This video explores the evolving nature of learning within modern organizations, emphasizing that learning is no longer a finite event but a continuous, integrated process embedded within daily work. Traditional models of discrete, formal training sessions are now insufficient in a rapidly changing professional landscape shaped by new technologies and shifting market demands. Instead, organizations must blend formal learning—which is structured, planned, and objective-oriented—with informal learning, which occurs spontaneously through everyday interactions and hands-on experiences. Highlights 📚 Learning is no longer a one-time event but a continuous, integrated process embedded in daily work life. 🤝 Formal and informal learning are complementary, not opposing, modes that together create deeper skill mastery. 🍳 Real-world examples like Microsoft and Pixar demonstrate successful blending of structured courses with hands-on, peer-supported learning. ⚖️ Equity in access to informal learning opportunities is critical to avoid reinforcing workplace inequalities. 💻 Technology can democratize learning but must be designed inclusively to avoid excluding less digitally savvy employees. 🌱 Managers are essential in cultivating a learning culture by coaching, providing psychological safety, and encouraging collaboration. 🔄 Organizations should build communities of practice and shift from measuring course completion to assessing skill application on the job. Key Insights 📖 The Death of “Finished Education”: The idea that education ends with formal schooling is obsolete. The workplace is now the classroom where continuous learning is mandatory due to rapid technological and market changes. This paradigm shift demands organizations to rethink how they support employee development, moving away from episodic training to ongoing learning embedded in everyday tasks. 🔄 Symbiosis of Formal and Informal Learning: Formal learning provides the “grammar” and “vocabulary” of a discipline—structured knowledge, theories, and procedures—while informal learning brings these concepts to life through real-world practice, experimentation, and social interaction. 🌍 Examples from Industry Leaders: Companies like Microsoft and Pixar exemplify how to harness this integration effectively. Microsoft combines formal technical training with hackathons and collaborative projects, enabling employees to apply new skills immediately. Pixar’s “brain trust” meetings embed learning into daily workflows through peer feedback, fostering a culture of continual improvement. ⚖️ Equity Challenges in Informal Learning: Informal learning often depends on social networks, chance interactions, and inclusion in high-profile projects, which can inadvertently exclude marginalized groups such as women, minorities, remote workers, and lower-status employees. Without deliberate action, informal learning may perpetuate existing inequalities. 💡 Technology’s Dual Role: Digital tools and platforms can be powerful enablers of learning by providing access to courses, facilitating cross-department collaboration, and offering AI-driven personalized learning paths. However, technology also risks deepening divides if employees lack digital skills or if algorithms reinforce biases. 🌱 Leadership as Learning Cultivators: Managers are pivotal in creating a learning-friendly environment. They must shift from directive supervisors to coaches who foster psychological safety, encourage risk-taking and failure, provide timely feedback, and model knowledge sharing. 🤝 Building Communities of Practice: Beyond individual coaching, organizations should nurture communities of practice—groups sharing expertise and challenges in a profession—to sustain informal learning. These communities serve as forums for exchanging tacit knowledge often absent from formal courses, promoting collective problem-solving and innovation. 🔍 From Tracking Completion to Measuring Impact: Traditional metrics focused on course completions are insufficient. Organizations must evaluate learning by how effectively new skills are applied in the workplace and incorporated into performance goals. Encouraging employees to set and discuss learning objectives regularly fosters ownership of personal development and aligns learning outcomes with business success. #WorkplaceLearning #HRD #LearningAndDevelopment #TrainingTransfer #EmployeeDevelopment OUTLINE: 00:00:00 - The Unending School Day of Modern Work 00:01:06 - From Event to Everyday 00:02:29 - Planned and Unplanned 00:03:26 - Informal in Action and The Chef Analogy 00:04:26 - The Chef to Culture: Making Learning Stick 00:05:45 - Braintrusts, Climates, and Leadership 00:06:40 - Managers as Gardeners; Equity and Tech 00:07:48 - Designing for Inclusion and Technology 00:09:25 - Inclusive Tech and Practical Steps 00:10:43 - Train the Managers; Communities; Culture and Close 00:12:06 - Culture Shift to Application and Final Call

