Leaders in the Loop

Episode Overview To prepare, Dan and Gaurav fed transcripts from the season into NotebookLM (recently rebranded Gemini Notebook) and had a dialogue with it about recurring themes — not to outsource the interpretation, but to make sure they weren't missing something across twelve very different conversations. What they found wasn't groupthink, despite guests who never met each other converging on similar ideas. It was a shared set of intellectual influences — Bandura, Kotter, Ethan Mollick — filtered through very different professional contexts, plus a genuinely broad and intentional ethical stance rather than narrow, uncritical enthusiasm. The conversation moves through the season's central metaphor (AI as augmentation, not replacement), into AI literacy as a practice rather than a vocabulary, AI as a leadership rehearsal space, the role ethics played across nearly every guest conversation, and where leaders are most tempted to hand off responsibility to AI. They close by marking the milestone of surpassing 1,000 downloads, announcing a production grant from the Association of Leadership Educators, and previewing what's ahead for season two. ----more---- Topics Covered Why twelve guests converging on similar themes isn't groupthink — it's shared foundational influences (Bandura's self-efficacy, Kotter's Eight-Step Change Model, Kurt Fischer's Dynamic Skill Theory) applied independently The season's defining metaphor: AI as an Iron Man suit, not a Terminator — augmentation with guardrails, not autonomous replacement Why "it saves time" undersells what generative AI actually does — it makes previously unattempted work possible The risk side of the metaphor: AI can amplify bias as easily as it amplifies skill "Tooling" language vs. "partnering" language — why Dan and Gaurav deliberately avoid anthropomorphizing chatbots in their own practice Ethan Mollick's Co-Intelligence as the season's most-cited book, and why Gaurav steers people away from more dystopian or overly enthusiastic alternatives AI literacy as a disciplined practice, not just vocabulary — and why it's a human development issue, not an access issue Dan's work proposing an undergraduate AI literacy course, drawing on learning goals frameworks from institutions across the U.S. The "hiring decision" custom GPT exercise Dan runs in workshops to surface real ethical stakes around AI in employment decisions AI as a leadership and communication practice simulator — psychological safety, private failure, and the value of a "flight simulator" with no human audience Why ethics kept surfacing as a leadership practice rather than a compliance checklist across the season Hallucination as an unexpected teaching moment for trust, verification, and moral agency AI and performance reviews — the promise of consistent standards vs. the discomfort of being evaluated by a system Modeling AI use publicly as a leadership behavior — demystifying the workflow instead of hiding it Where leaders are most tempted to outsource responsibility to AI, and why it usually comes down to being rushed, not lazy Season one milestones: surpassing 1,000 downloads and a new production grant from the Association of Leadership Educators What's ahead in season two: cheating and academic/workplace integrity beyond the classroom, communities of practice for AI experimentation, agentic AI and "vibe coding," and moving from what is AI to how do we scale it responsibly Key Takeaways Convergence isn't groupthink. Guests from completely different fields and backgrounds kept landing on similar ideas — not because they influenced each other, but because they're drawing on the same underlying leadership and psychology frameworks, applied through very different lived experience. AI as augmentation, not replacement, is the season's throughline. The Iron Man/Terminator framing shows up again and again: real capability enhancement, but never without human responsibility for judgment, ethics, and relationshi

Dan and Gaurav are joined by three researchers who are doing some of the most focused, longitudinal work in higher education on how assessment professionals are actually experiencing generative AI — not in theory, but in practice, in policy, and in the profession itself: Dr. Ruth Slotnick is Director of Assessment at Bridgewater State University and a co-lead of a national GenAI pulse survey for higher education assessment professionals that has now run across four administrations since early 2025. Dr. Will Miller is Associate Vice President for Continuous Improvement and Institutional Performance and SACSCOC Liaison at Embry-Riddle Aeronautical University. He oversees institutional effectiveness and accreditation across more than 100 sites globally and serves on the SACSCOC Board of Trustees. Dr. John Hathcoat is Associate Professor of Graduate Psychology and Associate Assessment Specialist in the Center for Assessment