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

  • 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 ⑧] Burnout and Employee Experience: The Psychological Contract HR Must Rewrite

    [2026 HR Trend ⑧] Burnout and Employee Experience: The Psychological Contract HR Must Rewrite

    This is the eighth and final article in the 2026 HR Trend series. If the previous articles covered AI accountability lines, performance management, hiring, upskilling, hybrid workforces, and polywork, this article looks at how these changes converge in employee experience and burnout.

    The conclusion for HR in 2026 is simple. Organizations demand higher productivity and faster change, while employees demand better rewards, growth, flexibility, and respect. When this balance breaks down, employee experience deteriorates and burnout becomes recurring.

    Employee experience is not a benefits event; it is a psychological contract

    SHRM’s 2026 State of the Workplace summary addresses employee expectations and workplace issues based on data from more than 1,800 HR professionals and more than 2,000 workers. The survey population and sample become a starting point for discussing employee experience because they show both HR practitioners’ observations and worker respondents’ perspectives. SHRM’s public summary states that 72% of HR professionals recognize rising employee expectations of employers, showing that employee experience is not a matter of benefits programs but of aligning expectations between the organization and employees.

    The psychological contract is different from the employment contract written in documents. Employees ask, “What do I gain if I work hard in this organization?” Organizations ask, “How will employees keep up with the performance and change we require?” Employee experience is where these two questions meet.

    Burnout is not an individual resilience problem; it is a work-design problem

    SHRM’s 2026 Talent Trends summary states that about 70% of HR professionals have difficulty hiring full-time employees, and 42% experienced difficulty retaining full-time employees over the past 12 months. In an environment where staffing is difficult and attrition risk rises, work is likely to concentrate on the employees who remain.

    If burnout is explained only as a lack of individual resilience, the solution becomes narrow. Meditation apps, well-being campaigns, and encouraging vacations may be necessary, but if actual workloads and priorities do not change, their effects are limited. HR must look at workload, role expectations, manager feedback, and workforce planning together.

    As AI raises productivity, the manager’s role becomes more important

    SHRM 2026 HR Trends states that 89% of CEOs expect AI to redefine how organizations create and capture value. At the same time, SHRM explains that AI is connected to cost, risk, productivity, and better workforce decisions. As AI increases the speed of work, employees may be asked for more output and faster responses.

    Therefore, employee experience in the AI era is determined not by the rate of technology adoption but by manager behavior. If managers cannot clarify priorities, AI becomes not a tool that reduces work but a pressure to process more work faster. Conversely, if managers clarify goals, expectations, feedback, and standards for rest, AI can become a tool that reduces employee burden.

    Hybrid workforces and polywork shake the boundaries of employee experience

    SHRM 2026 HR Trends shows Workforce Fragmentation, the increased use of independent contractors, gig workers, and freelancers, and the trend of employees holding two income sources together. In particular, the figure that 72% of CEOs expect increased use of independent contractors, gig workers, and freelancers in 2026 shows that the boundaries of employee experience cannot remain only inside the full-time workforce.

    When connected to SHRM’s “Employees Work Harder, Smarter… and Collect Two Pay Checks” trend, employee experience becomes more complex. Full-time employees collaborate with external experts, use AI tools, and at times do other work outside the company themselves. In this context, organizational culture is revealed not through office events but through collaboration rules, information access rights, performance accountability, and conflict-of-interest standards. Employee experience is no longer only “the experience inside our company”; it expands into “the experience of work connected to our organization.”

    Korean companies must rewrite employee experience as a performance contract

    When Korean companies redesign employee experience in 2026, the starting point is not to increase the number of benefits items. They must examine whether the performance the organization demands, the speed of change, the learning burden, and the mode of collaboration are in balance with the rewards, growth opportunities, flexibility, and manager support provided to employees.

    In practice, three questions are needed. First, what more is our organization asking of employees? Second, what more are we providing in line with those demands? Third, in which roles and under which managers is the imbalance between demands and provision growing? If HR cannot answer these questions, employee experience is reduced to managing survey scores, and burnout remains an individual problem.

    The core of the 2026 HR Trend series ultimately converges into one point. AI, performance management, hiring, upskilling, external workforces, and polywork are not separate issues. They are signals that organizations must redesign how work gets done. HR should not be a department that simply creates more systems; it should take on the role of rewriting the contract between the performance the organization demands and the conditions under which employees can work sustainably.

    2026 HR Trend series articles

    The final article synthesizes the previous seven topics from the perspective of employee experience and the psychological contract.

    Read the HR Trend series together

    This article is part of the 2026 HR Trend series. Reading AI adoption, accountability lines, performance management, hiring, upskilling, hybrid 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 State of the Workplace, 2026 HR Trends, and commentary on 2026 HR trends. The body connects the survey scope of more than 1,800 HR professionals and more than 2,000 workers in SHRM’s 2026 State of the Workplace summary, the 72% of HR professionals who recognize rising employee expectations of employers, and the AI and Workforce Fragmentation trends in SHRM 2026 HR Trends. Because burnout, mental health, and labor-risk issues can vary depending on organizational context and legal systems, this article should be read as an HR operating interpretation rather than medical or legal advice.

