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

  • 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 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 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