Current Issue: AI Strategy: The Journal of Organizational AI Implementation, Transformation, and Competitive Advantage
eISSN: 3144-590X
doi.org/10.70175/aistrategy.2026
Volume 1 Issue 1 (Download Issue)
Received March 10, 2026; Accepted for publication August 1, 2026; Published Early Access September 10, 2026
Title: Adaptive Capacity in the Age of Artificial Intelligence: A Critical Extension of
Workforce Resilience Frameworks to Human Resource Management Functions
Authors: Jonathan H. Westover, Utah Valley University, Future State University, Work & AI,
Catalyst Center for Work Innovation
Abstract: The rapid advancement of artificial intelligence (AI) technologies presents unprecedented challenges for workforce management, particularly within human resource (HR) and people management functions that simultaneously face high AI exposure and serve as organizational architects of workforce adaptation. This article critically reviews and extends the emerging adaptive capacity framework introduced by Manning and Aguirre (2026), which measures occupation-level worker characteristics relevant for navigating job transitions following AI-induced displacement. While their framework advances understanding of differential workforce vulnerability, its occupation-level aggregation obscures critical within-function heterogeneity, particularly in HR domains where roles range from transactional administration to strategic business partnership. We extend the adaptive capacity framework by applying it specifically to HR functional areas, disaggregating people management occupations into distinct role clusters with varying exposure-capacity profiles. Drawing on strategic HRM theory, including the resource-based view and ability-motivation-opportunity frameworks, we develop a multi-level adaptive capacity model integrating individual, occupational, organizational, and institutional factors. Our analysis reveals that HR functions exhibit pronounced bifurcation: transactional and administrative HR roles demonstrate high AI exposure coupled with low adaptive capacity, while strategic HR business partners and organizational development specialists show moderate exposure with substantially higher adaptive capacity. Using paradox theory, we examine how HR practitioners must navigate the tension between facilitating organizational AI adaptation and experiencing their own occupational transformation. We also address equity implications, examining how differential adaptive capacity may interact with existing workforce inequities. The article offers both theoretical refinement and practical guidance for HR leaders, policymakers, and management scholars concerned with workforce resilience in an era of accelerating technological change.
Keywords: artificial intelligence, adaptive capacity, human resource management, workforce resilience, technological displacement, strategic HRM, paradox theory, labor economics
doi.org/10.70175/aistrategy.2026.1.1.1
Suggested Citation:
Westover, Jonathan H. (2026). Adaptive Capacity in the Age of Artificial Intelligence: A Critical Extension of Workforce Resilience Frameworks to Human Resource Management Functions. AI Strategy: The Journal of Organizational AI Implementation, Transformation, and Competitive Advantage, 1(1). doi.org/10.70175/aistrategy.2026.1.1.1
Received March 20, 2026; Accepted for publication August 15, 2026; Published Early Access September 12, 2026
Title: Building an AI-First HR Organization: Transforming People Operations for the Digital Age
Authors: Jonathan H. Westover, Utah Valley University, Future State University, Work & AI,
Catalyst Center for Work Innovation
Abstract: This mixed-methods study examines how organizations develop AI-first HR functions by fundamentally reimagining people operations rather than merely adopting new technologies. Drawing on survey data from 152 HR leaders, 24 in-depth interviews, and longitudinal case studies of 8 organizations during 2022-2024, the research identifies three foundational pillars of successful AI-HR integration: augmented capabilities, data-driven culture, and ethical governance. Organizations implementing AI-first approaches demonstrate significant improvements in talent acquisition efficiency (38%), performance management effectiveness (27%), and learning personalization (41%). However, implementation success is hindered by data quality issues (74% of organizations), capability gaps (68%), and employee resistance (61%). Statistical analysis reveals that organizations emphasizing augmentation over automation achieve significantly higher implementation success rates (p < .01), with change management effectiveness emerging as the strongest predictor of successful adoption (β = .42, R² = .53). The resulting evidence-based implementation framework provides practical guidance for organizations navigating this complex transformation, highlighting the critical importance of strategic alignment, process redesign, data architecture, capability development, change management, and ethical governance in realizing the potential of AI-HR integration.
Keywords: AI-first HR, people operations, augmented capabilities, ethical governance, talent acquisition, performance management, change management, implementation framework, data-driven culture, learning personalization
doi.org/10.70175/aistrategy.2026.1.1.2
Suggested Citation:
Westover, Jonathan H. (2026). Building an AI-First HR Organization: Transforming People Operations for the Digital Age. AI Strategy: The Journal of Organizational AI Implementation, Transformation, and Competitive Advantage, 1(1). doi.org/10.70175/aistrategy.2026.1.1.2
Received April 10, 2026; Accepted for publication August 22, 2026; Published Early Access September 15, 2026
Title: Navigating the AI Revolution: A Critical Framework for Organizational Transformation
Authors: Jonathan H. Westover, Utah Valley University, Future State University, Work & AI,
Catalyst Center for Work Innovation
Abstract: This research article examines the imminent transformation of business and labor structures driven by artificial intelligence, critically analyzing adoption timelines and organizational impacts. Drawing on both primary survey data from 127 organizations and secondary research from institutional reports, the study quantifies workforce disruption projections while interrogating their methodological limitations. Through mixed-methods analysis combining quantitative industry data with qualitative case studies, the research presents an evidence-based three-dimensional strategic framework for organizational response: comprehensive upskilling that fosters behavioral change rather than mere tool adoption; an all-organization approach enabling distributed innovation; and strategic integration aligning systems and structures across departments. The study acknowledges significant implementation barriers including regulatory uncertainty, organizational resistance, and ethical considerations. It concludes that organizational adaptation capacity—not merely technological investment—will determine which organizations successfully navigate this period of transformation, while recognizing that adoption timelines may vary significantly across geographic and industry contexts.