and Research Studies at James Madison University, where his work focuses on measurement theory, validity, and the emerging challenge of assessing AI literacy. ----more---- Episode Overview Dan Jenkins and Gaurav Khanna open the episode by framing assessment as one of higher education's most persistent challenges — and one of the most consequential sites for navigating generative AI. Ruth, Will, and John bring longitudinal survey data, institutional case studies, and sharp theoretical perspective to a conversation that moves from the origins of their research collaboration, through what the data actually show about adoption and institutional readiness, into deeper questions about what it means to assess student learning when AI is always in the room. Dan also brings firsthand perspective from his own institution's AI task force and from a recent program review he conducted using AI tools for the first time. Topics Covered How Ruth, Will, and John each came to generative AI independently — and how they found each other at the Assessment Institute in Indianapolis Ruth's early 2022–2023 experiments using Bing, Bard, and ChatGPT for qualitative data analysis alongside colleague Joanna Boeing John's shift from skepticism about early automated scoring systems to genuine excitement after a JMU book club read Ethan Mollick's Co-Intelligence Will's conviction that AI disruption — like COVID before it — creates rare windows for meaningful change in institutions that otherwise resist it The difference between programmatic/institutional assessment and course-based assessment — and why that distinction matters for how AI enters the work Gaurav's framing: adoption has won; institutionalization has not The GenAI Pulse Survey: four administrations since early 2025, more than 278 assessment leaders in Spring 2026, longitudinal tracking of use, tools, policy, training, and influence Key survey findings: 79% regular or occasional users; 87% report efficiency gains; 87% self-taught; 71% report ongoing privacy and ethical concerns What "self-taught" reveals about the gap between individual adaptation and institutional support The policy question: why moving from individual use to institutional guidelines is harder than it looks — and why guidelines may serve institutions better than formal policy The "tragedy of the commons" dynamic when institutions lack shared AI expectations John's three-frame model for assessing in an AI-enabled world: what students can do without AI, with AI, and across new contexts Will's challenge: we know how to verify math before allowing calculators; we don't yet know how to verify critical thinking before letting students go all-out with AI John's concept of "co-constructed performance" — when learning and assessment no longer happen separately, but in real time alongside AI Ruth's argument for innovation space: "skunk works" thinking, cross-functional data collaboration, and the role of curriculum design Dan's firsthand experience using AI (Copilot and Claude) during a program review at J

Dan and Gaurav are joined by Annie Hardy, Global AI Architect and Futurist at Cisco Systems, for a wide-ranging conversation about what it actually takes to lead AI adoption inside a large organization — not through engineering or edict, but through empathy, movement-building, and a fierce commitment to the humans who get left behind when leaders skip straight to the technology. ----more---- Episode Overview Annie traces her path from communications major and White House intern to community health worker to boutique agency founder to Cisco's Generative AI Architect — and makes a case that every turn in that journey shaped her ability to do what engineers and consultants often cannot: connect the technology to the people it affects. The conversation moves from Cisco's early generative AI adoption story, through the creation of the Generative AI Explorers community, into the deeper question of what it means to re-architect the cognitive workforce for the age of agents. Along the way, Dan and Gaurav bring in frameworks from leadership theory, organizational learning, and their own classroom and practitioner experience to push the ideas further. Topics Covered Annie's non-traditional path from communications to Global AI Architect How she helped lead and evangelize Cisco's Generative AI Explorers community from five people to tens of thousands Why creating open AI communities actually decreases organizational risk — not increases it – through visibility and support to enable best practices. Navigating legal, governance, and information security teams during AI adoption The difference between corporate value programs and genuine innovation culture Why most organizations throw tools at people without re-architecting the workforce Convergent vs. divergent thinking and what it means for job design in the age of AI The data/information/knowledge/wisdom pyramid as