Adaptive Capacity in the Age of Artificial Intelligence: A Critical Extension of Workforce Resilience Frameworks to Human Resource Management Functions

1. Introduction

The accelerating integration of artificial intelligence into organizational processes has generated substantial scholarly and practitioner attention regarding the future of work (Brynjolfsson & McAfee, 2014; Autor, 2015; Acemoglu & Restrepo, 2019). A growing body of research estimates the degree of AI exposure across occupations, identifying which jobs contain tasks that AI systems possess capabilities to perform (Brynjolfsson et al., 2018; Webb, 2020; Felten et al., 2023; Eloundou et al., 2023). These exposure measures have revealed a counterintuitive pattern: higher-income, highly educated occupations often face the greatest exposure to current AI capabilities, particularly large language models (LLMs), challenging earlier assumptions that automation primarily threatens routine manual labor (Autor et al., 2003; Autor & Dorn, 2013).

However, exposure alone provides an incomplete picture of workforce vulnerability. As Manning and Aguirre (2026) observe, “exposure may often be a necessary but not sufficient condition for AI-driven displacement” (p. 2). Their innovative adaptive capacity framework shifts analytical attention from mere technological exposure toward understanding which workers possess characteristics that enable successful navigation of job transitions should displacement occur. By constructing an occupation-level index incorporating net liquid wealth, skill transferability, geographic density, and age, they demonstrate that AI exposure and adaptive capacity are positively correlated (r = 0.502), suggesting that many highly exposed workers possess relatively strong means to manage job transitions.

This finding carries significant implications but also raises important questions requiring further investigation. Chief among these is the recognition that occupation-level aggregation may obscure critical heterogeneity within broad occupational categories. Nowhere is this concern more salient than in human resource management and people management functions, which occupy a uniquely paradoxical position in the AI transformation landscape. HR professionals are simultaneously: (a) responsible for facilitating organizational workforce adaptation to technological change; (b) positioned in occupations with substantial AI exposure themselves; and (c) distributed across roles with vastly different task compositions, skill requirements, and adaptive capacity characteristics.

The present article addresses this gap by critically reviewing and extending the adaptive capacity framework with specific application to HR and people management functional areas. Our contribution is fivefold. First, we provide a critical appraisal of the Manning and Aguirre (2026) framework, identifying both its theoretical advances and limitations requiring refinement. Second, we disaggregate HR occupations into distinct functional clusters and analyze their differential exposure-capacity profiles, revealing patterns obscured by occupation-level aggregation. Third, we propose a multi-level adaptive capacity model grounded in strategic HRM theory that integrates factors operating at individual, occupational, organizational, and institutional levels. Fourth, drawing on paradox theory, we develop a theoretically grounded analysis of how HR professionals navigate the tension between facilitating organizational transformation and experiencing their own occupational disruption. Fifth, we examine equity implications of differential adaptive capacity, considering how AI-driven transformation may interact with existing workforce inequities.

This interdisciplinary analysis speaks to multiple scholarly communities. For labor economists, we extend task-based frameworks (Autor et al., 2003; Acemoglu & Autor, 2011) by incorporating organizational and institutional moderators of technological displacement. For management scholars, we bridge macro-level labor market analyses with micro-level human capital and strategic HR literatures, integrating resource-based and ability-motivation-opportunity perspectives. For public policy researchers, we illuminate considerations relevant to workforce development, reskilling investments, and social insurance design in the context of AI-driven occupational transformation.

The article proceeds as follows. Section 2 presents a critical review of the adaptive capacity framework, situating it within broader theoretical traditions while identifying limitations. Section 3 develops our extension to HR functional areas, presenting a typology of HR roles, analyzing their differential vulnerability profiles, and examining the HR paradox through the lens of paradox theory. Section 4 proposes the multi-level adaptive capacity model grounded in strategic HRM theory. Section 5 addresses equity and inclusion implications. Section 6 discusses implications for HR practice, organizational strategy, and public policy. Section 7 concludes with directions for future research.

2. Critical Review of the Adaptive Capacity Framework

2.1. Theoretical Foundations and Conceptual Advances

The adaptive capacity framework introduced by Manning and Aguirre (2026) represents a significant conceptual advance in understanding AI’s labor market implications. Their framework builds upon the task-based approach pioneered by Autor et al. (2003), who reconceptualized occupations as bundles of tasks that can be allocated between human labor and technological capital. This perspective was further developed by Acemoglu and Autor (2011) and Acemoglu and Restrepo (2019), who identified three primary mechanisms through which technology affects labor demand: displacement effects (substitution for human labor), scale effects (productivity-driven output expansion), and reinstatement effects (creation of new tasks requiring human comparative advantage).

What distinguishes the adaptive capacity framework is its explicit recognition that exposure measures alone cannot predict welfare consequences for workers. As the authors note, “the negative impacts of displacement are not borne evenly across all affected workers” (Manning & Aguirre, 2026, p. 5). This insight draws upon a substantial empirical literature documenting heterogeneous displacement costs across worker characteristics (Jacobson et al., 1993; Sullivan & Von Wachter, 2009; Couch & Placzek, 2010).

The framework’s four components—net liquid wealth, skill transferability, geographic density, and age—each possess theoretical grounding in the displacement literature:

Net liquid wealth addresses consumption smoothing during job search. Chetty (2008) demonstrated that liquidity constraints force workers to accept suboptimal job matches prematurely, and subsequent research has confirmed that low liquid wealth constrains job search decisions and may compel workers to accept positions below their productive potential. The framework’s use of log-transformed median net liquid wealth by occupation captures this buffer against income shocks.

Skill transferability reflects human capital portability across occupations. Building on Gathmann and Schönberg’s (2010) skill distance measures and Eggenberger et al.’s (2022) analysis of general versus occupation-specific training returns, the framework measures cosine similarity between occupation skill profiles weighted by destination occupation growth projections. This growth-weighting innovation recognizes that transferability value depends not merely on skill similarity but on labor demand in potential destination occupations.

Geographic density captures labor market thickness effects. Bleakley and Lin (2012) demonstrate that thick labor markets reduce occupational switching costs, enabling displaced workers to remain in roles matching their existing skills. Research consistently shows that larger labor markets improve reemployment outcomes, particularly for workers with specialized human capital, by providing more potential job matches within commuting distance.

Age addresses the well-documented challenges older workers face in displacement recovery. Farber (2017) documents substantially lower reemployment rates for workers aged 55-64 compared to younger cohorts, while broader research on displaced older workers shows persistent employment losses and earnings declines. The framework incorporates the fraction of workers aged 55 or older as a negative contributor to adaptive capacity.

2.2. Methodological Contributions

Beyond its theoretical integration, the framework offers methodological innovations meriting recognition. The authors harmonize multiple national datasets—including the Survey of Income and Program Participation (SIPP), American Community Survey (ACS), Occupational Employment and Wage Statistics (OEWS), Bureau of Labor Statistics employment projections, Lightcast geographic data, O*NET skill databases, and AI exposure measures from Eloundou et al. (2023)—into a unified occupational taxonomy covering 356 occupations representing 95.9% of the U.S. workforce.

The composite index construction follows established guidelines for composite indicators (OECD & European Commission, Joint Research Centre, 2008), employing employment-weighted z-scores with winsorization to reduce outlier influence, equal weighting across components, and transformation to employment-weighted percentile rankings. The authors’ robustness testing across 57 alternative specifications—varying transferability measures, normalization methods, age measures, geographic density inclusion, and aggregation methods—demonstrates that core findings persist across methodological choices.

Particularly noteworthy is the growth-weighted skill transferability calculation, which advances beyond static skill distance measures. This formulation recognizes that skill transferability’s value depends on labor demand trajectories in potential destination occupations—a dynamic consideration absent from earlier static approaches. By weighting skill similarity scores by projected employment growth in destination occupations, the framework captures not just where workers could transition but where viable opportunities are likely to exist.

2.3. Key Empirical Findings

The Manning and Aguirre (2026) framework yields several empirical findings with significant implications for understanding workforce vulnerability to AI-induced displacement.

First, and perhaps most notably, the analysis reveals a positive correlation (r = 0.502) between AI exposure and adaptive capacity at the occupation level. This finding challenges narratives suggesting that AI will predominantly harm already-vulnerable workers. Instead, many occupations facing high AI exposure—including management, business and financial operations, and computer and mathematical occupations—also exhibit high adaptive capacity due to their workers’ greater financial resources, more transferable skills, and concentration in thick labor markets.

Second, the framework identifies a concerning bifurcation within white-collar work. While professional and managerial occupations tend to exhibit the favorable combination of high exposure with high adaptive capacity, clerical and administrative support occupations demonstrate high exposure coupled with low adaptive capacity. Office clerks, administrative assistants, and similar roles face substantial AI exposure while their workers possess limited financial buffers, skills that primarily transfer to other declining administrative occupations, and older age profiles that may impede reemployment.

Third, the analysis reveals that healthcare support occupations occupy a uniquely favorable position, exhibiting the lowest AI exposure coupled with high adaptive capacity. This pattern reflects both the physical and interpersonal nature of healthcare support tasks (difficult for current AI to perform) and the strong projected growth in healthcare employment creating robust demand for transferable skills.

Fourth, geographic variation in adaptive capacity proves substantial. Workers in metropolitan areas with dense labor markets demonstrate higher adaptive capacity than those in rural or smaller urban areas, even within the same occupations, due to the greater availability of alternative employment opportunities.

2.4. Limitations and Areas Requiring Extension

Despite these contributions, several limitations warrant critical attention and motivate our extension to HR functional areas.

