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AIHR

Covers HR strategy, policies, operating practices, data, cases, and decision-making insights related to AIHR.

  • 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.

    References

  • When Frontline Workforce Data Moves Into AI Analytics, HR Must Control Metrics Before Dashboards

    When Frontline Workforce Data Moves Into AI Analytics, HR Must Control Metrics Before Dashboards

    Key Takeaways

    • A June 19, 2026 announcement about Indeavor shows a more operational scene than AI writing HR documents. Frontline workforce data such as scheduling, absence, and overtime is being connected directly to natural-language queries and dashboards.
    • The core of this change is not “adopting an AI dashboard.” In 24/7 operating environments, the issue is who can view scheduling and absence data, by what standards, and for which decisions.
    • For Korean companies, the point to study is less the vendor feature itself and more the data dictionary, permissions, accountable owners, and metric interpretation standards. Absence and overtime indicators in particular should be read as organizational operating signals before they are connected directly to individual evaluation.

    Before AI dashboards, HR must define frontline data

    A Techrseries announcement published on June 19, 2026 described Indeavor’s AI Analytics Hub as a “natural language reporting platform.” The target environment is also relatively clear. It refers to complex 24/7 operations and four industries where frontline shifts and regulation matter: manufacturing, food and beverage, energy, and nuclear.

    The notable point is the data being connected. The announcement explains that the tool connects directly to scheduling and absence data. From an HR perspective, this is not a small shift. When recruitment, attendance, staffing, absence, and overtime remain in separate tables and systems, more time is spent checking consistency than analyzing the workforce. Before AI makes the screen look polished, HR has to decide whether the same word, such as “absence,” means the same thing by department, site, and period.

    That is why frontline workforce AI analytics is closer to an operating-model issue than to a subfunction of People Analytics. If field names, aggregation months, absence types, overtime formulas, and exception rules are unclear, AI may answer quickly while the organization becomes unstable slowly. Fast numbers make meetings easier. They do not necessarily mean the numbers are right.

    Natural-language queries widen access but can blur permission boundaries

    The announcement says users can ask questions in plain English instead of using SQL or spreadsheets. The examples are concrete: compare absence trends by facility last month, or show overtime in the production department last week. It also emphasizes that site managers, HR, and enterprise leadership can see the information directly without analyst or IT support.

    This accessibility is clearly useful. Frontline leaders do not have to wait for every Excel extract, and HR can reduce the burden of answering the same questions repeatedly. But weak permission design creates another problem. How much of another facility’s absence trend may one site manager see? By what standard is personally identifiable data masked? Who audits the AI query log?

    Natural-language querying does not simply make analysis easy for everyone. It tests the boundaries of analytical permission more often. HR should decide at least three things before adoption: first, role-based viewing scopes; second, minimum display thresholds for individual-, team-, and facility-level data; and third, separate approval procedures when sensitive indicators are moved into performance evaluation or disciplinary decisions.

    Overtime and absence metrics are operating signals, not productivity scores

    The examples in the announcement are absenteeism trends and production department overtime. They are questions tied to periods and units, such as last month’s absence by facility or last week’s overtime in production. It also explains that smart insights can reveal risks and trends such as overtime spikes and staffing gaps.

    What HR must be careful about here is the speed of interpretation. An increase in overtime does not immediately mean productivity has improved. A rise in absence cannot be assigned directly to individual responsibility either. The same week of overtime may hide different causes, including a demand surge, equipment issues, insufficient training, shift-table design, or a leadership gap.

    AI analytics results should therefore begin as a question sheet, not an evaluation sheet. If overtime suddenly jumps by site, HR should review staffing, work reallocation, safety risk, and manager approval patterns together. If absenteeism rises, HR should examine health, burnout, commuting, rest time between shifts, and even how absence codes are entered. Metrics are not tools for marking people down. They are signals for finding bottlenecks in operations.

    Korean companies should set data dictionaries and accountable owners before vendor adoption

    The announcement presents benchmarking and standardization, automated delivery, and standardized dashboards as features. These give HR a practical hint. Benchmarking is an attractive word, but comparison quickly becomes distorted without standard definitions. Even the same absence rate can become a completely different number depending on how paid leave, sick leave, unauthorized absence, and shift changes are classified.

    If Korean companies review tools of this kind, they should first build a data dictionary. It is a document that organizes field names, formulas, denominators, base months, exclusions, approvers, and editing permissions. The second step is to designate accountable owners. If it is unclear whether HR owns the metric, production and operations own it, or IT is responsible for data quality, the AI tool may produce answers but execution will stop.

    Finally, the purpose of automated reports must be limited. A standard dashboard sent weekly to executives is different from a report for frontline improvement meetings. If data is used for evaluation, discipline, or compensation decisions, review procedures and appeal channels are also needed. The success or failure of an AI analytics tool is likely to be determined more by operating rules than by the model itself.

