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

In this HCI Webinar, Dr. Jonathan H. Westover talks with Josefine Campbell about developing your strengths to create a positive change. Josefine Campbell is a distinguished executive coach, educator, and inspirational speaker who empowers multinational leaders, companies, and employees to unleash their full potential. Through meaningful conversations, she elevates clients' situations by maintaining an open, non-judgmental mindset, sharing data-driven insights that challenge conventional thinking, and emphasizing actionable outcomes that create tangible impact.

A significant transformation is underway in workplaces, driven by artificial intelligence (AI) that is revolutionizing how employee turnover and management effectiveness are understood and addressed. As of January 7th, 2026, AI technologies are capable of detecting subtle indicators from digital interactions such as emails, meetings, and learning tool usage to predict when employees might be considering leaving their jobs. This early warning system provides leaders with invaluable intelligence, allowing them to intervene proactively to retain valuable talent. Highlights 🤖 AI detects subtle digital signals predicting employee turnover before it happens. 🔍 People often leave managers, not companies—AI reveals this with clarity. 📉 Traditional annual surveys and exit interviews are slow and general, hiding localized problems. 💸 Poor management triggers costly turnover, burnout, and loss of organizational knowledge. 🔄 AI enables real-time, daily insights into management effectiveness and team health. 🤝 Emphasizing coaching and developmental feedback over punishment builds trust and growth. 🔐 Privacy, transparency, and fair governance are essential when using AI-driven management analytics. Key Insights 🤖 AI as an Early Warning System: The adoption of AI to analyze patterns across digital work interactions—emails, meetings, and learning tools—marks a shift from reactive to proactive talent management. By predicting potential turnover days, weeks, or even months before it manifests, organizations gain lead time to intervene, potentially saving significant costs and preserving institutional knowledge. 👥 Managerial Impact on Retention: The longstanding belief that employees leave bad managers, not companies, is now quantifiable with AI data. This revelation shifts accountability to individual managers rather than diffuse organizational blame, highlighting the need for personalized leadership development and targeted interventions at the managerial level. 📊 Limitations of Traditional Feedback Tools: Annual surveys and exit interviews, while historically useful, fail to capture the nuanced and dynamic nature of workplace morale and leadership effectiveness. They provide only a snapshot, often averaging out critical localized issues and allowing toxic managerial behaviors to persist unnoticed between survey cycles. 💥 The Vicious Cycle of Poor Management: Ineffective leadership cascades into tangible organizational damage—financial losses from turnover, project delays, burnout, and attrition. This cycle erodes psychological safety, causing employees to withdraw, avoid risks, and diminish innovation and collaboration. AI exposes these patterns early, offering a chance to break the cycle. 🛤 The Fork in the Road – Punishment vs. Development: The introduction of AI-driven managerial insights presents a dilemma: organizations can either use these tools to penalize managers, risking fear, gaming of metrics, and erosion of trust, or they can adopt a coaching-oriented approach. The latter fosters a culture of continuous learning, transparency, and improvement, which is ultimately more sustainable. 🔍 The Importance of Transparent, Explainable AI: For AI-driven management tools to succeed, stakeholders must trust the data and models. Transparency about how metrics are defined, how conclusions are reached, and what data is collected is essential. Managers should have the opportunity to review and contextualize their data, ensuring fairness and preventing misinterpretation. 🔐 Protecting Privacy and Ensuring Fair Governance: Balancing the power of AI insights with employee privacy is crucial. Data must be aggregated and anonymized to avoid singling out individuals unfairly. Governance structures should safeguard against misuse, ensuring that insights focus on team-level trends and skill development rather than punitive individual scrutiny. 🌱 Psychological Safety as a Core Skill: AI analytics highlight psychological safety as the most critical managerial skill influencing retention and team health. Managers must learn to invite honest input, respond constructively, and foster environments where employees feel safe to take risks, ask questions, and innovate. Regular, authentic growth conversations anchored in AI insights can cultivate this environment. If you found this helpful, please like and share the video. #PeopleAnalytics #AIinHR #ManagerialAccountability #Retention #LeadershipDevelopment OUTLINE: 00:00:00 - AI Reveals Hidden Truths 00:00:58 - The Limits of Yesterday's Tools 00:01:47 - The Cost of a Blind Spot 00:02:46 - Using Data For Growth