2.4.1. Occupation-Level Aggregation and Within-Occupation Heterogeneity

The framework’s most significant limitation is its occupation-level unit of analysis. Manning and Aguirre (2026) acknowledge that “occupation-level aggregation masks within-occupation heterogeneity in adaptive capacity” (p. 20), but do not systematically address this constraint. Within broad occupational categories, workers vary substantially in task composition, skill levels, organizational contexts, and individual characteristics affecting adaptive capacity.

This limitation proves particularly consequential for HR and people management functions, where occupational categories encompass vastly different roles. The Standard Occupational Classification (SOC) code 13-1071 (Human Resources Specialists) includes both transactional administrators processing paperwork and HR business partners engaged in strategic workforce planning. Similarly, SOC 11-3121 (Human Resources Managers) spans operational supervisors overseeing benefits administration and Chief Human Resource Officers shaping enterprise talent strategy. Aggregating these diverse roles obscures precisely the variation most relevant for understanding AI’s differentiated impact on HR functions.

2.4.2. Absence of Organizational-Level Factors

The framework focuses exclusively on individual and occupational characteristics, omitting organizational factors that may substantially influence displacement likelihood and adaptive capacity. Organizational characteristics—including firm size, industry, technological maturity, HR practices, and internal labor market structures—shape both the pace of AI adoption and workers’ access to reskilling resources, internal mobility pathways, and transition support.

This omission proves significant for HR functions specifically. HR professionals embedded in organizations with robust learning and development infrastructure, internal talent mobility programs, and strategic workforce planning capabilities likely possess greater adaptive capacity than counterparts in organizations lacking such resources—regardless of their individual or occupational characteristics. The strategic HRM literature, including the resource-based view (Barney, 1991; Wright et al., 2001) and ability-motivation-opportunity framework (Appelbaum et al., 2000; Jiang et al., 2012), provides theoretical resources for understanding how organizational practices shape workforce capabilities that the current framework does not incorporate.

2.4.3. Static Measurement of Dynamic Processes

The framework provides a point-in-time snapshot of adaptive capacity but does not model how capacity evolves through workforce development investments, career progression, or changing labor market conditions. This static approach may underestimate adaptive capacity for workers in occupations or organizations actively investing in skill development, while overestimating capacity for those in stagnating contexts.

For HR professionals, this limitation interacts with the paradox that HR functions are simultaneously subject to AI transformation and responsible for facilitating organizational adaptation. HR practitioners who successfully develop AI-related competencies may substantially enhance their adaptive capacity, while those who do not may find their positions increasingly vulnerable despite strong baseline characteristics.

Furthermore, AI capabilities and adoption trajectories are rapidly evolving. Exposure assessments based on current AI capabilities may not reflect conditions in five or ten years as technology advances. The relationship between exposure and capacity may also be endogenous over time—workers in high-exposure roles may anticipate displacement and invest in capacity enhancement, while organizations may invest more heavily in adaptive capacity infrastructure for high-exposure functions.

2.4.4. Limited Treatment of Institutional Factors

The framework incorporates geographic density as a labor market thickness proxy but does not systematically address institutional factors—including professional credentialing systems, union representation, occupational licensing, and social insurance provisions—that shape both displacement risk and recovery trajectories.

For HR professionals, institutional factors carry particular salience. Professional certifications from organizations such as the Society for Human Resource Management (SHRM) and HR Certification Institute (HRCI) may signal competencies relevant to post-displacement job matching. Conversely, the relative absence of licensure requirements (compared to other professional occupations) may reduce barriers to entry while simultaneously increasing competitive pressure from adjacent occupations and AI systems.

2.5. Summary: Advancing the Framework

The adaptive capacity framework represents a meaningful advance in understanding differential workforce vulnerability to AI-induced displacement. However, its occupation-level focus, absence of organizational and institutional factors, and static measurement approach limit its applicability to contexts characterized by substantial within-occupation heterogeneity and organizational variation—precisely the conditions characterizing HR and people management functions. The following sections develop our extension addressing these limitations.

3. Extending the Framework to Human Resource Management Functions

3.1. The Strategic Position of HR in AI Transformation

Human resource management occupies a distinctive position in organizational AI adaptation that warrants specialized analytical attention. This distinctiveness stems from three interrelated factors.

First, HR functions face substantial AI exposure in their own task domains. Research on LLM capabilities identifies HR Specialists among occupations with moderate-to-high exposure, reflecting the text-intensive nature of HR work including policy documentation, employee communications, recruiting correspondence, and compliance reporting (Eloundou et al., 2023). Empirical evidence from AI adoption surveys indicates growing utilization of AI tools for resume screening, candidate assessment, employee onboarding, benefits administration, and performance management analytics (Tambe et al., 2019). Recent research specifically examining AI applications in HR documents expanding use cases across the employee lifecycle, from talent acquisition through performance management and workforce planning (Cheng & Hackett, 2021).

Second, HR functions bear primary organizational responsibility for workforce adaptation to technological change. Strategic HR practices including workforce planning, talent acquisition, learning and development, and organizational design directly shape organizational capacity to navigate technological disruption (Wright & McMahan, 1992; Becker & Huselid, 2006). This responsibility places HR practitioners at the center of AI transformation initiatives while simultaneously positioning them as potential subjects of such transformation.

Third, HR functions exhibit pronounced internal heterogeneity that occupation-level analysis obscures. The evolution of HR from primarily administrative personnel functions toward strategic business partnership has created a functional landscape encompassing vastly different roles, competencies, and organizational positions (Ulrich, 1997; Ulrich & Dulebohn, 2015). This heterogeneity generates differential exposure-capacity profiles within the HR domain requiring disaggregated analysis.

3.2. HR Technology Transformation: Historical Context and Current Trajectory

To contextualize our analysis, we briefly review the trajectory of HR technology transformation, drawing on research examining how technological change has reshaped HR work.

HR functions have experienced multiple waves of technology-driven transformation. The introduction of HR information systems (HRIS) in the 1980s and 1990s automated record-keeping and transaction processing, enabling efficiency gains but also displacing clerical positions (Bondarouk & Ruël, 2009). The subsequent development of HR shared services models in the 2000s consolidated transactional HR activities into centralized units, often leveraging technology platforms to standardize and streamline processes (Cooke et al., 2005; Farndale et al., 2009). These transformations provided lessons relevant to current AI-driven change: affected workers faced displacement pressure, but those who developed new competencies (e.g., HRIS administration, shared services management) often found opportunities within transformed functions.

The current wave of AI-driven transformation differs in scope and nature. Unlike earlier technologies that primarily automated structured, rule-based tasks, AI systems—particularly large language models—demonstrate capabilities in unstructured text interpretation, nuanced communication, and judgment-requiring analysis (Eloundou et al., 2023). Research on AI in HR identifies expanding applications including: intelligent resume screening and candidate matching; conversational AI for employee inquiries and onboarding; predictive analytics for turnover, performance, and workforce planning; natural language processing for sentiment analysis and engagement measurement; and automated generation of job descriptions, policies, and communications (Tambe et al., 2019; Cheng & Hackett, 2021).

These capabilities expose a broader range of HR tasks to potential automation than previous technologies, extending into professional and analytical work previously considered automation-resistant. However, research also identifies significant implementation challenges—including data quality, algorithmic bias, employee acceptance, and regulatory constraints—that may moderate displacement effects (Tambe et al., 2019). Understanding current AI capabilities and limitations informs our assessment of differential exposure across HR role clusters.

3.3. A Typology of HR Functional Roles

To enable disaggregated analysis, we develop a typology of HR functional roles drawing on Ulrich’s (1997) HR role model and subsequent refinements (Ulrich & Brockbank, 2005; Ulrich et al., 2012). While various role taxonomies exist in the HR literature, Ulrich’s framework has achieved substantial practitioner adoption and provides useful analytical categories for our purposes.

We acknowledge that Ulrich’s model has been subject to scholarly critique. Caldwell (2003) questioned whether the model reflects empirical reality or aspirational positioning, finding that many HR professionals struggle to enact strategic partner roles. Pritchard (2010) argued that the model oversimplifies HR role complexity and underestimates tensions between different role demands. Keegan and Francis (2010) critiqued the model’s managerialist assumptions and its potential to marginalize employee advocacy dimensions of HR work. We incorporate these critiques by: (a) treating role categories as analytical ideal types rather than descriptions of actual positions; (b) acknowledging role hybridization and boundary permeability in practice; and (c) recognizing that organizational context shapes which role configurations are feasible.

We identify four primary role clusters with distinct task compositions and, consequently, differential AI exposure and adaptive capacity profiles.

3.3.1. Transactional HR Administration

Transactional HR administration encompasses roles focused on processing standardized HR transactions including payroll processing, benefits enrollment, employee record maintenance, leave administration, and compliance documentation. These roles correspond to portions of SOC categories including Human Resources Specialists (13-1071), Payroll and Timekeeping Clerks (43-3051), and Human Resources Assistants (43-4161).

Task composition is characterized by high routine cognitive intensity: applying established rules to structured inputs, verifying data accuracy, and executing standardized procedures. Following the task-based framework (Autor et al., 2003; Autor & Dorn, 2013), these routine cognitive tasks have historically been susceptible to computerization and now face substantial LLM exposure. Natural language processing capabilities enable AI systems to interpret policy documents, respond to employee inquiries, process benefits changes, and verify compliance status with minimal human intervention.

O*NET data for relevant SOC codes confirms this task profile. Human Resources Assistants (43-4161) shows high importance ratings for tasks including “Maintain records of personnel-related data,” “Process paperwork for new employees,” and “Explain company personnel policies”—precisely the structured, rule-based activities amenable to AI automation. Eloundou et al. (2023) assign moderate-to-high exposure scores to clerical and administrative support occupations based on task overlap with LLM capabilities.