    Practical checklist questions

    • Are the data definitions for shift work, absence, overtime, and replacement work the same across departments?
    • For natural-language query users, which facility-, team-, and individual-level data can each role view?
    • Who reviews and acts on staffing gaps or overtime spikes suggested by AI?
    • Is an automatically delivered dashboard classified as decision material, monitoring material, or evaluation material?
    • Before vendor adoption, are the data dictionary, permission table, audit log, and exception approval process documented?

    References: Techrseries, “Indeavor Launches AI Analytics Hub to Turn Frontline Workforce Scheduling and Absence Data Into Real-Time Insights With AI”, 2026-06-19. https://techrseries.com/hr/indeavor-launches-ai-analytics-hub-to-turn-frontline-workforce-scheduling-and-absence-data-into-real-time-insights-with-ai/

  • AI Is Said to Eliminate Entry-Level Roles, but HR Should First Redesign Work

    AI Is Said to Eliminate Entry-Level Roles, but HR Should First Redesign Work

    The claim that AI will eliminate entry-level jobs spreads quickly. But the first question HR should examine is slightly different. It is less about which jobs disappear and more about how the tasks once handled by entry-level employees are being broken apart and recombined.

    A survey summary released by Cognizant and Pearson on June 18 shows this distinction clearly. It said that in India, 37% of entry-level role tasks are already performed by AI, while the global average is 33%. At the same time, 94% of HR leaders said AI will create new entry-level roles within the next five years. Replacement and creation appear in the same table.

    The entry-level jobs debate should start with task composition, not replacement rates

    The most striking figure in the survey summary is 37%. That is the share of entry-level job tasks in India that AI already performs. It is higher than the global average of 33%. In addition, 18% of HR leaders said AI is handling more than half of entry-level work. Looking only at the numbers, anxiety can come first.

    But if HR reads these figures immediately as “reduced entry-level hiring,” its judgment becomes too blunt. Even when some tasks move to AI, the whole job does not necessarily disappear. Recruiters should instead separate the repeated data entry, drafting, information search, verification, customer response, and internal coordination tasks inside job descriptions. Some tasks will be automated, while others will require more human judgment.

    Hiring criteria are moving from majors to the ability to work with AI

    In the Cognizant and Pearson survey, 96% of HR leaders expected entry-level roles to evolve toward supervising or managing AI systems within five years. Another 94% said AI will create new entry-level roles that do not exist today. This point means the focus of hiring criteria is moving from “Can this person use AI?” to “Can this person review AI output and adapt it to the context?”

    What is interesting is that the summary does not emphasize technical majors alone. It reported that 97% of HR professionals said soft skills have become more important, and 69% said a broad interdisciplinary background is more important for early-career talent than narrow specialization. If Korean companies revisit their entry-level hiring scorecards, they should look beyond major names, certificates, and tool experience. Problem definition, AI-output verification, and the ability to explain work collaboratively should be evaluated together.

    Training demand is rising, but L&D is falling behind the pace

    According to the survey summary, 91% of HR professionals said demand for AI training among employees increased over the past 12 months. Yet 60% said L&D programs are not keeping up with the pace of AI-driven job change, and the figure was presented as 63% among respondents in India. The gap between training demand and training supply has already become an operating issue.

    At this point, HRD needs to create task maps by job before adding one-off AI lectures. For example, in entry-level sales, marketing, development support, and HR operations roles, HRD should separate the drafting, search, and classification tasks handled by AI from the judgment tasks people must confirm. Training indicators also cannot stop at the number of participants. Actual task-transition rates after training, manager feedback, error-review standards, and changes in onboarding time should be checked together.

    Middle managers become the bottleneck in AI hiring and onboarding

    In the Cognizant and Pearson survey, 95% of HR leaders said middle managers are important in ensuring employees use AI effectively. Another 92% said middle managers play an important role in redefining job roles as AI changes daily work. Even if a company hires entry-level employees, change will stop at the wording of the job posting if frontline managers cannot redistribute work between AI and people.

    HR’s next diagnostic questions therefore need to be concrete. First, has the organization written down the tasks AI has taken over and the new verification tasks for each entry-level role? Second, does onboarding teach judgment standards and prohibited uses, not only how to use AI tools? Third, have middle managers been given role-redesign authority and coaching language? Fourth, for companies that maintain large-scale early-career hiring, as in Cognizant’s case of hiring 20,000 entry-level employees in 2025 and planning to exceed that number in 2026, are education, placement, and managers’ execution capabilities expanding together?

    The same percentages cannot be applied directly to Korean companies. The survey covered three countries—the United States, the United Kingdom, and India—and surveyed 750 director-level or higher HR professionals at companies with more than 1,000 employees. The sample and respondent composition were collected through an online survey from March 23 to April 3, 2026. Still, the message is clear. The core question in entry-level hiring in the AI era is not “How many people can we reduce?” but “Which tasks must we redesign, and which skills must we build early?” If HR misses this question, AI becomes not the answer to workforce planning but another cause of onboarding failure.

