Elevate Your AIQ

Charlene Li, analyst, author, and disruptive leadership expert, returns to Elevate Your AIQ to discuss with Bob her newly released book Winning with AI, co-authored with Dr. Katia Walsh. Charlene makes the case that most organizations are failing with AI because they treat it as a technology initiative rather than a strategic one — and lays out a 90-day, 12-step framework for building a foundation that creates real enterprise value. The conversation revisits themes from her Fall 2024 appearance, including responsible AI and the human-AI partnership, and explores how the landscape has evolved. Key topics include AI fluency as an organizational imperative, workforce reinvestment over workforce reduction, and the emerging concept of integrated intelligence — where human and AI capabilities combine to create something genuinely superhuman. Keywords Charlene Li, Winning with AI, Katia Walsh, AI strategy, AI fluency, AI literacy, integrated intelligence, superhuman worker, workforce planning, reskilling, pilot purgatory, responsible AI, ethical AI, governance, human centricity, talent transformation, future of work, organizational disruption, values-based AI, co-intelligence Takeaways Lead with business strategy, not AI technology — the question is never "what can we do with AI?" but "how can AI help us accomplish what we're already trying to do?" AI fluency, not just literacy, is the goal — fluency means reaching for AI naturally, trusting it, and using it to learn how to use it better, like chopsticks becoming second nature Organizations stuck in pilot purgatory are procrastinating real decisions — pilots give everyone an excuse not to commit, and that dooms projects from the start Successful examples show a better path: use AI to raise workforce quality first, then expand customer value, then reinvent the business entirely Reskilling requires both organizational imagination and honest values — the IKEA story turned 8,500 displaced service reps into a $1B design business Integrated intelligence combines AI's speed and scale with uniquely human traits — empathy, judgment, intuition, self-reflection, and wisdom — to create superhuman capability AI fluency in hiring is shifting from a red flag to a baseline expectation — how candidates use AI reveals curiosity, creativity, and adaptability far better than traditional interviews Responsible AI governance done right isn't a compliance burden — a gold-standard internal policy means regulation becomes a checkbox, not a crisis Quotes "You don't need an AI strategy — you already have a business strategy. Figure out what of your business strategy could really be impacted with AI." "Automating a broken process is the definition of madness. Because of AI, could we do this in a completely different way?" "AI can only be as creative as your questions are. It can only be as empathetic as you are." "We should stop doing pilots. It's just another way to procrastinate having to say yes or no." "The first thing they said was, we are not going to use AI to cut people. That is not the intent going in." "You aim for a higher level than any regulation would ever want. You go for the gold standard and whatever they ask of you, of course you do those things." Chapters 00:03 Welcome and guest introduction 01:27 Catching up since Fall 2024 and the impetus for Winning with AI 02:45 The 90-day framework and leading with business strategy 05:46 Reimagining work versus automating broken processes 09:22 AI fluency as an organizational imperative 14:06 Making AI practice habitual and learning in community 17:54 Embedding AI in the flow of work and escaping pilot purgatory 20:07 Workforce reinvestment and a recent case study 26:35 Reskilling, redeployment, and the IKEA story 29:54 Getting C-suite and boards to embrace a human-centric approach 33:38 Starting with customers and thinking beyond efficiency 38:30 Building AI fluency fast and making the investment 41:38 AI fluency in recruiting and hiring for AI capability 47:52 Integrated intelligence and the rise of the superhuman worker 50:42 From individual productivity to team and organizational impact 52:14 Values-based AI and imbuing organizational values into AI systems 55:53 Responsible and ethical AI as a strategic advantage 59:38 Goldilocks governance and the 90-day blueprint 01:00:21 Closing thoughts and book information Charlene Li: https://www.linkedin.com/in/charleneli “Winning With AI”: https://winningwithaibook.