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AIHR

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

  • 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.

    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.

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