In this HCI Webinar, Dr. Jonathan H. Westover talks with  WiLL R. Young about his book, The Enough Equation: Wealth, Wisdom, Virtue, and the Pursuit of Less. WiLL R. Young is a behavioral finance expert and practicing Stoic philosopher. The Enough Equation: Wealth, Wisdom, Virtue and the Pursuit uses a modern parable and principles from Stoic philosophy and behavioral psychology to explore how to define "enough" and find contentment. The book combines a narrative with wisdom to question our modern, often story-driven relationship with money, which it argues is influenced by beliefs and behaviors, not just math. It encourages readers to pursue less to achieve greater fulfillment.

The video explores the transformative impact of algorithmic management on modern workplaces, focusing on two workers, Eleanor, a warehouse employee, and Sam, a delivery driver. Unlike traditional management, where a human supervisor directs, supports, and evaluates workers, algorithmic management replaces these roles with computer programs that assign tasks, monitor performance, and enforce rules. This approach uses data to optimize efficiency, consistency, and cost savings for companies, promising faster operations and potentially lower prices for consumers. However, the system also brings significant challenges: workers lose autonomy, face relentless monitoring, and may experience stress, burnout, and feelings of unfairness when the algorithm fails to account for real-world complexities. The video advocates for a balanced approach where leaders “lead the machine” by ensuring transparency, incorporating human judgment, and fostering open communication channels. By doing so, organizations can harness the benefits of algorithmic management while safeguarding fairness, trust, and worker well-being, ultimately creating a sustainable and humane future of work. Highlights 🤖 Algorithmic management replaces human supervisors by assigning tasks, monitoring, and evaluating workers through computer programs. 📈 Businesses use algorithms to boost efficiency, streamline operations, and reduce costs, benefiting both companies and customers. ⚖️ Algorithms provide consistent, impartial rule enforcement, eliminating human biases in management decisions. 😓 Workers often feel stressed and powerless under constant surveillance and rigid, data-driven performance metrics. 🚦 Lack of transparency and inability to explain or appeal algorithmic decisions erode trust and increase perceptions of unfairness. 🗣️ Effective leadership requires transparency, explainability, and enabling workers to provide feedback and appeal decisions. 🌟 The future of work depends on balancing technology’s efficiency with empathy, fairness, and human oversight. Key Insights 🤖 Algorithmic Management as a New Supervisor: Algorithmic management automates traditional managerial functions—task assignment, performance tracking, and enforcement—through data-driven software. This shift from human to machine oversight fundamentally changes the worker experience. While it enhances operational precision, it reduces interpersonal interactions, which can undermine worker morale and engagement. 📊 Efficiency and Scale as Driving Forces: The main rationale behind algorithmic management is maximizing efficiency. Algorithms can analyze vast datasets and optimize complex logistics, such as warehouse navigation and delivery routes, far beyond human capabilities. This efficiency translates into cost savings and competitive advantages for companies, potentially benefiting consumers through lower prices and faster service. ⚖️ Impartiality and Consistency vs. Human Nuance: Algorithms excel in applying uniform rules without favoritism or mood swings, addressing common flaws in human management like bias or inconsistency. This standardization can foster a surface-level fairness where workers understand expectations and consequences clearly. 😓 Psychological Costs of Constant Surveillance: Continuous monitoring with real-time performance feedback can create a stressful work environment. Workers under algorithmic management often feel pressured by the “glowing rectangle” that times and judges every action, contributing to anxiety and burnout. 🔍 Transparency as a Foundation of Trust: One of the biggest challenges with algorithmic management is its “black box” nature—workers are often left in the dark about how decisions are made. Transparency about what data is collected, how it is used, and the rationale behind algorithmic decisions is essential to build trust. 🗣️ Human-in-the-Loop Systems and Worker Agency: Incorporating human oversight and feedback mechanisms is crucial in mitigating the limitations of algorithmic management. Providing workers with clear channels to report issues, appeal unfair evaluations, and contribute real-world knowledge empowers them and restores a sense of agency. 🌱 Leadership’s Role in Shaping Ethical Algorithmic Workplaces: Leaders must become architects of systems that balance technology’s power with human values. This involves demanding transparency from technology providers, conducting regular audits to detect biases, and including workers in governance and design decisions. By actively listening and engaging employees, organizations can create resilient, sustainable workplaces where efficiency and respect coexist. #AlgorithmicManagement #AIAtWork #HumanInTheLoop #WorkplaceAI #Leadership #Transparency #WorkerWellbeing #DrWestover

Abstract: The American Job Quality Study reveals a critical workforce gap: 60% of U.S. workers lack quality jobs across five dimensions—financial well-being, workplace safety and respect, growth opportunities, voice in decisions, and schedule control. This nationally representative survey of over 18,000 workers demonstrates that poor job quality correlates with diminished employee satisfaction, reduced retention, and weaker business performance. Organizations face mounting pressure to address systemic deficits in worker agency, with 62% lacking schedule control and 55% reporting limited input on decisions affecting them. This article presents evidence-based interventions spanning transparent communication, procedural justice, capability development, operating model redesign, and comprehensive benefits. Building long-term workforce resilience requires organizations to recalibrate psychological contracts, distribute leadership authority, and embed continuous learning systems that elevate job quality from compliance checkbox to strategic advantage.

