Elevate Your AIQ

Bob Pulver is joined by Tim Borys, a leader who wears many hats across executive coaching, workplace wellbeing, entrepreneurship, and podcasting. Drawing on Tim’s journey from elite athletics to advising leaders and organizations, the conversation explores sustainable human performance, burnout, adaptability, and leadership in times of constant change. Together, Bob and Tim examine why human-centric thinking is more critical than ever as AI reshapes work—and how individuals and organizations can thrive without losing sight of wellbeing, purpose, and agency. Keywords Tim Borys, Fresh Group, workplace wellbeing, human performance, burnout, executive coaching, leadership, adaptability, AI and work, human-centric AI, WRKdefined Podcast Network, Elevate Your AIQ Takeaways Sustainable performance requires focusing on human fundamentals like rest, recovery, and mindset High-performing corporate cultures often neglect wellbeing until burnout occurs Adaptability and learning are the most critical skills for thriving amid AI-driven change Leadership and communication skills will be essential for managing both people and AI agents Human performance, leadership, and business strategy must be addressed together AI should augment—not replace—human agency and critical thinking Quotes “Corporate high performers seem to think the rules of human performance don’t apply to them.” “Work sucks for a lot of people—and it doesn’t have to.” “Every human has a human operating system, and most people never optimize it.” “Adaptability is the number one human skill for thriving.” “As technology becomes more powerful, the human side matters even more.” Chapters 00:02 Welcome and introduction 00:43 Tim’s journey from elite athletics to executive coaching 02:39 Applying human performance principles to corporate work 04:32 Burnout, sleep, and sustainable performance 07:22 Human potential and wellbeing at work 09:05 The human operating system 12:06 Human-centric AI and the cost of efficiency 14:12 Adaptability, learning, and future skills 18:06 Fear, uncertainty, and career resilience 23:10 Leadership skills for managing AI agents 29:49 Performance-managing AI and responsible use 36:29 Frontline leaders vs. executive perspectives 43:52 Mindset, perception, and human agency 47:27 Personal AI tools and experimentation 51:30 The Working Well podcast and closing Tim Borys: https://timborys.com/ Working Well podcast: https://wrkdefined.com/podcast/the-working-well-podcast For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver welcomes Lance Thompson, President of VIVI, a hospitality-focused AI company formerly known as SAVI. Lance shares his journey from luxury hospitality to tech entrepreneurship, highlighting how VIVI is bringing human-centered design to voice AI. They discuss the evolution of guest experiences, the importance of multilingual support, and how AI is being responsibly deployed to reduce friction for both guests and staff. From room service to HR to golf tee times, VIVI’s solutions demonstrate what happens when deep hospitality know-how meets cutting-edge AI. Keywords Lance Thompson, VIVI, SAVI, hospitality tech, voice AI, multilingual support, hotel operations, HR automation, guest experience, AI adoption, Microsoft Azure, Kinetic Solutions Group, Four Seasons, Vail Resorts, Aspen Hospitality, AI in travel, shadow AI, responsible AI, agentic search, reservations automation, guest personalization Takeaways Lance's career spans luxury hospitality, including Four Seasons and Vail Resorts, before shifting into tech with the founding of SAVI, now VIVI VIVI is leveraging AI voice agents to support hotel operations, from answering phones to making reservations and handling HR inquiries Multilingual capabilities are critical in hospitality; VIVI agents can fluently switch between languages in real time Lance emphasizes the importance of consistency in service delivery — AI can ensure high-quality, brand-aligned experiences across time zones and locations Unlike traditional decision-tree systems, VIVI’s tools rely on conversational AI that listens, adapts, and can be interrupted mid-sentence Shadow AI poses risks for companies — Lance urges leaders to develop clear internal policies for responsible use and governance VIVI's architecture is designed with data privacy and security in mind, with each client having its own isolated knowledge base The future of hospitality AI lies in scalable, personalized tools that blend human empathy with machine precision Quotes “I wanted to be in a space where I could help people have a better experience in life — and hospitality gave me that.” “If it can’t be interrupted, it’s not a conversation. And that’s what real guest service is about.” “We don’t want to replace Janet in Reservations — we want to scale her.” “Guests don’t want a link. They want an answer — fast, accurate, and in their language.” “People aren’t afraid of AI. They’re asking when they can start using it to be more effective at their jobs.” “We’re not building a static product. As the models improve, our tools do too.” Chapters 00:00 - Intro and background from Carmel to Colorado 02:47 - Lance’s early passion for hospitality 05:09 - Discovering the limits of legacy systems 07:10 - The spark behind founding SAVI (now VIVI) 08:48 - Early demos, use cases, and multilingual potential 11:36 - Why real conversational AI matters 14:59 - Shadow AI and responsible adoption 17:54 - Building secure, client-specific AI agents 23:33 - Creating community through consistent service 26:39 - Managing real-time updates and seasonal accuracy 29:39 - Rethinking apps and improving discoverability 32:19 - The magic of humanlike conversations 36:02 - Delivering 5-star experiences through AI 39:30 - Personalizing brand voice (yes, even “absolutely”) 41:09 - Customizing user experience in real-time 43:03 - Transparency, trust, and guest empowerment 46:25 - What’s next for VIVI and hospitality AI 48:00 - Expanding into HR, golf, and reconciliation tools 51:06 - The travel planning use case 53:19 - New challenges in AI-driven SEO 53:23 - Final reflections and what’s ahead Lance Thompson: https://www.linkedin.com/in/lance-thompson-92a5476 VIVI: http://www.vivi.bot/ For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

In this insightful and forward-looking conversation, Bob Pulver speaks with Adam Gordon, co-founder and CEO of Poetry, about the rise of hiring enablement and how AI can be used to create consistency, speed, and scalability in talent acquisition. Adam reflects on his entrepreneurial journey from Candidate.ID to Poetry, unpacks the MOLT framework (Marketing, Operations, Learning, Tools), and explains how Poetry integrates AI to support recruiters and hiring managers with streamlined processes and guardrails to ensure quality and compliance. They also explore deeper workforce challenges like trust, burnout, and AI’s societal impact—especially in the context of shrinking employee tenure and the future of work. Keywords Adam Gordon, Poetry, hiring enablement, recruiter enablement, AI agents, MOLT framework, Candidate.ID, talent acquisition, recruiter productivity, ATS integration, AI guardrails, employer brand, candidate experience, AI governance, trust in leadership, DEI, burnout, workforce automation, staffing industry, responsible AI, talent intelligence Takeaways Adam Gordon’s journey from recruiting to tech entrepreneurship has been shaped by the need to empower recruiters with better tools and processes. Poetry was created as a hiring enablement workspace to reduce reliance on fragmented point solutions and to streamline recruiter workflows. The MOLT framework (Marketing, Operations, Learning, Tools) organizes recruiter needs in a way that supports end-to-end hiring activity. Poetry emphasizes product design simplicity and consistency, integrating AI without exposing users to the risks of hallucination or inconsistent prompts. Recruiters using Poetry can save up to 25% of their time per day, but there's concern about how organizations reinvest those gains. Guardrails are built into Poetry to ensure a consistent employer brand, tone, and candidate experience—especially important given drops in organizational trust. The move from “recruiter enablement” to “hiring enablement” reflects how recruiters and hiring managers must work together in today’s TA ecosystems. A new Poetry workspace tailored for staffing companies is set to launch in Q2 2026, signaling the platform’s evolution and market expansion. Quotes “Recruiting is a team sport.” “We’ve put such strong guardrails in place, it’s not possible for Poetry to hallucinate.” “We wanted to eliminate recruiters having to log into 30 different tools to do their job.” “I’ve described it as an age of employment brutality—CEOs don’t want more people on