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 reconnects with former IBM colleague Oded Dubovsky, founder of STRAIX (Strategy for AI Execution), an advisory practice helping organizations adopt AI thoughtfully and effectively. Oded shares a career journey spanning over two decades at IBM Research's Haifa Lab — where he led pioneering cognitive computing and computer vision projects — through applied AI work at Intel, and into independent consulting. The conversation explores why 95% of organizations struggle to move beyond AI aspiration to real execution, and what it takes to build a solid foundation before layering in AI. Bob and Oded also reflect on the enduring value of human ingenuity, originality, and orchestration in an increasingly AI-assisted world. Keywords Oded Dubovsky, STRAIX, AI strategy, AI execution, AI adoption, cognitive computing, computer vision, IBM Research, Haifa Lab, Watson, automation, generative AI, vibe coding, AI-assisted coding, responsible AI, human centricity, AI readiness, orchestration, innovation, shadow AI Takeaways Only about 5% of companies successfully adopt AI — most struggle with where to start, what tools to use, and how to build the right foundation before scaling AI is the "penthouse" built on top of decades of IT, software engineering, and automation experience — that foundational knowledge remains critical The human role is shifting from execution to orchestration and architecture — developers and knowledge workers are becoming "team leads" directing AI agents Responsible AI development means thinking through security, data, scalability, and governance from the start — not as an afterthought Slowing down to think carefully before prompting or building — echoing Einstein's 55/5 rule — leads to better, more scalable outcomes Early cognitive computing projects at IBM (food recognition, augmented reality for remote guidance) were ahead of their time, foreshadowing capabilities now taken for granted Human originality and the ability to generate truly novel ideas remain a distinctly human trait that AI has not replicated Quotes "AI is kind of the top level, like the penthouse on top of all of that." "95% are just saying we need AI — they kind of don't know how to absorb that, how to start using it." "Once I crossed the line, I couldn't go back." "Think about it — you just got a promotion. You're a team lead now. You don't micromanage. You give them the bigger picture." "If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and five minutes thinking about the solution." — Einstein, as quoted by Oded "Slow down to speed up." Chapters 00:02 Welcome and introductions 01:04 Oded's background and career journey from IBM to Intel to STRAIX 08:08 Early cognitive computing at IBM — the Watson era and the "What Did I Eat?" project 13:01 From research to product — augmented reality, 3D cameras, and lessons learned 17:54 How AI adoption is accelerating and compressing what once took a decade 20:14 Why 95% of organizations struggle to execute on AI 24:54 How STRAIX works — mapping pain points, building a heat map, and guiding implementation 29:47 Automation tools, vibe coding, and the value of foundational experience 33:13 Human readiness and the mindset shift required to embrace AI 37:22 AI agents, social networks, and the human as orchestrator 44:20 Responsible AI development — building with guardrails from the start 51:26 Asking better questions and thinking architecturally before building 53:31 Closing thoughts and how to connect with Oded Oded Dubovsky: https://www.linkedin.com/in/odeddubovsky STRAIX: www.straix.biz 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 Jacob Bank, Co-founder and CEO of Relay.app, whose career arc — from Stanford's Multi-Agent Systems Lab to founding Timeful (acquired by Google in 2015) to leading Gmail and Google Calendar product teams — represents one of the most continuous threads in AI agent development. Jacob frames AI agents not as software to configure, but as employees to hire, coach, and manage, arguing that great people managers are naturally suited to the AI era. He maps out a three-tier AI stack everyone should adopt and explores how knowledge work will be restructured, why AI literacy is non-negotiable, and how small businesses can now compete at scales once unimaginable. Keywords Jacob Bank, Relay.app, AI agents, agentic workflows, autonomous workers, workflow automation, small business, AI literacy, people management, Timeful, Google Calendar, Gmail, knowledge work, G&A, go-to-market, responsible AI, human-in-the-loop, SaaS evolution Takeaways The right mental model for AI agents is employee management: give them a job description, set expectations, provide feedback, and apply the same code of conduct as any team member Everyone needs three AI tools: a chatbot for conversation, a copilot for real-time task delegation, and an autonomous agent platform for proactive, repeatable work Relay runs on 9 humans and ~60 AI agents — and Jacob sees a path to serving 100x more customers with roughly the same team size AI levels the playing field for