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AIHR

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

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

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

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

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

    As AI adoption rises, recruiting accountability becomes broader

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

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

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

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

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

    When AI agents become team members, job definitions change first

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

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

    Skills-based hiring asks about judgment capability before technology names

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

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

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

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

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

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

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

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

    What HR should look at next

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

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

    References

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

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

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

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

    Expertise is narrowing from major to job-related experience

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

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

    Work experience becomes evidence of adaptability, not a specification

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

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

    AI recruiting requires prior notice and verification before efficiency

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

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

    Candidate experience now includes explainability in AI screening

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

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

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

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

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

    Even if hiring volume declines, selection difficulty does not fall

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

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

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

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

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

    What HR should look at next

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

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

    References

  • 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?

    What HR should look at next

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

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

    References: 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.

    What HR should look at next

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

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

    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?

    What HR should look at next

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

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

    References

  • [2026 HR Trend ⑤] Real-time upskilling: HRD must design the flow of work

    [2026 HR Trend ⑤] Real-time upskilling: HRD must design the flow of work

    This is the fifth article in the 2026 HR Trend series. If the fourth article argued that skills criteria must come before recruiting automation, this article addresses the next question. If it is difficult to hire enough of the needed skills from outside, what must HRD change?

    The answer is not to open more training courses. Upskilling in 2026 is not a training calendar, but a matter of work flow. Organizations need a system that helps employees identify and practice the skills they need at the moment they take on new work, at the moment goals change, and at the moment customer requirements shift.

    Upskilling becomes a talent acquisition strategy, not a training schedule

    The SHRM 2026 Talent Trends summary, based on a sample of more than 2,000 HR professional respondents, addresses hiring difficulties, retention, and skills shortages together. According to the public summary, about 70% of HR professionals struggle with full-time hiring, and 41% train current employees for roles that are difficult to fill.

    These figures show how HRD’s role is changing. Upskilling no longer means only the annual operation of courses by the training department. It becomes a talent acquisition strategy: how to develop internally the roles that are difficult to fill through external hiring. Therefore, training plans cannot be separated from recruiting plans, internal mobility, and performance management.

    Real-time learning begins where work changes occur

    The same Talent Trends summary explains that 42% of HR professionals experienced difficulty retaining full-time employees during the past 12 months. If it is hard both to hire and to retain people, organizations must create more sophisticated pathways for employees to move from their current work to the next role.

    Real-time upskilling is not about making every employee log into a learning platform every day. It is about pointing out needed skills at the point where work changes. Moments such as assignment to a new project, job rotation, development of promotion candidates, adoption of AI tools, and changes in customer response methods become the starting point of learning.

    HRD must move from course designer to workflow designer

    SHRM 2026 HR Trends connects AI use in 2026 to cost reduction, productivity improvement, and better workforce decisions. In addition, the sample of more than 2,000 HR professional respondents in 2026 Talent Trends shows hiring difficulties and skills shortages together. Applied to HRD, this perspective means AI does not remain merely a tool for recommending training content. It can become a signal for identifying which skills employees lack, what work experiences they need, and what feedback is being repeated.

    Therefore, HRD must move from course designer to workflow designer. Managing only course titles, training hours, and satisfaction scores is not enough to solve skills shortages. Core skills by role, work assignments, manager feedback, peer coaching, and internal project placement must be connected into one learning pathway.

    If performance management and upskilling are separated, learning will not lead to execution

    SHRM’s 2026 trend commentary suggests that AI coaching and People Analytics can change the flow centered on annual performance reviews. As seen in the third article, performance management in the age of AI coaching moves toward more frequent connections among goals, feedback, and development. Upskilling must also sit within this flow. If training completion records remain but are not connected to performance goals, learning will not lead to execution.

    Managers are closest to seeing what skills employees need. HRD should not translate this signal only into training courses, but connect it to work assignments and feedback loops. For example, if data analysis capability is lacking, the design should not end with taking an online course; it should include actual report writing, review, and improvement assignments.

    Korean companies should look first at skills application indicators, not training completion rates

    For a long time, HRD in Korean companies has treated training hours, completion rates, satisfaction, and statutory training compliance as important management indicators. These indicators are still necessary, but they are insufficient to explain the skills transition of 2026. What matters is whether employees used what they learned in their work.

    First, core skills by role must be defined. Next, each skill must be converted into behavioral criteria that can be observed in work. Then, over 30, 60, and 90 days after training, organizations should examine how actual work outputs and manager feedback have changed. HRD’s performance should be verified not inside the classroom, but in the workplace.

    The task for HRD in 2026 is not to secure more training content. It is to design the organization’s flow so that changes in work become the starting point of learning. Real-time upskilling is not a training program; it is a redesign of how work is done.

    2026 HR Trend series articles

    The upskilling article connects internal development and workflow design after hiring difficulties.

    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 State of the Workplace. It used the survey scopes confirmed in public summaries, including the sample of more than 2,000 HR professional respondents in Talent Trends and data from more than 1,800 HR professionals and more than 2,000 workers in State of the Workplace. 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 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.

    So, 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.

    Read the HR Trend series together

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

    What HR should look at next

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

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

    References

    This article was written based on SHRM’s 2026 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. So, job requirements, interview questions, work-sample assessments, onboarding, and training plans must use the same language.

    Recruiting teams and HRD must use the same skills language

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

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

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

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

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

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

    Read the HR Trend series together

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

    What HR should look at next

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

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

    References

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

  • [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.

    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.

    What HR should look at next

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

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

    References

    This article was written based on SHRM’s 2026 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. So, 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.

    So, 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.

    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.

    What HR should look at next

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

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

    References

    This article was written based on SHRM’s 2026 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.