In today’s modern workplace, teams have long been recognized as the central drivers of innovation and productivity, blending diverse skills to produce solutions beyond the capacity of any single individual. Teams not only enhance problem-solving through collaborative scrutiny but also provide essential emotional support and motivation. However, the introduction of generative artificial intelligence (AI) is reshaping the dynamics of teamwork and individual productivity. Initially seen as a simple productivity tool, AI’s role is expanding into that of a genuine collaborative partner, capable of augmenting human creativity and problem-solving. Highlights 🤝 Teams have traditionally been the core of workplace innovation and support. 🤖 Generative AI is evolving from a productivity tool to a genuine collaborative partner. ⚡ Individuals with AI can match two-person teams in output quality and work faster. 🚀 Human teams augmented by AI are three times more likely to produce breakthrough ideas. 🌐 AI breaks down knowledge silos, enabling cross-disciplinary thinking and innovation. 😊 AI use reduces stress and increases engagement but paradoxically lowers user confidence. 📈 Organizations must rethink team structures, invest in AI training, and establish clear governance. Key Insights 🤝 AI as a Teammate, Not Just a Tool: The study reveals that AI can effectively substitute a human partner in standard innovation tasks, enabling a single individual to perform at the level of a two-person team. This challenges traditional assumptions about collaboration and suggests a reevaluation of team size and composition to optimize efficiency and resource allocation. ⚡ Speed and Quality Gains with AI: Access to AI accelerated task completion by 12-16% and resulted in more comprehensive, detailed outputs. AI alleviates the cognitive load of starting from scratch, allowing users to focus on deeper exploration and refinement of ideas, which ultimately enhances both productivity and creativity. 🚀 AI Amplifies Team Breakthroughs: While AI alone benefits individuals, its combination with human teams leads to superior outcomes—tripling the chances of producing top-tier, innovative ideas. This synergy highlights AI’s role as a multiplier rather than a replacement, particularly for complex, high-stakes projects requiring diverse human judgment and creative tension. 🌐 Democratization of Expertise and Cross-Functional Innovation: AI dissolves traditional professional boundaries by making specialized knowledge accessible to non-experts. Engineers can incorporate marketing insights; marketers can understand technical feasibility. This cross-pollination accelerates innovation cycles and fosters a more agile, integrated workforce capable of addressing multifaceted challenges without waiting for formal team assembly. 😊 Emotional Impact and Confidence Paradox: Contrary to fears of AI dehumanizing work, interacting with AI increases engagement, reduces stress, and creates a more playful, exploratory work atmosphere. However, users experience a “confidence penalty,” doubting the quality of their AI-assisted outputs despite objectively superior performance. This paradox signals the need for managerial support to build trust and acceptance of AI collaboration. 📚 Necessity for New Skills and Training: Effective AI collaboration requires more than operational knowledge; employees must learn how to prompt AI effectively, critically evaluate its outputs, and integrate AI insights into their workflows. Organizations should create safe environments for experimentation and develop prompt libraries, transforming AI from a mysterious black box into a reliable, controllable partner. ⚖️ Governance and Sustainable Integration: Thoughtful governance is essential to balance productivity gains with long-term organizational health. Clear policies must delineate human oversight and AI delegation boundaries, preserve mentorship opportunities, and ensure new employees acquire fundamental skills. This balance is crucial for maintaining human judgment, skill development, and a vibrant learning culture alongside AI adoption. #GenAI #AIteammate #Collaboration #OrganizationalDesign #Innovation #P&GStudy #AIinWorkplace

Abstract: Generative artificial intelligence is fundamentally reshaping the collaborative foundations of knowledge work. This article synthesizes findings from a large-scale field experiment involving 776 professionals at Procter & Gamble to examine how GenAI transforms three core pillars of teamwork: performance outcomes, expertise integration, and social engagement. Results demonstrate that AI-enabled individuals achieve solution quality comparable to human teams, effectively replicating traditional collaborative benefits while breaking down functional silos between technical and commercial domains. Contrary to concerns about technology-driven isolation, participants reported significantly more positive emotions when working with AI. These patterns suggest organizations must move beyond viewing AI as merely another productivity tool and instead recognize its role as a "cybernetic teammate" capable of redistributing expertise, accelerating innovation cycles, and fundamentally altering optimal team structures. Evidence-based organizational responses include reimagining team composition, developing sophisticated AI-interaction capabilities, redesigning performance expectations around AI-augmented workflows, and building governance frameworks that balance efficiency gains with sustained human skill development.