payroll.” “The trust barometer is dropping, and without trust, the candidate experience and employer brand collapse.” “Just because you can build something doesn’t mean you’ve built a technology company.” Chapters 00:00 - Introduction and Adam’s Background 01:17 - From Social Media Search to Candidate.ID 05:32 - The Vision Behind Poetry 07:27 - Simplicity, Product Design, and AI Agents 09:16 - MOLT: Marketing, Operations, Learning, Tools 11:16 - ATS Integration and 25% Time Savings 14:05 - The Reinvestment Dilemma 18:34 - Talent Intelligence and Bite-Sized Research 22:01 - Guardrails Over Free Prompting 24:51 - Mitigating Risk and Ensuring Consistency 29:58 - From Recruiter to Hiring Enablement 33:40 - Empowering Employer Brand and Talent Attraction 37:50 - The Importance of Trust and Communication 43:25 - Turnover, Tenure, and the Workforce Equation 49:22 - Responsible AI and Societal Impact 54:35 - Creative AI Tools and Industry Disruption 56:44 - Building a Scalable Tech Company 59:46 - 2026 Preview: Poetry for Staffing Companies Adam Gordon: https://www.linkedin.com/in/adamwgordon/ Poetry: https://www.poetryhr.com/ For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver talks with Vijay Swami, Co-Founder and CEO of Draup, a global leader in AI-powered talent intelligence. Vijay shares his journey from early roles in call center forecasting to founding a management consultancy and then TalentNeuron, later acquired by CEB. With deep roots in data science and a vision for empowering internal analytics teams, Vijay built Draup to tackle labor market complexity using advanced AI, unstructured data, and rich taxonomies. Vijay and Bob discuss building trusted, AI-powered talent intelligence platforms that bridge data complexity and business decision-making, and how human-centric, explainable AI is reshaping strategic workforce planning. They cover the growing importance of verification skills, ethical AI practices, the future of people analytics, the architecture of trusted and explainable AI systems, and the evolving role of humans and agents in enterprise workflows. Keywords Vijay Swami, Draup, AI in HR, People Analytics, Strategic Workforce Planning, verification skills, ethical AI, talent intelligence, agentic AI, skills-based hiring, cloud data, explainability, trust, synthetic data, digital twins, ETTER, Curie, job displacement, augmented intelligence, transparency Takeaways AI's value in HR lies in sense-making from complex and unstructured data, not just simplifying workflows. Verification skills—like content and narrative validation—are emerging as critical in a world flooded with AI-generated data. Draup’s AI agent Curie supports HR and analytics professionals with leadership-ready narratives and scenario planning. The platform's ETTER model goes beyond job descriptions to assess real work through contracts, SLAs, and KPIs. Transparency and traceability are foundational to building trust in AI systems; Draup compares its models against industry benchmarks. Ethical AI practices include open documentation, interpretability, and empowering analysts to correct or clarify information. AI should not be viewed solely as a job killer; clear, specific skills definitions in job postings can increase hiring and help target investments. True transformation requires shifting from jobs to workflows and task orchestration, blending human effort, AI agents, and automation. Quotes “We want to tell the story—not just show the data—to help people analytics become a leadership engine.” “Verification skills are the next battery of capabilities organizations must build for a trustworthy enterprise.” “Transparency is about giving customers the right to know—even if they don’t ask.” “HR has the opportunity to become heroes in this AI wave by unlocking the true nature of work.” “We should be therapists for data anxiety—helping organizations see what’s real versus what’s a myth.” “I’m a net AI job creator guy—because there’s no shortage of work, just a need to match skills and workflows more intelligently.” Chapters 00:05 - Introduction and Vijay’s background 00:57 - From forecasting analyst to AI-powered platforms 03:18 - Rethinking labor intelligence beyond job descriptions 05:39 - Building a sense-making engine from complex data 07:42 - Storytelling, context, and executive alignment 11:15 - The rise of verification skills 14:04 - Creating a trusted and transparent AI ecosystem 19:31 - Unlocking the true nature of work through ETTER 22:44 - Ethical AI and human-centric design 32:19 - How data becomes a therapeutic tool 35:14 - AI’s real impact on jobs and skills demand 45:25 - Strategic work planning beyond job roles 49:19 - Optimism, augmentation, and future-proofing teams 50:34 - Closing thoughts and appreciation Vijay Swami: https://www.linkedin.com/in/vijay-swaminathan-a44101/ Draup: https://draup.com/ For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

In this milestone 100th episode, host Bob Pulver reflects on the journey of Elevate Your AIQ, sharing why he started the podcast, what he's learned from nearly 100 conversations, and what’s ahead for the show and its community. He revisits recurring themes such as AI literacy, responsible innovation, and human-centric transformation—connecting them to his personal experiences, professional background, and passion for empowering others. This solo conversation is both a look back and a call to action for individuals and organizations to embrace AI thoughtfully and elevate their AIQ together. Keywords AIQ, AI literacy, responsible AI, human-centric design, talent transformation, skills-based hiring, human potential, CHRO of the future, work redesign, education reform, podcasting, Substack, transformation leaders, automation strategy, AI readiness, AI ethics, trust, transparency, fairness, lifelong learning, community, AI-powered workforce Takeaways Podcasting is a powerful outlet for exploring curiosity, storytelling, and continuous learning—especially for neurodivergent thinkers. Human-centric AI readiness is not just about tools or tech—it’s about mindset, adaptability, and lifelong learning. AIQ exists on three levels: individual, team, and organizational—each requiring a blend of skills, tools, and ethical judgment. Responsible AI is central to modern transformation—touching on transparency, fairness, ethics, and explainability. CHROs and people leaders have dual responsibilities as strategic architects of work and catalysts for responsible innovation. Hiring for skills and potential—rather than pedigree—is crucial to unlocking hidden talent and countering bias. Education and talent development must evolve to equip students and workers with the durable skills of the AI-powered future. Communities of practice and peer generosity are vital to collective learning and resilience in this era of rapid change. Quotes “Use AI where you should, not wherever you can.” “We’ve always adapted to new technologies—this time is no different.” “Human-centricity and human potential are key overarching themes of this show, and of the future of work.” “AIQ isn’t just about literacy—it’s about readiness, judgment, and mindset.” “If you are a DEI advocate, you are now a responsible AI advocate.” “You can control your own destiny—you’re capable of more than you think.” Chapters 00:00 Welcome and Gratitude for Episode 100 00:50 Human-Centric AI and the Purpose of the Show 02:32 Authenticity, Creativity, and Focus 04:35 My Background: Corporate to Independent 07:18 Early Exposure to AI at IBM and Personal Stakes 09:55 Start with Processes and Business Challenges, Not Tech 11:48 Three Levels of AIQ: Individual, Team, Org 13:45 Beyond Prompting: Augmenting Capabilities 15:20 Responsible AI: Use and Design 17:30 The Role of Trust, Transparency, and Fairness 19:50 DEI and Responsible AI Are Inseparable 21:10 Skills-Based Hiring and Hidden Potential 23:00 Designing Work for Human + AI Partnership 25:40 Lifelong Learning and the Future of Education 27:20 CHROs as Architects and Innovation Catalysts 29:30 Offense and Defense in Responsible Innovation 31:00 A Call to Action for Listeners and the Community 32:10 What’s Next: Live Shows, Events, Writing, and Community 33:20 Closing Gratitude and Future Outlook For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver sits down with Ross Dawson, world-renowned futurist, serial entrepreneur, and creator of the Humans + AI community. With decades of foresight expertise, Ross shares his evolving vision of human-AI collaboration — from systems-level transformation to individual cognitive augmentation. The conversation explores why organizations must reframe their approach to talent, capability, and value creation in the age of AI, and how human agency, trust, and fluid talent models will define the future of work. Keywords Ross