HR Lens

AIHR

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    Before recruiting automation, skill criteria must be redefined

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

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

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

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

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

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

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

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

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

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

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

    Five things HR departments should check first in 2026

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

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

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

    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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    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.

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

  • AI Agent Work Transformation Is Shaking Up HR Data Approval Structures Again

    AI Agent Work Transformation Is Shaking Up HR Data Approval Structures Again

    In May 2026, the Work Trend Index item did not describe AI agents as mere work-assistance tools. Its core sentence is brief: when AI and agents take on execution, human agency expands. From an HR perspective, this is where the question divides. Before asking whether employees can do more work, HR must first ask whether it can leave a record of who approved which execution based on what data.

    When the actor executing work changes, approver records are the first thing to become unstable

    The latest annual Work Trend Index report item, published on 2026-05-05, uses the phrase “AI and agents take on execution.” It means that execution is moving in part from human hands to tools and agents. This shift is likely to spread first across work that HR already handled with data, such as writing job postings, classifying candidates, recommending training, and preparing performance conversations.

    For that reason, HR operating documents need at least 3 fields. First, the scope of work executed by the agent. Second, the point in time when a person approved it and the approver. Third, the procedure for reversing the outcome when it works to a person’s disadvantage. Without these 3 items, productivity improvement cases may remain, but the order of accountability becomes blurred.

    The format of surveys and observational studies makes HR ask about the reference month for metrics

    The Work Trend Index page describes this body of materials as research based on “global, industry-spanning surveys” and “observational studies.” It is also important that the 2024, 2025, and 2026 annual reports are arranged together. This is because the discussion of AI at work is not a one-time technology announcement, but a signal of a shift in ways of working that has continued for more than 3 years.

    HR data teams should recheck the reference month for their metrics here. To compare before and after AI adoption, the reference months must align for hiring lead time, training completion rates, internal mobility applications, and time spent writing performance feedback. If one department uses data after May 2026 while another uses standards from the timing of the June 2025 follow-up report, even the same dashboard will tell different stories.

    Audit logs are needed between personal information and People Analytics

    The public menu of the Personal Information Protection Commission separately lists items such as corporate policy, pseudonymization and combination of pseudonymized information, ISMS-P, and privacy impact assessment. This does not mean that these items immediately impose the same obligations on every HR AI tool. However, it is clear that when Korean companies handle People Analytics and AI automation together, they cannot avoid the language of personal information processing, security certification, and impact assessment.

    In practice, internal logs come before vendor contracts. HR must record which HR data entered the model input, who distinguished raw data from pseudonymized data, and when a person reviewed the recommendation results. In particular, for groups with a large impact on individuals, such as candidates, low performers, and targets of training recommendations, it is necessary to manage the data dictionary and approval records separately.

    Next quarter’s decisions will hinge more on exception handling than on the scope of adoption

    The question posed by the Work Trend Index is close to whether organizations are ready to seize this opportunity. In HR meetings, it is not enough to read this sentence only as a yes-or-no question about adoption. In AIHR reviews for the second half of 2026, the more difficult issue is not “how far to automate,” but “who will stop it when an exception occurs.”

    Four items should be placed on next quarter’s review sheet: the list of tasks AI agents will execute, tasks that must not proceed without human approval, data reference months and denominators, and channels for objections or requests for reconsideration. If these four fields are empty, AI adoption may look fast. In HR operations, however, records that can be retraced last longer than fast execution.

    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.

    Public materials referenced
    • Microsoft WorkLab, Work Trend Index
    • Personal Information Protection Commission policy, laws and corporate policy guidance

  • 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.

    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.

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