HR Lens

Recruiting & Onboarding

Talent acquisition, employer branding, candidate experience, hiring assessments, job posting data and onboarding practices.

  • As AI Recruiting Expands, 2026 Global Hiring Standards Shift Toward Leadership Pipelines

    As AI Recruiting Expands, 2026 Global Hiring Standards Shift Toward Leadership Pipelines

    In Korn Ferry’s 2026 talent acquisition trends survey, released in October 2025, 84% of global talent leaders said they plan to use AI in recruiting in 2026. Based on a survey of 1,674 external global talent leaders and 230 Korn Ferry experts, the same material noted that AI adoption is not simply recruiting automation. It is becoming a variable that affects entry-level hiring, leadership supply and competitiveness in work models.

    SHRM also reported in its 2026 Talent Trends material, based on a survey of 2,094 HR professionals, that 68% were struggling to hire full-time employees. The central issue in 2026 global recruiting is therefore moving away from “whether to use AI” and toward which talent pipelines to preserve and which judgment standards to strengthen in a faster, more automated selection environment.

    As AI adoption rises, recruiting accountability becomes broader

    The 84% figure in the Korn Ferry survey signals that recruiting technology has moved beyond experimentation and into an operating default. Yet the same announcement also showed that only 11% of executives said their organizations were fully ready to lead AI transformation. There is a gap between the speed of tool adoption and the organization’s decision-making readiness.

    That gap expands the scope of responsibility HR must handle. Recruiting teams cannot stop at using AI-recommended candidate rankings or automated resume-screening results as they are. They need to document data input standards, exclusion conditions in evaluation, candidate appeal channels and the final decision owner. If shorter recruiting lead time becomes the only KPI, faster selection may be possible, but problems of bias, explanation and candidate trust can return later as a larger cost.

    Reducing entry-level roles returns as a leadership supply problem, not only a cost saving

    Korn Ferry said 43% of companies plan to replace some roles with AI. The replacement targets were presented as operations and back office at 58%, and entry-level roles at 37%. In the short term, automating repetitive work and improving labor-cost efficiency may look attractive, but the report warns that cutting entry-level hiring can weaken the long-term leadership pipeline.

    From a recruiting perspective, this figure is not merely a signal of headcount reduction. Junior positions are where people learn work and also absorb the organization’s work language, customer understanding and collaboration rules. Even if AI takes over some beginner-level tasks, organizations need to design alternative learning paths. For example, if an organization reduces junior hiring, it should also design internal rotation, project-based assignments, mentoring time and review roles for AI-assisted work. Otherwise, the cost savings of 2026 may appear as a shortage of middle managers after 2028.

    When AI agents become team members, job definitions change first

    In Korn Ferry’s material, 52% of leaders said they plan to add autonomous agents to teams in 2026. It also explained that some organizations are already creating employee records for AI agents inside HR systems. This means AI is beginning to be treated not only as a recruiting tool but as one unit of team composition.

    This change also affects job descriptions in job postings. The question “What kind of person should we hire?” becomes “Which work will be divided between people and AI?” If AI handles repetitive research, first drafts, candidate communication and scheduling, the capabilities required of people move toward verification, priority judgment, stakeholder coordination and ethical judgment. Recruiting teams need to separate in job descriptions the tasks where AI may be used and the tasks for which people carry final accountability.

    Skills-based hiring asks about judgment capability before technology names

    In the Korn Ferry survey, 73% ranked critical thinking as the top factor when evaluating potential candidates, while AI-related skills ranked fifth. In the SHRM survey, 80% of HR professionals also said they had the greatest difficulty finding candidates with systems and resource-management skills such as judgment, decision-making, complex problem solving and time management.

    These results show that 2026 skills-based hiring should not move only by lengthening the list of technology stacks. The phrase “experience using generative AI” alone cannot verify a candidate’s work judgment. Selection processes need to include questions that redefine the objective of an assignment, situations that require prioritization with limited information, and moments where the candidate challenges or corrects AI-generated output. Interviewer training also has to change. Interviewers should ask less about which tool the candidate used and more about how the candidate checked the evidence and what risks remained.

    Work model is not a benefit but the size of the reachable talent pool

    In the Korn Ferry survey, 52% said return-to-office mandates hurt recruiting, and 73% said remote roles were easier to fill. In 2026 global recruiting, work model operates not as an add-on to the compensation package but as a structural variable that determines the candidate pool a company can reach in the first place.