Dawson, Humans + AI, AI roadmap, ThoughtWeaver, AI teaming, digital twins, augmented thinking, talent marketplaces, future of work, systems thinking, AI in organizations, AI in education, trust in AI, AI-enabled teams, cognitive diversity, latent talent, fluid talent, organizational design Takeaways The “Humans + AI” framework centers on complementarity, not substitution — AI should augment and elevate human potential. AI maturity is not just technical — it requires cultural readiness, mindset shifts, and systems-level thinking. Trust in AI must be calibrated; both over-trusting and under-trusting limit value creation. AI-enabled teams will rely on clear role design, thoughtful delegation of decision rights, and frameworks for collaborative intelligence. Digital twins and AI agents offer different organizational advantages — one mimics individuals, the other scales domain expertise. Organizations must reimagine work as networks of capabilities, not boxes of job descriptions. Talent marketplaces are an early expression of fluid workforce models but require intentional design and leadership buy-in. The most human-centric organizations will be best positioned to attract talent and thrive in the AI era. Quotes “AI should always be a complement to humans — not a substitute.” “We live in a humans + AI world already. The question is how we shape it.” “Mindset really frames how much value we can get from AI — individually and societally.” “You know more than you can tell. That gap between tacit knowledge and what AI can access is where humans still shine.” “Start with a vision — not a headcount reduction. Ask what kind of organization you want to become.” “We can use AI not just to apply existing capabilities but to uncover and expand them.” Chapters 00:00 - Welcome and Ross Dawson’s introduction 01:10 - From futurism to Humans + AI: key focus areas 03:30 - How AI is shifting public curiosity and mindset 06:00 - Systems-level thinking and responsible AI use 08:20 - AI in education and enterprise transformation 11:10 - The rise of AI-augmented thinking 14:00 - Calibrating trust in AI and human roles in teams 17:00 - Designing humans + AI teaming frameworks 20:30 - Delegation models and decision architecture 23:20 - Digital twins vs synthetic AI agents 26:00 - The value of tacit knowledge and cognitive diversity 30:00 - Empowering individuals amidst career uncertainty 32:10 - Breaking out of job “boxes” with fluid talent models 35:00 - Talent marketplaces and barriers to adoption 38:00 - Human-centric leadership in AI-powered transformation 41:00 - Strategic roadmaps and vision-led change 45:30 - Ross’s personal AI tools and experiments 52:00 - Final thoughts on AI’s role in augmenting human creativity Ross Dawson: https://www.linkedin.com/in/futuristkeynotespeaker Humans + AI: https://humansplus.ai For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver speaks with Jeff Riley, former Massachusetts Commissioner of Education and Executive Director of Day of AI, a nonprofit launched out of MIT. They explore the urgent need for AI literacy in K-12 education, the responsibilities of educators, parents, and policymakers in the AI era, and how Day of AI is building tools, curricula, and experiences that empower students to engage with AI critically and creatively. Jeff shares both inspiring examples and sobering warnings about the risks and rewards of AI in the hands of the next generation. Keywords Day of AI, MIT RAISE, responsible AI, AI literacy, K-12 education, student privacy, AI companions, Common Sense Media, AI policy, AI ethics, educational technology, AI curriculum, teacher training, creativity, critical thinking, digital natives, student agency, future of education, AI and the arts, cognitive offloading, generative AI, AI hallucinations, PISA 2029, AI festival Takeaways Day of AI is equipping teachers, students, and families with tools and curricula to understand and use AI safely, ethically, and productively. AI literacy must start early and span disciplines; it’s not just for coders or computer science classes. Students are already interacting with AI — often without adults realizing it — including the widespread use of AI companions. A core focus of Day of AI is helping students develop a healthy skepticism of AI tools, rather than blind trust. Writing, critical thinking, and domain knowledge are essential guardrails as students begin to use AI more frequently. The AI Festival and student policy simulation initiatives give youth a voice in shaping the future of AI governance. AI presents real risks — from bias and hallucinations to cognitive offloading and emotional detachment — especially for children. Higher education and vocational programs are beginning to respond to AI, but many are still behind the curve. Quotes “AI is more powerful than a car — and yet we’re throwing the keys to our kids without requiring any kind of driver’s ed.” “We want kids to be skeptical and savvy — not just passive consumers of AI.” “Students are already using AI companions, but most parents have no idea. That gap in awareness is dangerous.” “Writing is thinking. If we outsource writing, we risk outsourcing thought itself.” “The U.S. invented AI — but we risk falling behind on AI literacy if we don’t act now.” “Our goal isn’t to scare people. It’s to prepare them — and let young people lead where they’re ready.” Chapters 00:00 - Welcome and Introduction to Jeff Riley 01:11 - From Commissioner to Day of AI 02:52 - MIT Partnership and the Day of AI Mission 04:13 - Global Reach and the Need for AI Literacy 06:37 - Resources and Curriculum for Educators 08:18 - Defining Responsible AI for Kids and Schools 11:00 - AI Companions and the Parent Awareness Gap 13:51 - Critical Thinking and Cognitive Offloading 16:30 - Student Data Privacy and Vendor Scrutiny 21:03 - Encouraging Creativity and the Arts with AI 24:28 - PISA’s New AI Literacy Test and National Readiness 30:45 - Staying Human in the Age of AI 34:32 - Higher Ed’s Slow Adoption of AI Literacy 39:22 - Surfing the AI Wave: Teacher Buy-In First 42:35 - Student Voice in AI Policy 46:24 - The Ethics of AI Use in Interviews and Assessments 53:25 - Creativity, No-Code Tools, and Future Skills 55:18 - Final Thoughts and Festival Info Jeff Riley: https://www.linkedin.com/in/jeffrey-c-riley-a110608b Day of AI: https://dayofai.org For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob sits down with Jeff Pennington, former Chief Research Informatics Officer at the Children’s Hospital of Philadelphia (CHOP) and author of You Teach the Machines, and his daughter Mary Jane (MJ) Pennington, a recent Colby College graduate working in rural healthcare analytics. Jeff and MJ reflect on the real-time impact of AI across generations—from how Gen Z is navigating AI’s influence on learning and careers, to how large institutions are integrating AI technologies. They dig into themes of trust, disconnection, data quality, and what it truly means to be future-proof in the age of AI. Keywords AI literacy, Gen Z, future of work, healthcare AI, trusted data, responsible AI, education, automation, disconnection, skills, strategy, adoption, social media, transformation Takeaways Gen Z’s experience with AI is shaped by a rapid-fire sequence of disruptions: COVID, remote learning, and now Gen AI Both podcast and book You Teach the Machines serve as a “time capsule” for capturing AI’s societal impact Orgs are inadvertently cutting off AI-native talent from the workforce Misinformation, over-hype, and poor PR from big tech are fueling widespread public fear and distrust of AI AI adoption must move from top-down mandates to bottom-up innovation, empowering frontline workers Data quality is a foundational issue, especially in healthcare and other high-stakes domains Real opportunity is in leveraging AI to elevate human work through augmentation, creativity, and access Disconnection and over-reliance on AI are emerging as long-term social risks, especially for younger generations Quotes “It’s a universal fear now. Everyone has to ask: what makes you AI-proof?” “The vitality of democracy depends on popular knowledge of complex questions.”  “We're not being given the option to say no to any of this.” “I’m 100% certain the current winners in AI will not be the winners in five to ten years.”  