Applying the adaptive capacity framework components, transactional HR roles likely exhibit:

• Net liquid wealth: Below-median, reflecting lower compensation levels for administrative versus professional roles. Bureau of Labor Statistics data shows median annual wages for Human Resources Assistants (43-4161) at approximately $\mathrm{45,000},substantiallybelowthe$ 67,000 median for Human Resources Specialists (13-1071) overall.
• Skill transferability: Moderate to low, as skills primarily transfer to other declining administrative occupations. This pattern is consistent with Manning and Aguirre’s (2026) finding that clerical workers’ skills transfer primarily to other administrative roles facing similar automation pressure.
• Geographic density: Variable, dependent on location, with potential concentration in headquarters locations but also distribution across organizational facilities.
• Age: Potentially skewing older, as administrative roles may accumulate longer-tenured workers, though empirical data on age distribution by HR role cluster is limited.

The Manning and Aguirre (2026) findings regarding clerical and administrative support occupations—which demonstrate high AI exposure coupled with low adaptive capacity—likely extend to transactional HR administration, placing these roles in the high-vulnerability category.

3.3.2. HR Service Delivery and Employee Relations

HR service delivery and employee relations encompasses roles serving as primary interfaces between HR functions and employee populations. These roles handle escalated inquiries, interpret policies for complex situations, mediate workplace conflicts, conduct investigations, and facilitate employee lifecycle transitions including onboarding, transfers, and separations. Portions of Human Resources Specialists (13-1071) and Training and Development Specialists (13-1151) fall within this cluster.

Task composition combines routine elements (delivering standardized communications, processing standard requests) with non-routine interpersonal dimensions (navigating emotionally sensitive situations, exercising judgment in policy interpretation, building trust relationships with employees). This task heterogeneity generates mixed AI exposure—some tasks are highly susceptible to automation while others require human judgment, empathy, and contextual sensitivity that current AI systems cannot fully replicate.

O*NET task data for Human Resources Specialists (13-1071) reveals this heterogeneity. Tasks such as “Interpret and explain human resources policies” and “Address employee relations issues” contain elements automatable by AI (policy explanation) alongside elements requiring human judgment (navigating sensitive interpersonal dynamics). Research on AI limitations identifies social-emotional intelligence, contextual judgment, and trust-building as capabilities where humans retain comparative advantage (Frey & Osborne, 2017; Autor, 2015).

Adaptive capacity components likely show:

• Net liquid wealth: Near-median, reflecting professional compensation above administrative levels but below managerial tiers.
• Skill transferability: Moderate, as interpersonal and conflict resolution skills transfer across service delivery roles and industries, including customer service management, employee assistance, and social services.
• Geographic density: Moderate, with roles distributed across organizational locations to provide employee access.
• Age: Balanced distribution across career stages, as service delivery roles serve as both entry points and mid-career positions.

This role cluster occupies an intermediate position—neither in the high-vulnerability category (high exposure/low capacity) nor the fully protected category (low exposure/high capacity).

3.3.3. HR Business Partnership and Strategic Advisory

HR business partnership and strategic advisory encompasses roles serving as strategic partners to business unit leaders, translating organizational strategy into talent implications, and consulting on workforce planning, organizational design, and change management. These roles correspond to senior Human Resources Specialists (13-1071), Human Resources Managers (11-3121), and portions of Management Analysts (13-1111) engaged in HR consulting.

Task composition emphasizes non-routine analytical and interpersonal competencies: interpreting ambiguous organizational dynamics, building trust relationships with senior leaders, facilitating strategic dialogue, and synthesizing complex information into actionable recommendations. While AI tools may augment analytical capabilities (e.g., workforce analytics, scenario modeling), core tasks involve contextual judgment, political navigation, and relationship management less susceptible to full automation.

The HR architecture literature (Lepak & Snell, 1999, 2002) provides theoretical grounding for understanding these roles’ strategic value. HR business partners engage in work characterized by high human capital value and high uniqueness—the configuration Lepak and Snell associate with internal development and commitment-based employment modes rather than transactional arrangements. This strategic positioning may provide some protection against displacement even amid functional transformation.

O*NET data for Human Resources Managers (11-3121) emphasizes tasks including “Advise managers on organizational policy matters,” “Analyze and modify compensation and benefits policies,” and “Plan and conduct new employee orientation”—activities combining analytical, advisory, and interpersonal elements with substantial contextual complexity.

Adaptive capacity components likely demonstrate:

• Net liquid wealth: Above-median, reflecting professional/managerial compensation levels. Bureau of Labor Statistics data shows median annual wages for Human Resources Managers exceeding $130,000, providing substantial financial buffer.
• Skill transferability: High, as strategic advisory, consulting, and organizational development skills transfer across industries and into adjacent functions (strategy consulting, general management, organizational development consulting).
• Geographic density: Variable, with concentration in corporate headquarters and major metropolitan areas where strategic decision-making occurs.
• Age: Variable, trending toward mid-career given seniority requirements for strategic roles.

This role cluster likely exhibits the pattern Manning and Aguirre (2026) identify for professional and managerial occupations: substantial AI exposure accompanied by high adaptive capacity, positioning workers relatively well to navigate transitions if displacement occurs.

3.3.4. HR Centers of Excellence and Technical Specialization

HR centers of excellence and technical specialization encompasses roles providing deep functional expertise in specific HR domains including compensation design, benefits strategy, talent acquisition strategy, learning architecture, HR information systems (HRIS), and people analytics. Portions of Compensation and Benefits Managers (11-3111), Training and Development Managers (11-3131), and specialized Human Resources Specialists (13-1071) fall within this cluster.

Task composition varies substantially by specialization but generally combines technical expertise with analytical capabilities. Compensation specialists apply complex statistical methodologies; learning architects design instructional experiences; HRIS specialists implement and maintain technological infrastructure; people analytics professionals conduct workforce analyses. AI exposure varies accordingly—some technical domains (e.g., analytics, data management) face significant tool-based transformation, while others (e.g., compensation philosophy, learning strategy) retain substantial human judgment components.

Research on HR analytics (Angrave et al., 2016; Marler & Boudreau, 2017) suggests that analytical and technical HR roles face both displacement pressure and augmentation opportunity. AI tools can automate routine analytical tasks while creating demand for professionals who can interpret AI outputs, ensure data quality, address algorithmic bias, and translate analytical insights into organizational action.

Adaptive capacity components likely show:

• Net liquid wealth: Above-median, reflecting specialized professional compensation.
• Skill transferability: Variable by specialization—analytics skills transfer broadly to data science and business intelligence roles; compensation and benefits expertise transfers primarily within HR domain; HRIS skills transfer to IT functions.
• Geographic density: Concentrated in major metropolitan areas with professional labor market depth.
• Age: Variable, with specialization development occurring across career stages.

This cluster occupies an intermediate-to-favorable position, with exposure-capacity profiles varying substantially by technical specialization.

3.4. Addressing Role Hybridization and Generalist Positions

The typology presented above represents analytical ideal types rather than descriptions of actual positions. In practice, substantial role hybridization exists, and many HR professionals perform tasks spanning multiple clusters. This boundary permeability requires explicit consideration.

Hybrid roles combining elements of multiple clusters are common, particularly in mid-sized organizations where functional differentiation is incomplete. An HR generalist may process transactions, handle employee relations issues, consult with managers on talent decisions, and administer HRIS systems. For such hybrid roles, adaptive capacity assessment requires task-level analysis of time allocation across cluster activities. A generalist spending 60% of time on transactional activities faces different exposure-capacity dynamics than one spending 60% on advisory activities, even if both carry the same job title.

Organizational size substantially moderates typology applicability. Large organizations with differentiated HR structures enable specialization into distinct role clusters. Smaller organizations typically feature generalist roles spanning multiple functions. Research on HR in small and medium enterprises (SMEs) documents that HR work in smaller organizations is less professionalized, more informal, and more integrated with general management (Harney & Dundon, 2006). For SME contexts, the typology may be less applicable, and adaptive capacity assessment should consider the full range of tasks performed rather than assuming cluster membership.

Career progression often involves movement across clusters, with professionals beginning in transactional or service delivery roles and advancing toward business partnership or specialization. This progression pattern has implications for adaptive capacity dynamics—workers in transactional roles may be developing competencies enabling future mobility, while those who have remained in transactional roles over extended periods may face greater vulnerability.

3.5. Empirical Illustration: Mapping SOC Codes to Role Clusters

To illustrate how the role typology maps to available occupational data, Table 1 presents relevant SOC codes with O*NET-derived task profiles and available exposure and capacity indicators. This illustration demonstrates how the framework could be operationalized with role-level data while acknowledging limitations of currently available occupation-level measures.

This mapping reveals several patterns. First, compensation levels vary substantially across HR-related SOC codes, with clerical/assistant roles earning roughly one-third of manager compensation—a gap that directly affects the net liquid wealth component of adaptive capacity. Second, the broad SOC 13-1071 (Human Resources Specialists) category encompasses multiple role clusters, confirming that occupation-level aggregation obscures meaningful heterogeneity. Third, AI exposure estimates track task composition, with transactional roles facing higher exposure than strategic and managerial roles.

3.6. Differential Vulnerability Across HR Role Clusters

Table 2 synthesizes expected AI exposure and adaptive capacity profiles across the four HR role clusters, generating a vulnerability assessment with supporting rationale.

This typological analysis reveals pronounced bifurcation within HR functions, paralleling the professional-clerical gap that Manning and Aguirre (2026) identify across the broader occupational landscape. Transactional HR administration exhibits the high-exposure/low-capacity profile characterizing vulnerable occupations, while strategic HR roles demonstrate patterns consistent with more resilient professional occupations.