    ReferencesCognizant Newsroom, 37% of Entry-Level Tasks in India Already Done by AI, Finds Cognizant and Pearson Study, 2026-06-18

  • AI Training Participation Lags Adoption, Exposing a Bottleneck in the HR Operating Model

    AI Training Participation Lags Adoption, Exposing a Bottleneck in the HR Operating Model

    Installing AI tools is not the same as people actually changing how they work. Aon’s June 17 article makes that gap quite clear. It notes that nearly three-quarters of organizations worldwide have deployed AI or are running pilots, while only 18% of organizations have most employees participating in AI reskilling and upskilling.

    That number signals that HR teams need to change the first question they ask about AI projects. The question should not start with “which tool did we introduce?” It should start with “who learned it, which work changed, and is that change visible in performance indicators?” Even when adoption rates are high, low learning participation and weak operating standards reveal the bottleneck inside the HR operating model.

    The gap between deployment and learning participation appears first

    Aon’s insight dated June 17, 2026 explains that AI deployment and pilot programs are already widespread. In numerical terms, nearly three-quarters of organizations worldwide have deployed AI or are testing it. From the employer perspective, it also says that more than three-quarters have already rolled out AI tools. On the surface, AI transformation looks fast.

    But the second number in the same material is more uncomfortable. Only 18% of organizations have most employees participating in AI reskilling and upskilling. The gap between deployment and learning participation is not simply a delayed training schedule. It may mean that HRD budgets, job-based priorities, manager roles, and work redesign are not connected on the same operational view.

    Counting usage frequency alone does not show the return on AI investment

    Aon points out that AI use is still often measured by “frequency of use.” How many people logged in, how many prompts were entered, and which teams used tools most frequently can work as early diffusion indicators. But those indicators alone cannot show whether hiring lead time, training conversion rates, customer response quality, document review time, or the speed of managerial decision-making have improved.

    Training coverage shows the same problem. Aon wrote that fewer than one-third of employers have failed to train even 10% of their workforce, and that one in six has not trained any employees at all. If an AI project meeting ends after reviewing only “number of users,” this gap remains hidden. HR needs to connect training audiences, job groups, use cases, and before-and-after performance indicators.

    HRD and People Analytics need to look at the same dashboard

    AI training is now difficult to run as a standalone campaign. The 18% participation figure is not only an HRD issue. It is an operating indicator that People Analytics, HRBPs, IT, and business leaders need to review together. For example, organizations should not look only at training completion rates. They should also place next to them the share of work actually using AI after training, the number of approved use cases, and the number of processes that have completed risk review.

    John McLaughlin said that organizations are deploying AI but are not providing enough clarity, direction, and operating model support for people to use it effectively. That sentence can be read as a checklist for the HR operating model. Are there job-specific standards for AI use? What outputs should managers approve? What should be compared 30, 60, and 90 days after training? Without these questions, AI use is left to individual curiosity.

    The next question for Korean companies is readiness, not tools

    Aon’s material is written from a global consulting perspective, so it does not replace explanations of Korean companies’ legal obligations or industry-specific rules. In this automated run, the sample, survey scope, and industry distribution of respondents were not checked in detail, so the figures should be read as signals for workforce-readiness diagnosis. Still, there is a warning HR practitioners can apply: if AI transformation is treated only as a solution deployment project, training, roles, performance measurement, and accountability structures will not catch up.

    In the next AIHR meeting, it may be better to open a readiness table before reviewing the feature list. HR should check, line by line, training participation by job group, actual applied work, manager approval standards, prohibited use cases, performance indicators, and data-security review status. If the tools are already inside the organization, the question needs to be asked before it is too late: are we increasing the number of people who use AI, or are we redesigning work that can actually use AI?

    References

  • [2026 HR Trend ⑥] The Limits of Full-Time-Centric HR and Hybrid Workforce Operations

    [2026 HR Trend ⑥] The Limits of Full-Time-Centric HR and Hybrid Workforce Operations

    This is the sixth article in the 2026 HR Trend series. If the fifth article covered upskilling that develops internal talent in real time, this article addresses an operating model that includes talent outside the organization. The workforce structure of 2026 is difficult to explain with full-time employees alone.

    Freelancers, gig workers, external experts, independent contractors, and project-based partners work together, and AI tools are added to the mix. HR’s question moves from ‘Whom should we hire?’ to ‘Which roles should be handled through which employment arrangements and accountability structures?’

    Full-time-centered workforce planning alone cannot explain 2026

    SHRM 2026 HR Trends states that 72% of CEOs expect increased use of independent contractors, gig workers, and freelancers in 2026. At the same time, the SHRM 2026 Talent Trends summary addresses hiring difficulties and retention challenges based on a sample of more than 2,000 HR professional respondents.

