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

In the dynamic and ever-evolving world of organizational change, the role of the change champion has become increasingly critical. I've witnessed firsthand the transformative impact that capable and committed champions can have on the success of change initiatives. In this module introduction, we'll explore the key attributes of a capable change champion, delve into the importance of influencing without authority, and outline the seven practices for cultivating these invaluable catalysts of change. We'll also reflect on the critical process of entering and contracting as OD practitioners. In today's rapidly evolving business environment, the role of the change champion has become increasingly critical to the success of organizational transformation initiatives. As we've explored in this module introduction, these invaluable catalysts of change possess a unique blend of personal and professional attributes that enable them to drive sustainable transformation from within the organization. By cultivating a network of capable change champions and empowering them to influence without authority, organizations can dramatically increase the likelihood of successful change and unlock the full potential of their transformation efforts. As OD practitioners, we play a pivotal role in supporting organizations through the process of organizational change, beginning with the critical phase of entering and contracting. By establishing a collaborative partnership with our clients and laying the foundation for a trust-based engagement, we can more effectively support the change champions, navigate the complexities of organizational transformation, and drive sustainable, impactful change.

Artificial intelligence is shifting from individual tools toward computational collectives, where specialized digital agents coordinate to perform complex tasks. This research highlights that while these AI groups mimic human organizational structures, their success depends on contextual transaction costs and architectural design rather than social factors like trust. Simply imitating human hierarchies often leads to failure; instead, effective systems prioritize shared-state memory and adversarial verification to maintain accuracy. As these collectives integrate into professional workflows, the role of human workers transitions from execution to strategic validation and accountability oversight. Organizations must develop new interface structures and transparent audit trails to ensure AI collaboration remains reliable and ethically sound. Ultimately, the research argues that mastering context architecture is essential for capturing the true benefits of collective machine intelligence.

In the ever-evolving landscape of organizational change, the role of middle managers has taken on increasing significance. I've witnessed firsthand the critical contributions that middle managers can make in driving successful transformation. In this module introduction, we'll explore the evolving world of middle management, recognize the key roles they play in contributing to change, and outline the seven practices for increasing their involvement. We'll also reflect on the different facets of the service learning project, and discuss the importance of data gathering in supporting organizational change. In today's rapidly evolving business environment, the role of middle managers has become increasingly critical to the success of organizational transformation initiatives. As we've explored in this module introduction, middle managers are uniquely positioned to serve as catalysts for change, leveraging their deep contextual knowledge, cross-functional relationships, and problem-solving skills to drive sustainable transformation from within the organization. By actively engaging and empowering middle managers, organizations can dramatically increase the likelihood of successful change and unlock the full potential of their transformation efforts. This involves clearly defining their roles, providing them with targeted training and support, and fostering a culture of collaboration, empowerment, and continuous improvement. As OD practitioners, we play a crucial role in supporting organizations through the process of change, leveraging robust data gathering to inform the design and implementation of targeted interventions. The service learning project has been instrumental in bridging the gap between academic theory and real-world practice, equipping our students with the skills and practical expertise needed to drive successful organizational transformation.

In the dynamic and ever-evolving world of organizational change, the role of the change champion has become increasingly critical. I've witnessed firsthand the transformative impact that capable and committed champions can have on the success of change initiatives. In this module introduction, we'll explore the key attributes of a capable change champion, delve into the importance of influencing without authority, and outline the seven practices for cultivating these invaluable catalysts of change. We'll also reflect on the critical process of entering and contracting as OD practitioners. In today's rapidly evolving business environment, the role of the change champion has become increasingly critical to the success of organizational transformation initiatives. As we've explored in this module introduction, these invaluable catalysts of change possess a unique blend of personal and professional attributes that enable them to drive sustainable transformation from within the organization. By cultivating a network of capable change champions and empowering them to influence without authority, organizations can dramatically increase the likelihood of successful change and unlock the full potential of their transformation efforts. As OD practitioners, we play a pivotal role in supporting organizations through the process of organizational change, beginning with the critical phase of entering and contracting. By establishing a collaborative partnership with our clients and laying the foundation for a trust-based engagement, we can more effectively support the change champions, navigate the complexities of organizational transformation, and drive sustainable, impactful change.

In this HCI Webinar, I talk with Kathi Enderes about The Josh Bersin Company roadmap for the embracing of agentic AI into HR by 2030. Kathi Enderes is Senior Vice President of Research and a global industry analyst at the human capital advisory firm The Josh Bersin Company, the world's largest community for HR. She has over 20 years global experience in human capital, talent and performance management, and change management from consulting with IBM, PwC and EY and industry, working with companies of various sizes, from Fortune 50 companies to start ups, in multiple industries including technology and healthcare, and leading research on all topics of HR, talent and technology. She is passionate about making work better and more meaningful.

Artificial intelligence is shifting from individual tools toward computational collectives, where specialized digital agents coordinate to perform complex tasks. This research highlights that while these AI groups mimic human organizational structures, their success depends on contextual transaction costs and architectural design rather than social factors like trust. Simply imitating human hierarchies often leads to failure; instead, effective systems prioritize shared-state memory and adversarial verification to maintain accuracy. As these collectives integrate into professional workflows, the role of human workers transitions from execution to strategic validation and accountability oversight. Organizations must develop new interface structures and transparent audit trails to ensure AI collaboration remains reliable and ethically sound. Ultimately, the research argues that mastering context architecture is essential for capturing the true benefits of collective machine intelligence.

Abstract: Artificial intelligence systems are evolving beyond individual assistants into organized collectives of specialized agents—planners, reviewers, synthesizers, and memory managers—that coordinate work before outputs reach human decision-makers. This article examines the organizational behavior of agentic AI and its implications for human work, drawing on recent computational research alongside established organization theory. While agent collectives exhibit familiar organizational patterns—role differentiation, boundary work, routines, and collective outcomes—their coordination mechanisms differ fundamentally from human organizations. Instead of trust, authority, or professional identity, agent coordination depends on context architecture: prompts, schemas, memory structures, and validation rules. The article introduces contextual transaction cost as a core mechanism explaining when multi-agent forms create value versus dysfunction. Evidence from computational studies reveals that familiar organizational forms—hierarchies, committees, pipelines—often underperform in agentic systems when they prioritize human-like structure over context preservation. Organizations embedding agentic AI must therefore design interface structures that align computational context coordination with human accountability requirements. The practical implication extends beyond technology adoption to fundamental questions of organizational design: how to audit agent traces, where to preserve human judgment, and when collective AI intelligence enhances rather than obscures decision quality. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Artificial intelligence is shifting from individual tools toward computational collectives, where specialized digital agents coordinate to perform complex tasks. This research highlights that while these AI groups mimic human organizational structures, their success depends on contextual transaction costs and architectural design rather than social factors like trust. Simply imitating human hierarchies often leads to failure; instead, effective systems prioritize shared-state memory and adversarial verification to maintain accuracy. As these collectives integrate into professional workflows, the role of human workers transitions from execution to strategic validation and accountability oversight. Organizations must develop new interface structures and transparent audit trails to ensure AI collaboration remains reliable and ethically sound. Ultimately, the research argues that mastering context architecture is essential for capturing the true benefits of collective machine intelligence. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.