring teams

Most people involved in hiring are not recruitment specialists. In SMEs and startups, hiring often falls to team leads, technical profiles, or founders who rely on intuition.

To apply skills-based hiring correctly, hiring teams need to know how to:

• define skills,
• design assessments,
• interpret evidence,
• make consistent decisions,
• document the process.

Without training or experience, the process becomes inconsistent, hard to justify, and highly vulnerable to bias.

Bias persists when skills are poorly defined

Evaluating skills does not automatically produce fair processes. If skills are fuzzy, if assessments are based on gut feeling, or if evaluators lack training, bias simply shifts from one signal to another.

For example:

• treating “communication” as a criterion without specifying what it means,
• then penalizing accents, personal styles, or communication norms.

Skills-based hiring only reduces bias when skills are well defined, tied to real work, and assessed through objective evidence that applies equally to every candidate. Otherwise, the process is as arbitrary as before, just with more modern terminology.

Skills-First Hiring Requires a Full Redesign of the Process

The skills-first approach only works when it is embedded across every stage of hiring. This is the biggest barrier companies face today. The steps are:

1. Define the role by skills and expected outcomes

Hiring managers must identify the tasks that make up the role, the essential skills required to perform them, and the indicators that define strong performance. This requires upfront work and domain knowledge.

2. Search for candidates in ATS platforms by skills, not job titles

Most job boards and ATS searches still rely on job titles (“product manager”, “data analyst”). Searching by skills opens new talent pools and reduces bias.

3. Design assessments that simulate critical tasks

A good test is not academic, generic, or disguised free labor. It should mirror real work as closely as possible: analysis, decision-making, problem-solving, tool usage, and judgment.

4. Document evaluation criteria that apply to all candidates

Teams must document what is being evaluated, how it is evaluated, what evidence counts, and the weight of each criterion. Documentation forces more consistent, defensible decisions and reduces bias.

The challenge is that implementing all of this requires time, intention, and training for everyone involved in hiring. Many companies lack the resources to do it well, even when they genuinely want to.

The Future of skills-based hiring

Skills-based hiring is not the final destination of the labor market. It is a transition phase. We are moving from a system driven by degrees and job titles to one that demands live, observed, and continuously updated evidence of capability.

Between 2026 and 2030, we will see a deep shift in how roles are defined, how candidates are evaluated, which signals companies trust, and how professionals demonstrate what they can do.

Ten labor-market predictions for 2026–2030

1. Skills will update faster than certifications

Generative AI dramatically shortens the lifecycle of skills. A static certification will not keep pace. This will force the creation of living credentials: modular, short-duration, and tied to applied evidence that expires or renews through real performance.

2. The market will learn to discard certifications that don’t measure real execution

Companies will need to separate those who “hold a certification” from those who can actually apply the skill in real workflows.

It’s the same tension we saw with university degrees. If the market doesn’t learn the lesson, we’ll face credentialism 2.0, AI edition.

3. AI as a continuous learning layer and complementary evaluator

AI will not only teach. It will personalize learning, generate exercises, evaluate deliverables, and identify gaps. This benefits candidates and accelerates learning, while also making it more observable, which strengthens the value of skills-based hiring.

4. Convergence of taxonomies: a common language for skills

Europe is moving ahead with ESCO and microcredentials; LinkedIn is shifting the Economic Graph toward skills; OpenAI and other vendors will build their own taxonomies.

By 2030, we will see some degree of convergence, because without a shared language companies cannot compare skills or make their data interoperable.

5. Growing divide between regulated and non-regulated roles

Legally regulated professions (healthcare, law, education) will continue moving slowly, but all of them will incorporate digital and applied-AI skills even if degrees remain the entry gate.

Non-regulated roles will evolve much faster and adopt selection models grounded in tasks, projects, and applied evidence.

6. Expansion of alternative pathways into employment

Bootcamps, microcredentials, modern vocational training, AI-assisted learning, project-based work, portfolios… For an increasing number of roles, this mix will be as valid as a degree. University will stop being “the default pathway” and become one of many.

7. Talent decentralization: companies will hire more for “skills liquidity”

Smaller, more dynamic, AI-augmented teams will demand specialized freelancers, liquid talent, and project-based contributors. Skills-based hiring works especially well in these environments.

8. Skill portability: the rise of verifiable “skills passports”

By 2030, it will be normal to have a skills wallet containing verifiable abilities, project evidence, and interoperable digital credentials. This decentralizes validation, reduces vendor lock-in, and improves mobility.