The video transcript presents a comprehensive examination of the current state of job quality in the United States, revealing a troubling reality: 60% of American workers lack quality jobs. This issue is not merely a workforce concern but a critical business challenge that impacts organizational performance, employee well-being, and economic stability. The discussion unpacks job quality into five essential dimensions—financial well-being, workplace culture and safety, growth and development, agency and voice, and operational structure—each serving as a pillar supporting what constitutes a “good job.” Poor job quality leads to significant negative consequences such as high turnover, reduced productivity, and diminished customer service, particularly affecting sectors like retail, food service, and care work. Highlights 🔍 60% of U.S. workers hold jobs that do not meet quality standards, revealing a widespread economic and social crisis. 💰 Financial well-being includes fair pay, predictable hours, and benefits such as health insurance and retirement plans. 🛠️ Workplace safety and a respectful culture are essential for employee security and satisfaction. 📈 Growth and development opportunities are critical to preventing career stagnation and enhancing employee engagement. 🗣️ Agency and voice empower workers by involving them in decision-making processes, improving morale and commitment. ⚠️ Poor job quality causes high turnover, lower productivity, and impacts customer service negatively, especially in key service sectors. 🚀 Companies like Gap Inc., Kaiser Permanente, and Costco demonstrate that investing in job quality boosts both worker well-being and business outcomes. Key Insights 💡 The Hidden Business Cost of Poor Job Quality: The revelation that 60% of U.S. jobs are low quality underscores a systemic problem that affects economic productivity and business sustainability. Poor job quality leads to high turnover and lost institutional knowledge, which directly undermines operational efficiency and profitability. Leaders must recognize that job quality equates to a strategic business asset, not just a human resources issue. 💰 Financial Well-being is Foundational but Insufficient Alone: While fair pay is critical, the research highlights that financial compensation alone does not define job quality. Predictability of income and access to benefits are equally important for economic security. Unstable hours and inconsistent pay schedules create stress and uncertainty, reducing workers’ ability to plan their lives and meet basic needs, which in turn affects their performance and loyalty. 🛡️ Workplace Safety and Culture Shape Employee Retention: Physical safety and a respectful, hazard-free environment are non-negotiable pillars of a quality job. Beyond physical safety, psychological safety—feeling respected and valued—is crucial to reducing employee stress and absenteeism. The absence of such a culture fosters disengagement and increases the risk of workplace accidents and mental health issues. 📚 Growth and Development Opportunities Drive Motivation and Retention: Jobs that lack clear career pathways and skill development trap workers in low-wage cycles, limiting both their personal growth and economic mobility. Investing in training, mentorship, and education not only satisfies intrinsic human desires for advancement but also enhances organizational capability by building a more skilled and adaptable workforce. 🗳️ Employee Voice Enhances Engagement and Operational Excellence: Empowering employees with meaningful input into daily decisions and policies disrupts traditional top-down management models, fostering a culture of trust and shared ownership. This participative approach leads to better decision-making, increased employee commitment, and higher overall productivity, as workers feel their insights and concerns are valued. 🔄 Operational Redesign is Critical for Sustainable Job Quality: Addressing job quality requires rethinking work design, staffing models, and scheduling practices. Moving away from reactive “just-in-time” scheduling toward stable, predictable schedules, and cross-training employees for flexibility, builds resilience and improves service delivery. This structural shift can reduce burnout and absenteeism while enhancing team cohesion. 🌟 Real-world Corporate Success Stories Validate the Approach: The examples of Gap Inc., Kaiser Permanente, CVS Health, and Costco provide compelling evidence that improving job quality is achievable and beneficial. These companies show improvements in worker well-being, reduced absenteeism, enhanced safety, and better customer service, proving that investment in employees yields measurable returns, including higher profitability and competitive advantage. #JobQuality #Workforce #Leadership #EmployeeRetention #HRStrategy