    If full-time hiring is difficult and the use of external talent is increasing, the unit of workforce planning must also change. Previously, planning centered on departmental headcount, levels, roles, and labor costs. Now, core roles, external expertise, project duration, data access rights, and performance accountability must be designed together.

    A hybrid workforce is not outsourcing, but a change in the operating model

    The workforce fragmentation trend presented by SHRM is different from a simple expansion of outsourcing. The 2026 shift toward greater use of independent contractors, gig workers, and freelancers means organizations do not secure needed capabilities through a single employment contract alone.

    Therefore, hybrid workforce operations cannot be seen only as a matter of procurement departments or business units using external talent when needed. It is an operating model issue that determines who handles the organization’s core knowledge, who contacts customers, who prepares decision-making materials, and who is accountable for performance and quality.

    When AI and external talent are combined, accountability lines become more complex

    SHRM states that 89% of CEOs expect AI to redefine how organizations create and capture value in 2026. When AI is combined with external workforce operations, accountability lines become more complex. When an external expert uses AI tools to create outputs for internal decision-making, organizations must decide who holds final responsibility.

    For example, if an external consultant creates a People Analytics report, AI helps summarize data, and a business leader decides workforce deployment based on the results, accountability is divided across multiple layers. HR must clarify the contract scope, data access rights, output reviewer, and final approver.

    HR must differentiate onboarding and performance criteria by employment form

    The SHRM 2026 Talent Trends summary explains that about 70% of HR professionals struggle with full-time hiring, and 42% experienced difficulty retaining full-time employees during the past 12 months. In this situation, using external talent becomes not a temporary stopgap but part of the workforce portfolio.

    However, all workers cannot be managed with the same onboarding and performance management criteria. For full-time employees, organizational culture, long-term growth, and internal mobility must be considered. For freelancers and external experts, project scope, deliverable standards, and security and data access criteria matter more. For AI tools, purpose of use, review responsibility, and recordkeeping standards are needed.

    Korean companies should first map their workforce portfolio

    When Korean companies prepare for hybrid workforce operations, the first task is not deciding whether to increase or reduce the use of external talent. It is to map what workforce combinations are currently performing the organization’s work. They must identify which work involves full-time employees, contract employees, dispatched or outsourced workers, freelancers, external experts, and AI tools.

    Next, risk levels should be divided by role. Roles that access customer information, HR information, core technology, or strategic decisions require higher standards. Conversely, roles centered on short-term deliverables need clear scope and quality criteria. HR must organize these criteria together with business units, legal, security, and procurement.

    The task for HR in 2026 is not a simple choice between reducing full-time employees and increasing external talent. It is to decide how to keep core roles inside, where to use external capabilities, and which judgments AI tools should support. Hybrid workforce operations are not a cost-cutting strategy, but an organization design strategy.

    2026 HR Trend series articles

    The hybrid workforce article addresses an operating model that includes capabilities outside the organization after upskilling.

    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.

    References

    This article was written based on SHRM’s 2026 HR Trends, 2026 Talent 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.

  • [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. Therefore, 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.

    2026 HR Trend series articles

    The recruiting and skills article redefines talent criteria between performance management and upskilling.

    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.

    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.

  • [2026 HR Trend ③] The End of Annual Reviews: Redesigning Performance Management in the Age of AI Coaching

    [2026 HR Trend ③] The End of Annual Reviews: Redesigning Performance Management in the Age of AI Coaching

    This is the third article in the 2026 HR Trend series. The first article covered the redesign of HR’s operating model, and the second covered AI accountability lines. This article focuses on performance management. In SHRM’s 2026 trends, AI coaching and People Analytics signal that annual-review-centered performance management is losing relevance.

    This does not mean performance evaluation disappears. Rather, it means goal setting, feedback, capability development, and managerial judgment must be connected more frequently. AI coaching should be seen not as a technology that replaces evaluators, but as an operating mechanism that changes the rhythm of performance management.

    Annual reviews are under pressure not because of the review cycle itself, but because work has accelerated

    SHRM’s 2026 HR Trends explains that AI remains a central HR agenda item in 2026 and that organizations must connect it to real business impact while considering both cost and risk. In the same flow, SHRM’s 2026 trend commentary addresses the view that AI coaches may accelerate the end of annual performance reviews.

    The important point here is not the slogan of “abolishing annual reviews.” It is that the speed of work has increased, roles change frequently, and required skills change in short cycles. Managing employee growth and organizational performance at the same time is difficult with a method that checks goals and assigns ratings only once a year.

    AI coaching increases the frequency of feedback rather than replacing evaluators

    SHRM explains AI use in connection with cost reduction, productivity improvement, and better workforce decisions. Applied to performance management, this perspective clarifies the role of AI coaching. AI is not a mechanism that makes final evaluations on behalf of managers, but a supporting mechanism that drafts feedback, increases the frequency of conversations, and connects goals with behavior.