Keywords: Artificial intelligence, organizational transformation, workforce disruption, upskilling, strategic integration, distributed innovation, implementation barriers, regulatory uncertainty, organizational resistance, ethical considerations, adoption timelines, mixed-methods analysis, behavioral change, adaptation capacity
doi.org/10.70175/aistrategy.2026.1.1.3
Suggested Citation:
Westover, Jonathan H. (2026). Navigating the AI Revolution: A Critical Framework for Organizational Transformation. AI Strategy: The Journal of Organizational AI Implementation, Transformation, and Competitive Advantage, 1(1). doi.org/10.70175/aistrategy.2026.1.1.3
Received May 10, 2026; Accepted for publication August 25, 2026; Published Early Access September 10, 2026
Title: How Generative AI is Reshaping Work, Skills, and Organizational Strategy
Authors: Jonathan H. Westover, Utah Valley University, Future State University, Work & AI,
Catalyst Center for Work Innovation
Abstract: This research examines how generative artificial intelligence, large language models, and machine learning are transforming work across industries. Through a mixed-methods approach comprising interviews with 42 organizational leaders (January-June 2024), a survey of 157 knowledge workers, and comparative analysis of 18 implementation case studies, the study develops an integrated sociotechnical framework for understanding AI's impact. Findings indicate that generative AI is shifting from task automation to cognitive augmentation, with implementation success varying significantly based on organizational context and approach. Quantitative results show that organizations employing comprehensive strategic approaches achieved 3.2 times higher ROI than those with narrower implementations. Distributional analysis reveals uneven impact patterns, with knowledge workers in higher-status positions experiencing 27% greater augmentation benefits than those in routine roles. The research contributes to sociotechnical systems theory by identifying specific mechanisms through which technological capabilities interact with organizational factors to reshape work. Implications for theory, practice, and policy are discussed, with particular attention to how organizations can implement these technologies while mitigating potential workforce inequalities.
Keywords: generative AI, large language models, machine learning, cognitive augmentation, sociotechnical framework, knowledge workers, task automation, workforce inequality, organizational strategy, mixed-methods
doi.org/10.70175/aistrategy.2026.1.1.4
Suggested Citation:
Westover, Jonathan H. (2026). How Generative AI is Reshaping Work, Skills, and Organizational Strategy. AI Strategy: The Journal of Organizational AI Implementation, Transformation, and Competitive Advantage, 1(1). doi.org/10.70175/aistrategy.2026.1.1.4
Received May 20, 2026; Accepted for publication September 15, 2026; Published Early Access September 25, 2026
Title: When Experts Get the Future Wrong: Strategic Planning in an Era of
Systematically Underestimated AI Progress
Authors: Jonathan H. Westover, Utah Valley University, Future State University, Work & AI,
Catalyst Center for Work Innovation
Abstract: Recent evidence from the Forecasting Research Institute reveals a striking pattern: domain experts and superforecasters have systematically underestimated the pace of artificial intelligence progress across capability benchmarks, economic adoption indicators, and diffusion metrics. This article examines the organizational and strategic implications of this persistent forecasting bias. Drawing on cognitive psychology, strategic foresight literature, and emerging enterprise data, the article explores why human forecasters—even highly credentialed ones—anchor on linear mental models when confronted with exponential technological change. It then outlines evidence-based organizational responses for leaders who must make investment, workforce, and governance decisions against a backdrop of radical uncertainty. These responses include scenario-based planning architectures, adaptive resource allocation, workforce capability building, and governance frameworks designed for rapid recalibration. The article concludes with forward-looking pillars for building lasting organizational resilience in a world where the best available expert forecasts may consistently trail reality.
Keywords: artificial intelligence forecasting, expert prediction bias, exponential technological change, strategic foresight, organizational resilience, scenario-based planning, adaptive resource allocation, workforce capability building, AI governance, radical uncertainty
doi.org/10.70175/aistrategy.2026.1.1.5
Suggested Citation:
Westover, Jonathan H. (2026). When Experts Get the Future Wrong: Strategic Planning in an Era of Systematically Underestimated AI Progress. AI Strategy: The Journal of Organizational AI Implementation, Transformation, and Competitive Advantage, 1(1). doi.org/10.70175/aistrategy.2026.1.1.5
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Orem, Utah, USA