Featuring scholarly and practitioner insights from HR and people leaders, industry experts, and researchers.

In this podcast episode, Dr. Jonathan H. Westover talks with Eric Saylors about how AI is a "cognitive acid test" for leaders. Eric Saylors is the author of Quantifying a Negative (2015), Sense Making in Risk Assessments (2022), and The 7-Year Active Shooter Study (2026). He is a Fire Chief with over 30 years of experience and holds a Doctorate in Leadership from the University of Southern California. In addition to serving as Product Lead for HEN Technologies, Eric currently serves as Fire Chief of the El Cerrito–Kensington Fire Department, having previously spent 25 years with the Sacramento City Fire Department. Over the course of his career, he has served in every rank from firefighter/paramedic to Fire Chief, including engineer, captain, battalion chief, and assistant chief. His operational experience includes assignments on ambulances, fire engines, ladder trucks, a Type 1 HazMat team, and a heavy rescue unit. He has responded to and managed a wide range of incidents, including high-rise fires, large commercial fires, wildland-urban interface incidents, basement fires, and balloon-frame construction fires. Prior to joining executive leadership, he served as Battalion Chief 1 in downtown Sacramento, one of the busiest operational areas in the region. In addition to his doctorate, Chief Saylors holds two associate degrees, a bachelor’s degree in finance, and a master’s degree in Security Studies from the Naval Postgraduate School. He regularly consults with fire departments nationwide on active shooter response, leadership development, succession planning, and post-incident analysis, and has published more than twenty articles on homeland security and fire service topics.

This research explores the organizational transition from conversational AI to agentic AI, which focuses on autonomous task delegation rather than simple information retrieval. While technical teams were the first to adopt these tools, non-technical departments are now integrating them at a much faster rate by leveraging established infrastructure and shared knowledge. The research argues that achieving true productivity gains requires restructuring workflows and shifting human roles toward verification and coordination rather than direct execution. Consequently, deep domain expertise remains vital, as experts are uniquely qualified to supervise complex automated processes and ensure quality control. To manage these shifts, the research advocates for staged rollouts, adaptive governance, and a cultural shift toward systematic AI integration across all business functions.

This research explores the organizational transition from conversational AI to agentic AI, which focuses on autonomous task delegation rather than simple information retrieval. While technical teams were the first to adopt these tools, non-technical departments are now integrating them at a much faster rate by leveraging established infrastructure and shared knowledge. The research argues that achieving true productivity gains requires restructuring workflows and shifting human roles toward verification and coordination rather than direct execution. Consequently, deep domain expertise remains vital, as experts are uniquely qualified to supervise complex automated processes and ensure quality control. To manage these shifts, the research advocates for staged rollouts, adaptive governance, and a cultural shift toward systematic AI integration across all business functions.

Abstract: This article examines the evolving patterns of agentic artificial intelligence adoption in organizational settings, with particular focus on the transition from conversational to task-delegating AI systems. Drawing on recent large-scale usage data and established technology diffusion theory, the analysis reveals that while technical workers remain early adopters of agentic AI tools, the most significant organizational shifts occur when non-technical functions rapidly integrate these capabilities. The evidence demonstrates that late-adopting departments often exhibit accelerated transition timelines compared to early adopters, suggesting that organizational learning and infrastructure development by pioneering groups reduce implementation friction for subsequent adopters. These patterns have important implications for workforce planning, organizational design, and the strategic deployment of AI capabilities across diverse business functions. The findings suggest that organizations should anticipate compressed adoption cycles as agentic AI diffuses beyond engineering teams, requiring proactive attention to workflow redesign, governance frameworks, and skill development across multiple organizational layers. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This research explores the organizational transition from conversational AI to agentic AI, which focuses on autonomous task delegation rather than simple information retrieval. While technical teams were the first to adopt these tools, non-technical departments are now integrating them at a much faster rate by leveraging established infrastructure and shared knowledge. The research argues that achieving true productivity gains requires restructuring workflows and shifting human roles toward verification and coordination rather than direct execution. Consequently, deep domain expertise remains vital, as experts are uniquely qualified to supervise complex automated processes and ensure quality control. To manage these shifts, the research advocates for staged rollouts, adaptive governance, and a cultural shift toward systematic AI integration across all business functions. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This research explores the organizational transition from conversational AI to agentic AI, which focuses on autonomous task delegation rather than simple information retrieval. While technical teams were the first to adopt these tools, non-technical departments are now integrating them at a much faster rate by leveraging established infrastructure and shared knowledge. The research argues that achieving true productivity gains requires restructuring workflows and shifting human roles toward verification and coordination rather than direct execution. Consequently, deep domain expertise remains vital, as experts are uniquely qualified to supervise complex automated processes and ensure quality control. To manage these shifts, the research advocates for staged rollouts, adaptive governance, and a cultural shift toward systematic AI integration across all business functions. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This research explores the organizational transition from conversational AI to agentic AI, which focuses on autonomous task delegation rather than simple information retrieval. While technical teams were the first to adopt these tools, non-technical departments are now integrating them at a much faster rate by leveraging established infrastructure and shared knowledge. The research argues that achieving true productivity gains requires restructuring workflows and shifting human roles toward verification and coordination rather than direct execution. Consequently, deep domain expertise remains vital, as experts are uniquely qualified to supervise complex automated processes and ensure quality control. To manage these shifts, the research advocates for staged rollouts, adaptive governance, and a cultural shift toward systematic AI integration across all business functions. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This research examines the systemic prevalence of academic bullying, highlighting how hierarchical structures and prestige-driven cultures enable persistent mistreatment within higher education. The research argues that traditional features like tenure and intense power imbalances often shield perpetrators, leading to severe consequences for research integrity, institutional productivity, and individual mental health. To address these issues, the research proposes evidence-based interventions, such as independent reporting systems, bystander activation programs, and structural reforms to mentorship models. It emphasizes that leadership accountability and psychological safety are essential for shifting academic culture away from normalized suffering toward genuine intellectual flourishing. Ultimately, the research serves as a call for institutions to align their stated values with the lived experiences of students and faculty through proactive, data-driven reform.