Chapters 00:02 Welcome and Guest Introductions 00:48 MJ’s Path: From Computational Biology to Rural Healthcare 01:52 Why They Launched the Podcast You Teach the Machines 03:25 Jeff’s Work at CHOP and the Pediatric LLM Project 06:47 Making AI Understandable: The Book’s Purpose 09:11 Navigating Fear and Trust in AI Headlines 11:31 Gen Z, AI-Proof Careers, and Entry-Level Job Loss 16:33 Why Resilience is Gen Z’s Underrated Superpower 18:48 Disconnection, Dopamine, and the Social Cost of AI 22:42 AI’s PR Problem and the Survival Signals We're Ignoring 25:58 Chatbots as Addictive Companions: Where It Gets Dark 29:56 Choosing to Innovate: A More Hopeful AI Future 32:11 The Dirty Truth About Data Quality and Trust 36:20 How a Brooklyn Coffee Company Fine-Tuned AI with Their Own Data 40:12 Why “Throwing AI on It” Isn’t a Strategy 44:20 Measuring Productivity vs. Driving Meaningful Change 48:22 The Real ROI: Empowering People, Not Eliminating Them 53:26 Healthcare’s Lazy AI Priorities (and What We Should Do Instead) 57:12 How Gen Z Was Guided Toward Coding—And What Happens Now 59:37 Dependency, Education, and Democratizing Understanding 1:04:22 AI’s Impact on Educators, Students, and Assessment 1:07:03 The Real Threat Isn’t Just Job Loss—It’s Human Disconnection 1:10:01 Defaulting to AI: Why Saying "No" Is No Longer an Option 1:12:30 Final Thoughts and Where to Find Jeff and MJ’s Work Jeff Pennington: https://www.linkedin.com/in/penningtonjeff/ Mary Jane Pennington: https://www.linkedin.com/in/maryjane-pennington-31710a175/ You Teach The Machines (book): https://www.audible.com/pd/You-Teach-the-Machines-Audiobook/B0G27833N9 You Teach The Machines (podcast): https://open.spotify.com/show/4t6TNeuYTaEL1WbfU5wsI0?si=bb2b1ec0b53d4e4e For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver sits down with community builder and HR influencer Enrique Rubio, founder of Hacking HR. Enrique shares his journey from engineering to HR, his time building multiple global communities, and why he ultimately returned “home” to Hacking HR to pursue its mission of democratizing access to high-quality learning. Bob and Enrique discuss the explosion of AI programs, the danger of superficial “prompting” education, the urgent need for governance and ethics, and the risks organizations face when employees use AI without proper training or oversight. It’s an honest, energizing conversation about community, trust, and building a responsible future of work. Keywords Enrique Rubio, Hacking HR, Transform, community building, democratizing learning, HR capabilities, AI governance, AI ethics, shadow AI, responsible AI, critical thinking, AI literacy, organizational risk, data privacy, HR community, learning access, talent development Takeaways Hacking HR was founded to close capability gaps in HR and democratize access to world-class learning at affordable levels. The community’s growth accelerated during COVID when others paused events; Enrique filled the gap with accessible virtual learning. Many AI programs focus narrowly on prompting rather than teaching leaders to think, govern, and transform responsibly. Companies must assume employees and managers are already using AI and provide clear do’s and don’ts to mitigate risk. Untrained use of AI in hiring, promotions, and performance management poses serious liability and fairness concerns. Critical thinking is declining, and generative AI risks accelerating that trend unless individuals stay engaged in the reasoning process. Community must be built for the right reasons—transparency, purpose, and service—not just lead generation or monetization. AI strategies often overlook workforce readiness; literacy and governance are as important as tools and efficiency goals. Quotes “Hacking HR is home for me.” “We’re here to democratize access to great learning and great community.” “Prompting is becoming an obsolete skill—leaders need to learn how to think in the age of AI.” “Assume everyone creating something on a computer is using AI in some capacity.” “If managers make decisions based on AI without training, that’s a massive liability.” “Most AI strategies can be summarized in one line: we’re using AI to be more efficient and productive.” Chapters 00:00 Catching up and meeting in person at recent events 01:18 Enrique’s career journey and return to Hacking HR 04:43 Democratizing learning and supporting a global HR community 07:17 The early days of running virtual conferences alone 09:39 Why affordability and access are core to Hacking HR’s mission 13:13 The rise of AI programs and the noise in the market 15:58 Prompting vs. true strategic AI leadership 18:21 The importance of community intent and transparency 20:42 Training leaders to think, reskill, and govern in the age of AI 23:05 Dangers of data misuse, privacy gaps, and dark-web training sets 26:08 Critical thinking decline and AI’s impact on cognition 29:16 Trust, data provenance, and risks in recruiting use cases 31:48 The need for organizational AI manifestos 32:47 Managers using AI for people decisions without training 35:12 Why governance is essential for fairness and safety 39:12 The gap between stated AI strategies and people readiness 43:54 Accountability across the AI vendor chain 46:18 Who should lead AI inside organizations 49:28 Responsible innovation and redesigning work 53:06 Enrique’s personal AI tools and closing reflections Enrique Rubio: https://www.linkedin.com/in/rubioenrique Hacking HR: https://hackinghr.io For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver and Sandra Loughlin explore why most narratives about AI-driven job loss miss the mark and why true productivity gains require deep changes to processes, data, and people—not just new tools. Sandra breaks down the realities of synthetic experts, digital twins, and the limits of current enterprise data maturity, while offering a grounded, hopeful view of how humans and AI will evolve together. With clarity and nuance, she explains the four pillars of AI literacy, the future of work, and why leaning into AI—despite discomfort—is essential for progress. Keywords Sandra Loughlin, EPAM, learning science, transformation, AI maturity, synthetic agents, digital twins, job displacement, data infrastructure, process redesign, AI literacy, enterprise AI, productivity, organizational change, responsible innovation, cognitive load, future of work Takeaways Claims of massive AI-driven job loss overlook the real drivers: cost-cutting and reinvestment, not productivity gains. True AI value depends on re-engineering workflows, not automating isolated tasks. Synthetic experts and digital twins will reshape expertise, but context and judgment still require humans. Enterprise data bottlenecks—not technology—limit AI’s ability to scale. Humans need variability in cognitive load; eliminating all “mundane” work isn’t healthy or sustainable. AI natives—companies built around data from day one—pose real disruption threats to incumbents. Productivity gains may increase demand for work, not reduce it, echoing Jevons’ Paradox. AI literacy requires understanding technology, data, processes, and people—not just tools. Quotes “Only about one percent of the layoffs have been a direct result of productivity from AI.” “If you automate steps three and six of a process, the work just backs up at four and seven.” “Synthetic agents trained on true expertise are what people should be imagining—not email-writing bots.” “AI can’t reflect my judgment on a highly complex situation with layered context.” “To succeed with AI, we have to lean into the thing that scares us.” “Humans can’t sustain eight hours of high-intensity cognitive work—our brains literally need the boring stuff.” Chapters 00:00 Introduction and Sandra’s role at EPAM 01:39 Who EPAM serves and what their engineering teams deliver 03:40 Why companies misunderstand AI-driven job loss 07:28 Process bottlenecks and the real limits of automation 10:51 AI maturity in enterprises vs. AI natives 14:11 Why generic LLMs fail without specialized expertise 16:30 Synthetic agents and digital twins 18:30 What makes workplace AI truly dangerous—or transformative 23:20 Data challenges and the limits of enterprise context 26:30 Decision support vs. fully autonomous AI 31:48 How organizations should think about responsibility and design 34:21 AI natives and market disruption 36:28 Why humans must lean into AI despite discomfort 41:11 Human trust, cognition, and the need for low-intensity work 45:54 Responsible innovation and human-AI balance 50:27 Jevons’ Paradox and future work demand 54:25 Why HR disruption is coming—and why that can be good 58:15 The four pillars of AI literacy 01:02:05 Sandra’s favorite AI tools and closing thoughts Sandra Loughlin: https://www.linkedin.com/in/sandraloughlin EPAM: https://epam.com For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver speaks with Keith Langbo, CEO and founder of Kelaca, about redefining recruitment in the AI era. Keith shares why he founded Kelaca to prioritize people over process, how core values like kindness and collaboration shape culture, and why trust and choice must be built into AI-powered recruiting tools. Bob and Keith explore evolving models of hiring, including fractional workforces, agentic systems, and data-informed decision-making — all rooted in a future where