Companies worldwide are investing billions in generative AI, anticipating significant boosts in productivity and innovation. However, the vast majority of these investments fail to yield substantial returns, with about 95% of enterprise AI pilots never advancing to full production. This stark disparity creates a divide between organizations experimenting with AI and those achieving tangible business impact. The core reason behind this divide is not merely technical but rooted in a fundamental learning gap within enterprise AI systems. These systems often lack persistent memory, fail to improve from user feedback, and cannot adapt effectively to dynamic workflows, which severely limits their ability to scale beyond pilot phases. Highlights 🤖 95% of enterprise generative AI pilots fail to reach full production, revealing a major adoption challenge. 🧠 The core issue is a fundamental learning gap: AI systems often lack memory, feedback loops, and workflow adaptation. 📉 Only 5% of AI projects starting at exploration make it to full-scale deployment, highlighting the pilot-to-production chasm. 🔄 Back-office automation provides the most consistent, measurable ROI compared to high-profile customer-facing AI applications. 👥 Over 90% of knowledge workers use consumer AI tools unofficially, indicating gaps in enterprise AI offerings. 🤝 Partnering with specialized AI vendors nearly doubles the success rate compared to building AI solutions internally. 🚀 Distributed AI experimentation across teams uncovers practical use cases faster than centralized labs. Key Insights 🧩 Learning Gap as the Central Barrier: The failure of AI pilots to scale is predominantly due to enterprise AI systems’ inability to learn continuously. Without persistent memory, AI tools forget past interactions, making them ineffective in building user context or improving over time. This gap prevents AI from evolving into smarter assistants, limiting their practical utility and user adoption in complex workflows. Organizations must prioritize AI solutions with learning capabilities to overcome this barrier. 🔄 Broken Feedback Loops Limit Improvement: Effective AI systems require mechanisms for users to provide feedback and corrections that the system can incorporate. Many enterprise pilots fail because there is no easy way for users to correct errors or guide the AI outputs. This rigidity causes repeated mistakes and user frustration, pushing employees toward more flexible consumer AI tools like ChatGPT. Integrating seamless, user-driven feedback into AI workflows is essential to improve accuracy and trust. 🛠️ Workflow Adaptation is Vital: AI must fit naturally into the dynamic and fluid nature of real-world work. Successful AI tools learn team-specific patterns, exceptions, and historical resolutions to become genuinely helpful. For example, an AI tool for IT support should adapt recommendations based on past ticket outcomes and user groups. Without this adaptability, AI solutions remain rigid and disconnected from actual operational needs, undermining adoption and value generation. 💼 Back-Office Automation Delivers Tangible ROI: While customer-facing AI applications attract attention, the most reliable and measurable benefits come from automating back-office tasks such as invoice processing, compliance checks, and internal support. These processes are critical to business operations and well-suited to AI-driven efficiency gains. Prioritizing these areas allows organizations to realize early wins, build momentum, and develop internal AI capabilities before tackling more complex initiatives. 👥 Shadow AI Reveals User Needs and Pain Points: The widespread unofficial use of consumer AI tools by employees—referred to as shadow AI—signals a clear demand for AI solutions that enterprise tools fail to meet. Rather than viewing shadow AI as a compliance problem alone, leaders should analyze its usage patterns to identify unmet needs, pain points, and potential pilot projects that align with actual user behavior and preferences. 🤝 Partnering Accelerates AI Success: Companies partnering with specialized AI vendors enjoy nearly double the success rate in crossing the generative AI divide compared to those building solutions internally. Vendors bring domain expertise, industry-specific training data, and proven deployment experience, enabling faster delivery of refined AI products. This approach allows organizations to leverage AI effectively without diverting excessive resources toward building foundational models and infrastructure. #GenAI #EnterpriseAI #AIAdoption #AITransformation #AgenticWeb OUTLINE: 00:00:00 - The Great GenAI Divide 00:01:08 - Why Most AI Projects Stall 00:02:55 - Memory, Feedback, and Adaptation 00:04:34 - The Back Office, Shadow AI, and the Agentic Future 00:06:09 - Cross the Divide Before It’s Too Late 00:07:31 - Partner, Distribute, and Demand Learning 00:09:10 - Deploy What Learns, Integrates, and Scales

In this HCI Webinar, Dr. Jonathan H. Westover talks with Jennifer Carlson about the role of apprenticeships in preparing the future workforce. Jennifer Carlson serves as the CEO of Apprenti. Apprenti is a non-profit, apprenticeship intermediary and workforce consulting organization that delivers a secondary pipeline of tech talent to address U.S. domestic digital skills shortages. A former business leader with AIG, Progressive and adjunct professor at Seattle University, Jennifer also serves on the Tech Councils of North America (TECNA) foundation board, and as an Advisory Board Member - Apprenticeships for America.