small businesses, enabling work at a scale previously only achievable by much larger organizations Jacob's three-level delegation progression: tasks you already do, tasks you're capable of but never have time for, and tasks you'd otherwise hire an expert for AI literacy is not optional — it's becoming a baseline requirement for effective work, equivalent to basic computer literacy Quotes "We're all managers now — that is the skill set we need." "If you have a job that is just to write the blog post about X, that job is not going to exist anymore." "It's not optional. This is going to be a requirement of being an effective worker in the future." "Whenever I have an AI agent doing a classification task, I always ask the AI to explain its rationale — because then you can correct it for next time." "At some point you'll cross this tipping point where you don't have to tell yourself to go use AI — it'll suck you in." Chapters 00:02 Welcome and introductions 00:56 Jacob's origin story and agent-oriented programming 02:54 From Timeful to Google 04:41 Pre-LLM AI features in Gmail and Calendar 06:15 AI coworkers vs. productivity tool nudges 07:39 Early agent research and org disruption 09:24 Restructuring knowledge work 11:45 Evolving human roles and AI literacy 13:32 The social complexity of scheduling 15:16 Credentialed jobs at risk 17:24 AI leveling the playing field for small business 18:17 Inside Relay — 9 humans and 60 agents 19:41 The three-tier AI stack 22:38 Relay as intelligent workflow automation 23:42 SaaS selection in the agent era 26:47 Platform consolidation and SaaS business models 28:13 Deploying agents across G&A, GTM, and R&D 33:16 Agent collaboration and human oversight 34:21 When to build vs. buy 37:56 Three levels of AI delegation 39:50 Scaling AI readiness across organizations 42:22 Responsible AI and the employee management lens 44:14 Evaluating agents vs. testing software 45:51 The blast radius problem 48:09 Bias, coachability, and correcting agents 49:29 Closing advice — go one step further 50:45 What's next for Relay Jacob Bank: https://www.linkedin.com/in/jacobbank https://relay.app For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Pulver and Melissa Reeve explore AI transformation and organizational design through the lens of Melissa's Hyperadaptive framework. They unpack what it means to become AI native, why most enterprises stumble by neglecting human support structures, and how governance, AI activation hubs, and AI leads create always-on learning organizations. The conversation tackles the reinvestment dilemma — what to do with capacity freed by AI — and makes the case for durable skills, systems thinking, and career lattices over ladders. Both Bob and Melissa draw on their non-linear careers and share the belief that humans remain essential connective tissue in any AI-powered future. Keywords Hyperadaptive, AI native, AI transformation, support structures, AI activation hubs, AI leads, dynamic governance, systems thinking, durable skills, adjacent competencies, agentic workflows, responsible AI, triple bottom line, career lattice, organizational design, value streams, Melissa Reeve, Elevate Your AIQ Takeaways Most AI transformations fail not because of technology, but because organizations underinvest in support structures — from AI councils to activation hubs to frontline AI leads Becoming AI native is a gradual five-stage journey: foundation, workflow integration, agentic AI, scaling agents, and full hyper-adaptivity The bifurcation problem is real: a small percentage self-direct their AI learning while the majority are left behind without programmatic support Individual productivity gains are a vanity metric — what matters is whether AI unlocks new organizational capabilities and a more ambitious mission The shift for workers is from doing the task to building, monitoring, and maintaining the AI that does it — durable skills like systems thinking are central to that transition Adjacent competencies unlocked by AI are where breakthrough innovation happens, especially at the intersection of previously siloed domains Responsible AI and the triple bottom line — people, profit, and planet — must be woven into AI native organizations from the start Quotes "A piano is easy to use — you can dink around on the keys all day, but it's not really easy to learn." "You can't get 21st century results with the 20th century operating system." "With great power comes great responsibility — and I don't think there's enough attention being put to the implications of AI." "The shift is from creating to building, monitoring, or maintaining — and there will always be room for the artisans." "AI changes who can do what — and that's where the innovation is, at the overlay of disciplines." Chapters 00:02 