9. Massive asymmetry between companies that adopt AI and those that don’t

Companies that combine operational AI copilots, skills-based evaluation, and continuous learning integrated into work will have a massive competitive advantage.

Those anchored in degrees and job titles will struggle to attract or retain adaptable talent.

10. Risk of turning certifications into the new filters

If employers, platforms, and training providers repeat past mistakes, we will return to the same problem:

• endless lists of certifications,
• processes filtered by “credentials,”
• very little real performance assessment.

Skills-based hiring will only work if the market truly values applied evidence, not badge collections.

What companies need to do to prepare (2026–2030)

Companies cannot wait until 2026 to adapt. This is not an “HR project.” It is a fundamentally different way of defining, evaluating, and developing talent; and it will determine competitiveness.

1. Define roles by skills, not by job titles

Many job descriptions still describe roles that don’t exist as written. By 2026, that will be unsustainable. Companies must define:

• which tasks drive the role,
• which skills are essential vs. optional,
• which indicators define strong performance.

And this cannot be a once-a-year project. It must be reviewed quarterly using real data from tasks, performance, and technology shifts.

2. Build assessment processes grounded in real tasks

Tests should resemble actual work. They must measure judgment, decision-making, reasoning, tool usage, and the ability to work with AI in real contexts. This reduces bias, improves prediction, and speeds up post-hire learning.

3. Train anyone involved in hiring… in Hiring

You can’t evaluate candidate skills well if evaluators don’t know how to assess them. Companies will need to train every employee involved in hiring.

4. Connect hiring, onboarding and learning

Evaluation does not end at hiring. If companies understand which skills are missing, they can design onboarding that is faster and more impact-oriented.

5. Avoid overreliance on vendor-driven certifications

The key question is not “Do they have the certification?” but:

Companies will need to distinguish between:

• certifications that reduce uncertainty,
• and certifications that simply create more identical resumes.

What this skills-first model means for candidates

As we’ve seen, we all need to learn to demonstrate what we can do. This brings many opportunities for job seekers, but also new expectations.

1. Continuous learning is no longer optional

The market will value what you can do today, not where you studied years ago. You will unlock more pathways if you show practical, verifiable skill.

This does not mean endless studying. It means short learning cycles, tied to work and oriented toward results.

2. A more open market for self-taught talent

Learning has never been cheaper or more accessible. AI can personalize your learning process: tutorials, guided exercises, simulations, AI-assisted projects. And if not, YouTube is always there.

3. A visible portfolio linked inside the resume

Companies will want examples, outputs, deliverables, evidence of judgment, and how you approach real cases. You should add this to your resume, but because you cannot go into detail there, you must link your portfolio and explain it clearly.

4. Changing industries will be easier with transferable skills

Good news for the curious. Digital skills, analytical capabilities, communication skills, and AI-driven workflows are increasingly transferable across sectors. This will enable career jumps that were unthinkable years ago without prior experience.

5. More competition, but also fairer processes

Competition will increase, yes. But if companies implement this model well, processes will also be fairer. When the dominant signal is applied skill, not personal history, hiring becomes more meritocratic.

Conclusion

Everything outlined in this report is the logical consequence of a labor market where tasks evolve faster than job titles, where technology reshapes work every few months, and where companies can no longer afford to hire based on intuition or inherited credentials.

Skills-based hiring does not solve every problem, but it forces us to confront something the market avoided for far too long: defining what it actually means to do a job well, and how to evaluate that fairly, with evidence.

The challenge, for both companies and candidates, is to adopt a new mindset. One that recognizes that learning is continuous, that talent often comes from nonlinear paths, and that today’s relevant skills may not be tomorrow’s.

Between 2026 and 2030, we will see stronger regulatory pressure, deeper standardization, more AI inside hiring processes, and higher expectations for demonstrated capability.

We will also see mistakes: empty certifications, shallow interpretations, and companies replicating the same credentialism as before, only with a different label.

The difference will come from those who can build evaluation and development systems that measure what matters, not what has always been measured.

In this landscape, tools and methodologies that help people identify, organize, and communicate their skills clearly and with evidence will become essential.

That has always been the purpose of CandyCV: to understand how hiring systems and technologies actually work, and to help people articulate what they can do, what they can contribute, and the evidence that backs their trajectory, without relying on credentials that no longer reflect anyone’s potential.

The future of work will be more complex, but it will also be fairer for those who can demonstrate their value.

And it all starts with the same principle: put skills at the center and build everything else around them, not the other way around.