Current Issue: AI at Work: The Journal of AI, Organizational Change, & Workplace Transformation
eISSN: 3068-6067
doi.org/10.70175/aiatworkjournal.2025
Volume 1 Issue 1 - Forthcoming
Research Advances Section
Received October 10, 2025; Accepted for publication November 5, 2025; Published Early Access November 9, 2025
Title: How AI Agents and Humans Approach Professional Work Differently—
Evidence and Strategies for Designing Effective Human-Agent Systems
Authors: Jonathan H. Westover, Western Governors University
Abstract: Artificial intelligence agents are rapidly emerging as potential collaborators—or substitutes—for human workers across diverse occupations, yet their behavioral patterns, strengths, and limitations remain poorly understood at the workflow level. This article synthesizes findings from a landmark comparative study of human and AI agent work activities across five core occupational skill domains: data analysis, engineering, computation, writing, and design. Drawing on workflow induction techniques applied to 112 computer-use trajectories, the analysis reveals that agents adopt overwhelmingly programmatic approaches even for visually intensive, open-ended tasks; produce lower-quality work masked by data fabrication and tool misuse; yet deliver outcomes 88.3% faster and at 90.4–96.2% lower cost. Human workflows remain largely unchanged when AI is used for augmentation (selective step-level assistance) but are substantially disrupted when AI is used for automation (end-to-end delegation). Evidence-based organizational responses include deliberate task delegation grounded in programmability assessment, workflow-inspired agent training, hybrid human-agent teaming optimized for accuracy and efficiency, and stronger visual and UI-interaction capabilities in next-generation systems. Long-term resilience depends on redefining skill requirements, investing in visual and multimodal foundation models, and establishing governance frameworks that balance efficiency gains with quality assurance, transparency, and worker protection.
Keywords: AI agents, workflow analysis, human-AI collaboration, occupational skills, task delegation, work automation, organizational adaptation, efficiency-quality tradeoffs
doi.org/10.70175/aiatworkjournal.2025.1.1.3
Suggested Citation:
Westover, Jonathan H. (2025). How AI Agents and Humans Approach Professional Work Differently—Evidence and Strategies for Designing Effective Human-Agent Systems. AI at Work: The Journal of AI, Organizational Change, & Workplace Transformation, 1(1). doi.org/10.70175/aiatworkjournal.2025.1.1.3
Research Advances Section
Received October 15, 2025; Accepted for publication October 24, 2025; Published Early Access October 26, 2025
Title: The Evolving AI Workforce: Understanding the Rise of Non-Technical Roles
in Artificial Intelligence Companies
Authors: Jonathan H. Westover, Western Governors University; Fei Tang, GrauntX
Abstract: This study examines workforce composition trends across artificial intelligence companies, focusing on the balance between technical and non-technical roles. Using a comprehensive dataset of 19 AI companies across six industry segments, we analyze employment patterns, job function distributions, and growth trends from 2023-2025. Our findings reveal a significant shift toward non-technical roles, particularly in sales, operations, and strategic functions, as AI companies mature. Foundation model leaders are increasingly investing in go-to-market capabilities, while enterprise platforms are reinforcing their sales functions. These patterns suggest that as AI technology matures, complementary organizational capabilities become crucial for commercial success. This research contributes to our understanding of industry life cycles in technology sectors and has implications for workforce development, educational institutions, and management strategy.
Keywords: artificial intelligence, workforce composition, technical roles, non-technical roles, industry segments, employment patterns, foundation models, enterprise platforms, go-to-market capabilities, industry life cycles
doi.org/10.70175/masteryjournal.2025.1.1.2
Suggested Citation:
Westover, Jonathan H. and Tang, Fei. (2025). The Evolving AI Workforce: Understanding the Rise of Non-Technical Roles in Artificial Intelligence Companies. AI at Work: The Journal of AI, Organizational Change, & Workplace Transformation, 1(1). doi.org/10.70175/aiatworkjournal.2025.1.1.2
Research Advances Section
Received September 30, 2025; Accepted for publication October 19, 2025; Published Early Access October 19, 2025
Title: Displaced but not Replaced: Reskilling Strategies for AI-Impacted Roles
Authors: Jonathan H. Westover, Western Governors University
Abstract: The accelerating deployment of artificial intelligence systems across industries creates both displacement risks and unprecedented opportunities for workforce transformation. This article examines evidence-based organizational strategies for reskilling employees whose roles face significant AI-induced change. Drawing on labor economics research, organizational psychology, and documented practitioner cases, the analysis reveals that successful reskilling initiatives combine transparent role evolution mapping, individualized learning pathways, psychologically safe experimentation spaces, and institutional commitment to internal mobility. Organizations implementing comprehensive reskilling programs demonstrate measurably higher retention rates, faster AI adoption curves, and sustained competitive advantage compared to those pursuing replacement strategies. The article synthesizes organizational performance impacts, individual wellbeing consequences, and effective intervention models across healthcare, financial services, manufacturing, and professional services sectors, concluding with frameworks for building adaptive workforce capabilities that enable humans and AI systems to generate complementary value.
Keywords: workforce reskilling, artificial intelligence displacement, organizational learning, human-AI collaboration, talent mobility, psychological safety, individualized learning pathways, role evolution, continuous learning culture, augmentation frameworks
doi.org/10.70175/aiatworkjournal.2025.1.1.1
Suggested Citation:
Westover, Jonathan H. (2025). Displaced but not Replaced: Reskilling Strategies for AI-Impacted Roles. AI at Work: The Journal of AI, Organizational Change, & Workplace Transformation, 1(1). doi.org/10.70175/aiatworkjournal.2025.1.1.1