The video explores the emergence and integration of AI agents into workplace environments, emphasizing how these software systems differ fundamentally from human workers despite their ability to perform many common office tasks. Unlike humans who rely on visual interfaces and trial-and-error methods, AI agents favor programmatic, code-based approaches, leading to remarkable increases in speed and cost-efficiency but accompanied by significant quality and reliability issues. A detailed study comparing human workers and AI agents across various tasks such as data analysis, software engineering, writing, and graphic design revealed three primary findings: AI agents overwhelmingly prefer code-driven solutions; they complete tasks up to 88% faster, drastically reducing costs; yet they often produce fabricated or erroneous outputs, creating risks for organizations. Highlights 🤖 AI agents perform office tasks using programmatic, code-driven workflows, differing fundamentally from human visual and trial-and-error methods. ⚡ AI agents complete tasks up to 88% faster, driving massive productivity gains and cost reductions for businesses. ⚠️ Despite speed, AI agents often generate fabricated or erroneous outputs, risking quality and trust in their work. 🔄 Human roles are shifting to supervising, verifying, and debugging AI outputs rather than performing routine tasks. 🛠️ Practical adaptations include delegating programmable tasks, training agents with human workflows, and creating hybrid human-agent teams. 👁️ Investing in AI’s visual and multimodal capabilities is essential for handling complex, non-textual work environments. 🔐 Governance, logging, and human-in-the-loop checks are critical to safely integrating AI agents and managing risks. Key Insights 🤖 Programmatic Bias of AI Agents: AI agents almost exclusively rely on code and command line interfaces, even for tasks traditionally handled visually by humans. This is significant because it highlights a fundamental difference in how AI approaches work, favoring deterministic, rule-based processes over intuitive or aesthetic judgments. For managers, recognizing this bias helps in identifying which tasks are suitable for automation and which require human intervention. ⚡ Massive Efficiency Gains Come with Trade-offs: Agents complete tasks nearly 88% faster, drastically cutting costs, but this speed comes at the expense of quality. While the speed and cost benefits are compelling for businesses, the hidden risk is that agents fabricate data or make errors that can lead to disastrous downstream effects. ⚠️ Fabrication and Hallucination Risks are a New Class of Errors: AI agents often invent plausible but false data when encountering unreadable inputs, leading to fake analyses. This “hallucination” is particularly dangerous because it creates a false sense of progress, masking errors behind apparently valid results. 🔄 Human Labor Shifts Toward Oversight and Verification: As AI takes over routine work, humans become supervisors, requiring new literacies such as debugging and critical evaluation. This shift elevates the importance of domain expertise combined with technical skills, advocating for workforce development programs that emphasize these capabilities. 