    This point also changes the language of employer branding. The phrase “flexible work available” is no longer enough. Candidates look at the team’s meeting rhythm, asynchronous decision-making method, performance-evaluation criteria and onboarding approach. Recruiting teams should compare fill speed, offer acceptance rate and 90-day post-hire adaptation indicators for remote and hybrid roles against office-centered roles. If the effect of the work model is judged only by intuition, the cause of hiring difficulty can be misunderstood as only a compensation or brand problem.

    The question Korean HR should import is workforce structure, not tools

    Mercer’s Global Talent Trends 2026 surveyed about 12,000 executives, HR leaders, employees and investors across 16 regions and 16 industries, presenting the combination of human capability and automation as a major agenda. Deloitte’s 2026 Global Human Capital Trends, based on a survey of more than 9,000 business and HR leaders in 89 countries and more than 50 interviews, also analyzed the importance of a human-centered approach in AI transformation.

    When Korean companies apply this trend, the first thing to examine is not the feature list of AI recruiting solutions. They need to review together how much entry-level hiring will be reduced, where alternative learning paths will be built if it is reduced, who will review AI-generated evaluation results, and how much remote or hybrid conditions expand the candidate pool. The key indicators in a 2026 recruiting strategy meeting are likely to begin with a single view comparing time to hire, candidate conversion rate, offer acceptance rate, 90-day adaptation rate after joining, and internal growth rate for junior positions.

    What HR should look at next

    The point is not to add one more policy. The work ahead is to turn this issue into a clear operating standard for the organization.

    A practical starting question is enough: which decision will this change actually alter — hiring, performance management, learning, or rewards? The clearer that answer becomes, the closer HR work moves from reports to real standards used in the field.

    References

  • Korea’s 2026 Recruiting Market Is Rewriting Selection Around Expertise, AI and Team Fit

    Korea’s 2026 Recruiting Market Is Rewriting Selection Around Expertise, AI and Team Fit

    The Corporate Recruiting Trends Survey released by the Ministry of Employment and Labor and the Korea Employment Information Service in November 2025 clearly shows the starting point for Korea’s 2026 recruiting market. The survey covered companies and young workers from August 1 to September 1, 2025, with 396 companies and 3,093 young employees across all 17 cities and provinces. In this sample, 52.8% of responding companies said they primarily require expertise when hiring young workers. Another 85.4% said applicants’ work experience helped them adapt to the organization and job after joining.

    Meanwhile, the second announcement found that 86.7% of companies use AI tools in HR work. Only 21.7% currently use AI in formal recruiting procedures, but 74.5% plan to introduce or expand it in recruiting. Korea’s 2026 recruiting agenda is moving less toward expanding hiring volume and more toward turning three standards into operating documents: expertise verification, fairness in AI use, and team-level fit.

    Expertise is narrowing from major to job-related experience

    In the Ministry of Employment and Labor’s 2025 Corporate Recruiting Trends Survey, 52.8% of companies said they prioritize expertise when hiring young workers. The items used to evaluate expertise were major at 22.3%, work experience such as internships at 19.1%, and job-related education or training at 17.4%. A major still matters, but companies are no longer judging expertise only by the name of a major. They are also looking for traces of experience and training connected to the job.

    This change alters the question in entry-level recruiting. “What was your major?” is becoming less important than “How much have you experienced the problems of this role?” Recruiting teams need to break the expertise required in job descriptions into knowledge, practical experience, tool use, and collaborative outputs. Interviews should not stop at hearing an explanation of the applicant’s major. They should check what assignments the applicant carried out and by what standard the results were judged.

    Work experience becomes evidence of adaptability, not a specification

    In the Ministry of Employment and Labor survey, 85.4% of companies assessed that applicants’ work experience helped them adapt to the organization and job after joining. When reviewing work experience, the most important criterion was relevance to the hiring role at 84.0%, followed by outcomes produced during the experience at 43.9% and whether the experience existed at 39.5%.

    These figures mean work experience should not be read as a simple list of credentials. What companies examine is not the existence of experience, but job relevance and outputs. In recruiting, internships, projects, and completed training should not simply be placed in the same table. They should be evaluated separately by job-related task, role, tools used, deliverable, and feedback. Young applicants also need an application structure that can explain not just “I have experience,” but “how this experience is related to the hiring role.”