In this podcast episode, Dr. Jonathan H. Westover talks with Josefine Campbell about developing your strengths to create a positive change. Josefine Campbell is a distinguished executive coach, educator, and inspirational speaker who empowers multinational leaders, companies, and employees to unleash their full potential. Through meaningful conversations, she elevates clients' situations by maintaining an open, non-judgmental mindset, sharing data-driven insights that challenge conventional thinking, and emphasizing actionable outcomes that create tangible impact. Check out all of the podcasts in the HCI Podcast Network (https://www.podbean.com/podcast-network/HCI) !

This conversation explores how AI-enabled people analytics are transforming workplace management by making managerial performance visible through real-time data. While traditional metrics often obscured localized issues, machine learning can now link high employee turnover and low engagement to specific supervisors rather than just broad company policies. The text argues that organizations face a choice between using these insights for punitive monitoring or as a catalyst for leadership development and coaching. To succeed, companies must implement robust data governance and ensure that accountability is handled with procedural justice. Ultimately, they suggest that embracing this transparency allows businesses to address root causes of attrition and foster a culture of continuous improvement. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Abstract: Artificial intelligence is transforming organizational capabilities in talent analytics, enabling real-time detection of retention patterns previously obscured by aggregated metrics and delayed feedback cycles. This shift threatens to expose a longstanding organizational blind spot: the localized nature of attrition, engagement decline, and talent development failures that cluster around individual managers rather than systemic policies. Drawing on research in people analytics, psychological safety, and leadership development, this article examines how AI-driven insights will make managerial performance visible in unprecedented ways, creating both accountability pressures and developmental opportunities. We explore evidence-based organizational responses including transparent coaching systems, capability-building frameworks, and governance structures that position data as a developmental tool rather than a punitive mechanism. Organizations that proactively address this transition can transform retention from a lagging HR metric into a dynamic leadership development signal, while those that delay face cultural backlash, legal risks, and accelerated talent loss among their strongest performers.