This bifurcation carries significant implications. From a workforce planning perspective, organizations must attend to differential vulnerability within their HR functions, rather than treating HR as a homogeneous population. From a career development perspective, HR professionals in transactional roles face strong incentives to develop competencies enabling mobility toward service delivery, business partnership, or technical specialist roles with more favorable adaptive capacity profiles.

3.7. The HR Paradox: A Theoretical Analysis Through the Lens of Paradox Theory

Our analysis illuminates a paradox central to HR’s position in organizational AI adaptation. HR functions bear primary responsibility for designing and implementing workforce transition strategies—reskilling programs, internal mobility pathways, change management initiatives, and separation support—that enable organizational adaptation to technological change. Yet HR professionals simultaneously experience the transformation they are charged with facilitating.

To develop a more rigorous understanding of this paradox, we draw on organizational paradox theory (Smith & Lewis, 2011; Schad et al., 2016). Paradox theory examines how organizations and individuals navigate contradictory yet interrelated demands that persist over time. Smith and Lewis (2011) identify four categories of organizational paradox: learning (tensions between existing knowledge and new knowledge), organizing (tensions between control and flexibility), belonging (tensions between individual and collective identity), and performing (tensions between multiple stakeholders or goals).

The HR paradox exhibits characteristics of multiple paradox types. It involves learning paradox as HR professionals must simultaneously apply existing expertise while developing new AI-related competencies that may render current expertise obsolete. It involves performing paradox as HR professionals serve organizational interests in efficiency-enhancing automation while serving employee interests in job security—interests that may directly conflict when automation affects HR positions. It involves belonging paradox as HR professionals’ professional identity centers on workforce advocacy and development, yet their organizational role may require facilitating workforce reduction affecting their own colleagues.

3.7.1. Psychological and Identity Dimensions

The paradox generates significant psychological demands on HR professionals. Identity threat theory (Petriglieri, 2011) suggests that challenges to valued aspects of self-concept produce defensive responses including denial, rationalization, or resistance. HR professionals whose professional identity centers on employee advocacy may experience identity threat when required to implement AI systems displacing HR colleagues. This identity threat may manifest as:

• Denial or minimization of AI’s potential impact on HR functions, leading to underinvestment in personal adaptive capacity development
• Emotional labor demands as HR professionals manage their own anxiety about occupational change while projecting confidence to employees facing similar uncertainty
• Moral distress when organizational efficiency goals conflict with perceived obligations to affected colleagues

Research on emotional labor in HR roles (O’Brien & Linehan, 2014) documents that HR professionals routinely manage tensions between organizational demands and employee welfare. The AI transformation context intensifies these tensions by adding personal stake to what was previously primarily vicarious concern.

3.7.2. Behavioral Dynamics and Potential Perverse Incentives

The paradox may generate behavioral dynamics warranting organizational attention. HR professionals facing displacement risk from AI initiatives they are responsible for implementing may experience conflicting incentives:

• Information asymmetry: HR professionals possess specialized knowledge about workforce implications of AI adoption. This knowledge could be used to advocate for responsible implementation—or to selectively emphasize risks that slow AI adoption in HR functions while supporting adoption elsewhere.
• Implementation quality: HR professionals responsible for implementing AI in their own functions control implementation quality. Conscious or unconscious resistance could manifest as slow rollout, inadequate change management, or emphasis on implementation challenges.
• Self-serving advocacy: HR professionals may genuinely believe that human judgment is essential for their functions while holding different standards for other organizational areas—a form of motivated reasoning that serves self-interest while appearing principled.

These dynamics do not impugn HR professionals’ integrity; they reflect normal human responses to threatening situations. However, organizations should anticipate such dynamics and design governance structures that address potential conflicts of interest.

3.7.3. The Paradox as Opportunity

Paradox theory emphasizes that contradictory tensions, when navigated skillfully, can become sources of creativity and transformation (Smith & Lewis, 2011). The HR paradox offers potential advantages if approached constructively:

• Experiential credibility: HR professionals experiencing AI transformation firsthand develop practical knowledge unavailable to those merely studying it. This lived experience enhances credibility when guiding other functions through similar transformations.
• Empathetic insight: Personal experience with displacement anxiety enables deeper empathy with affected employees, potentially improving the quality of transition support and change management.
• Innovation motivation: The imperative to demonstrate HR’s continued value may motivate innovation in HR service delivery, analytics, and strategic contribution that would not occur absent competitive pressure.
• Model demonstration: HR functions that successfully navigate their own transformation provide organizational proof-of-concept for workforce adaptation, demonstrating that proactive reskilling and role evolution can address AI disruption.

Successfully navigating the paradox requires what Smith and Lewis (2011) term “dynamic equilibrium”—ongoing engagement with tensions rather than resolution through elimination of one pole. For HR professionals, this means neither denying AI’s transformative potential nor abandoning advocacy for affected workers, but rather holding both concerns simultaneously while developing creative responses.

3.8. Implications of Within-Function Heterogeneity

The heterogeneity revealed by our HR role typology carries implications extending beyond the HR domain. If substantial within-occupation variation exists in HR functions, similar patterns likely characterize other occupational categories encompassing diverse role types. Finance and accounting functions, for instance, likely exhibit comparable bifurcation between transactional processing roles and strategic advisory roles. Legal functions may show similar patterns between document review and compliance roles versus strategic counsel roles.

This suggests that occupation-level adaptive capacity measures, while valuable for broad labor market analysis, require supplementation with finer-grained role-level analysis for organizational workforce planning purposes. Organizations cannot assume that occupation-level vulnerability assessments apply uniformly across all workers within those occupational categories. Instead, role-level analysis considering specific task compositions, compensation levels, and career pathway positions provides more actionable intelligence for workforce strategy.

4. A Multi-Level Adaptive Capacity Model Grounded in Strategic HRM Theory

The preceding analysis motivates development of a more comprehensive theoretical framework integrating factors across multiple levels of analysis. We propose a multi-level adaptive capacity model grounded in strategic HRM theory, incorporating individual, occupational, organizational, and institutional factors that interact to shape workforce resilience to AI-induced displacement.

4.1. Theoretical Foundations in Strategic HRM

Our multi-level model draws on two foundational strategic HRM frameworks: the resource-based view (RBV) and the ability-motivation-opportunity (AMO) model.

The resource-based view (Barney, 1991; Wright et al., 2001) theorizes that sustained competitive advantage derives from resources that are valuable, rare, inimitable, and non-substitutable. Applied to human capital, the RBV suggests that workforce capabilities meeting these criteria provide strategic value that organizations will seek to retain and develop. Wright et al. (2001) extended RBV to argue that HR practices create sustainable advantage not through the practices themselves (which are imitable) but through the human capital pools and employee behaviors they generate.

For adaptive capacity, the RBV offers several insights. First, workers whose capabilities are valuable, rare, and difficult to substitute possess stronger labor market positions that enhance adaptive capacity—their skills are sought by multiple employers, improving reemployment prospects. Second, organizations have stronger incentives to invest in developing and retaining workers with strategically valuable capabilities, providing access to organizational adaptive capacity resources. Third, AI exposure may be inversely related to capability non-substitutability—tasks that AI can perform are, by definition, substitutable, while tasks requiring uniquely human capabilities are more resistant.

The ability-motivation-opportunity (AMO) framework (Appelbaum et al., 2000; Jiang et al., 2012) theorizes that employee performance and organizational outcomes result from the interaction of employee abilities, motivation to perform, and opportunities to contribute. High-performance work systems enhance outcomes by investing in ability development (through selection and training), motivation (through incentives and engagement), and opportunity (through participation and job design).

For adaptive capacity, the AMO framework suggests that organizational practices influence not just current performance but capacity for adaptation. Organizations that invest in ability development build skill breadth enhancing transferability. Organizations that maintain high motivation foster engagement with continuous learning. Organizations that provide opportunity for skill deployment enable practice and refinement of capabilities. The framework thus identifies mechanisms through which organizational practices shape individual adaptive capacity.

4.2. Individual-Level Factors

At the individual level, adaptive capacity reflects personal characteristics that influence job search effectiveness, reemployment probability, and post-displacement earnings recovery. The Manning and Aguirre (2026) framework operationalizes several such factors at occupation-level aggregation; our model retains these while recognizing that meaningful variation exists within occupations.

Financial resources. Net liquid wealth provides consumption smoothing during job search and enables longer search duration leading to better job matches (Chetty, 2008). Individual variation in savings behavior, family wealth transfers, and debt burden creates substantial within-occupation heterogeneity in this dimension. Two HR business partners with identical occupational positions may possess vastly different financial buffers affecting their capacity to weather displacement. Research on precautionary saving (Carroll, 1997) suggests that workers facing greater income uncertainty should hold larger financial buffers—though behavioral factors often prevent optimal saving.

Human capital profile. Individual skill portfolios vary in transferability to alternative occupations. From an RBV perspective, workers with capabilities valuable across multiple contexts possess greater labor market power. Workers who have accumulated diverse experience spanning multiple functions or industries likely possess more transferable skills than those with narrow, specialized backgrounds, regardless of occupation-level averages. An HR generalist with prior experience in operations and finance may demonstrate greater skill transferability than one whose entire career has been confined to a single HR subdomain.

Social capital and networks. Professional networks facilitate job information transmission and referral-based hiring (Granovetter, 1995; Ioannides & Loury, 2004). Workers with broader, more diverse networks likely experience faster reemployment and better job matches. HR professionals with extensive professional network connections—through industry associations, alumni networks, or cross-functional relationships—may possess valuable social capital not captured in occupation-level measures. Research on network structure suggests that weak ties to diverse contacts provide greater job search value than strong ties to similar contacts (Granovetter, 1973).