  • [2026 HR Trend ⑦] Polywork and the Rise of Side Work: Redesigning Rewards and Engagement Strategy

    [2026 HR Trend ⑦] Polywork and the Rise of Side Work: Redesigning Rewards and Engagement Strategy

    This is the seventh article in the 2026 HR Trend series. If the sixth article examined the limits of full-time-employee-centered HR, this article looks at how individual employees’ ways of working are changing. Polywork, side work, and side projects are no longer exceptions limited to a few occupations.

    The issue cannot be reduced to “whether to allow or prohibit side jobs.” In an era when employees have multiple income streams and multiple roles, organizations must design rewards competitiveness, engagement, conflicts of interest, information security, and performance criteria together.

    Side work is not personal misconduct; it is a rewards signal

    SHRM 2026 HR Trends presents the trend “Employees Work Harder, Smarter… and Collect Two Pay Checks.” The phrase shows that, on the 2026 HR agenda, employees are being asked to achieve higher productivity while also seeking additional income sources. The fact that SHRM addresses both the productivity effects and the costs and risks of AI on the same trends page also suggests that this shift is not only an individual choice but an organizational operating issue.

    Employees take side jobs for many reasons. Cost-of-living pressure, an uncertain employment environment, a lack of growth opportunities, and the desire to test their expertise in the market are all intertwined. When viewed together with SHRM’s 2026 State of the Workplace summary, which covers employee expectations and organizational issues based on data from more than 1,800 HR professionals and more than 2,000 workers and states that 72% of HR professionals recognize rising employee expectations of employers, side jobs should be read as signals about rewards, growth, and employee experience. If HR treats all of this only as problematic behavior, it misses the cause. Conversely, if it leaves the issue unmanaged without any standards, the risks of lower performance, conflicts of interest, and information leakage may grow.

    In the polywork era, the key questions are engagement and conflicts of interest

    The Workforce Fragmentation trend in SHRM 2026 HR Trends shows a shift in which work outside the organization and work inside the organization are becoming more loosely connected. The figure that 72% of CEOs expect increased use of independent contractors, gig workers, and freelancers in 2026 suggests that the external labor market is moving more deeply into organizational operations.

    This trend also affects full-time employees. Inside the company, an employee is a member of the organization; outside the company, the same person may be a freelancer, creator, instructor, adviser, or online seller. HR’s core question is not “Does the employee have a side job?” but “Does that activity conflict with the performance of the primary job, the company’s interests, or customer information?”

    Total Rewards becomes the design of choices, not a salary table

    SHRM’s 2026 State of the Workplace summary addresses employee expectations and organizational issues based on data from more than 1,800 HR professionals and more than 2,000 workers. The 72% of HR professionals in SHRM’s public summary who recognize rising employee expectations shows that rewards cannot be explained by wage levels alone.

    Total Rewards in the polywork era is not limited to a bundle of base pay, incentives, and benefits. Flexible work, growth opportunities, financial well-being, recognition, career mobility, and psychological safety work together. If employees are seeking additional income and opportunities outside the company, HR must examine not only the salary table but the total value employees gain inside the organization.

    As AI lowers the barrier to side jobs, policies must change as well

    SHRM states that 89% of CEOs expect AI to redefine how organizations create and capture value in 2026. AI raises productivity in the primary job while also lowering the barrier to side jobs. Content creation, data analysis, document drafting, training-material development, and online sales operations can be started with less time and cost than before.

    Therefore, existing concurrent-employment policies must be reviewed. External activities during working hours, the use of company devices and accounts, the use of company data, transactions with competitors or clients, and paid activities similar to one’s company role each require different standards. Even if outputs are produced with AI, risks grow when company materials or customer information are mixed in.

    Korean companies should define judgment criteria before prohibition clauses

    The easiest approach for Korean companies dealing with side jobs and polywork is to strengthen prohibition clauses. But as the trends in SHRM 2026 HR Trends show, employees’ external activities and multiple income streams are moving in a broader direction. A simple ban makes actual behavior difficult to understand and may instead increase hidden risks.

    HR must define at least four judgment criteria. First, does the activity infringe on primary-job performance and working time? Second, is it connected to the company’s trade secrets, personal information, or customer information? Third, is there a conflict of interest with competitors, clients, or partners? Fourth, does it affect the company’s reputation and job ethics? These criteria should be operated together with work rules, security policies, performance management, and manager training.