ThIS video highlights a critical, often overlooked cost in traditional organizational management—the reliance on outdated hierarchical org charts. These charts, depicting a neat, top-down structure, create a false sense of order and control but fail to capture the complex, dynamic networks of relationships that truly drive a company’s performance. The world and its businesses have become intricately connected systems where success depends on understanding not just roles and reporting lines but the invisible webs of interactions, trust, and information flow between people, teams, and technologies. Highlights 🔍 Traditional org charts create a false sense of control but hide critical informal networks. 🌐 Organizations function as complex networks of nodes (people, teams, tech) and edges (relationships). 🏥 Real-world examples reveal how ignoring informal connections can cripple operations and risk safety. 🚀 Understanding and leveraging networks fosters resilience, innovation, and faster problem-solving. 📊 Shift leadership focus from static charts to dynamic network graphs for deeper insight. 🤖 AI operates on network patterns, so leaders must see and manage these connections to stay competitive. 🌱 Future leadership requires cultivating relationships, not just managing roles—leading the graph, not the org chart. Key Insights 🔗 The Network Model Replaces the Hierarchy Model: The traditional hierarchical org chart is insufficient for today’s complex organizational realities. Viewing companies as networks of interconnected nodes and edges better reflects how work actually gets done. This mental model shift is crucial for leaders to unlock hidden efficiencies and innovation. Understanding the network enables leaders to see beyond formal roles to the actual flows of information, influence, and collaboration that drive outcomes. 🏥 Invisible Connections Have Real Consequences: The hospital example illustrates that critical contributions often come from informal relationships that don’t appear on org charts, such as a nurse’s vigilance or a technician’s rapport with the pharmacy. Disrupting these informal networks can jeopardize performance and safety. This insight underscores the high stakes of network blindness, where leaders’ inability to see these connections can cause costly errors and operational breakdowns. 🌪️ Network Awareness Enables Agility During Crises: The manufacturing example shows how network insight can turn a potential supply chain disaster into a manageable disruption. Recognizing informal ties to secondary suppliers through network maps allows rapid problem-solving that hierarchical views miss. This demonstrates how network visibility builds organizational resilience and agility in times of uncertainty, a capability increasingly vital in a volatile global economy. 🔍 Start Small, Focused, and Combine Data with Stories: Leaders don’t need to map entire organizations at once. Beginning with a focused question and combining quantitative data (surveys, communication flows) with qualitative insights (interviews, narratives) creates a rich understanding of the network dynamics at play. This pragmatic approach makes network analysis accessible and actionable, accelerating adoption and delivering immediate value. 🌉 Leadership Is About Enabling Connections, Not Just Controlling Roles: The analogy of traffic cops versus city planners illustrates the fundamental leadership shift needed. Rather than rigidly directing workflows through fixed structures, leaders must design and nurture a system of connections enabling information and collaboration to flow naturally. This mindset embraces influence and enablement over mere control, aligning with the demands of a fast-paced, innovation-driven environment. 🤖 AI’s Power Lies in Network Understanding: AI excels at identifying patterns and optimizing flows within complex networks. Leaders who grasp their organizational networks can leverage AI to amplify influence, optimize collaboration, and uncover hidden influencers. Conversely, managers who cling to outdated hierarchical models risk being outpaced by AI-augmented competitors. Mastery of network thinking is a prerequisite for effectively integrating AI into leadership and decision-making. 🌱 The Future of Leadership Requires New Vision and Tools: The old org charts represent a world that no longer exists. The future belongs to leaders who see and shape the intricate dance of collaboration beneath the surface. This requires new tools, perspectives, and skills to visualize and influence human connections dynamically. Leading in the age of AI and complexity demands a vision that embraces networks as the core architecture of organizational success, enabling companies to innovate, adapt, and thrive. #GraphThinking #AILeadership #OrganizationalNetworkAnalysis #DigitalTransformation #ONA

Abstract: Organizations traditionally optimized through linear hierarchies face a fundamental challenge as artificial intelligence transforms business operations: the inability to perceive and manage complex networks. This brief examines "graph thinking"—the capacity to understand organizational and ecosystem structures as interconnected networks rather than linear processes—as an emergent leadership competency essential for AI integration and strategic resilience. Drawing on network science, organizational theory, and digital transformation research, the analysis demonstrates how graph-literate leaders diagnose hidden dependencies, protect critical relationship nodes, and architect contexts that enable human-AI collaboration. Evidence from platform companies reveals graph thinking as foundational to AI leadership advantage, while cases across healthcare, manufacturing, and services illustrate consequences of network blindness. The brief synthesizes evidence-based interventions—network mapping protocols, betweenness analysis, edge quality assessment, and ecosystem density optimization—alongside frameworks for building long-term network intelligence capabilities. As AI agents require explicit relationship architectures that human workers navigate implicitly, graph thinking transitions from technical specialty to core strategic competence, determining which organizations successfully integrate intelligent systems into collaborative workflows.

Modern organizations are shifting from linear hierarchies to interconnected networks to successfully integrate artificial intelligence. This transition requires a leadership competency known as graph thinking, which involves perceiving the enterprise as a complex web of nodes and relationships rather than a vertical chart. Leaders who master this mindset can identify critical bridge positions, reduce information delays, and build resilient ecosystems that traditional management styles often overlook. By making implicit social and operational connections explicit and machine-readable, graph-literate organizations create the necessary architecture for effective human-AI collaboration. Ultimately, those who fail to move beyond industrial-era logic risk systemic fragility and the inability to scale intelligent technologies. This conversation emphasizes that network intelligence is now a primary driver of strategic advantage and long-term viability in an automated world. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.