    For example, managers can use AI to summarize recent project records and organize an employee’s strengths and areas for improvement. But humans must decide what feedback to actually deliver, whether to leave a performance issue as a formal record, and whether to connect it to compensation or promotion decisions. If AI replaces evaluation, accountability becomes blurred; if AI helps prepare feedback, it can improve the quality of managerial conversations.

    The starting point for redesigning performance management is connecting goals, feedback, and development

    SHRM’s 2026 Talent Trends summary includes a sample of more than 2,000 HR professional respondents and addresses hiring difficulties and skill shortages together. According to the public summary, 41% of HR professionals train existing employees for hard-to-fill roles, and 42% experienced difficulty retaining full-time employees in the past 12 months.

    These figures show that performance management is not only a matter of evaluation and rewards. If it is hard to find the needed talent externally and also hard to retain existing employees, performance management must be more strongly connected to internal capability development. When goals change, required skills change as well, and feedback must extend to how those skills will be developed.

    The manager’s role does not shrink; it becomes clearer

    Some view the spread of AI coaching as reducing the manager’s role. In reality, the opposite is closer to the truth. As AI provides more data and wording, managers must explain more clearly what they used as the basis for their judgment.

    In performance management, managers should have three responsibilities. First, they must check whether feedback suggested by AI fits the actual work context. Second, they must distinguish messages to deliver to employees from content to leave as formal records. Third, they must judge whether goal adjustments or development plans connect to organizational priorities. AI can help, but it cannot take over these responsibilities.

    Korean companies should change the operating rhythm before the evaluation system

    In Korean companies, performance-management reform often begins with discussions of rating scales, relative evaluation, and the proportion reflected in compensation. But the 2026 change asks about operating rhythm before policy wording. When are goals reviewed, how often does feedback happen, and is the development plan connected to the next work assignment? These questions become more important.

    HR’s first task is not to choose an AI coaching tool but to map the flow of performance management. HR must identify where goal setting, interim check-ins, feedback, capability development, and reward decisions are disconnected. Only then should it decide where AI can help.

    The core of performance management in 2026 is not “let’s evaluate more often.” It is to identify more quickly what employees are doing well now, what they must learn for the next performance outcome, and what conversation managers need to have. AI coaching is most useful when it is a tool that helps prepare that conversation.

    2026 HR Trend series articles

    The performance-management article addresses manager feedback and operating rhythm after AI accountability.

    Read the HR Trend series together

    This article is part of the 2026 HR Trend series. Reading AI adoption, accountability lines, performance management, recruiting, upskilling, mixed workforces, Polywork, and employee experience together provides a more three-dimensional view of changes in the HR operating model.

    References

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

  • [2026 HR Trend ②] More Important Than AI Adoption: Designing HR’s Lines of Accountability for AI

    [2026 HR Trend ②] More Important Than AI Adoption: Designing HR’s Lines of Accountability for AI

    This is the second article in the 2026 HR Trend series. If the first article read the overall flow as a “redesign of the HR operating model,” this article focuses on AI within that flow. The key issue is not AI adoption rate. It is how far HR will use AI, who will review it, and how it will explain decisions to employees.

    SHRM expects AI to remain a central HR agenda item in 2026. At the same time, it explains that organizations must revisit whether AI has delivered the expected results and where costs and risks are hidden. It also presents the figure that 89% of CEOs expect AI to redefine how organizations create and capture value. As expectations grow, responsibility grows as well.

    As AI becomes a central HR agenda, lines of accountability must come first

    AI is rapidly entering recruiting, performance management, learning, workforce planning, and employee-experience analysis. But saying that HR uses AI is not a single action. AI that summarizes candidate documents, AI that recommends interview questions, AI that drafts performance feedback, and People Analytics tools that predict turnover risk each create different risks.

    The problem is that as tools multiply, the source of judgment becomes blurred. If there is no record of whether HR simply followed the AI output, whether a manager modified it, or what criteria justified an exception, employees will find it difficult to accept the result. Therefore, the first task for HR AI in 2026 is not “what should we adopt?” but “who is the final decision-maker?”

    HR AI accountability starts with three questions

    The first question is purpose of use. SHRM’s 2026 HR Trends explains that organizations should strip away excessive expectations around AI and use it where it truly matters. HR must therefore distinguish whether AI is being used for cost reduction, productivity improvement, or as a supporting tool for better workforce decisions. If the purpose is vague, performance is hard to measure.

    The second question is review responsibility. Who checks the recommendations made by AI? In recruiting, the roles of recruiters and business leaders differ; in performance management, the responsibilities of managers and HRBPs differ. The third question is documentation standards. Organizations must leave a record of what data was entered, by what criteria results were revised, and who approved exceptions.