Age and career stage. Age affects adaptive capacity through multiple mechanisms including residual working years over which to recoup transition investments, generational differences in technology comfort, and potential age discrimination in hiring (Farber, 2017). Career stage may interact with age—mid-career transitions may differ from late-career transitions even at similar ages, as mid-career workers may have greater flexibility to pursue substantial reskilling investments.

Learning agility and career adaptability. Individual differences in openness to new experiences, learning motivation, and cognitive flexibility influence capacity to acquire new competencies and adjust to changing role requirements. Savickas and Porfeli’s (2012) concept of career adaptability—encompassing concern, control, curiosity, and confidence regarding career development—captures psychological resources enabling effective career navigation. HR professionals with strong learning orientations may proactively develop AI-related competencies, enhancing their adaptive capacity even as their current roles face automation pressure. The AMO framework suggests that these individual dispositions interact with organizational opportunity structures—learning agility matters more when development opportunities exist.

4.3. Occupational-Level Factors

Occupational-level factors shape the collective characteristics of workers in particular roles and the structural position of occupations within the labor market.

Task composition and AI exposure. The mixture of routine, non-routine cognitive, and non-routine manual tasks determines occupational AI exposure (Autor et al., 2003). Following Eloundou et al. (2023), occupations with high concentrations of text-intensive, codifiable tasks face greater LLM exposure. Our HR role typology demonstrates how task composition varies substantially within nominally similar occupational categories.

Skill transferability profile. Occupational skill requirements determine transferability to alternative occupations. The Manning and Aguirre (2026) growth-weighted transferability measure captures this factor, recognizing that value depends on labor demand in destination occupations. From an RBV perspective, occupations whose skill profiles are valuable across multiple employment contexts demonstrate higher transferability than those with occupation-specific requirements. Occupations whose skills transfer primarily to growing fields (e.g., data analytics skills transferring to expanding technology roles) demonstrate higher adaptive capacity than those whose skills transfer to declining fields.

Occupational labor market dynamics. Growth or decline trajectories for the occupation itself and related occupations shape transition opportunities. Occupations in declining clusters offer fewer alternative pathways than those surrounded by growing related occupations. HR specialists whose skills align with growing people analytics and workforce planning demands face more favorable conditions than those whose skills align primarily with declining administrative functions.

Credentialing and professional infrastructure. Occupational licensing, certification requirements, and educational prerequisites affect both displacement risk and transition pathways. Credentialed occupations may face fewer competitive pressures but also present higher barriers to lateral entry. For HR professionals, certifications such as SHRM-CP/SCP or HRCI credentials may signal competence to prospective employers, potentially facilitating post-displacement matching. Professional associations provide networking, job placement, and professional development resources that enhance adaptive capacity for members.

4.4. Organizational-Level Factors: An Extended Analysis

Our model introduces organizational-level factors absent from the Manning and Aguirre (2026) framework but likely influential for HR and other functions. We develop this level more extensively given its novelty relative to the source framework.

4.4.1. Organizational AI Adoption Trajectory

Organizations vary substantially in AI adoption pace, scope, and approach (Brynjolfsson et al., 2018). Drawing on technology adoption literature (Rogers, 2003; Tornatzky & Fleischer, 1990), we identify several organizational postures with implications for workforce adaptive capacity:

Technology pioneers adopt AI early and aggressively, accepting implementation risk to capture first-mover advantages. Workers in pioneer organizations face earlier exposure to AI tools, providing both greater displacement risk and greater opportunity to develop AI-complementary skills before broader labor market adjustment.

Fast followers observe pioneers’ experiences before implementing proven applications. This posture provides somewhat later exposure with potentially smoother implementation, allowing workers more time to prepare but less opportunity to develop distinctive AI-related competencies.

Deliberate adopters proceed cautiously, implementing AI only after extensive evaluation and preparation. Workers in these organizations face delayed displacement risk but may find their AI-related skills lagging when broader labor market transformation occurs.

Technology resisters avoid or delay AI adoption due to resource constraints, cultural resistance, or perceived inapplicability. Workers in resistant organizations may face sudden disruption if competitive pressure eventually forces rapid adoption, with little preparation time.

The relationship between organizational adoption posture and worker adaptive capacity is complex. Early adoption creates immediate displacement pressure but also development opportunity; delayed adoption provides temporary protection but may disadvantage workers in long-term labor market positioning.

4.4.2. Internal Labor Market Structures

Organizations differ substantially in internal labor market (ILM) structures, with significant implications for adaptive capacity. Drawing on the extensive ILM literature (Doeringer & Piore, 1971; Althauser, 1989; Bidwell & Keller, 2014), we identify several relevant dimensions:

Job ladder structures. Organizations with well-defined career ladders and promotion-from-within policies provide internal mobility pathways enabling workers to advance beyond vulnerable entry-level positions. Workers in organizations with strong job ladders can build adaptive capacity through career progression within the firm.

Internal versus external hiring orientation. Organizations vary in preference for internal promotion versus external hiring for higher-level positions (Bidwell, 2011). Strong internal hiring preferences enable displaced workers to find alternative internal positions; external hiring orientation may leave displaced workers competing in external labor markets.

Cross-functional mobility. Some organizations encourage lateral moves across functions, building broad skill portfolios; others emphasize deep functional specialization. Cross-functional mobility enhances skill transferability and provides more diverse internal redeployment options.

Redeployment versus separation practices. When positions are eliminated, organizations differ in effort to redeploy affected workers internally versus separating them. Robust redeployment practices substantially enhance adaptive capacity by reducing dependence on external labor market success.

For HR professionals specifically, ILM structures shape whether displacement from one HR role enables transition to another internal position—in HR or adjacent functions—or requires external job search.

4.4.3. Learning and Development Infrastructure

Organizational investments in employee development shape workers’ capacity to acquire skills enabling adaptation. Drawing on the training and development literature (Noe et al., 2014; Blume et al., 2010), we identify several relevant dimensions:

Formal training investments. Organizations vary in training expenditure, access policies, and curriculum scope. High training investment provides workers with development opportunities enhancing skill breadth and currency. Research on training transfer (Blume et al., 2010) indicates that training effectiveness depends on transfer climate factors including supervisor support, opportunity to apply learning, and reinforcement of new behaviors.

Informal learning opportunities. Beyond formal training, organizations differ in support for informal learning through job rotation, stretch assignments, project participation, and communities of practice. These informal mechanisms may be particularly important for developing novel competencies (e.g., AI-related skills) not yet systematized into formal curricula.

Career development support. Organizations vary in provision of career counseling, mentoring, developmental feedback, and career pathing resources. Strong career development support helps workers identify skill gaps and development opportunities, enabling proactive adaptive capacity building.

Educational assistance. Tuition reimbursement, educational leave, and degree completion support enable workers to pursue formal education enhancing credentials and capabilities. These investments may be particularly valuable for substantial career transitions requiring new qualifications.

From an AMO perspective, L&D infrastructure represents organizational investment in workforce ability that enhances adaptive capacity. Workers in organizations with robust L&D infrastructure can develop competencies enhancing their own adaptive capacity, while those in organizations with limited development investments may face greater vulnerability. The relationship is not automatic, however—workers must take advantage of available opportunities, suggesting interaction with individual learning orientation.

4.4.4. Transition Support Provisions

Organizational policies regarding severance, outplacement support, extended benefits continuation, and alumni networks affect displacement costs when transitions do occur (Jacobson et al., 1993). These provisions operate as organization-provided supplements to individual financial resources.

Severance generosity. Severance pay provides financial buffer enabling longer job search and better matching. Organizations vary substantially in severance formulas, from minimal legal requirements to extended packages based on tenure and position level.

Outplacement services. Professional outplacement support including career counseling, job search assistance, resume development, and interview coaching improves reemployment outcomes. Quality and duration of outplacement services vary across organizations.

Extended benefits. Continuation of health insurance, retirement contributions, or other benefits during transition reduces financial pressure and preserves important protections during vulnerable periods.

Alumni network support. Some organizations maintain ongoing relationships with former employees through alumni networks, providing networking, job referral, and even rehiring pathways. Strong alumni networks provide continued access to social capital even after organizational exit.

Generous transition support can substantially buffer displacement impacts even for workers with limited personal financial resources. For HR professionals specifically, transition support provisions represent HR policy decisions that HR functions themselves often influence—creating another dimension of the HR paradox.

4.4.5. Organizational Culture and Change Readiness

Cultural factors shape how organizations and individuals respond to technological disruption, with implications for adaptive capacity development.

Psychological safety. Organizations vary in psychological safety—the extent to which individuals feel safe to take interpersonal risks, ask questions, and admit mistakes (Edmondson, 2019). High psychological safety facilitates experimentation with new technologies and approaches, enabling workers to develop AI-related competencies without fear of failure consequences.

Innovation orientation. Cultures emphasizing continuous improvement, experimentation, and innovation expose workers to ongoing change, potentially building change readiness and adaptability as organizational muscles. Workers accustomed to regular innovation may find AI-driven transformation less disorienting.

Learning culture. Organizations differ in valuation of continuous learning, tolerance for developmental activity during work time, and celebration of capability development. Strong learning cultures reinforce individual learning orientation and create social expectations supporting development.

Change management capability. Organizations with strong change management capabilities—honed through previous transformations—may implement AI transitions more effectively, reducing disruptive impacts on workers. Poor change management may amplify displacement effects through inadequate communication, training, and support.

4.5. Institutional-Level Factors

Institutional factors operating beyond individual organizations shape the broader context within which displacement and adaptation occur.

Professional association resources. Professional bodies provide networking, certification, professional development, and job placement services that may facilitate transitions. For HR professionals, organizations including SHRM, HRCI, and WorldatWork offer resources potentially enhancing adaptive capacity beyond what individual or organizational resources provide. These associations increasingly offer AI-focused professional development, enabling members to build competencies relevant to technological transformation.