The video, authored by Dr. Jonathan H. Westover, explores the rapidly evolving role of artificial intelligence (AI) in education and how it can be integrated thoughtfully to enhance rather than diminish human learning capabilities. It opens by acknowledging the widespread and growing use of AI tools by students worldwide, highlighting the challenge this presents to educators: how to harness AI without undermining the fundamental goal of education, which is to develop independent thinking and critical skills. Highlights 🤖 AI tools are increasingly used by over half of high school and university students worldwide. ⚠️ AI-generated work can create a dangerous illusion of competence by bypassing critical learning processes. 🤝 A balanced approach treats AI as a learning partner, supporting but not replacing student thinking. 🧠 AI can help reduce cognitive load, enabling students to focus on higher-order thinking and metacognition. 📚 Assessments must shift from final products to portfolios that capture the learning journey, including drafts and reflections. 🌍 Addressing the digital divide and teaching AI literacy are crucial for equitable AI integration. 🔒 Data privacy concerns arise from student interactions with AI tools owned by major tech companies. Key Insights 🤖 Widespread AI adoption is transforming education rapidly: The fact that over half of high school and university students already use AI tools daily shows that AI is no longer a niche or future issue but a present reality in education. This transformation demands immediate and thoughtful responses from educators, policymakers, and institutions. Ignoring or resisting AI adoption is futile; instead, systems must adapt to this new norm. ⚠️ Illusion of competence threatens authentic learning: When AI completes tasks like writing essays or generating code, students may receive high grades without engaging in essential cognitive processes such as research, critical analysis, drafting, and revision. This risks producing graduates who can produce polished outputs but lack deep understanding and independent problem-solving skills. 🤝 AI as a partner rather than a replacement encourages deeper learning: Rather than banning AI or allowing it to do all the work, positioning AI as a supportive assistant empowers students to focus on complex thinking tasks. For example, automating citation formatting or summarizing information frees mental resources for evaluating arguments, synthesizing ideas, and reflecting on learning. 🧠 Metacognition is a critical skill enhanced by AI: Using AI tools to teach students how to think about their own thinking—metacognition—can profoundly improve learning outcomes. AI can scaffold this process by prompting students to reflect on their reasoning, question their assumptions, and revise their work thoughtfully. 📚 Redesigning assessment to focus on learning processes is essential: Traditional grading based solely on final products is inadequate in an AI-augmented environment. Portfolio-based assessments that include brainstorming sessions, multiple drafts with documented revisions, AI prompt histories, reflective writings, and peer feedback provide a richer, more transparent picture of student learning. 🌍 Equity and access must be at the forefront of AI integration: The digital divide poses a significant barrier; students without reliable internet, modern devices, or quiet study spaces are disadvantaged. Furthermore, disparities in AI literacy mean some students can leverage AI tools more effectively than others. 🔒 Ethical considerations, especially data privacy, are paramount: Many AI tools collect and store sensitive student data on corporate servers, raising concerns about privacy, consent, and data security. Educational institutions must advocate for transparent data policies, protect student information, and educate students about the implications of sharing personal data with AI platforms. If this helped, please like and share the video. #AIinEducation #EdTech #Metacognition #AssessmentReform #JonathanWestover #TeachingWithAI OUTLINE: 00:00:00 - Intro — Title & Table of Contents 00:00:39 - The AI Challenge in Our Classrooms 00:01:34 - The Risk of Replacing Thought 00:02:39 - AI as a Learning Partner 00:03:35 - Redesigning Our Classrooms and Assessments 00:04:38 - Building an Ethical and Equitable AI Future

Abstract: The integration of artificial intelligence into educational settings presents a fundamental challenge: how to harness powerful generative technologies without undermining the very cognitive capabilities required to use them wisely. This paper examines the pedagogical implications of AI adoption across educational institutions, drawing on cognitive science, instructional research, and emerging practice to propose evidence-based responses. Analysis reveals that 92% of British undergraduates now use AI tools, yet much of this usage exists in a zone of ambiguity that risks hollowing out critical thinking, domain expertise, and analytical reasoning. Rather than treating AI as either a threat requiring surveillance or a solution demanding wholesale adoption, this paper argues for a third path: embedding AI use within transparent, reflective frameworks that make technology a catalyst for deeper learning. Key recommendations include managing cognitive load through purposeful AI integration, explicitly teaching metacognition alongside AI literacy, celebrating intellectual risk-taking through collaborative sense-making, and redesigning assessment as ongoing conversation rather than one-time product evaluation. The evidence suggests that institutional success depends less on technological sophistication than on grounding innovation in longstanding principles of how humans actually learn—principles that become more rather than less essential as machine capabilities advance.

