Explore Dr. Jonathan H. Westover’s insights in this 5-minute explainer: "Automation Isn’t Killing Jobs—The Contract Is Changing." Learn how AI, robotics, and algorithmic management are redefining job security — from guaranteed roles to guaranteed employability — and discover evidence-based responses: transparent transition planning, skills-based redeployment, participatory automation design, hybrid human‑machine models, and continuous learning. If this helped you, please like and share to spread practical strategies for sustainable automation. #Automation #FutureOfWork #JobSecurity #Reskilling #HRStrategies Hashtags Mention

In this HCI Webinar, Dr. Jonathan H. Westover talks with Jan Yuhas and Jillian Yuhas about navigating team conflict and balancing resolution and productivity in the modern workplace. Jan Yuhas, M.A., MFT, and Jillian Yuhas, M.A., MFT, are Conflict and Communication Strategists and International Best-Selling Authors of Boundary Badass. They specialize in helping business leaders and teams master communication that transforms conflict into growth opportunities, cultivates psychological safety, and develops resilient organizations rooted in collaboration and trust. Learn more at ⁠www.twentyeightconsultancy.com⁠.

Artificial intelligence (AI) offers groundbreaking capabilities such as coding, medical scan analysis, and predictive analytics. However, many organizations fail to realize these benefits due to a fundamental disconnect termed the “integration gap.” This gap arises because existing organizational structures and processes are not designed to accommodate or leverage advanced AI tools effectively. AI’s potential is vast, but business value only emerges when AI and human work are seamlessly integrated through redesigned jobs, workflows, and governance frameworks. Highlights 🤖 AI’s revolutionary potential is hindered by outdated organizational structures, creating an integration gap. ⚙️ Accelerating parts of a workflow with AI without redesigning the whole process often creates bottlenecks. 👥 AI changes human roles, causing anxiety and resistance unless roles and responsibilities are clearly redefined. ⚖️ Lack of governance leads to risks like biased decisions, legal liabilities, and operational failures, especially in regulated industries. 🔍 Small, controlled pilots and experiments are essential to safely integrate AI and learn what works. 🗣️ Transparent, honest communication from leadership is critical to foster trust and effective AI adoption. 🌱 The ultimate goal is to augment human capabilities through AI, not to replace people. Key Insights 🤖 Integration Gap as a Core Barrier: The core obstacle to AI value realization is not the technology itself but the organizational mismatch. This insight shifts the focus from acquiring AI tools to transforming organizational design, processes, and culture to fully harness AI’s benefits. It highlights that AI’s power is only as effective as the environment in which it operates. ⚙️ Systemic Workflow Redesign is Crucial: AI tools often speed up isolated tasks, but without holistic redesign of interconnected workflows, new bottlenecks emerge. This reveals the importance of viewing organizations as complex systems rather than isolated processes. Effective AI integration requires systemic thinking to synchronize all parts of the workflow for genuine efficiency gains. 👥 Human Role Transformation Requires Leadership Attention: Automating routine tasks changes job definitions and employee expectations, often causing fear and resistance. This insight underscores that AI adoption is as much a human and cultural challenge as a technological one. Proactive role redefinition and clear communication are vital to maintain morale, accountability, and productivity. ⚖️ Governance is a Non-Negotiable for Safe AI Use: AI’s susceptibility to bias, errors, and risk necessitates a structured governance framework. Without clear rules on when to trust AI, how to verify outputs, and who is accountable, organizations expose themselves to significant financial, legal, and reputational harm. This is especially true in sensitive domains like finance and healthcare where compliance and fairness are paramount. 🔍 Iterative Experimentation Enables Safe and Effective AI Integration: Since no universal AI integration blueprint exists, organizations must adopt a learning mindset, using small-scale experiments to test redesigns in work, roles, and governance. This reduces risk, accelerates learning, and fosters innovation by replacing fear with curiosity. It also supports continuous adaptation in a rapidly evolving AI landscape. 🗣️ Radical Transparency Builds Trust and Alignment: Transparent communication from leadership about the purpose, scope, and uncertainties of AI-driven changes is essential. This builds credibility, reduces anxiety, and helps align teams around a shared vision where AI amplifies human capabilities. Honesty about unknowns also encourages collaborative problem-solving rather than resistance. 🌱 Augmentation, Not Replacement, is the Vision: The ultimate aspiration for AI integration is to augment human skills and decision-making rather than eliminate human roles. This mindset shapes how organizations approach AI adoption, ensuring that technology empowers employees and enhances outcomes rather than creating fear of displacement. It calls for a human-centric approach to AI strategy. #AIIntegration #OrganizationalChange #WorkRedesign #AILeadership #ChangeManagement #Experimentation OUTLINE: 00:00:00 - The Real Reason AI Isn't Working for You 00:01:17 - Outdated Workflows in an AI World 00:02:27 - Role Confusion and Human Anxiety 00:03:51 - The Wild West of AI Governance 00:04:59 - How to Fix It Now