humans remain in control of the technology that serves them. Keywords Keith Langbo, Kelaca, recruitment, hiring, talent acquisition, AI in recruiting, agentic systems, culture add, core values, psychometrics, responsible AI, fractional workforce, gig economy, recruiting automation, candidate experience, structured interviews, Kira, human-centric design, AI trust, global hiring, digital agents, recruitment tech, NLP sourcing, recruiting innovation Takeaways Keith founded Kelaca to humanize the recruitment experience, treating people as partners — not products. Modern recruiting must shift from transactional, resume-driven models to more consultative, intelligence-based practices. AI’s greatest value lies in giving candidates and clients choice, not replacing humans — especially for real-time updates and communication preferences. Recruiters should move from “human-in-the-loop” to “humans in control” — using AI to augment but not automate judgment. Future hiring models may rely on digital agents representing both candidates and employers, enabling richer, data-driven matches. Core values — like kindness, accountability, and enthusiasm — are essential to maintaining culture across full-time and fractional teams. Structured data is key to overcoming bias and improving hiring quality, but psychometrics alone can't capture experience or growth. Many current tools automate broken processes; real innovation requires first rethinking what “better” hiring looks like. Quotes “I wanted to treat people like people, not like products.” “AI powered but human driven — that’s the experience I want to create.” “Resumes are broken. Interviews are often charisma contests. We can do better.” “Humans don’t just need to be in the loop — they need to be in control.” “I don’t care if you’re full-time or fractional. You still need to show kindness and a willingness to learn.” “We’re on the verge of bots talking to bots. That’s exciting — and terrifying.” Chapters 00:00 Introduction and Keith’s mission behind founding Kelaca 02:35 The candidate and client frustrations with traditional recruiting 05:10 Why resumes and interviews are broken — and what to do instead 07:10 Building feedback loops and AI-enabled candidate communication 10:45 Choice and context in AI tools: respecting human preference 13:44 From “human in the loop” to “human in control” 18:12 Agentic hiring and the rise of digital representation 25:10 Gig work and applying culture fit to fractional talent 29:34 Core values as the foundation of culture, not employment status 33:22 Responsible AI, fairness, and trust in hiring decisions 40:00 The hype cycle of recruiting tech and design thinking 42:56 AI as the modern calculator: from caution to capability 47:16 Global perspectives: AI adoption in US vs UK recruiting 53:08 Keith’s favorite AI tools and Kelaca’s new product, Kira 56:28 Closing thoughts and appreciation Keith Langbo: https://www.linkedin.com/in/keithlangbo Kelaca: https://kelaca.com/ KIRA Webinar Series: https://www.eventbrite.com/e/how-to-fix-the-first-step-in-hiring-to-drive-retention-introducing-kira-tickets-1853418256899 For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver talks with Dan Riley, CEO and Co-founder of RADICL, about reshaping work through connection, trust, and clarity. From his roots as a punk rock musician to building Modern Survey and RADICL, Dan shares how creativity, curiosity, and courage fuel his leadership philosophy. Together, they explore the balance between human imperfection and technological advancement, why “high tech” must still serve human needs, and how organizations can build cultures that learn, listen, and adapt. The discussion spans themes of AI strategy, responsible design, employee listening, and the enduring value of genuine human connection. KeywordsDan Riley, RADICL, Modern Survey, Aon, employee listening, people analytics, connection, trust, AI ethics, human-AI collaboration, imperfection, curiosity, creativity, collective intelligence, organizational network analysis, people analytics world, Unleash, Transform, learning culture, human connection, responsible AI Takeaways Imperfection is a defining strength of humanity — and the source of creativity and innovation. The best technology solves real human problems in the flow of work, not just productivity gaps. AI is a mirror, amplifying human intent and behavior; if we lead with empathy and ethics, AI learns from that. Clarity, communication, and transparency are critical to avoiding “AI chaos” inside organizations. Continuous listening and connection are the new foundations for engagement and trust. Curiosity and conversation are essential skills for navigating the fast-moving future of work. The most effective teams balance diverse strengths rather than relying solely on “rock stars.” True progress happens when we keep the human conversation going — across roles, hierarchies, and perspectives. Quotes “I define myself as an artist first — a musician, filmmaker, who randomly fell into HR and tech.” “The most beautiful part about being human is that we’re imperfect — that’s where the best ideas come from.” “AI doesn’t fix our flaws; it amplifies them. It’s a mirror of how we show up.” “For technology to work, it has to be solving a human problem in the flow, not just adding to the stack.” “It’s okay to say, ‘We don’t have it all figured out yet’ — just be transparent about where you are.” “You’ll never regret having a conversation about something important.” Chapters 00:03 – Welcome and Dan’s background: from punk rock to HR tech 01:45 – Founding Modern Survey and RADICL’s mission around trust and impact 05:14 – The changing landscape of work 06:42 – Highlights from People Analytics World, Transform, and Unleash 09:50 – Rise of human connection as the dominant theme in work tech 13:10 – Clarity, communication, and the need for an AI strategy 16:19 – Productivity, balance, and reinvesting in people 18:36 – The risk of over-automation and the value of learning 22:16 – Teaching curiosity and critical thinking in an AI world 27:25 – Why open conversations about AI matter more than ever 33:51 – Employee listening, continuous dialogue, and the evolution of engagement 37:22 – How AI enhances understanding and connection between teams 40:06 – Organizational network analysis and adaptive learning 43:21 – Connection, mentorship, and collective intelligence 46:03 – AI as a mirror: amplification of human behavior and bias 48:36 – Building balanced, imperfect, and effective teams 51:48 – Tools, curiosity, and the limits of generative AI 55:35 – Trusting your judgment and maintaining critical thinking 56:34 – Staying human amid synthetic connection 57:45 – Closing reflections and the call for ongoing dialogue Dan Riley: https://www.linkedin.com/in/dan-riley-57b9431 RADICL: http://www.radiclwork.com For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver talks with creative technologist and entrepreneur Brad Topliff about building more human-centered systems for the AI era. Brad reflects on his nonlinear career—from early work in design and user experience, to many years at data and analytics company TIBCO, to his latest venture, SelfActual, which helps people and teams cultivate self-awareness, strengths, and alignment. Together, Bob and Brad explore the intersections of identity, trust, data ownership, and imagination in the workplace, and how understanding ourselves better can make AI more supportive—not more invasive. The conversation bridges psychology, technology, and ethics to imagine a future of work where humans remain firmly in control of their data, choices, and growth. Keywords Brad Topliff, SelfActual, TIBCO, self-awareness, positive psychology, data ownership, digital identity, AI ethics, imagination, human-centric design, trust, internal mobility, talent data, distributed identity, psychological safety, future of work Takeaways Self-awareness is foundational to effective teams and ethical AI use. Personal data about strengths and values should be owned by the individual, not the employer. AI can serve as a mirror and reframing tool, helping people build perspective—not replace human judgment. Internal mobility and growth depend on psychological safety and discretion around what employees share. Positive psychology and imagination can help teams align without reducing people to static personality types. The next era of HR tech should prioritize trust, transparency, and consent in how personal data is used. True human readiness for AI means combining durable human skills with thoughtful technology design. Quotes “I became a translator between the arts, the engineers, and leadership—and that’s carried through everything I’ve done.” “When you create data about yourself, who owns it? You? Your organization? The answer matters for trust.” “Most