Abstract: U.S. higher education faces mounting existential pressures—enrollment declines, cost escalation, political skepticism, and administrative managerialism that prioritizes short-term institutional survival over long-term scholarly mission. Despite widespread critique, business management faculty have largely failed to mount effective resistance to managerialist interventions, even as these practices erode academic autonomy and institutional purpose. This paradox deepens when considering that many senior administrators implementing managerial reforms lack formal training in management and strategy, sometimes producing poorly conceived interventions that damage institutions while expanding administrative ranks. This essay examines why business faculty—who possess expertise to recognize problematic management practices—often remain complicit in or complacent toward managerialism. Drawing on identity theory and organizational scholarship, we argue that typical business faculty identities neither frame managerialism as a personal threat nor create obligation to apply professional expertise to institutional challenges. Before mounting effective response, business management faculty may need to cultivate alternative identities as stewards of organizational practice, not merely teachers of management abstracted from institutional context.

Colleges and universities today face significant challenges including declining enrollment, rising operational costs, and persistent doubts about their societal relevance. In response, many institutions have adopted managerialism — a corporate-inspired approach that emphasizes market-driven efficiencies, quantitative metrics, and top-down strategic planning. While managerialism aims to create more resilient and responsive institutions, its application often reduces complex academic missions to simplistic performance indicators, leading to unintended negative consequences. A notable paradox is the silence of business school faculty, who study management but often refrain from applying their expertise to critique or improve their own universities’ managerial practices. Highlights 📉 Colleges face declining enrollment and rising costs, fueling uncertainty in higher education. 🏢 Managerialism applies corporate strategies to universities, emphasizing metrics and efficiency. 🤐 Business faculty often remain silent despite expertise in management, missing opportunities to improve institutions. 🎓 University leaders may lack management training, learning administration on the job, sometimes adopting ineffective business fads. 📋 Excessive strategic initiatives create fatigue and resource dilution without clear alignment. 🎯 Overreliance on simplistic metrics distorts institutional priorities, focusing on numbers rather than quality. 🤝 Business faculty should engage early in problem-solving processes and embrace stewardship for institutional well-being. Key Insights 📊 Managerialism’s Double-Edged Sword: While managerialism aims to enhance institutional resilience by importing business efficiency, it often oversimplifies academic missions. Treating education as a product measured by spreadsheets reduces complex values like intellectual inquiry and community engagement to mere metrics, risking the erosion of the university’s foundational purpose. 🤝 The Silence of Business Faculty as a Missed Opportunity: Business school faculty possess critical expertise in strategy, finance, and organizational behavior but often perceive their role narrowly as scholars and teachers. Their reluctance to intervene in institutional management represents a lost opportunity for universities to benefit from informed, evidence-based governance. 🎓 Academic Leaders’ Management Deficit: Many senior administrators rise through academic ranks due to research excellence rather than managerial competence. Without formal training in organizational theory and leadership, they may implement popular management fads without fully understanding their implications, contributing to flawed strategies and cultural issues. 🔄 Initiative Fatigue and Strategic Fragmentation: Universities often respond to uncertainty by launching numerous strategic initiatives simultaneously to appear proactive and innovative. However, this “strategy by long list” approach stretches resources and leads to fatigue among faculty and staff, undermining morale and effectiveness. 🎯 Flawed Metrics Distorting Behavior: When institutional success is measured primarily by quantitative indicators like enrollment or graduation rates, faculty and staff may prioritize meeting these targets over improving educational quality. 🛠️ Early Engagement of Business Faculty in Problem Definition: Involving faculty experts in finance, strategy, and organizational design at the earliest stages of institutional challenges allows for a more accurate framing of problems. This can redirect efforts from reactive, superficial fixes toward systemic, sustainable solutions. 🌱 Stewardship as a Path Forward: Stewardship entails business faculty recognizing their management knowledge as a valuable institutional asset and embracing a broader responsibility to act for the common good. This requires shifting from a passive role to active participation in governance, driven by a commitment to preserving the university’s academic mission and long-term health. If this resonated, please like and share to spread the conversation. #HigherEducation #Managerialism #AcademicLeadership #BusinessFaculty #OrganizationalChange OUTLINE: 00:00:00 - Managerialism and the Muted Professor 00:01:25 - Why Expertise Stays on the Sidelines 00:02:27 - The Institutional Cost of Poor Management 00:03:34 - Evidence-Based Pathways for Engagement 00:04:25 - Reclaiming the University's Core Mission

Abstract: As artificial intelligence reshapes labor markets globally, organizational leaders face a fundamental strategic question: which capabilities truly predict performance in AI-augmented work environments? While public discourse fixates on job displacement projections—the World Economic Forum estimates 92 million job losses against 170 million new roles by 2030—emerging research reveals a critical distinction between superficial AI adoption and transformative capability development. This article synthesizes evidence from leading academic institutions and consulting firms to demonstrate that technical AI proficiency alone provides minimal competitive advantage. Instead, six meta-competencies—adaptive learning capacity, deep AI comprehension, temporal leverage, strategic agency, creative problem-solving, and stakeholder empathy—distinguish high performers from surface-level experimenters. Drawing on cost-benefit frameworks from McKinsey, capability models from Harvard and Stanford, and organizational case studies spanning healthcare, professional services, and manufacturing, we provide evidence-based guidance for developing sustainable AI fluency. The synthesis reveals that return-on-investment literacy for automation decisions has emerged as a core executive competency, separating productive implementation from expensive overhead creation.