Abstract: Organizations have moved beyond questioning whether artificial intelligence delivers value. The critical challenge has shifted to organizational integration: restructuring work, redefining roles, and redesigning processes to capture demonstrated AI value while managing risks inherent in sociotechnical transformation. This article examines the AI integration gap—the distance between technical capability and organizational value realization—and synthesizes evidence on effective change leadership practices. Drawing on organizational change theory, technology adoption research, and emerging practitioner accounts, it identifies patterns in how leading organizations navigate structural ambiguity when established implementation models do not exist. The analysis reveals that successful AI integration requires simultaneous attention to work redesign, capability development, governance frameworks, and psychological contracts, with experimentation emerging as the dominant change methodology in the absence of proven blueprints.

In this podcast episode, Dr. Jonathan H. Westover talks with Jan Yuhas and Jillian Yuhas about navigating team conflict and balancing resolution and productivity in the modern workplace. Jan Yuhas, M.A., MFT, and Jillian Yuhas, M.A., MFT, are Conflict and Communication Strategists and International Best-Selling Authors of Boundary Badass. They specialize in helping business leaders and teams master communication that transforms conflict into growth opportunities, cultivates psychological safety, and develops resilient organizations rooted in collaboration and trust. Learn more at www.twentyeightconsultancy.com (http://www.twentyeightconsultancy.com/)   Check out all of the podcasts in the HCI Podcast Network (https://www.podbean.com/podcast-network/HCI) !