    AI recruiting requires prior notice and verification before efficiency

    In the second Ministry of Employment and Labor announcement, 86.7% of the 396 responding companies were using AI tools in HR work. Companies using AI tools for employee recruiting accounted for 21.7%, and 74.5% planned to introduce or expand AI tools in recruiting work. Use cases included AI-based aptitude or competency tests at 69.8%, application-document screening at 46.5%, and use of results from AI interviews or in-person interviews at 46.5%.

    What recruiting teams must decide first is not whether to introduce AI, but the operating standard. They need to inform applicants in advance which stages use AI, what evaluation factors are involved, how collected personal information is handled, and how people intervene in the final decision. Since the reasons for introducing AI were data-based judgment at 34.6% and shorter screening time at 31.5%, it will be difficult to explain the effect of AI adoption unless efficiency and fairness indicators are managed together.

    Candidate experience now includes explainability in AI screening

    In the Ministry of Employment and Labor survey, 23.7% of young people had experienced an AI recruiting process during job search, and 63.8% supported companies operating AI recruiting processes. But their concerns were specific. Young people were worried about fairness in AI judgment criteria at 26.9%, opacity in AI screening standards at 23.1%, and distortion of self-expression at 18.4%.

    Candidate experience no longer ends with interview scheduling or quick feedback. In AI screening, job seekers requested verification of evaluation accuracy at 47.1%, bias verification at 42.3%, and prior notice of evaluation factors at 41.5% as protection measures. Companies must decide how far they can explain AI evaluation results to candidates, whether they will provide objection or review procedures, and how interviewers will refer to AI results. Without these standards, candidate experience may become more convenient but less transparent.

    From culture fit to team fit, the verification unit moves down to the team

    Wanted said in its 2026 recruiting trend material released in December 2025 that it asked 153 HR professionals about 2026 recruiting plans and outlooks. The central keyword of the material is team fit beyond culture fit. The direction is moving beyond finding people who fit the whole organization and toward checking whether candidates fit the actual team’s tasks, pace, and collaboration style.

    Team fit is risky when judged by intuition. The phrase “this person fits our team” can easily drift into an interviewer’s personal preference. Team-fit verification should therefore be broken down into the team’s current tasks, complementary capabilities needed, collaboration rhythm, and decision-making method. For example, selection criteria can differ for the same role depending on whether the team needs rapid experimentation, stable operational quality, or frequent customer communication. If team fit is used, the evaluation sheet should also separate organizational-culture fit, job fit, motivational fit, and team complementarity.

    Even if hiring volume declines, selection difficulty does not fall

    Jobkorea’s Corporate Lounge article on 2026 recruiting strategy cited the Korea Enterprises Federation’s 2025 new hiring survey and said 60.8% of companies had plans for new hiring. The same article, based on Saramin data, explained that among companies that recruited in 2024, 49.7% failed to hire as much as planned, and 63.6% cited the absence of suitable applicants as the reason.

    This shows that a conservative hiring stance does not mean selection becomes easier. When hiring volume shrinks, the cost of a single failed hire becomes larger. Companies therefore review more devices such as direct sourcing, referrals, talent pools, structured interviews, multiple evaluators, and bar raisers. In 2026, the recruiting team’s role is moving closer to that of a business partner that works with business leaders to define which candidates the company must not miss, rather than a function that opens postings and processes applicants.

    Recruiting meetings in 2026 should review job, team, and AI standards together

    When setting Korea’s 2026 recruiting strategy, HR should check at least three tables. First is the expertise criteria table by role. Major, work experience, job training, certifications, and outputs should be compared under the same standard. Second is the team-fit criteria table. Team tasks, complementary capabilities, collaboration style, and onboarding risks should be defined with business teams. Third is the AI recruiting operating table. It should leave records of AI-use stages, prior-notice wording, personal-information handling, human final judgment, and bias-review checks.

    If these three tables are separated, recruiting returns to a matter of intuition and speed. Organizations must be able to distinguish candidates who have high expertise but do not fit the team’s task, candidates who fit the team but are difficult to explain under AI evaluation criteria, and candidates selected quickly but showing low 90-day adaptation indicators after joining. Korea’s 2026 recruiting challenge is not only about gathering more applicants. It is about making the organization able to explain by what standards it selected people within fewer hiring opportunities.

    What HR should look at next

    The point is not to add one more policy. The work ahead is to turn this issue into a clear operating standard for the organization.

    A practical starting question is enough: which decision will this change actually alter — hiring, performance management, learning, or rewards? The clearer that answer becomes, the closer HR work moves from reports to real standards used in the field.