    If these three questions are not settled, AI may make HR faster, but it will not make HR more trusted.

    For recruiting AI, explainability matters more than screening speed

    In 2026 Talent Trends, SHRM addresses hiring difficulties and skill shortages based on data from more than 2,000 HR professionals. According to the public summary, about 70% of HR professionals still face difficulty hiring full-time employees, and 42% experienced difficulty retaining full-time employees in the past 12 months.

    In this situation, recruiting AI looks like an attractive solution because it can quickly summarize applications, classify candidates, and generate interview questions. Yet, as SHRM points out, automation and algorithms alone cannot solve hiring problems. If job requirements are outdated and evaluation criteria are unclear, AI will only repeat that ambiguity faster.

    Therefore, the core of recruiting AI is not speed but explainability. HR must be able to explain why a candidate was excluded, what capabilities were judged insufficient, and how humans reviewed the AI recommendation.

    Performance-management AI should make managerial judgment more transparent

    AI coaching and People Analytics are also changing performance management. SHRM’s 2026 HR Trends explains that AI can go beyond cost reduction and productivity improvement and lead to better workforce decisions. SHRM’s 2026 trend commentary also addresses the idea that AI coaches may accelerate the end of annual performance reviews. This does not mean evaluation disappears. Rather, feedback must become more frequent, more specific, and more data-based.

    Here again, accountability lines matter. AI can draft an employee development plan. But managers must decide what feedback to actually deliver, what goals to adjust, and what performance issues to leave as formal records. HR should not let AI replace managerial judgment; it should use AI as a mechanism that makes the judgment process more consistent and transparent.

    Korean companies should keep records of AI use and exception criteria

    The first task for Korean companies is not a grand AI ethics declaration but the maintenance of operating documents. Translating SHRM’s framing of the 2026 AI agenda as a matter of cost, risk, productivity, and workforce decisions into Korean HR operations means separating AI-use standards first in areas that affect employees, such as recruiting, performance management, learning recommendations, and turnover-risk analysis.

    For example, recruiting must distinguish whether AI only summarizes applications or also ranks candidates. Performance management must separate whether AI feedback wording is reference material or formal evaluation evidence. HR data analytics needs standards for whether individual-level predictions are provided to managers or used only as organization-level indicators.

    The competitiveness of HR AI in 2026 does not lie in using more tools. It lies in creating a structure in which people can review and explain the judgments made by AI. That is the starting point for HR to turn AI into an asset of organizational trust.

    2026 HR Trend series articles

    The AI accountability article connects the flow of HR AI operations when read together with the hub article and the performance-management article.

    Read the HR Trend series together

    This article is part of the 2026 HR Trend series. Reading AI adoption, accountability lines, performance management, recruiting, upskilling, mixed workforces, Polywork, and employee experience together provides a more three-dimensional view of changes in the HR operating model.

    References

    This article was written based on SHRM’s 2026 HR Trends, 2026 Talent Trends, and 2026 HR trend commentary. In particular, the HR professional respondent sample and public figures from 2026 Talent Trends were used as article-level evidence. Only figures and wording verifiable in public materials were used as evidence in the body, and nonpublic content from member-only detailed reports was not cited.

  • [2026 HR Trend ①] HR’s Operating Model Must Change Before AI

    [2026 HR Trend ①] HR’s Operating Model Must Change Before AI

    This is the first article in the 2026 HR Trend series. In one sentence, the 2026 HR trends released by SHRM are not a message to simply “adopt AI.” More precisely, they point more directly to the need for HR to redesign its operating model as AI, hiring difficulties, changing skills, and rising employee expectations arrive at the same time.

    If many organizations through 2025 focused on AI experiments, automation tools, and recruiting-system improvements, the question for 2026 is somewhat different. Has this technology produced real performance? Has the way employees work become clearer? Are managers giving better feedback? Has hiring become fairer and more accurate? SHRM’s 2026 HR Trends, Talent Trends, and State of the Workplace materials raise these questions from several angles.

    The research base is also broad. SHRM’s 2026 Talent Trends summary addresses recruiting and retention based on data from more than 2,000 HR professionals, while the State of the Workplace summary presents employee experience and burnout issues based on responses from more than 1,800 HR professionals and more than 2,000 workers. This article should therefore be read less as a set of individual predictions and more as a reading of the operating signals repeatedly appearing in the public summaries.

    The challenge of AI is not adoption rate but performance and control

    SHRM expects AI to remain a central HR agenda item in 2026. The mood, however, differs from the early optimism. Pressure is growing to verify what effects AI has on cost reduction, productivity, and workforce decision-making.

    At this point, HR’s role is not simply to introduce tools. SHRM notes that 89% of CEOs expect AI in 2026 to redefine how their organizations create and capture value. Because expectations are high, HR must design standards for AI use, the scope of data use, bias checks, and lines of decision accountability together. As recruiting AI screens candidates, performance-management AI suggests feedback, and HR analytics tools predict turnover risk, the question “who makes the final judgment?” becomes increasingly important.