This video introduces a transformative framework for integrating AI into enterprise workflows through “Claude’s skills,” a novel, simple, and highly adaptable method for AI tool creation. Unlike traditional complex AI systems that required specialized engineering teams, lengthy development cycles, and substantial capital, Claude’s skills are small, self-contained folders that package AI instructions in human-readable markdown files. These files contain clear, step-by-step guidance, supported by additional resources like scripts and data files, enabling domain experts to build AI-powered tools without deep programming knowledge. Highlights 🛠️ Claude’s skills simplify AI integration by using plain-language markdown files as self-contained AI tools. ⚡ Radical simplicity lets non-engineers create AI skills quickly, speeding up development cycles from months to days. 🌍 Democratization of AI tool creation empowers domain experts to build customized solutions close to real workflows. 📈 Skills codify institutional knowledge, preserving expertise and accelerating organizational learning and agility. 🔐 Governance frameworks are essential to balance freedom with safety, ensuring quality and compliance. 🎓 Comprehensive training programs teach employees to write clear AI instructions and test outputs effectively. 🚀 Leadership’s proactive role in culture, infrastructure, and incentives is crucial for sustainable human-AI collaboration. Key Insights 🧩 Modular, human-readable AI “skills” redefine enterprise AI development: The use of markdown files and simple folder structures as AI instruction sets radically departs from legacy, code-heavy plugin architectures. This modularity makes AI tools approachable for knowledge workers without programming expertise, democratizing access and enabling rapid iteration. The system’s transparency and simplicity foster faster feedback loops and continuous improvement cycles, transforming AI from a specialized IT project into a pervasive organizational capability. ⚙️ Radical simplicity leverages the interpretive strengths of large language models: Instead of rigid API calls or complex coding, skills rely on natural language instructions that models can dynamically understand and execute. This leverages the core strength of modern language models—contextual comprehension and flexible reasoning—allowing AI to perform multi-step workflows reliably when guided by well-structured, hierarchical prompts. 🌐 Shifting AI development from central IT to domain experts accelerates innovation and adoption: By empowering frontline workers who intimately understand their tasks to create AI tools, organizations eliminate bottlenecks and misalignment between technology and user needs. This decentralization leads to more relevant, practical AI assistants, enhances employee engagement, and fosters a culture where continuous process improvement is embedded into daily work. 📚 Skills function as living repositories of institutional knowledge: Each skill captures expert knowledge in a reusable, updatable form, preserving critical processes and insights that might otherwise be lost due to employee turnover. Over time, this builds a dynamic AI knowledge library that supports organizational resilience and scalability, ensuring expertise is broadly accessible rather than siloed. ⚖️ Governance and quality control are non-negotiable to prevent risks from decentralized skill creation: Without oversight, the ease of skill creation could lead to poor-quality or harmful AI outputs, risking business decisions, compliance, and reputation. A tiered governance model—distinguishing personal experimentation from team-shared skills and certified, formally validated skills—balances innovation speed with necessary safeguards, ensuring reliability and trustworthiness. 🤝 Human workforce considerations must be integral to AI skill adoption strategies: Automation of tasks traditionally performed by specialists can provoke fear and resistance. Positioning skills as augmentation tools that empower rather than replace experts is essential. Including employees in skill creation and implementation fosters buy-in, reduces anxiety, and helps evolve job roles to complement AI capabilities rather than compete with them. 🚀 Leadership’s active role in infrastructure, culture, and incentives is vital for sustained success: Leaders must invest in secure platforms for skill management, define career paths for skill authors, encourage experimentation, and embed clear training programs. Cultivating internal communities of practice and tracking usage metrics helps refine approaches and drives continuous learning, making AI a core, adaptive part of organizational infrastructure rather than an isolated technology project. #ClaudeSkills #Anthropic #EnterpriseAI #Markdown #SkillsEngineering

Abstract: Claude Skills, introduced by Anthropic in October 2025, represent a paradigm shift in organizational AI adoption through radical simplification. Unlike previous approaches requiring complex protocols and substantial technical infrastructure, Skills employ a deceptively simple architecture: Markdown files containing task instructions, optional supporting scripts, and minimal metadata. This simplicity enables organizations to rapidly develop and deploy specialized AI capabilities across functions without extensive engineering resources. This article examines how Skills redefine work design by democratizing AI capability development, enabling rapid organizational learning cycles, and potentially flattening traditional skill hierarchies. Drawing on research in organizational learning and technology adoption, we analyze Skills' implications for capability building, knowledge management, and workforce transitions. Organizations that strategically cultivate "skills engineering" as a core competency while addressing governance challenges stand to gain significant competitive advantage in the evolving landscape of human-AI collaboration.