a framework for understanding AI's impact on roles Being in the loop, on the loop, and out of the loop — and what each means for job descriptions Annie's Code of AI Ethics and its five principles The Job Lab: an AI-powered career pivot tool she built as a Georgetown capstone project Shadow AI, personal infrastructure spending, and what it reveals about leadership gaps Why responsible AI practitioners are being sidelined — and why that should concern everyone The innovation gap facing women in AI Community colleges as faster, more agile partners in AI workforce education Range, cognitive diversity, and why a workforce of strategists is a disaster Key Takeaways Human-centeredness and strategic influence are both required to build movements. Empathy alone doesn't create change. Neither does strategy without connection. Annie argues that the most effective AI leaders in organizations combine both — and that this combination is rare. Creating a community around AI reduces risk, it doesn't increase it. When employees have a visible, moderated space to explore generative AI, governance teams gain visibility into what's actually happening. Shadow AI thrives in silence, not in community. Agentic AI begins with process design, not technology. Before deploying agents, organizations need internal experts who understand both line-of-business workflows and AI capabilities. The knowledge transfer has to happen first. Leaders are failing workers by skipping the "what comes next" conversation. It is irresponsible, Annie argues, for executives to signal that jobs are at risk from AI without defining what new roles look like and building pathways to get there. Job descriptions need to be rewritten around the human-in-the-loop. Most organizations haven't begun this work. The question isn't just what AI will automate — it's where human judgment must remain, and how to write that into the role itself. Cognitive diversity is an asset, not a problem to manage. Not everyone is a strategist. Re-architecting a workforce requires understanding the cognitive profiles already present — and

Dan and Gaurav are joined by Dr. Mary Tabata and Dr. Kevin Bottomley to preview the International Leadership Association’s 2026 AI & Leadership Virtual Summit, The Integration Frontier: Leading With AI Across Sectors. The summit takes place live on May 6–7, 2026, with on-demand access available afterward. Mary Tabata serves as associate faculty at American Public University System and Eastern University. Kevin Bottomley is an assistant professor of Global Leadership at Indiana Tech. In this conversation, they discuss how the summit came together, why cross-sector dialogue matters right now, and what leaders can expect from this year’s program. ----more---- Episode Overview This episode introduces the purpose and structure of the ILA 2026 AI & Leadership Virtual Summit and explains why its cross-sector focus matters. The conversation highlights how AI is reshaping leadership practice in education, business, healthcare, and organizational development, while also raising questions about ethics, governance, literacy, and implementation. The summit is designed as a space for leaders, educators, researchers, and practitioners to explore those questions together. The official event page describes the summit as ILA’s second virtual AI summit and emphasizes themes such as ethical and responsible AI, AI literacy, innovation, productivity, leadership development, and reskilling. Event details and registration Topics Covered Preview of the ILA 2026 AI & Leadership Virtual Summit The summit theme: The Integration Frontier: Leading With AI Across Sectors The growth of the International Leadership Association’s AI and Emerging Technologies member community Early leadership and teaching use cases for ChatGPT and Claude Hallucinations, verification, and responsible use AI literacy and implementation frameworks Leadership challenges in business, education, and healthcare Human-centered leadership in AI-enabled systems Key Takeaways The ILA 2026 AI & Leadership Virtual Summit is designed to connect leaders across sectors around shared AI adoption challenges. AI implementation is not only technical; it also requires leadership in governance, culture, and change. Early experiences with generative AI revealed both practical value and clear limitations, especially around fabricated references and unreliable links. AI literacy needs to be developed with role, context, and sector in mind. Cross-sector dialogue can help leaders identify common patterns in AI adoption. Human judgment remains essential in high-stakes environments such as education and healthcare. The summit emphasizes ethical, inclusive, responsible, and impactful AI leadership. Examples & References Discussed Mary and Kevin describe their early experiments with generative AI in professional and academic settings. Those experiences included testing