    Therefore, the core keyword for AIHR in 2026 is not automation but explainability. HR must build an organization that does not simply accept AI outputs, but can review AI-generated judgments and explain them to employees.

    Performance management is moving from annual reviews to real-time feedback

    Another strong signal from SHRM is the change in performance management. As AI coaching and People Analytics spread, an annual-review-centered approach is losing persuasiveness. In an environment where work moves faster and roles change frequently, evaluating people all at once against goals set a year earlier cannot keep pace with learning on the ground.

    Future performance management must operate more frequently, more specifically, and with more data. Managers become people who adjust priorities, behavioral standards, and growth direction during the flow of work, not people who assign scores during review season. To support this, HR must revise feedback questions, manager training, performance data, and the way performance connects to rewards.

    The important point is that AI coaching does not mean replacing managers. Rather, the quality of managerial judgment becomes more visible. AI may recommend feedback wording, but the leader remains responsible for deciding what conversation is needed in what context.

    Before recruiting automation, skill criteria must be redefined

    SHRM’s 2026 Talent Trends sees hiring difficulties as still widespread. Difficulty hiring full-time employees, skill shortages in critical roles, and retention problems are not issues that will disappear quickly. The notable direction here is skills-based hiring and internal talent development.

    Many companies place hope in recruiting automation, but SHRM’s concern is more fundamental: algorithms alone do not complete good hiring. In its public summary, SHRM states that about 70% of HR professionals still face difficulty hiring full-time employees, and 42% experienced difficulty retaining full-time employees in the past 12 months. Hiring difficulty is therefore not simply a matter of job-posting exposure or screening speed, but of job requirements and retention strategy.

    HR must first rewrite what capabilities are actually required for each role. It must examine whether degrees, tenure, or specific industry experience are truly essential, and change interview questions, assignments, and scorecards toward skill verification. It is also important that, as SHRM notes, 41% of HR professionals train existing employees for hard-to-fill roles. Internal mobility and L&D paths are no longer separate tasks for the training department; they become part of the recruiting strategy.

    The workforce structure is shifting from full-time-centered to mixed

    SHRM presents a workforce structure that mixes freelancers, independent contractors, gig workers, small project teams, and AI agents as an important change. This can be seen as workforce fragmentation and the spread of fractional work. SHRM’s 2026 HR Trends page notes that 72% of CEOs expect increased use of independent contractors, gig workers, and freelancers in 2026.

    This change is not unfamiliar to Korean companies. External experts for projects, short-term contracts, platform workers, and automation tools are already entering simultaneously. The problem is that systems are not keeping up with this speed. Who is a member of the organization? What information can they access? How is performance evaluated? How far do security and compliance responsibilities extend?

    An HR operating model is no longer sufficient if it manages only full-time employees. On the assumption that internal employees, external experts, and automation tools work together, roles, authority, accountability, and reward criteria must be reorganized.

    Employee experience and rewards again become a matter of the psychological contract

    SHRM’s State of the Workplace material treats rising employee expectations, burnout, and employee experience as important challenges for 2026. At the same time, HR Trends mentions side jobs, polywork, side hustles, financial pressure, and changes in rewards strategy.

    This does not simply mean adding more benefits. Employees may be asked to deliver more performance and adapt more, while feeling that the stability and growth opportunities provided by the organization are shrinking. If this gap widens, it leads to lower engagement, burnout, turnover, and weakened culture.

    Total Rewards is therefore not a matter of a wage table or benefits package, but a task of redesigning the psychological contract between employees and the organization. Compensation, growth, flexible work, well-being, manager quality, and the meaning of work must be connected together.

    Five things HR departments should check first in 2026

    If SHRM’s 2026 trends are translated into practical tasks for Korean companies, they can be summarized in five questions.

    First, are the purpose of use, owner, and review criteria documented for each AI tool? Second, does performance management operate as a continuous feedback structure rather than an annual review? Third, have hiring criteria changed to verify actual skills rather than education and experience? Fourth, is there a clear authority system for internal employees, external workers, and automation tools working together? Fifth, does the employee experience and rewards strategy address both heightened expectations and burnout risk?

    The 2026 HR trends are not a list of new buzzwords. AI realization, redesigning performance management, skills-based hiring, real-time upskilling, mixed workforce structures, employee experience, and Total Rewards ultimately converge in one direction: HR must move beyond being a function that operates systems and become a function that designs how the organization works.

    2026 HR Trend series articles

    This hub article is the starting point of a series that reframes SHRM’s 2026 HR trends as HR operating agendas for Korean companies. The following articles divide each issue into detailed topics.

    Read the HR Trend series together

    This article is part of the 2026 HR Trend series. Reading AI adoption, accountability lines, performance management, recruiting, upskilling, mixed workforces, Polywork, and employee experience together provides a more three-dimensional view of changes in the HR operating model.