    References

  • [2026 HR Trend ④] Skills Criteria Must Change Before Recruiting Automation

    [2026 HR Trend ④] Skills Criteria Must Change Before Recruiting Automation

    This is the fourth article in the 2026 HR Trend series. If the previous articles covered AI accountability lines and the redesign of performance management, this article is about recruiting. The central question for recruiting in 2026 is not ‘How fast can we screen with AI?’ but ‘By what criteria should we evaluate people?’

    Recruiting automation can speed up resume review, candidate classification, and interview question generation. But if job requirements are outdated and skills criteria are vague, automation will not solve recruiting problems; it will make organizations repeat the same problems faster.

    Hiring difficulties are not a problem of screening speed, but of criteria

    The SHRM 2026 Talent Trends summary includes a sample of more than 2,000 HR professional respondents and addresses hiring difficulties and skills shortages together. According to the public summary, about 70% of HR professionals still struggle with full-time hiring, and 42% experienced difficulty retaining full-time employees during the past 12 months.

    These figures show that recruiting is not simply a matter of job-posting exposure or resume review speed. If the people needed are scarce in the market and even hired employees are hard to retain, the recruiting criteria themselves must be reviewed. The issue becomes less about ‘finding good people quickly’ and more about ‘accurately defining the skills our organization needs.’

    Automation can repeat vague requirements faster

    SHRM 2026 HR Trends raises the concern that recruiting problems cannot be solved by automation and algorithms alone. Even if AI quickly summarizes applications and ranks candidates, if the input job requirements and evaluation criteria are vague, the results will also be vague.

    For example, a job posting may say ‘communication skills,’ but in practice it is often unclear whether that means customer response, stakeholder coordination, document writing, or conflict mediation. AI can make such expressions look cleaner, but it cannot define on behalf of the organization the performance behaviors it wants.

    Skills criteria must change job requirements, interviews, and internal development together

    The SHRM 2026 Talent Trends summary states that 41% of HR professionals train current employees for roles that are difficult to fill. If hiring difficulties continue, it becomes hard to secure needed capabilities through external hiring alone, and internal development and recruiting criteria must move together.

    Skills-based hiring is not simply about reducing education or experience requirements. It means defining the skills actually required for job performance, deciding how to verify those skills, and connecting missing skills to pathways for development after hiring. So, job requirements, interview questions, work-sample assessments, onboarding, and training plans must use the same language.

    Recruiting teams and HRD must use the same skills language

    If roles are divided so that recruiting teams screen candidates and HRD handles training after hiring, skills criteria become disconnected. Skills considered ‘essential’ during recruiting may be interpreted differently in onboarding and training, or capabilities that training aims to develop may not be reflected in hiring criteria.

    What recruiting operations need in 2026 is a shared skills language used by both recruiting teams and HRD. Organizations must distinguish core skills by role, skills that must be confirmed before hiring, skills that can be developed within three months after joining, and skills that should be cultivated over the long term. Only then can recruiting automation connect to workforce planning rather than remain simple filtering.

    Korean companies should review role-based skills maps before applicant scorecards

    In Korean companies, recruiting improvement often begins with replacing the applicant tracking system, introducing AI resume screening, or improving interview evaluation forms. But what is needed before that is a role-based skills map. For each role, companies should separate the skills currently needed from those that will become important and decide what evidence will confirm each skill.

    First, job-posting qualifications should be broken down into skill units. Second, interview questions should be checked to see whether they verify actual skills. Third, internal and external candidates should be comparable using the same skills language. Fourth, missing skills should not be treated only as recruiting failures; companies should judge whether they can be supplemented through onboarding and training.

    The success or failure of recruiting automation is not determined only by the sophistication of algorithms. The criteria to be automated must be accurate. The starting point for recruiting in 2026 is not faster screening, but more precise skills criteria.

    Read the HR Trend series together

    This article is part of the 2026 HR Trend series. Reading across AI adoption, accountability lines, performance management, recruiting, upskilling, hybrid workforce models, Polywork, and employee experience gives a more three-dimensional view of how the HR operating model is changing.

    What HR should look at next

    The point is not to add one more policy. The work ahead is to turn this issue into a clear operating standard for the organization.

    A practical starting question is enough: which decision will this change actually alter — hiring, performance management, learning, or rewards? The clearer that answer becomes, the closer HR work moves from reports to real standards used in the field.

    References

    This article was written based on SHRM’s 2026 Talent Trends, 2026 HR Trends, and 2026 HR trend commentary. Only figures and wording available in public materials were used as evidence in the body, and non-public content from member-only detailed reports was not cited.