    Ultimately, the spread of polywork and side jobs is not a simple story about weaker employee loyalty. It means an era has arrived in which the rewards and growth opportunities an organization provides to employees are compared with other choices in the market. HR should not see side jobs only as a hidden problem; it should read them as a signal to revisit rewards strategy and engagement strategy.

    2026 HR Trend series articles

    The polywork article examines the point where the hybrid workforce trend extends into employees’ individual rewards and engagement issues.

    Read the HR Trend series together

    This article is part of the 2026 HR Trend series. Reading AI adoption, accountability lines, performance management, hiring, upskilling, hybrid 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 public materials for 2026 HR Trends and the 2026 State of the Workplace. The body connects SHRM 2026 HR Trends’ “Employees Work Harder, Smarter… and Collect Two Pay Checks,” Workforce Fragmentation, and AI-related trends, and uses the survey scope of more than 1,800 HR professionals and more than 2,000 workers in SHRM’s 2026 State of the Workplace summary as the basis for interpreting employee expectations and Total Rewards. Judgments about side jobs, concurrent employment, discipline, and conflicts of interest may vary by national law and each company’s work rules, so this article should be read not as legal advice but as operating criteria from an HR perspective.

  • [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.

  • Deloitte 2026 Human Capital Trends: AI performance debate shifts to HR operating-model redesign

    Deloitte 2026 Human Capital Trends: AI performance debate shifts to HR operating-model redesign

    Deloitte Insights’ 2026 Global Human Capital Trends shifts the AI discussion away from technology purchasing or productivity tools and toward the redesign of work. One finding is especially hard for HR to ignore: among the 100 C-suite leaders surveyed, 59% take a technology-centered approach to AI, and those organizations are 1.6 times more likely than human-centered organizations to fail to achieve AI investment returns that exceed expectations. In other words, AI performance is determined less by adoption rates than by the structure of work.

    A 59% technology-centered approach exposes the blank spaces in AI investment review sheets

    In Deloitte’s survey of 100 C-suite leaders, 59% of organizations approach AI from a technology-centered perspective. The same source explains that technology-centered organizations are 1.6 times more likely than human-centered organizations to fall short of AI investment returns that exceed expectations. This figure is not simply a warning label in AIHR budget reviews. It is a signal that performance measurement itself is incomplete unless organizations ask how purchased tools will change work judgment, approvals, collaboration, and learning.

    HR therefore needs to change its AI adoption review sheet. Comparing only feature lists and license costs is not enough. The same table should include the roles that will use the tool, data access rights, reviewers of outputs, error-reporting methods, training audiences, and whether performance indicators will change. The 1.6-times figure points not only to the technology team’s performance but also to HR’s responsibility for operating-model design.

    Advantage comes from real-time orchestration of people, skills, and data, not static placement

    Deloitte’s original report explains that as AI accelerates work, competitive advantage is moving from static talent allocation to the real-time orchestration of people, skills, data, and technology. This sentence is about a change in operating rhythm rather than an organizational-chart redesign. Annual workforce planning, semiannual capability diagnostics, and quarterly training applications alone cannot keep pace with changing work demand.

    In HR practice, the first thing to check is the refresh cycle for skills data. HR should examine which roles use which tools, whether internal mobility candidates can be identified within days when new work emerges, and whether project staffing is captured in performance management and learning records. Orchestrating people, skills, and data in real time is a demand to change data quality and decision-making cycles before introducing another platform.

    HR functions are reassembled as outcome-centered capability bundles, not silos

    The report says traditional functions such as HR, finance, and IT are slow and siloed for today’s business needs. The same section also raises the need to deconstruct and reassemble functions into outcome-centered capabilities. From HR’s perspective, this means that a model in which recruiting, learning, performance, and HRIS teams each execute only their own annual plans may clash with how work changes in the AI era.

    For example, if an organization introduces customer-service AI, recruiting cannot look only for prompt-writing experience. Learning also cannot stop at teaching people how to use the tool. Performance management must decide how to evaluate AI-generated drafts and human-revised judgment. HRIS must retain logs and permissions data. If function-specific KPIs remain unchanged, one side of the organization will accelerate adoption while another handles risk after the fact.

    Continuous learning is not a training course but adaptive capability inside the flow of work

    Deloitte views traditional change management and training as potentially too slow to match the adaptation speed required of organizations and employees. The original report also adds that AI brings learning, adaptation, and skill application into the flow of work. This point expands HRD’s role from managing training application or completion rates to managing learning data generated while work is being done.

    At the next quarterly HR meeting, three metrics are worth asking about. First, after an AI-related work change occurs, within how many days is the training content for that role updated? Second, is data captured on the guidance, coaching, and review procedures employees actually use in their work? Third, are new skills reflected in performance reviews and internal mobility decisions? The core message of 2026 Human Capital Trends is not to buy more AI. It is about how quickly organizations redesign the way people make judgments, learn, and collaborate.

    Public sources referenced
    Deloitte Insights, 2026 Global Human Capital Trends.