people think they’re self-aware—but only about twelve percent actually are.” “A job interview is two people sitting across the table from each other lying. We both present what we think the other wants to hear.” “If you give people autonomy and psychological safety, they’ll show up more fully as themselves.” “In the presence of trust, you don’t need security.” Chapters 00:03 – Welcome and Brad’s background in design, Apple roots, and TIBCO experience 05:46 – From UX to data: connecting human insight with enterprise technology 07:48 – Self-awareness, ownership of personal data, and building SelfActual 11:00 – The tension between authenticity, masking, and “bringing your whole self” to work 18:19 – Digital credentials, resumes, and rethinking candidate data ownership 23:08 – Internal mobility, verifiable credentials, and distributed identity 32:51 – Broad skills vs. specialization and the role of AI in talent matching 34:48 – Self-awareness, imagination, and positive psychology at work 46:48 – Rethinking internal mobility and autonomy for well-being and growth 49:26 – Human-centric AI readiness and the limits of automation 58:40 – Trust, security, and ownership of data in organizational AI systems 01:02:37 – Reflections on digital twins, imagination, and collective intelligence 01:08:06 – Closing thoughts and Self Actual’s human-first approach Brad Topliff: https://www.linkedin.com/in/bradtopliff SelfActual: https://selfactual.ai For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver speaks with Prem Kumar, CEO and Co-founder of Humanly.io, about the evolution of hiring technology and the company's transition from a conversational AI tool to a full-fledged AI-powered hiring platform. Prem discusses the impact of Humanly’s recent acquisitions, expansion into post-hire engagement, and how they help employers address challenges in both high-volume and knowledge worker recruiting. Prem emphasizes the need for responsible, inclusive, and human-centric AI design, and explains how Humanly is helping organizations speed up hiring without sacrificing quality, fairness, or candidate experience. Keywords Humanly, conversational AI, AI interviewing, responsible AI, candidate experience, recruiting automation, employee engagement, AI acquisitions, ethics, RecFest, quality of hire, neurodiversity, candidate feedback, interview intelligence, AI coach, sourcing automation Takeaways Humanly’s evolution includes three strategic acquisitions that expand its platform from candidate screening to post-hire engagement. The company’s mission is to help employers talk to 100% of their applicants—not just the 5% that typically make it through—and reduce time-to-hire. Prem highlights how AI can reduce ghosting by creating 24/7 availability and real-time Q&A touchpoints for candidates. Interview feedback tools and coaching features are being developed for both candidates and recruiters. The importance of AI workflow integration is critical—tools must operate within a recruiter’s day-to-day flow to be effective. Humanly’s platform helps uncover quality-of-hire insights by connecting interview behaviors with long-term employee outcomes. The need for third-party AI audits and ethical guardrails. Insights from diverse candidate populations—including neurodiverse candidates and early-career talent—are shaping Humanly’s inclusive design practices. Quotes “It’s not human vs. AI—it’s AI vs. being ignored.” “Our goal is to reduce time-to-hire without compromising quality or fairness.” “We’re obsessed with the problem, not just the solution. That’s what keeps us grounded as we scale.” “Responsible AI should be audited just like SOC 2 or ISO—trust is foundational in hiring.” “The best interview for one role won’t be the same for another. That’s where personalization and learning matter.” “Everything we’ve done to improve access for neurodiverse candidates has made the experience better for everyone.” Chapters 00:00 – Intro and Prem’s Background 01:00 – Humanly's Origins and the Candidate Experience Gap 03:00 – 2025 Growth, Funding, and Acquisition Strategy 05:15 – From Conversational AI to Full-Funnel Hiring Platform 06:30 – High-Volume and Knowledge Workers 08:00 – Combating Ghosting and Delays with AI Speed 10:30 – Candidate Support and Interview Feedback 12:00 – Creating a 24/7 Conversational Layer for Applicants 13:45 – Data-Driven Hiring and Candidate Self-Selection 15:00 – Interview Coaching and Practice Tools 17:00 – Acquisitions and Platform Consolidation Feedback 18:45 – Responsible AI and Third-Party Auditing 21:00 – Partnering with Values-Aligned Teams and Investors 22:00 – Measuring Candidate Experience Across All Interactions 24:00 – Connecting Interview Behavior to Quality of Hire 26:00 – Coaching Recruiters and Interview Intelligence 28:45 – Expanding Into Post-Hire and Internal Conversations 30:00 – The Future of AI in HR and Internal Use Cases 34:00 – Designing Inclusively for Diverse Candidate Needs 36:00 – Modalities, Accessibility, and Equity in Interviewing 39:00 – Generative AI Reflections and Everyday Use 42:00 – Wrapping Up: What's Next for Humanly Prem Kumar: https://www.linkedin.com/in/premskumar Humanly: https://humanly.io For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

In this lively and wide-ranging conversation, Bob Pulver welcomes William Tincup, Co-founder of the WRKdefined Podcast Network, HR tech expert, and longtime friend of the show. Together they explore the evolution of podcasting, from its early scrappy days to today’s community-driven, AI-enhanced ecosystem. William shares his philosophy on personal authenticity, the rise of “PSO” — podcast search optimization — and why he believes we’re moving from search to conversation as the new model of discovery. They also dive into the ethics of personalization, digital identity, and privacy in a world where every click is data. From the practical uses of AI in podcast production to the philosophical questions about digital twins and second lives online, this episode blends humor, honesty, and the kind of deep reflection that defines both William and the WRKdefined network of shows. Keywords AI in podcasting, HR tech, authenticity, podcast search optimization, personalization, digital identity, privacy, digital twins, agentic internet, audience engagement, AI tools, discoverability, content creation, automation, human connection Takeaways Podcasting has evolved from a solo pursuit to a collaborative, AI-empowered craft. Optimization now means being discoverable by AI, not just by search engines. AI is already embedded throughout the creative workflow — from editing to marketing. Personal authenticity builds lasting trust in an algorithmic world. Digital twins and personalization raise questions about identity, privacy, and consent. Good content isn’t manipulation — it’s value shared with intention and empathy. True innovation comes from staying curious, playful, and human. Quotes “We’ve moved from search to conversation — people don’t Google anymore, they ask.” “Independent podcasting can be lonely, but community turns it into a craft.” “You can’t automate authenticity, but AI can help you amplify it.” “If your content has value, you’re not gaming the system — you’re serving people.” “Privacy is an illusion. So, make the ads you see worth your time.” “Digital twins may not replace us, but they’ll definitely outlive us.” Chapters 00:00 – Welcome and introduction 00:26 – William’s 25-year journey in HR tech and podcasting 02:47 – The evolution of Elevate Your AIQ and lessons from early episodes 05:25 – From SEO to PSO: Optimizing for AI discoverability 09:06 – Why AI-driven content isn’t manipulation when it adds real value 10:39 – Building community through the Work Defined Podcast Network 13:44 – Experimentation, creativity, and learning from other hosts 16:23 – How AI is transforming podcast production workflows 19:17 – Forgetting, hallucinations, and the limits of AI memory 21:48 – Digital twins and the blurred lines between personal and professional identity 26:32 – Authenticity online: the “one-dimensional self” 31:39 – Privacy illusions and the myth of online anonymity 33:57 – The “agentic internet” and the power of individual terms 38:25 – Advertising, personalization, and the importance of relevance 41:58 – Lazy marketing, weak signals, and bad outreach 46:46 – Aggregating knowledge and curating content intelligently 51:01 – Content creation, subscriptions, and the value of giving before selling 53:43 – AI, equity, and unlocking untapped talent 57:34 – Closing reflections and the case for empathy in technology William Tincup: https://www.linkedin.com/in/tincup WRKdefined: https://wrkdefined.com For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver talks with Dan Chait, CEO and co-founder of Greenhouse, about how