Social insurance provisions. Unemployment insurance generosity, duration, and eligibility requirements affect consumption smoothing and job search duration (Card et al., 2007; Nekoei & Weber, 2017). Institutional variation across states and countries generates differential adaptive capacity independent of individual or occupational characteristics. Workers in states with more generous unemployment insurance can sustain longer job searches, potentially securing better post-displacement matches.

Active labor market policies. Government-provided training programs, job matching services, and reemployment assistance shape transition pathways (LaLonde, 1995). The effectiveness and accessibility of such programs varies substantially across contexts. Trade Adjustment Assistance and similar programs may provide resources supporting displaced workers, though evidence on program effectiveness remains mixed.

Regional economic conditions. Local labor market dynamics including unemployment rates, industry composition, and growth trajectories affect reemployment opportunities. The geographic density measure in Manning and Aguirre (2026) partially captures this factor, but broader regional economic conditions also matter. HR professionals in economically vibrant metropolitan areas face more favorable transition prospects than those in economically distressed regions, regardless of individual characteristics.

Regulatory environment. Employment protection legislation, notice requirements, and collective bargaining coverage affect both displacement likelihood and transition processes (OECD, 2020). Institutional protections may slow displacement while creating adjustment time, or may redirect adjustment costs from workers to employers. Stronger employment protections may provide workers additional time to prepare for transitions, though they may also reduce employer incentives to invest in workforce development.

4.6. Theoretical Integration: Relationships Among Levels

The multi-level model recognizes that factors across levels interact in shaping adaptive capacity. Drawing on RBV and AMO frameworks, we theorize several relationship patterns.

4.6.1. Compensatory Versus Threshold Relationships

A key theoretical question concerns whether factors across levels relate compensatorily (strong resources at one level offsetting weak resources at another) or whether minimum thresholds at each level are necessary regardless of strength elsewhere.

We theorize primarily compensatory relationships with partial thresholds. Strong organizational resources can partially compensate for limited individual resources—a worker with minimal personal savings but generous organizational severance and robust redeployment programs possesses meaningful adaptive capacity. However, some minimum individual capacity (e.g., basic learning ability, minimal financial buffer) appears necessary for organizational investments to translate into actual adaptation. Similarly, favorable institutional provisions (generous unemployment insurance, active labor market programs) can compensate for organizational limitations, but cannot fully substitute for individual effort and capability.

From an AMO perspective, organizations can enhance worker abilities and provide opportunities, but cannot fully substitute for individual motivation. Workers must engage with development opportunities for organizational investments to generate capacity returns.

4.6.2. Amplifying and Dampening Interactions

Factors across levels may interact to amplify or dampen main effects:

Individual × organizational amplification. Individual learning orientation amplifies returns to organizational L&D investment—workers with strong learning motivation extract greater value from development opportunities. Conversely, limited individual learning orientation may dampen organizational investment returns.

Occupational × organizational interactions. Occupational vulnerability plays out differently across organizational contexts. High-exposure occupations in technology-forward organizations may face faster displacement than counterparts in technology-lagging organizations. Conversely, proactive organizations may invest in workforce transformation enabling adaptation before displacement occurs.

Organizational × institutional interactions. Organizational practices interact with institutional contexts. Strong internal labor markets matter more where external labor market institutions are weak; organizational L&D investments complement public training systems. In regions with robust public workforce development infrastructure, organizational investments may be less critical; in regions lacking such infrastructure, organizational resources become more consequential.

4.6.3. Temporal Dynamics

While our model presents factors at a point in time, we recognize that adaptive capacity evolves dynamically:

Anticipatory adaptation. Workers and organizations may anticipate displacement and invest in capacity enhancement before displacement occurs. Workers in high-exposure roles may proactively develop transferable skills; organizations may invest in reskilling programs for at-risk populations. These anticipatory responses complicate static assessment.

Feedback loops. Successful adaptation builds confidence and competence for future adaptation; unsuccessful adaptation may deplete resources and discourage future effort. Adaptive capacity is not static but evolves through experience.

Technology trajectory. AI capabilities continue advancing, potentially exposing new task domains while human capabilities may also develop. Current exposure assessments represent point-in-time snapshots that may not reflect medium-term conditions.

4.7. Applying the Multi-Level Model to HR Functions

Applying the multi-level model to HR functions illuminates several insights obscured by occupation-level analysis alone.

At the individual level, HR professionals vary substantially in financial resources (given compensation variation across transactional and strategic roles), human capital profiles (generalist versus specialist backgrounds), network breadth (local versus profession-wide connections), and learning orientation. These individual differences generate meaningful adaptive capacity variation within occupational categories.

At the occupational level, our role typology reveals that HR encompasses multiple distinct occupational positions with different exposure-capacity profiles. Transactional roles face high exposure and limited transferability; strategic roles face moderate exposure with high transferability to consulting, general management, and other professional occupations.

At the organizational level, HR professionals embedded in organizations with strong L&D infrastructure, internal mobility pathways, and change-ready cultures likely possess greater adaptive capacity than counterparts in organizations lacking such resources—regardless of individual or occupational characteristics. This organizational embedding represents a significant adaptive capacity factor not captured in the Manning and Aguirre (2026) framework.

At the institutional level, HR professionals benefit from professional association resources (certification, networking, job boards) that may enhance adaptive capacity beyond what occupation-level measures suggest. However, the relative absence of licensure (compared to other professional occupations) may reduce barriers to entry, increasing competitive pressure.

The multi-level model thus provides a more complete picture of HR workforce vulnerability, recognizing that adaptive capacity emerges from the interaction of factors operating at multiple levels rather than from occupation-level characteristics alone.

5. Equity and Inclusion Implications

AI-driven workforce transformation does not affect all workers equally, and the adaptive capacity framework reveals systematic patterns of differential vulnerability. This section examines equity implications of these patterns, considering how AI transformation may interact with existing workforce inequities.

5.1. Demographic Composition of Vulnerable Role Clusters

Transactional and administrative roles facing highest vulnerability exhibit demographic compositions that raise equity concerns. Bureau of Labor Statistics data indicates that administrative support occupations disproportionately employ:

• Women: Women represent approximately 72% of administrative support workers, compared to 47% of the overall workforce. If transactional HR administration roles follow similar patterns, AI-driven displacement may disproportionately affect women workers.
• Workers without advanced degrees: Administrative roles typically require less formal education than professional and managerial positions. Workers without bachelor’s or advanced degrees may face greater barriers to transitioning into lower-vulnerability roles requiring higher credentials.
• Older workers: Administrative roles may accumulate longer-tenured workers who entered positions earlier in their careers and have not progressed to other roles. These workers face age-related adaptive capacity challenges documented by Farber (2017).

While empirical data specifically on demographic composition of HR role clusters is limited, if transactional HR mirrors broader administrative patterns, AI-driven transformation could disproportionately impact already-disadvantaged worker populations.

5.2. Geographic Dimensions of Vulnerability

The adaptive capacity framework reveals substantial geographic variation, with workers in dense metropolitan labor markets demonstrating higher adaptive capacity than those in smaller cities and rural areas. This pattern has equity implications:

• Rural and small-city workers face thinner labor markets with fewer alternative employment opportunities. Displaced HR professionals in metropolitan areas may find local alternatives; those in smaller communities may face relocation requirements adding to transition costs.
• Regional economic inequality may be exacerbated if AI-driven displacement concentrates in areas already facing economic challenges. Communities with struggling local economies offer fewer reemployment opportunities for displaced workers.
• Access to professional networks and resources varies geographically. Professional association events, networking opportunities, and professional development programs concentrate in major metropolitan areas, potentially advantaging workers in those locations.

5.3. Organizational Resources and Workforce Equity

Organizational investments in adaptive capacity infrastructure are unevenly distributed across employers:

• Resource-constrained organizations—including many public sector employers, nonprofits, and small businesses—may provide less generous transition support, learning and development investments, and internal mobility opportunities than well-resourced private sector organizations.
• Industry variation in organizational resources creates differential adaptive capacity for workers in different sectors. HR professionals in technology or financial services may have access to more robust organizational resources than counterparts in retail or hospitality.
• Precarious employment relationships—including contract, temporary, and gig arrangements—provide minimal organizational adaptive capacity resources. Workers in non-standard employment lack access to organizational L&D, transition support, and internal labor market protections.

5.4. Access to Professional Resources

Access to professional association resources and development opportunities that enhance adaptive capacity is not equally distributed:

• Professional association membership typically requires fees that may be prohibitive for lower-paid workers, potentially excluding transactional HR workers who would most benefit from association resources.
• Credential attainment requires time, money, and organizational support that may be less available to workers in transactional roles than to higher-status HR professionals.
• Networking opportunity access depends on geographic location, employer support for professional involvement, and social capital enabling entry into professional networks.

5.5. Implications for Equity-Conscious Practice

These equity considerations suggest that organizations and policymakers should explicitly attend to distributional consequences of AI-driven transformation:

• Targeted support for workers in high-vulnerability role clusters, recognizing that these workers may lack personal resources to navigate transitions independently.
• Proactive identification of demographic groups potentially facing disproportionate impact, with deliberate intervention to prevent exacerbation of existing inequities.
• Geographic considerations in workforce development and transition support, recognizing that labor market thickness varies substantially across locations.
• Accessible professional development that does not require resources (financial, time, employer support) unavailable to lower-status workers.
• Organizational accountability for equity implications of AI adoption decisions, moving beyond efficiency metrics to consider distributional impacts.

6. Implications for Practice and Policy

6.1. Implications for HR Practice

Our analysis carries direct implications for HR leaders managing their own functions through AI-driven transformation.