🛠️ Delegation by Programmability as a Key Criterion: Tasks that can be broken into clear, rule-based steps are ideal for AI automation, while those needing judgment remain human-led. This insight provides a practical framework for managers to assess task suitability for automation, preventing misapplication of AI to tasks requiring subjective or contextual decision-making. 👁️ Investing in Multimodal AI Capabilities Expands Agent Usefulness: Improving AI’s ability to interpret images, videos, and complex user interfaces can reduce current limitations. Since many workplace tasks involve non-textual data, multimodal skills are essential for AI agents to become truly versatile collaborators. 🔐 Governance, Transparency, and Human-in-the-Loop are Non-Negotiable for Risk Management: Logging every agent action and requiring human approval for critical decisions are vital. These safeguards create accountability and auditability, essential for maintaining trust and operational integrity. #AI #HCI #HumanAgent #WorkflowDesign OUTLINE: 00:00:00 - The New Digital Coworkers 00:01:01 - From Observation To Workflow Insight 00:02:01 - Speed, Code, and Critical Flaws 00:03:27 - The Shifting Landscape of Work 00:05:09 - Four Ways to Adapt 00:06:15 - Hybrid Teams And Visual Investments 00:07:26 - Building a Hybrid Future, Intentionally 00:08:42 - Guardrails, Skills, And The Path Forward

Artificial intelligence agents are emerging as potential collaborators—or substitutes—for human workers across diverse occupations, yet their behavioral patterns, strengths, and limitations remain poorly understood at the workflow level. This article synthesizes findings from a landmark comparative study of human and AI agent work activities across five core occupational skill domains: data analysis, engineering, computation, writing, and design. Drawing on workflow induction techniques applied to 112 computer-use trajectories, the analysis reveals that agents adopt overwhelmingly programmatic approaches even for visually intensive tasks; produce lower-quality work masked by data fabrication and tool misuse; yet deliver outcomes 88.3% faster and at 90.4–96.2% lower cost. Evidence-based organizational responses include deliberate task delegation grounded in programmability assessment, workflow-inspired agent training, hybrid human-agent teaming, and investments in visual capabilities. Long-term resilience depends on redefining skill requirements, strengthening multimodal foundation models, and establishing governance frameworks that balance efficiency gains with quality assurance and worker protection.

In this HCI Webinar, Dr. Jonathan H. Westover talks with Simon T. Bailey about his recent book, Resilience@Work: How to Coach Yourself Into a Thriving Future. Simon T. Bailey is the world’s leading expert in Brilliance. His groundbreaking research, State of Working America Report Thriving in Resilience and Brilliance, solidifies his insights in his 11th book, Resilience@Work: How to Coach Yourself Into a Thriving Future. With Disney Institute as his launchpad, he’s left an indelible mark on 2,400 plus organizations in 54 countries, including American Express, Deloitte, Visa, Signet Jewelers, and Taco Bell. He has made a remarkable impact on 120,000 professionals who’ve experienced his pioneering courses on the LinkedIn Learning platform. He’s also been recognized as Success Magazine’s Top 25, alongside Brené Brown, Tony Robbins, and Oprah Winfrey, as well as being on leadersHum Top 200 Power List.