technology, especially AI, is reshaping the hiring landscape — for better and worse. Dan shares Greenhouse’s origin story and the company’s mission to help every organization become great at hiring through structured, data-driven, and fair processes. Together, they explore the “AI doom loop” of automated applications and AI-written job descriptions, the tension between efficiency and authenticity, and how innovations like Real Talent and Dream Job aim to bring trust, fairness, and humanity back into hiring. The conversation also touches on identity verification, prompt injection risks, AI ethics, and the evolving skills that will define the workforce of the future. Keywords AI hiring, structured hiring, recruiting technology, Greenhouse, Real Talent, Dream Job, hiring fairness, candidate experience, identity verification, deepfakes, AI doom loop, prompt injection, job seeker experience, future of work, skills-based hiring, authenticity in hiring, mission-driven leadership, HR tech Takeaways AI can enhance hiring but must not replace human connection and judgment. The “AI doom loop” is eroding trust between employers and candidates. Real Talent helps companies identify legitimate, high-intent applicants. Dream Job empowers real people to rise above automated applications. Employers should be transparent about how AI is used in hiring decisions if they want to build trust while improving their employer brand. The résumé’s role is fading as new ways of showcasing skills emerge. The future of hiring belongs to organizations that unite data, empathy, and trust. Quotes “Our mission is to help every company be great at hiring — and that means putting structure and fairness at the center.” “We’re caught in an AI doom loop where both sides are using automation to outsmart the other — and no one’s winning.” “You can’t automate authenticity. The human element is what stands out most in a world full of AI slop.” “We can do anything, but we can’t do everything. So we focus on what matters most: helping people connect in meaningful ways.” “It’s not about banning AI — it’s about setting clear expectations for how to use it responsibly.” “The death of the résumé has been predicted for decades, but maybe this is finally the time.” Chapters 00:00 – Welcome and introduction 00:44 – Greenhouse origin story and mission 02:50 – Lessons from Dan’s early career and the importance of structured hiring 06:00 – Hiring for skills and potential over pedigree 08:20 – How structured interviews and scorecards create fairness and better data 11:00 – Balancing mission and business success at Greenhouse 13:40 – Introducing Real Talent and solving the “AI doom loop” 16:50 – Detecting fraud, misrepresentation, and risk in job applications 18:45 – Partnership with Clear for verified identities 20:00 – Digital credentialing and transparency in hiring 22:30 – The “AI vs. AI” challenge: automation on both sides of the hiring equation 25:00 – Dream Job: Human intent meets AI efficiency 27:50 – The candidate experience crisis and how to fix it 30:20 – Why resumes and job descriptions are losing meaning 32:00 – Bringing humanity back to hiring in an AI-dominated world 34:30 – The future of the HR tech ecosystem and partnerships 40:00 – Agentic AI and the next frontier of recruiting technology 43:00 – The death of the résumé and what replaces it 47:00 – Skills, AI literacy, and the next generation of workers 52:00 – Setting clear expectations for AI use in hiring 55:00 – Personal AI use: augmenting human connection 56:00 – Closing thoughts and reflections Dan Chait: https://www.linkedin.com/in/dhchait Greenhouse: https://greenhouse.com For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver speaks with Agi Garaba, Chief People Officer at UiPath, about the organization’s evolution from robotic process automation (RPA) to agentic AI and how that has impacted people, processes, and culture. Agi shares how HR can lead with a human-centric lens during AI transformation, the importance of AI literacy, and the practical steps UiPath is taking to balance innovation with responsible governance. This conversation blends strategic foresight with pragmatic execution and offers a roadmap for any leader navigating AI-enabled change. Keywords UiPath, agentic AI, automation, digital workers, RPA, HR technology, AI governance, AI literacy, talent acquisition, responsible AI, workforce transformation, human-centric design, reskilling, change management, future of work, CHRO, culture shift, AI readiness Takeaways UiPath’s transition from RPA to agentic automation marks a broader shift in how digital and human workers collaborate. HR has a central role in driving culture, trust, and adoption around emerging AI tools. A grassroots approach to agent development—crowdsourcing over 500 ideas from employees—ensures relevance and engagement. AI governance must evolve with technology; dedicated roles and frameworks are key to managing bias, access, and compliance. Building AI literacy across the organization—through tiered training and internal tooling—helps democratize innovation. Recruiting is transforming, but human relationships remain critical, especially in engaging passive candidates and senior-level talent. Not every task should be automated—some skills, like creative writing or candidate engagement, lose value when over-automated. Over-automation can create long-term talent gaps; junior roles are vital for succession and cultural continuity. Quotes “It’s not just a technology-led transformation. Culture has to be a core part of the AI journey.” “Over 50% of my HR team are citizen developers—we’ve built that capability into our DNA.” “We crowdsourced more than 500 ideas for agents across the organization—and everyone had a voice.” “Just because you can automate something doesn’t mean you should. Human context still matters.” “AI literacy is about imagination as much as it is about instruction. People need to see what’s possible.” “I’d like to create a workplace where human connection still matters—even as agents take on more tasks.” Chapters 00:00 – Introduction and Agi’s Career Path to UiPath 03:00 – From RPA to Agentic Automation 05:00 – HR at the Crossroads of Tech and Culture 07:15 – Org Design with Digital Coworkers 10:30 – Building Trust in Agentic Systems 13:40 – Responsible AI in HR Contexts 17:00 – Prioritizing and Tracking Agent Development 19:00 – Building AI Literacy Across the Organization 22:30 – From Vision to Execution: Pilots and Production 24:10 – Cross-functional Use Cases and Orchestration 26:45 – Governance, Compliance, and Continuous Oversight 30:00 – Redefining Human Skills in the Age of AI 33:00 – Knowing When Not to Automate 35:40 – Long-term Impacts on Junior Roles and Succession 38:45 – Strategic Workforce Planning and Digital Labor 41:00 – Agents in Recruiting: Limits and Opportunities 44:00 – Maintaining Human Relationships in Talent Acquisition 48:00 – Executive Search, Talent Advisors, and the Future of Recruiting 51:30 – Agi’s Personal Use and Reflections on GenAI 54:00 – Balancing Utility, Trust, and Critical Thinking 55:30 – Closing Thoughts and Wrap-up Agi Garaba: https://www.linkedin.com/in/agnesgaraba UiPath: https://uipath.com For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

In this compelling episode, Bob speaks with Lisa Yokana, a pioneering educator and global consultant, about how AI is reshaping the education landscape. Lisa shares her journey from traditional art and architecture teacher to building an experiential design lab, STEAM program, and social entrepreneurship course. Bob and Lisa explore how AI can serve as a catalyst for changing not just what we teach, but how we teach and why. With a focus on student agency, lifelong learning, and the shifting expectations of the future workforce, Lisa offers practical insights and inspiration for educators, parents, and community leaders looking to bring relevance, equity, and innovation into the classroom. Keywords AI in education, student agency, maker-centered learning, design thinking, STEAM, lifelong learning, workforce readiness, future of education, educational disruption, personalized learning, human skills, ethical AI, K-12 innovation Takeaways AI is a disruptor that can serve as a catalyst for rethinking teaching and learning. Student agency—not content mastery—is the core skill for future-ready learners. Traditional education systems are misaligned with the skills needed for the future workforce. Hands-on, project-based learning nurtures creativity, empathy, and real-world problem solving. Educators must experiment, fail forward, and reimagine their roles. Community support is critical for educational transformation. Ethics, responsible use, and digital literacy must be part of AI education, and must start early. AI levels the playing field