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.

In this HCI Webinar, Dr. Jonathan H. Westover talks with leveraging spiritual and emotional intelligence at work. Yosi Amram Ph.D. is a licensed clinical psychologist, a CEO leadership coach, and a best-selling and award-winning author. Previously the founder and CEO of two companies he led through successful IPOs, Yosi has coached over 100 CEOs—many of whom have built companies with thousands of employees and revenues in the billions. In addition to working with individuals, Yosi works with couples interested in passionate, conscious relationships that serve their psycho-spiritual healing and growth. With engineering degrees from MIT, an MBA from Harvard, and a Ph.D. in Psychology from Sofia University, he is a pioneering researcher in the field of spiritual intelligence whose research has received over 1000 citations. As a C-Suite, Amazon, B&N best-selling author of the Nautilus Book Award Gold Medal-winning Spiritually Intelligent Leadership: How to Inspire by Being Inspired, Yosi is committed to awakening greater spiritual intelligence in himself and the world. Yosi is also the founder of several non-profits, including trueMASCULINITY.org, Engendering-Love.org, and AwakeningSI.org.

This video introduces GDPVol, a groundbreaking benchmark designed to evaluate how effectively artificial intelligence performs complex, real-world professional tasks typically completed by highly skilled knowledge workers. Unlike simple tests or games, GDPVol focuses on sophisticated assignments such as drafting legal memos, creating marketing plans, and producing financial analyses—work that usually takes experts hours to complete. The benchmark reveals that leading AI models can match or surpass human experts in nearly half of these tasks, marking a profound shift from AI as a novelty to a practical tool capable of high-value economic work. This development signals a transformation in how professional work is done, emphasizing augmentation rather than outright replacement of human labor. Highlights 🚀 GDPVol benchmark measures AI’s ability to perform complex professional tasks with human-level quality. ⏱ AI completes tasks in minutes that traditionally take humans several hours, yielding massive efficiency gains. 🤖 Nearly half of tested tasks showed AI output equal or superior to expert human work. ⚠️ AI can produce critical errors, making human oversight and quality control essential. 🎯 Success requires task-specific AI deployment and redesigned human-AI workflows. 🧠 New employee skills in prompt engineering and clear communication with AI are vital. 📈 Organizations must adopt iterative, data-driven AI integration to stay competitive in the evolving workplace. Key Insights 🔍 Benchmarking Real-World AI Performance: GDPVol is a paradigm shift from traditional AI assessments, focusing on real-world, high-stakes professional tasks rather than simplistic benchmarks. This approach provides a realistic gauge of AI’s readiness for workplace integration, highlighting its practical economic impact rather than theoretical capability. ⚡ Exponential Productivity Gains Through Speed: The compression of task completion time from hours to minutes by AI represents not just a marginal improvement but a fundamental transformation in work pace. Even when allowing for human review, the combined human-AI workflow can achieve what once took a full day in a fraction of the time, enabling organizations to scale output and responsiveness dramatically. 🤝 Augmentation Over Replacement: The findings emphasize augmentation—using AI to offload routine, time-consuming work, freeing human experts to focus on high-value activities like strategy, relationship management, and complex judgment. This reframes the AI impact narrative away from job losses toward productivity enhancement and workforce empowerment. 🧩 Task-Level Analysis as the Foundation for AI Strategy: The recommendation to deconstruct work into individual tasks rather than focusing on job titles is a strategic insight. It allows organizations to identify which tasks are ripe for automation and which require human judgment, enabling a targeted and efficient AI adoption roadmap. ❗ Risks of AI Hallucinations and Errors: AI’s pattern-matching strength comes with a lack of true understanding or judgment, leading to unpredictable and sometimes dangerously flawed outputs. This necessitates stringent quality assurance protocols and human-in-the-loop systems, especially for high-risk tasks where errors can have severe consequences. 💡 The Critical Role of Prompt Engineering: The quality of AI output is highly sensitive to the clarity and detail of input instructions. Crafting precise, context-rich prompts emerges as a new, essential skill for employees working alongside AI, marking a shift in workplace competencies toward effective human-AI communication. 🔄 Continuous Adaptation and Governance: Given AI’s rapid capability advancements, organizations must establish governance frameworks for ongoing evaluation of AI models and workflows. Regular testing and iterative process redesign ensure that companies remain at the forefront of AI efficiency gains and risk mitigation. #GDPval #AI #WorkRedesign #HumanAICollaboration #OrganizationalStrategy OUTLINE: 00:00:00 - Understanding GDPval and Its Impact 00:02:13 - AI Performance and Its Tangible Benefits 00:04:18 - Where AI Still Falters 00:06:07 - Practical Steps for AI Integration 00:07:36 - Redesigning Work for the AI Era

Abstract: The recent introduction of GDPval—a benchmark evaluating AI model performance on economically valuable real-world tasks—signals a fundamental shift in how organizations must approach work design, workforce planning, and operational strategy. This research examines the organizational implications of frontier AI models approaching human expert-level performance across 44 knowledge-work occupations spanning nine major economic sectors. Analysis reveals that AI capabilities are advancing linearly, with leading models now matching or exceeding human deliverables in approximately half of evaluated tasks while offering potential time and cost advantages when paired with human oversight. For organizations, these findings suggest an urgent need to move beyond conceptual AI strategies toward systematic work redesign, requiring recalibration of role definitions, capability development frameworks, quality assurance processes, and governance structures. This paper synthesizes evidence from GDPval findings with broader organizational research to provide practitioners with evidence-based approaches for redesigning work in an era where AI can competently perform complex, multi-hour knowledge tasks across professional domains. The analysis demonstrates that competitive advantage will increasingly depend not on whether organizations adopt AI, but on how effectively they reconfigure human-AI collaboration, redistribute cognitive labor, and build adaptive capabilities for continuous work evolution.