This video explores the current state and challenges of implementing artificial intelligence (AI) in human resources (HR), emphasizing the significant gap between the high ambitions HR leaders have for AI and the reality of its practical application. Despite widespread recognition of AI’s potential to transform hiring, talent development, and employee support, most organizations are still in the nascent stages of using AI effectively. A major issue is the implementation gap—many companies purchase AI tools without integrating them properly into workflows, resulting in underutilized, costly technology that fails to deliver measurable benefits. Highlights 🤖 AI ambition in HR is high, but practical implementation lags behind reality. 🔍 AI in HR can be categorized into automation, augmentation, and agentic AI. ⚠️ Most AI failures stem from poor strategy, not faulty technology. 🛠️ Redesigning workflows before implementing AI is critical for success. 🎯 Clear, measurable business outcomes must drive AI projects. 🤝 Human-AI partnerships enhance trust and effectiveness in HR tasks. 📈 A real-world example shows how focused, user-centric AI can deliver significant value. Key Insights 🤖 Implementation Gap as the Core Challenge: Although HR leaders widely acknowledge AI’s potential, more than 80% see AI as essential for the future, yet fewer than 20% feel equipped with the skills or processes to deploy it successfully. This gap highlights the need for organizations to move beyond merely acquiring technology and focus on practical integration into daily workflows. The “shiny new tool” syndrome leads to wasted resources and skepticism about AI’s value. 🧩 Three Flavors of AI in HR Clarify Application Scope: Categorizing AI into automation, augmentation, and agentic AI helps HR leaders understand what AI can realistically do today. Automation handles rule-based repetitive tasks—freeing up time but limited to straightforward processes. Augmentation enhances human decision-making by analyzing data patterns, making recruiters or HR professionals more efficient and accurate. Agentic AI, though more advanced, can autonomously coordinate complex workflows, representing a future direction. 🚫 Technology-First Approach Leads to Failure: The video emphasizes that many AI projects fail because companies rush to buy new technology without analyzing or improving existing workflows. Automating flawed or inefficient processes simply accelerates problems rather than solving them. The failure cycle—technology buy-in without strategic fit, low user trust, absence of measurable value, abandonment—underscores the importance of starting with a process-first mindset. 🎯 Necessity of Specific, Measurable Business Goals: Vague aspirations like “improve candidate experience” or “increase employee engagement” are insufficient to guide AI projects. Instead, setting precise, quantifiable targets—such as “reduce time to hire by 15%” or “decrease first-year attrition by 5%”—creates clarity and accountability. This clarity enables organizations to track progress, prove return on investment, and maintain stakeholder support. 🤝 Human-Centric Design and Change Management Are Vital: AI tools often fail because they are developed in isolation by IT or analytics teams with little input from HR professionals—the actual end users. Involving users throughout the design and testing phases builds trust, ensures the tool addresses real pain points, and fosters adoption. Additionally, designing AI to complement human skills—empathy, judgment, ethical reasoning—instead of replacing humans builds a sustainable human-AI partnership. 🚀 Agile, Product Management Approach Enhances AI Implementation: Treating AI initiatives like products rather than one-off projects promotes iterative development, cross-functional collaboration, and ongoing refinement. Agile methodologies enable teams to test minimum viable products (MVPs), gather user feedback, and scale solutions only after demonstrating clear value. 📊 Case Study Demonstrates Best Practices in Action: The example of a global retailer’s AI hiring tool embodies the principles outlined. By mapping the entire recruitment workflow, the team identified a high-impact bottleneck—manual resume screening—and developed a focused AI augmentation tool. The tool surfaced the top 20% of candidates for human review, reducing shortlisting time by 40%. Crucially, recruiters were involved from the start, fostering trust and high adoption. #AIinHR #PeopleAnalytics #HRTech #HRLeadership OUTLINE: 00:00:00 - Ambition vs Reality in HR 00:01:46 - Three Flavors of AI 00:03:17 - Why So Many AI Projects Fail 00:04:39 - The Practical Playbook 00:07:00 - Making AI Work for People

Abstract: Despite surging interest in artificial intelligence within human resources, most organizations remain in the early stages of their AI journey, with two-thirds having less than one year of implementation experience. This article synthesizes research and practitioner insights from David Green's comprehensive September 2025 HR analytics review to examine why many HR departments struggle to realize value from their AI investments. The analysis explores the implementation gap between AI ambition and business outcomes, revealing that successful organizations prioritize workflow redesign over technology adoption, take a product-centric approach to implementation, and maintain a focus on human oversight. The article provides a structured framework for HR leaders to move beyond pilot implementations to achieve scalable, value-generating AI applications that augment rather than replace human capabilities.