for diverse learners but must be designed and used thoughtfully. Quotes “I never ask for permission. I just ask for forgiveness—and sometimes not even that.” “The big question is: what content is truly important for students to learn—and what can they master on their own?” “Agency is the kernel. If students have it, they can be resilient, adaptive, and self-directed.” “We want to create curious, empathetic humans who know they can change the world.” “AI doesn’t live a life—it can’t replace the embodied experience of being human.” “Schools need community conversations, not mandates, to adopt AI responsibly and equitably.” Chapters 00:00 – Lisa Yokana’s background and the early signs of educational misalignment 02:35 – Leaving the classroom to consult globally on innovation and mindset 03:25 – Reframing education: Skills vs. content 06:20 – Nurturing student agency and tackling big problems 09:01 – The disconnect between education and workforce needs 12:56 – How Lisa gained support and built the Scarsdale Design Lab 17:29 – Parent engagement and community buy-in 20:59 – Integrating AI in meaningful, ethical ways 24:06 – Educator mindsets and reframing pedagogy around AI 27:26 – AI use starts younger than we think 29:24 – Rethinking college in the age of AI 35:33 – Global patterns in AI adoption across education systems 39:20 – Addressing neurodiverse needs and accessibility 42:24 – Broadening community engagement and “thinking out loud” 43:38 – Responsible AI use and responsible design 49:11 – Big Tech’s role and thoughtful AI adoption in schools 53:03 – Final advice for parents, educators, and students Lisa Yokana: https://www.linkedin.com/in/lisa-yokana-81787ba Next World Learning Lab: https://nextworldlearninglab.com For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver welcomes Ben Zweig, CEO of Revelio Labs and labor economist, for a deep dive into the evolving world of workforce analytics. Drawing from their overlapping experiences at IBM, Bob and Ben explore how the early days of cognitive computing sparked a journey toward greater transparency in labor market data. Ben explains how Revelio Labs is building a “Bloomberg Terminal” for workforce insights—grounded in publicly available data and powered by sophisticated taxonomies of occupations, tasks, and skills. Together, they examine the importance of job architecture, the promise and pitfalls of AI in workforce analytics, and the complexities of measuring contingent and freelance labor. Ben also shares a preview of his upcoming book, Job Architecture, and how LLMs are being used to redefine how organizations model and respond to changes in work itself. Keywords Revelio Labs, Ben Zweig, labor market data, job architecture, workforce analytics, strategic workforce planning, AI in HR, cognitive computing, IBM, labor economics, generative AI, skills-based hiring, public labor statistics, contingent workforce, gig economy, talent intelligence Takeaways Revelio Labs aims to recreate company-level workforce insights using publicly available employment data, similar to how Bloomberg transformed financial markets. Job architecture is built on three distinct but interrelated taxonomies: occupations, tasks, and skills. Many orgs think of skills as the building blocks of jobs, rather than attributes of people—a conceptual misstep that limits strategic planning. Gen AI is being used to score the automation vulnerability of tasks, enabling better insights into how work is changing. Strategic workforce planning is often misnamed—what most companies do is operational, not truly strategic. Contingent and freelance labor remains a blind spot in many traditional labor statistics and HR systems. The ability to adjust for data bias, reporting lags, and incomplete workforce signals is critical for creating trustworthy insights. Revelio’s Public Labor Statistics offers an independent source of macro labor data, complementing BLS and ADP methodologies. Quotes “Skills are attributes of people. Tasks are the building blocks of jobs.” “What’s exciting is that these are hard problems with big upside—unlike finance, where most of the low-hanging fruit is gone.” “We’re asking LLMs to tell us what they’re good at—and how confident they are in that judgment.” “Most organizations don’t need to pay $1M to build a taxonomy anymore. They just need the right approach and the right data.” “There’s no reason we shouldn’t be repurposing labor market insights to help individuals, not just institutions.” Chapters 00:00 — Intro and HR Tech reflections 02:08 — Ben’s background in economics and IBM analytics 06:43 — Why labor market data lags behind capital markets 09:22 — Building a flexible, bias-adjusted analytics stack 14:19 — Empathy for job seekers and candidate friction 16:10 — Why job discovery is fundamentally an information problem 19:53 — Unpacking job architecture: occupations, tasks, and skills 24:28 — Scoring AI’s impact on tasks, not skills 28:39 — Summarization vs. hallucination in generative AI 38:45 — Introducing RPLS: Revelio Public Labor Statistics 45:40 — The challenge of tracking freelance and contingent work 51:58 — Dealing with ghost data and workforce ambiguity 53:35 — Real-life uses of AI and Ben’s curiosity mindset 54:42 — Closing thoughts Ben Zweig: https://www.linkedin.com/in/ben-zweig Revelio Labs: https://reveliolabs.com Job Architecture (pre-order): https://www.amazon.com/Job-Architecture-Building-Workforce-Intelligence/dp/1394369069/ For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver speaks with Emily Scace, Senior Legal Editor at Brightmine, about the intersection of AI, employment discrimination, and the evolving legal landscape. Emily shares insights on how federal, state, and global regulations are addressing bias in AI-driven hiring processes, the responsibilities employers and vendors face, and high-profile lawsuits shaping the conversation. They also discuss candidate experience, transparency, and the role of AI in pay equity and workforce fairness. Keywords AI hiring, employment discrimination, bias audits, compliance, workplace fairness, age discrimination, Title VII, DEI backlash, Workday lawsuit, SiriusXM lawsuit, EU AI Act, risk mitigation, HR technology, candidate experience Takeaways Employment discrimination laws apply at every stage of the talent lifecycle, from recruiting to termination. States like New York, Colorado, and California are setting the pace with new AI-focused compliance requirements. Employers face challenges managing a patchwork of state, federal, and international AI regulations. Recent lawsuits (Workday, SiriusXM) highlight risks of bias and disparate impact in AI-powered hiring. Candidate experience remains a critical yet often overlooked factor in mitigating both reputational and legal risk. Employers must balance the promise of AI with the responsibility to ensure fairness, accessibility, and transparency. Pay equity and transparency represent promising use cases where AI can drive positive change. Quotes “Discrimination can happen at any stage of the employment process.” “Some state laws go as far as requiring employers to proactively audit their AI tools for bias.” “Employers can’t just outsource their hiring funnel and blindly take the recommendations of AI.” “Class actions often succeed where individual discrimination claims struggle — they reveal systemic patterns.” “Even if candidates don’t get the job, a little touch of humanity goes a long way in making them feel respected.” “AI has real potential to help employers get to the root causes of pay inequity and model solutions.” Chapters 00:00 – Welcome and Introduction 00:36 – Emily’s background and role at Brightmine 02:38 – Overview of employment discrimination laws 05:27 – AI and compliance with existing legal frameworks 07:20 – California’s October regulations and employer liability 09:54 – Employer challenges with multi-state and global compliance 11:26 – Proactive vs reactive approaches to AI bias 13:06 – EU AI Act and global alignment strategies 15:37 – High-risk AI use cases in employment decisions 18:34 – DEI backlash and its impact on discrimination law 20:59 – Age discrimination and the Workday lawsuit 27:34 – Data, inference, and bias in AI hiring tools 31:25 – Candidate experience and black-box hiring systems 33:33 – Bias in interviews and the human role in hiring 37:43 – Transparency and feedback for candidates 42:44 – AI sourcing tools and recruiter responsibility 47:52 – Risks of misusing public AI tools in hiring 50:12 – The SiriusXM lawsuit and early legal developments 54:08 – Candidate engagement and communication gaps 59:19 – Emily’s views on AI tools and positive use cases Emily Scace: https://www.linkedin.com/in/emily-scace Brightmine: https://brightmine.com For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