6.1.1. Workforce Planning Within HR Functions

HR leaders must conduct rigorous workforce planning for their own functions, disaggregating roles by vulnerability profile rather than treating HR as a homogeneous population. This requires:

• Role mapping: Categorizing HR roles according to the transactional, service delivery, business partnership, and center of excellence clusters (or equivalent typology appropriate to organizational context), with explicit attention to hybrid roles and task heterogeneity within positions.
• Exposure assessment: Analyzing AI exposure for each role cluster based on task composition analysis using O*NET or internal task inventories, identifying which tasks are most susceptible to automation and which require continued human involvement.
• Capacity evaluation: Evaluating adaptive capacity characteristics of workers in each cluster using available data on compensation levels, tenure, skills inventories, demographics, and organizational tenure.
• Differentiated strategy development: Developing tailored strategies addressing distinct vulnerability profiles rather than applying uniform approaches across the HR function.

For transactional HR roles with high vulnerability profiles, strategies may include: accelerated automation of routine tasks coupled with reskilling investments enabling progression to service delivery or specialist roles; managed attrition through hiring freezes rather than active displacement; and enhanced transition support for workers unable or unwilling to reskill. Organizations should recognize that transactional HR workers face structural disadvantages in adaptive capacity and may require more intensive support than strategic HR colleagues.

For strategic HR roles with lower vulnerability profiles, strategies may include: integration of AI tools augmenting analytical and productivity capabilities; deliberate development of competencies in AI governance, algorithmic fairness, and technology-enabled HR; and positioning as internal consultants on workforce AI transformation. Strategic HR professionals should leverage their higher adaptive capacity to lead organizational AI integration rather than passively awaiting its impact.

6.1.2. Career Development and Mobility Pathways

HR leaders should design career development pathways enabling mobility from higher-vulnerability to lower-vulnerability role clusters. Drawing on the AMO framework, this requires investments in abilities, motivation, and opportunities:

Ability development:

• Clear specification of competency requirements distinguishing role clusters, enabling workers to understand development needs for career progression
• Programs specifically designed to bridge competency gaps between clusters, such as analytical skills training enabling transactional workers to transition to specialist roles
• Access to formal education supporting substantial capability development (e.g., tuition assistance for analytics or business degrees)

Motivation enhancement:

• Career pathing and counseling that helps workers see progression possibilities and understand paths from current to target positions
• Recognition and incentives for capability development, not just current role performance
• Manager accountability for developmental support and career advancement of direct reports

Opportunity provision:

• Rotation and stretch assignment opportunities enabling exposure to strategic and specialist work
• Project participation opportunities that build experience in higher-level activities
• Deliberate succession planning that creates advancement opportunities for developing professionals

Professional development investments should emphasize competencies enhancing adaptive capacity: analytical and data literacy skills applicable across contexts; consulting and advisory capabilities transferable to business partnership roles; and technology fluency enabling effective collaboration with AI systems.

6.1.3. Navigating the HR Paradox

HR leaders must thoughtfully navigate the paradox of facilitating organizational AI adaptation while managing their own functional transformation. Drawing on paradox theory’s emphasis on working with rather than resolving tensions:

• Transparent acknowledgment: Honest communication that HR functions face similar transformation pressures as other organizational areas, avoiding the appearance that HR views itself as exempt from changes it advocates for others. Transparency builds credibility and enables collective problem-solving.
• Demonstrated credibility: Successful management of HR’s own AI integration, demonstrating competence in navigating technological transformation before advising other functions. HR becomes a proof-of-concept for organizational adaptation.
• Distributional attention: Explicit consideration of consequences of HR automation decisions for affected workers, with proactive support for those facing displacement. This attention to distributional impacts models the approach HR advocates for the broader organization.
• Conflict of interest management: Recognition that HR professionals responsible for implementing AI in their own functions may face conflicting incentives. Governance structures should address potential conflicts through oversight, stakeholder involvement, and accountability mechanisms.
• Identity reframing: Helping HR professionals reframe their professional identity around workforce development and adaptation rather than specific administrative activities. This identity evolution enables engagement with transformation rather than resistance.
• Embracing the paradox’s advantages: Positioning experiential knowledge from HR’s own transformation as a source of credibility and insight for guiding organizational adaptation. The lived experience of transformation provides understanding that abstract knowledge cannot match.

6.2. Implications for Organizational Strategy

Beyond HR-specific considerations, our analysis illuminates broader implications for organizational workforce strategy.

6.2.1. Investing in Organizational Adaptive Capacity Infrastructure

The multi-level model highlights that organizational factors substantially influence workforce adaptive capacity independent of individual and occupational characteristics. Organizations can deliberately invest in infrastructure enhancing workforce resilience:

• Internal labor markets: Strengthening internal mobility pathways through transparent job posting, cross-functional rotations, and promotion-from-within preferences. This reduces external labor market dependence and enables organizational redeployment of workers displaced from specific roles.
• Learning and development infrastructure: Building robust capability development systems including formal training, informal learning opportunities, career development support, and educational assistance. Organizations that invest in continuous learning cultures position workers to adapt to technological change rather than being displaced by it.
• Transition support provisions: Enhancing severance, outplacement, and alumni support reduces displacement costs when transitions do occur. Generous transition support represents an organizational investment in adaptive capacity that benefits workers even when displacement cannot be avoided.
• Psychological safety and learning culture: Fostering environments where experimentation, questioning, and learning from failure are supported. These cultural investments enable workers to develop new competencies without fear of negative consequences from developmental struggles.

These investments represent organizational-level adaptive capacity resources benefiting workers across occupational categories. From a strategic perspective, organizations making such investments not only support individual workers but also build organizational resilience and reputational capital that may enhance talent attraction and retention.

6.2.2. Differentiated Transformation Strategies

Organizations should calibrate AI transformation strategies based on workforce vulnerability profiles. Drawing on the HR architecture literature (Lepak & Snell, 1999, 2002), we suggest differentiated approaches:

For areas with high-vulnerability workforces—high AI exposure combined with low adaptive capacity—transformation strategies should include:

• Extended timelines: Longer transformation periods enabling managed adjustment, providing workers time to reskill or prepare for transitions
• Enhanced investment: Greater investment in reskilling and internal redeployment relative to areas with higher-capacity workforces
• Robust support: Enhanced transition support for workers unable to adapt, recognizing that some workers may lack capacity for successful reskilling despite organizational investments
• Equity monitoring: Careful tracking of workforce equity implications, given that vulnerable populations often include workers with limited alternative options

For areas with high-exposure but high-capacity workforces, faster transformation may be feasible with appropriate communication and change management, as workers possess resources enabling self-directed adaptation. However, organizations should avoid assuming that high-capacity workers require no support—even capable workers benefit from organizational assistance in navigating transitions.

6.2.3. Emerging HR Roles and Function Evolution

Beyond managing displacement, organizations should anticipate new HR roles emerging from AI transformation:

• AI governance and ethics roles: Ensuring responsible AI deployment, addressing algorithmic bias, and managing regulatory compliance. These roles require HR expertise in employment law, diversity and inclusion, and employee advocacy combined with AI literacy.
• People analytics leadership: Integrating AI-enabled workforce analytics with strategic decision-making. These roles build on traditional HR analytical capabilities while requiring enhanced data science competencies.
• Employee experience design: Designing human-AI collaboration models that optimize both productivity and employee well-being. These roles draw on HR’s traditional employee advocacy focus while incorporating technology design perspective.
• Continuous learning architecture: Designing and managing organizational learning ecosystems enabling ongoing workforce adaptation. These roles extend traditional L&D responsibilities to encompass broader organizational learning strategy.

Strategic workforce planning should include deliberate development of talent pipelines for these emerging roles, recognizing that current HR professionals with appropriate development may be well-positioned to fill them.

6.3. Implications for Public Policy

Our analysis carries implications for public policy regarding workforce development and social insurance in the context of AI-driven occupational transformation.

6.3.1. Targeted Workforce Development Investments

The finding that AI exposure and adaptive capacity are positively correlated—many highly exposed workers are well-positioned to adapt—suggests that blanket workforce development programs targeting all AI-exposed occupations may misallocate resources. More targeted approaches would:

• Identify high-vulnerability occupational clusters: Using the exposure-capacity framework to identify occupations combining high AI exposure with low adaptive capacity as priority targets for public investment. Administrative support occupations, clerical functions, and routine information processing roles across industries represent likely priority targets.
• Conduct role-level assessment: Moving beyond occupation-level targeting to identify vulnerable role clusters within broader occupational categories, recognizing that occupation-level data may obscure within-occupation heterogeneity.
• Consider geographic targeting: Concentrating resources in regions with thinner labor markets where displaced workers face fewer alternative opportunities.
• Address equity dimensions: Explicitly targeting worker populations facing disproportionate displacement impact, including women, workers without advanced degrees, and older workers.

6.3.2. Enhanced Social Insurance

The adaptive capacity framework highlights that displacement costs fall most heavily on workers lacking financial buffers. This reinforces arguments for enhanced portable benefits and social insurance provisions:

• Unemployment insurance enhancement: Providing adequate replacement rates and duration for job search, recognizing that longer search durations may be necessary in AI-transformed labor markets where substantial reskilling may be required (Nekoei & Weber, 2017).
• Portable benefits designs: Enabling benefit continuation across employers, reducing the disruption associated with job transitions. Health insurance portability is particularly important for enabling workers to pursue transition opportunities without coverage loss.
• Wage insurance programs: Providing partial compensation for earnings losses during occupational transitions, recognizing that many transitions involve compensation reductions even when reemployment is successful.
• Retirement security: Ensuring that displacement does not jeopardize retirement security, particularly for older workers close to retirement age.