    References

  • AI HR adoption makes compliance infrastructure a core variable in HR Tech investment

    AI HR adoption makes compliance infrastructure a core variable in HR Tech investment

    The figure of 97 HR Tech deals and $2.8 billion in Q1 2026 may look like just another investment headline. Yet the more important point in HR Executive’s June 15 report is not the deal value, but the shifting center of gravity in investment judgment. As AI agents enter recruiting, performance management, and workforce planning, HR systems become more than tools that process work quickly. They become systems that must leave evidence of “who approved an action, whether communication was appropriate, and which workflow created regulatory exposure.”

    The key issue for HR leaders is not the name of a particular vendor or report. It is the compliance infrastructure that can easily be missed when AIHR investment meetings compare only automation features: approval logs, bias audits, data flows, and accountability are moving into the center of the HR operating model.

    HR Tech buying criteria are moving from automation speed to accountability tracking

    According to the Norwest Venture Partners analysis introduced by HR Executive, Q1 2026 HR Tech activity reached 97 deals worth $2.8 billion. ADP’s acquisition of WorkForce Software was cited at $1.2 billion, and Workday’s acquisition of Sana at $1.1 billion. On the surface, these are major M&A headlines. From an HR perspective, the more important signal is that governance and compliance layers are becoming more important than automation features alone.

    As AI agents spread across HR workflows, the compliance surface expands with them. For HR leaders, the point to watch is not the number of automation features, but whether accountability can be traced. Once job posting drafts, candidate screening, performance feedback drafts, and workforce planning scenarios pass through AI, keeping only the final output is not enough. Who approved the tool and under what standard, where exceptions are recorded, and how far model output replaced human judgment become purchasing requirements.

    The risk of AI recruiting and performance tools does not end with the vendor contract

    The legal and regulatory risks around AI HR tools are also becoming more concrete. Companies using AI in recruiting, performance management, and workforce planning need to explain accountability across a patchwork of regional requirements and existing anti-discrimination principles. Colorado’s impact-assessment requirements for high-risk AI systems, Illinois restrictions on AI video interviews, and New York City’s bias audit requirement for automated employment decision tools all point in the same direction.

    Because these are U.S. rules, Korean companies should not read them as directly applicable obligations. Still, they leave a clear common question for HR operations. The fact that a company used a third-party AI tool does not automatically reduce the employer’s responsibility. The comment by Littler’s Britney Torres, reported by HR Executive, points in the same direction: courts may look at both AI-specific authority and general anti-discrimination law when judging responsibility for biased employment decisions.

    Korean HR teams should ask first about approval logs and data flows, not feature lists

    Compliance and HR service management are connected to work that is difficult to stop, such as employee relations case management, compliance training, and background screening. The discussion is limited to the U.S. HR Tech market, but it is still meaningful that these operational items appeared alongside the Q1 2026 flow of 97 deals and $2.8 billion. In an environment where AI agents affect HR decisions at volume, one missing log can later become an impossible-to-explain decision.

    When Korean companies apply this discussion, it is more practical to map internal data flows first than to memorize U.S. regulatory names. They need to identify which systems handle sensitive HR data such as candidate information, evaluation comments, manager feedback, training completion records, and performance ratings, and where AI recommends, summarizes, classifies, or executes actions. The Colorado impact-assessment example, Illinois AI video interview restrictions, and New York City bias audit requirement should be read less as domestic legal obligations and more as signals to turn approvers, change histories, exception handlers, retention periods, vendor access rights, and bias-check cycles into standard review items.

    The next HR Tech review meeting is already late if it starts with “what can we automate?”

    The questions HR Tech review meetings need to ask are direct: who authorized the action, whether the communication was appropriate, and whether the workflow created regulatory exposure. Those three questions change the order of the agenda in HR Tech adoption meetings. If the first question is “what work can we automate?”, the demo screen can look impressive. If the first question is “what judgment will we later have to explain, and where will the evidence remain?”, the vendor comparison table changes.

    In practice, four points deserve early review. First, does the system preserve evidence when a person modifies AI-recommended candidates, evaluations, or workforce placements? Second, can managers and HRBPs briefly record why they accepted or rejected an AI suggestion? Third, are there indicators and review cycles for detecting repeated disadvantage to a specific group? Fourth, does the vendor contract cover not only functional SLAs but also data retention, audit log provision, model-change notice, and incident response time? Ultimately, an expanding compliance surface means more points where approval, communication, and regulatory risk can arise.

    Public sources referenced
    – HR Executive, “Compliance tech is becoming a strategic priority, as AI expands in HR”, 2026-06-15. Read the referenced report
    – Google News RSS field collection, AIHR·HR Tech / labor and employment field. This material was used only as a supplementary collection signal for topic selection.