This video discusses the challenges faced by the field of Organizational Development (OD) due to its lack of clear, standardized skills and competencies. Unlike professions such as plumbing, accounting, or engineering, OD has historically been vague and unstructured, making it difficult to hire qualified professionals, train new entrants, and prove the field’s value within organizations. This ambiguity has led to inconsistent results, skepticism, and a struggle for credibility. Highlights 🔧 Organizational Development (OD) has lacked clear standards, making hiring and training challenging. 📊 The MOST assessment introduces a validated framework based on socio-technical systems theory. 🤝 MOST breaks OD skills into Social, Technical, and Influence domains for comprehensive capability. ✔️ The model is scientifically validated, making it reliable and credible for workplace use. 🎯 MOST enables objective hiring, targeted training, and continuous development tracking. 🚀 The framework moves OD from vague “soft skills” to strategic, measurable impact. 🌱 MOST is a dynamic model requiring ongoing refinement to suit evolving organizational needs. Key Insights 🔍 Clarity in OD Skills is Essential: The absence of a clear skill set in OD has historically caused confusion among hiring managers and professionals, resulting in inconsistent outcomes and skepticism about the field’s value. Defining and structuring these skills is a prerequisite for professionalizing OD and gaining organizational trust. In-depth analysis: Without clarity, OD roles become a “black box” where outcomes are unpredictable, making it difficult to justify investment or measure success. The MOST model provides this clarity by categorizing essential skills, thus aligning expectations and outcomes. ⚙️ Socio-Technical Systems Theory as a Foundation: The MOST framework’s grounding in socio-technical systems theory highlights the necessity of balancing human (social) elements and technical processes for effective organizational change. In-depth analysis: This theory acknowledges that neither social dynamics nor technical solutions alone can sustain transformation. The integration ensures that change initiatives address both interpersonal relationships and operational realities, increasing the likelihood of lasting success. 🧠 Three Core Competency Areas (Social, Technical, Influence): Breaking down OD skills into social facilitation, technical problem-solving, and influence (leadership and ethics) covers the full spectrum of what successful change agents must master. In-depth analysis: Social skills foster trust and collaboration, technical skills provide structure and rigor, and influence ensures buy-in from decision-makers. Ignoring any one area risks failure—e.g., great plans with no stakeholder support or well-liked facilitators without clear outcomes. 📈 Validation Brings Credibility and Trust: The rigorous testing and validation of the MOST assessment transform it from a theoretical model into a trustworthy, evidence-based tool. In-depth analysis: Validation parallels clinical trials in medicine—without it, tools remain anecdotal and unreliable. MOST’s validation signals to organizations that investing in this framework is a low-risk, high-return proposition for improving OD practices. 🎯 Objective Hiring and Targeted Development: With MOST, hiring becomes a science rather than guesswork. Behavioral interview questions and practical tests aligned with the three competency areas reduce bias and improve candidate fit. Similarly, training can be precisely focused on identified weaknesses, saving time and resources. In-depth analysis: This approach shifts OD talent management from intuition-driven to data-driven, ensuring that organizations build stronger, more capable teams with measurable skill improvements. 🔄 Continuous Improvement Cycle: The model promotes ongoing assessment, targeted intervention, and follow-up evaluation, creating a feedback loop that drives continuous skill enhancement and better organizational outcomes. In-depth analysis: This cyclical process mirrors best practices in quality management and learning organizations, where static evaluation is replaced by dynamic, iterative refinement of skills and strategies. 🌍 Future Evolution and Collective Responsibility: The MOST model is presented as a living framework needing further research, adaptation, and curriculum development to meet diverse organizational contexts and evolving challenges. In-depth analysis: This openness to change ensures that the model remains relevant across industries and cultures, encouraging collaboration among researchers, educators, and practitioners to continuously elevate the OD profession. #MOSTAssessment #OrganizationDevelopment #OD #CompetencyModel #EvidenceBasedPractice #WorkforcePlanning

Abstract: Organization Development has long struggled with establishing empirically validated competency frameworks that balance theoretical rigor with practical application. The recent publication of the MOST (Mastering Organizational & Societal Transformation) competency model represents a significant step toward professionalizing OD practice. Grounded in socio-technical systems theory and validated through psychometric testing with over 1,100 participants, the MOST Assessment provides a research-based framework for defining and developing OD capabilities. This article examines the professional landscape that necessitated such validation, analyzes consequences of competency ambiguity in OD, and presents evidence-based strategies for leveraging validated competency models to enhance professional credibility, inform workforce planning, and support the field's evolution toward mainstream recognition.  

Abstract: Organizations invest heavily in people analytics infrastructure yet fail to translate insights into frontline management action. This article examines the persistent "last-mile problem" in human resources: the gap between centralized people data and the managers who need it for daily performance decisions. Despite unprecedented volumes of workforce analytics, structural barriers—data silos, governance hesitancy, and poor contextualization—prevent frontline leaders from accessing actionable intelligence. Research demonstrates that manager effectiveness drives 70% of variance in employee engagement, yet fewer than 30% of managers report having adequate people data to make informed decisions. This article synthesizes evidence on organizational and individual consequences of this gap, examines proven interventions including AI-enabled self-service analytics, contextual delivery systems, and capability-building frameworks, and proposes long-term strategies for democratizing people intelligence. Drawing on cases across technology, healthcare, retail, and financial services sectors, the analysis provides practitioner-oriented guidance for closing the last mile between HR insight and managerial impact.