6.3.3. Active Labor Market Policies

Active labor market policies including training, job matching, and reemployment assistance can enhance institutional-level adaptive capacity (LaLonde, 1995). Based on our analysis:

• Transferable skill focus: Training programs emphasizing competencies with broad transferability rather than narrow occupational preparation, recognizing that workers may need to transition across occupational boundaries. Digital literacy, data analysis, and interpersonal skills represent broadly applicable competencies.
• Career navigation support: Expanded career counseling helping workers identify transition pathways based on individual skill profiles, recognizing that optimal transitions vary across individuals. This goes beyond traditional job matching to include assessment, counseling, and pathway planning.
• Cross-occupational matching: Job matching services connecting displaced workers with appropriate opportunities across occupational boundaries, rather than focusing narrowly on same-occupation placement. This requires sophisticated understanding of skill transferability.
• Employer engagement: Partnering with employers to identify emerging skill needs and develop training programs aligned with actual labor demand, reducing mismatch between training and employment opportunities.

6.3.4. Professional Association Partnerships

For HR functions specifically, professional associations may partner with public workforce systems to provide occupation-specific transition resources:

• AI-focused professional development: Associations like SHRM and HRCI increasingly offer AI-related programming that could be integrated with public workforce development.
• Networking and placement: Professional association job boards and networking events can supplement public job matching services.
• Credential recognition: Public workforce programs could incorporate professional certifications into training pathways, recognizing their labor market signaling value.
• Accessible membership: Public subsidy of professional association membership for lower-income workers could enhance access to association resources.

7. Conclusion and Future Directions

7.1. Summary of Contributions

This article has critically reviewed and extended the adaptive capacity framework introduced by Manning and Aguirre (2026), applying it specifically to human resource management and people management functions. Our analysis yields several contributions to theory and practice.

First, we have identified limitations of the original framework’s occupation-level aggregation, demonstrating that meaningful vulnerability variation exists within broad occupational categories. HR functions exemplify this within-occupation heterogeneity, encompassing transactional administrative roles with high-vulnerability profiles and strategic professional roles with substantially higher adaptive capacity. This finding suggests that occupation-level analyses, while valuable for broad labor market assessment, require supplementation with finer-grained role-level analysis for organizational and policy applications.

Second, we have developed a typology of HR functional roles—transactional administration, service delivery and employee relations, business partnership and strategic advisory, and centers of excellence—revealing pronounced bifurcation in exposure-capacity profiles. Transactional HR roles exhibit patterns consistent with vulnerable administrative occupations identified by Manning and Aguirre (2026), while strategic HR roles demonstrate patterns consistent with resilient professional occupations. We have addressed role hybridization and boundary conditions, recognizing that organizational size and structure moderate typology applicability. Table 1 provides empirical illustration mapping SOC codes to role clusters with available exposure and capacity indicators.

Third, we have proposed a multi-level adaptive capacity model grounded in strategic HRM theory, integrating individual, occupational, organizational, and institutional factors. Drawing on the resource-based view (Barney, 1991; Wright et al., 2001) and ability-motivation-opportunity framework (Appelbaum et al., 2000; Jiang et al., 2012), we have developed theoretical mechanisms explaining how factors at different levels interact to shape adaptive capacity. The model addresses limitations of occupation-level analysis by recognizing that organizational context and institutional resources substantially influence workforce resilience independent of individual and occupational characteristics.

Fourth, drawing on paradox theory (Smith & Lewis, 2011; Schad et al., 2016), we have developed a theoretically grounded analysis of how HR professionals navigate the tension between facilitating organizational transformation and experiencing their own occupational disruption. We have examined psychological and identity dimensions, behavioral dynamics and potential perverse incentives, and the paradox’s potential advantages when navigated skillfully.

Fifth, we have examined equity and inclusion implications of differential adaptive capacity, considering how AI-driven transformation may interact with existing workforce inequities. Transactional roles facing highest vulnerability may disproportionately employ women, workers without advanced degrees, and workers in lower-resource organizational contexts, potentially exacerbating existing labor market inequalities.

Sixth, we have developed practical implications for HR leaders navigating their own functional transformation, organizational strategists designing AI adoption approaches, and policymakers considering workforce development and social insurance provisions. These implications are grounded in both the theoretical framework and practical organizational realities.

7.2. Limitations

Our analysis has several limitations warranting acknowledgment.

Empirical foundation. The application to HR functional areas relies substantially on qualitative mapping of role characteristics to adaptive capacity components rather than empirical measurement with role-level data. While we have provided empirical illustration using available occupation-level data (Table 1), direct empirical validation with role-level data would strengthen confidence in the differential vulnerability assessments. We have positioned key assessments as theoretical propositions requiring empirical validation rather than established findings.

Model complexity. The multi-level model proposes numerous factors and interactions, but we have provided limited guidance on relative magnitude or empirical operationalization. Future research is needed to estimate which factors are most consequential and under what conditions interactions amplify or attenuate main effects.

Temporal dynamics. While we have acknowledged temporal dynamics and feedback loops conceptually, our analysis does not model how adaptive capacity evolves dynamically over time. AI capabilities, worker investments, organizational practices, and institutional provisions all change over time in ways that complicate static assessment.

National context. Our analysis focuses on the U.S. context, reflecting the data sources underlying the Manning and Aguirre (2026) framework. Institutional factors shaping adaptive capacity—including social insurance provisions, employment protection, and labor market regulation—vary substantially across national contexts. Extension to other national settings would require adaptation to local institutional arrangements.

Typology boundaries. While we have addressed role hybridization and generalist positions, our typology represents analytical ideal types rather than empirical categories. The mapping between typology clusters and actual organizational positions requires contextual translation.

7.3. Future Research Directions

Several directions for future research emerge from this analysis.

Empirical validation of within-occupation heterogeneity. Research using role-level or task-level data could empirically test whether adaptive capacity varies within occupational categories as our analysis suggests. Surveys combining detailed role descriptions (task inventories, time allocation across activities) with measures of the four adaptive capacity components would enable direct assessment of within-occupation variation. Linked employer-employee data could assess how organizational context moderates occupation-level patterns.

Longitudinal analysis of adaptive capacity dynamics. Research tracking how adaptive capacity evolves over time—through workforce development investments, career progression, organizational changes, and labor market shifts—would address the static measurement limitation. Longitudinal designs could assess whether proactive investments in reskilling translate into enhanced adaptive capacity as theorized, and whether anticipatory adaptation moderates displacement impacts.

Organizational-level moderators. Research examining how organizational characteristics moderate the relationship between occupational exposure and worker outcomes would test the organizational-level factors proposed in our multi-level model. Matched employer-employee data could assess whether strong internal labor markets, L&D investments, and transition support provisions reduce displacement costs as hypothesized. This research could also estimate relative magnitude of different organizational factors.

Cross-national comparative analysis. Research comparing adaptive capacity patterns across countries with different institutional arrangements would illuminate how national-level factors shape workforce resilience. Such research could inform policy design by identifying institutional configurations associated with more favorable adaptation outcomes. Comparison of HR function structures and transformation patterns across national contexts would be particularly valuable.

HR-specific transition outcomes. Research specifically examining career transitions of HR professionals following AI-related role changes would provide direct evidence on the exposure-capacity profiles proposed in our typology. Tracking whether transactional HR workers indeed face greater displacement challenges than strategic HR counterparts, and what factors predict successful transitions, would validate and refine our framework.

Paradox navigation strategies. Research examining how HR functions and professionals navigate the paradox of facilitating and experiencing transformation would inform practical recommendations. Case studies of HR functions that have successfully (or unsuccessfully) managed their own AI-driven transformation could identify effective navigation strategies and common pitfalls.

Equity impacts. Research tracking whether AI-driven displacement disproportionately affects workers from disadvantaged demographic groups within HR and other functions would inform equity-conscious policy and practice. Intersectional analysis examining how gender, race, education, age, and other characteristics interact with role-level vulnerability would be particularly valuable.

Emerging HR role development. Research examining how new HR roles (AI governance, people analytics leadership, employee experience design) emerge and how workers develop the competencies for these roles would inform career development and workforce planning for the HR profession.

7.4. Concluding Observations

The transformation of work through artificial intelligence presents challenges that will unfold over coming decades. The adaptive capacity framework introduced by Manning and Aguirre (2026) represents an important advance in understanding which workers possess resources enabling successful navigation of AI-induced displacement. Our extension to HR functional areas and development of a multi-level adaptive capacity model grounded in strategic HRM theory advances this research program by addressing within-occupation heterogeneity, organizational context, and institutional factors.

For HR professionals specifically, our analysis reveals both challenge and opportunity. The challenge lies in the bifurcation of HR functions between high-vulnerability transactional roles and more resilient strategic roles—a pattern requiring differentiated workforce planning and development strategies. The opportunity lies in HR’s potential to lead organizational AI adaptation, leveraging functional expertise in workforce strategy, change management, and organizational development to guide enterprises through technological transformation.

The paradox confronting HR—facilitating organizational transformation while experiencing personal and functional disruption—need not be paralyzing. Paradox theory suggests that tensions, when engaged constructively rather than avoided, can become sources of creativity and insight. HR professionals who successfully navigate their own transformation develop experiential credibility and empathetic understanding that enhance their capacity to guide others through similar challenges.

Preparing workers for AI-driven transformation requires understanding not merely which occupations face technological exposure, but which workers possess resources enabling successful adaptation when disruption occurs. By extending adaptive capacity frameworks to account for within-occupation heterogeneity, organizational context, institutional factors, and equity implications, we move toward more complete understanding of workforce resilience in an era of accelerating technological change. This understanding, in turn, provides foundation for organizational strategies and public policies that enhance adaptive capacity across the workforce, ensuring that the benefits of AI advancement are more broadly shared rather than concentrated among those already well-positioned to navigate labor market transitions.