classroom assignments, reviewing AI-generated papers, and encountering hallucinated citations and broken links. These examples helped shape their approach to responsible AI use and to the kinds of summit sessions they believe leaders need now. The episode also traces the development of the AI and Emerging Technologies member community within the International Leadership Association. That work grew out of earlier conference conversations and collaborations around AI, ethics, education, and leadership. The summit program brings together voices from multiple sectors and includes sessions focused on leadership development, implementation, governance, AI literacy, and organizational practice. The event page also lists Ganna Pogrebna of Queen’s University Belfast as the closing keynote speaker. Mary highlights Pogrebna’s work at Queen’s University Belfast and the broader mix of speakers contributing to the summit. The conversation makes clear that one of the event’s central goals is to help attendees think more clearly about how leadership and AI intersect in real organizational settings. Resources & Mentions

In this episode, Dan and Gaurav are joined by Dr. Greg Allen, founder of The Leaders Lab.io, co-founder of Ascendant Global Leadership, LLC, and a professor of leadership at The Citadel. In this episode, they explore how AI can support leadership development through structured practice, reflection, and feedback. Check out the video version here: https://youtu.be/N5f_fzsGl5o ----more---- Episode Overview Greg shares the thinking behind The Leaders Lab.io and explains the developmental gap he set out to address: what happens after a leadership workshop, class, or assessment, when learners need opportunities to practice, receive feedback, and improve over time. The conversation focuses on AI as an augmentation tool for leadership learning, not a replacement for human coaching. Dan and Gaurav connect Greg’s approach to familiar leadership development practices, including assessments, coaching, communication feedback, and role-play. Topics Covered The gap between leadership learning and leadership practice Why Greg Allen built The Leaders Lab.io The role of Virtual Sapiens in communication feedback Transformational leadership as a foundation for the platform AI role-play, Practice Mirror, video Q&A, and uploaded video analysis Feedback on clarity, empathy, buy-in, framing, and trustworthiness Using AI to extend leadership development beyond workshops and assessments Greg’s perspective on safe, prosocial, human-centered use of AI Key Takeaways Leadership development often breaks down after the initial learning experience, when people need practice and feedback to turn ideas into behavior. The Leaders Lab.io is designed to support rehearsal, reflection, and improvement over time. Greg positions AI as a practice partner and feedback mechanism rather than a substitute for human coaching. Assessments become more useful when paired with conversation, reflection, and action. The platform’s feedback addresses both the content and delivery of communication. Practice matters more than simply adding more leadership content. Greg emphasizes the importance of using AI in ways that are constructive, ethical, and grounded in human development. Examples and References Discussed A recurring theme in the conversation is the difference between acquiring leadership knowledge and building leadership skills. Greg explains that leaders often gain insight from courses, workshops, and assessments, but still need structured ways to rehearse difficult conversations, test approaches, and receive feedback. Dan connects that point to strengths-based development and other assessment tools, while Gaurav emphasizes that the real need in leadership development is not more content, but more meaningful practice. Greg also walks through the main functions of The Leaders Lab.io, including AI role-play, Practice Mirror, video Q&A, and uploaded video analysis. He describes how the platform can generate feedback on both what a leader says and how they show up while saying it, including factors such as clarity, empathy, buy-in, eye contact, trustworthiness, posture, intonation, and filler words. The discussion also situates The Leaders Lab.io within the broader landscape of leadership and AI. Dan references an earlier Leaders in the Loop episode with Gary Lloyd and LeadershipSkillsLab.ai, and Greg explains how his work was shaped by leadership scholarship, practical coaching needs, and collaboration with Virtual Sapiens. In the closing segment, Greg also recommends Teaching with AI by José Antonio Bowen and C. Edward Watson. Resources and Mentions The Leaders Lab.io Virtual Sapiens The Citadel Gallup CliftonStrengths Leadership Practices Inventory (LPI) DiSC Myers-Briggs / MBTI Toastmasters Descript Audacity OpenAI Teaching with AI by José Antonio Bowen and C. Edward Watson Stay curious. Stay human.