By 2030, 85% of jobs that will exist have not yet been invented, largely driven by the rise of AI. The common debate around AI’s impact on jobs often misses a crucial factor: the growing gap in skills required to effectively work alongside AI. This is the AI skills paradox, where two individuals equipped with the same AI tool can experience vastly different outcomes based on their approach and mindset. The key to thriving in an AI-driven world is not mastering fleeting prompts or hacks but developing enduring human skills—referred to here as AI fluency. Highlights 🤖 By 2030, 85% of jobs will be new, driven by AI innovation. 🔑 The AI skills paradox: success depends on deep human skills, not just AI tools. 🧠 AI fluency means understanding AI’s strengths, biases, and limitations, not just memorizing commands. 🎯 Six meta skills form the foundation of AI fluency, vital for all employees. 🚀 Companies fail when they focus on tools over teaching judgment and critical thinking. 💡 Real productivity gains come from selectively using and evaluating AI outputs, not blind acceptance. 🌱 Continuous learning and safe experimentation are crucial for building lasting AI capabilities. Key Insights 🤔 The AI Skills Paradox: Human Judgment Trumps Tool Mastery The key differentiator between workers who succeed or struggle with AI is not technical prowess but how they think and apply AI in context. This highlights that AI adoption is fundamentally a human skills challenge, underscoring the importance of cultivating judgment, problem framing, and strategic thinking to complement AI’s capabilities. 🧩 AI Fluency as Digital Literacy for the AI Age AI fluency parallels traditional digital literacy, requiring not just operational knowledge but a nuanced understanding of AI’s nature—its pattern-based predictions, potential for hallucinations, and embedded biases. This fluency enables users to critically evaluate AI output rather than passively accepting it, reducing the risk of errors that can undermine work quality. 🔄 Meta Skills Over Tool-Specific Hacks Ensure Longevity Because AI tools evolve rapidly, skills tied to specific prompts or platforms quickly become obsolete. The six meta skills—deep comprehension, adaptability, empowerment, creative problem solving, temporal leverage, and stakeholder empathy—are transferable across tools and time, offering a sustainable competitive edge in a fast-changing environment. 🚧 The Pitfalls of Surface-Level AI Training in Organizations Many companies invest significantly in AI tools and one-off trainings but neglect the deeper cultural and skill shifts required. Without teaching employees how to critically assess AI outputs and integrate AI thoughtfully into workflows, organizations see AI projects stall and tools go unused, wasting resources and missing transformative potential. ⏳ Temporal Leverage: Measuring Real Time Saved AI can produce outputs rapidly, but if the cost of reviewing, fact-checking, or editing outweighs the time saved, the tool becomes a net loss. This insight stresses the need for teams to evaluate the end-to-end workflow impact of AI, not just raw output speed, ensuring AI adoption genuinely enhances efficiency. 🤝 Stakeholder Empathy Builds Trust and Adoption Successful AI integration requires a focus on the people impacted—customers, colleagues, and end users. Transparent communication about what AI can do, its limits, and data usage fosters trust, which is essential for adoption and ethical use of AI technologies. This empathy-driven approach positions AI as a tool that truly serves human needs. 📈 Leadership’s Role in Cultivating an AI-Fluent Culture Leaders must embed AI fluency into everyday work through continuous learning, real-world practice, and decentralized experimentation. By empowering teams to explore AI tools safely and share learnings openly, organizations can build a culture of innovation and resilience, avoiding the trap of chasing short-term hacks and instead driving sustainable growth. Like & share if this helped you rethink AI strategy. #AISkills #AIFluency #Automation #Leadership #AIROI OUTLINE: 00:00:00 - Why Chasing Hacks Fails + Defining Real AI Fluency 00:02:49 - Six Meta-Skills, Deep Comprehension, Adaptive Capacity, Strategic Agency 00:04:32 - From Meta-Skills Intro to Strategic Agency 00:05:50 - Creative Problem-Solving, Temporal Leverage, Stakeholder Empathy, People Not Platforms 00:08:08 - People, Not Just Platforms — ROI and Call to Action 00:09:18 - Final CTAs — Build Fluency, Not Hacks