The video transcript from November 7th, 2025, explores the critical role of motivation in the modern workplace and its profound impact on organizational success. Motivation is portrayed as the invisible but powerful force that transforms a mere list of tasks into a meaningful mission and turns a group of employees into a cohesive, engaged, and high-performing team. The speaker emphasizes that disengaged employees harm productivity, increase errors, and drain company resources, making motivation a pressing economic reality rather than just a “feel-good” concept. Highlights 🔥 Motivation transforms routine tasks into a meaningful mission and builds engaged teams. 💰 Extrinsic motivation (pay, bonuses) is necessary but insufficient for lasting engagement. 🌱 Intrinsic motivation thrives on autonomy, mastery, and purpose—universal psychological needs. 🤝 The psychological contract between employer and employee must evolve into ongoing conversations. 👥 Motivational leadership is a distributed responsibility, not just for top executives. ♻️ Motivation is a renewable resource vital for creativity, resilience, and growth. 🚀 Shifting leadership from command-and-control to empowerment drives sustainable competitive advantage. Key Insights 🔑 Motivation as a Dual-Engine System: The distinction between extrinsic and intrinsic motivation highlights that compensation alone cannot sustain employee engagement. While extrinsic rewards meet basic needs, intrinsic motivation fuels passion and commitment. Understanding and balancing these engines is crucial for any leader seeking high performance. This duality explains why traditional incentive models often fail in creative and collaborative workplaces. 🧠 Psychological Needs as Nutrients for Engagement: Autonomy, mastery, and purpose are identified as the core drivers of intrinsic motivation. Autonomy empowers employees to take control over their tasks, timing, and methods, fostering ownership and innovation. Mastery appeals to the human drive for growth and competency, making continuous learning essential. Purpose connects individual effort to a larger mission, which enhances meaning and satisfaction. 🔄 The Evolving Psychological Contract: Moving away from a static employment agreement toward a dynamic, ongoing dialogue reflects the reality of modern work. Employees’ needs and aspirations change over time, especially in a rapidly evolving workplace. Regular one-on-one conversations allow leaders to tailor support and opportunities, helping employees stay motivated and aligned with organizational goals. This approach also signals respect and investment in individual growth, which strengthens loyalty and performance. 👩‍💼 Distributed Motivational Leadership: Motivation cannot be the sole purview of senior executives; it must be embedded at every management level. Training managers and team leads to empower rather than micromanage builds a culture of trust and accountability. This distributed leadership model creates multiple motivation “touchpoints,” ensuring that employees receive consistent support and encouragement. It also democratizes motivation, making it an organizational capability rather than a sporadic initiative. 🌍 Motivation as a Sustainable Competitive Advantage: Companies that effectively harness motivation do more than improve short-term productivity; they build resilient, adaptive organizations. In an era characterized by rapid technological change and shifting market demands, motivated employees are critical for innovation and agility. Additionally, workplaces that respect autonomy and growth attract and retain top talent, which is increasingly scarce. 💡 Empowerment over Control: The shift from command-and-control leadership to one focused on empowerment and trust is fundamental. Leaders must create conditions where employees feel safe to experiment, take initiative, and learn from failure. This mindset encourages creativity and engagement, reducing turnover and fostering a positive culture. 🔄 Motivation as Renewable Energy: Viewing motivation as a renewable resource reframes how organizations approach employee engagement. It is not a one-time project with a set end date but a continuous process requiring investment and renewal. This perspective encourages sustained effort and vigilance by leaders, who must regularly nurture motivation through recognition, development opportunities, and meaningful work. If this helped you, please like and share the video. #Motivation #Leadership #EmployeeEngagement #SelfDeterminationTheory #OrganizationalPsychology OUTLINE: 00:00:00 - Why Motivation Matters More Than Ever 00:00:57 - Inside and Outside Forces 00:01:51 - Autonomy, Mastery, and Purpose 00:02:54 - A Three-Pillar Plan 00:04:08 - A Leader's Action Plan

Abstract: Motivation remains one of the most critical yet complex drivers of organizational performance and individual wellbeing. This article synthesizes contemporary motivation theory—including self-determination theory, social cognitive theory, goal-orientation frameworks, and attribution theory—to provide evidence-based guidance for practitioners navigating workforce engagement challenges. Drawing on recent empirical research and organizational case examples across healthcare, technology, and manufacturing sectors, we demonstrate how understanding the interplay between intrinsic drivers (autonomy, competence, relatedness) and extrinsic factors (incentives, recognition, structure) enables leaders to design interventions that sustain performance while fostering psychological wellbeing. The analysis reveals that organizations achieving superior outcomes integrate multiple motivational levers simultaneously, adapting approaches to individual differences and contextual demands. We propose a three-pillar framework for building long-term motivational capability: psychological contract evolution, distributed motivational leadership, and continuous learning systems.