Welcome and introductions  01:17 Melissa's non-linear path and the origins of Hyperadaptive  03:49 Systems thinking, transferable skills, and shared career philosophies  05:13 Unpacking AI native and what it means for organizational design  07:53 Why large enterprises are struggling and the aircraft carrier analogy  09:21 AI maturity, readiness, and knowing where to draw the line  11:14 The biggest mistake: neglecting human support structures  13:57 AI activation hubs, AI leads, and dynamic governance  19:14 Centralized vs. functional governance layers  23:14 Where most organizations stand in early 2026  26:16 Individual productivity as a vanity metric  28:02 Unlocking organizational potential beyond current capabilities  31:22 Adjacent competencies, durable skills, and the future of careers  37:48 Systems thinking and redesigning work  40:05 Career lattices, value streams, and Unilever's talent model  43:10 AI governance, responsible AI, and the triple bottom line  50:48 Melissa's book release details Melissa Reeve: https://www.linkedin.com/in/melissamreeve Hyperadaptive Solutions: https://hyperadaptive.solutions For advisory and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Jonathan Aberman — venture capitalist, entrepreneur, educator, and CEO of Hupside — joins Bob Pulver to explore why AI readiness is fundamentally a human potential problem. Hupside's Original Intelligence Quotient (OIQ) provides an objective measurement of human originality relative to AI output, giving organizations a clear signal of who can thrive in an AI-augmented environment, who needs development, and how to compose teams for transformation. Jonathan and Bob dig into the dangerous feedback loop that AI can create when misused, and why originality is the true competitive differentiator. The conversation spans higher education, venture capital, workforce design, and the future of digital credentials, all through the lens of keeping humans central to value creation. Keywords Jonathan Aberman, Hupside, OIQ, Original Intelligence Quotient, AI readiness, human originality, talent transformation, workforce design, higher education, venture capital, AI augmentation, digital credentials, collective intelligence, responsible AI, human-AI symbiosis Takeaways Hupside's OIQ objectively measures human originality against AI output, helping organizations identify who to develop, elevate, or support through AI transformation AI creates a self-reinforcing feedback loop that debilitates when misused — but as a tool, it can powerfully accelerate human creativity Originality equals novelty plus salience; AI can generate novelty, but humans remain essential for determining what's meaningful Higher education's real challenge isn't cheating prevention — it's teaching students to reason well with AI, then measuring output quality Misaligning high-OIQ talent with constrained roles leaves value on the table; matching autonomy to originality profiles is a key workforce design opportunity The greatest long-term AI risk may be whether rising capability gradually excludes people from competing as knowledge workers OIQ and AIQ scores are dynamic and improvable — making them well-suited for portable digital credential profiles Quotes "AI has a couple of limitations that make it different from every tool humans ever invented — it creates a self-reinforcing loop that can cause debilitation if not used properly." "We're the umpire in a baseball game. We're not the players — you and your listeners are the players." "AI is not a cheating problem, it's an education problem." "Originality is novelty plus salience. As long as humans are the ones consuming, AI will always be at best a lieutenant." "The more we [flood] society with sameness, the more people who stand out are going to be important." "I'm not worried about whether AI becomes sentient. I'm more worried about whether it raises the bar and starts to exclude people." Chapters 00:02 Welcome and introductions  02:58 The founding of Hupside and the OIQ origin story  05:35 AI readiness as a human potential problem  07:53 OIQ in higher education and rethinking assessment  09:11 K-12 considerations and bias mitigation  11:20 VC and portfolio applications of OIQ  15:11 Embedding OIQ into the talent lifecycle  19:56 Autonomy, role design, and workforce orchestration  24:42 Higher education, authenticity, and the value of originality  27:04 Innovation management and organizational barriers to AI adoption  34:52 Short-termism, Silicon Valley monoculture, and pushing back  39:25 Can LLMs become truly original? Shared novelty vs. human originality  43:20 Collective intelligence and the wisdom of crowds  48:53 Digital credentials, OIQ in talent profiles, and data ownership  54:43 What's next for Hupside and closing thoughts Jonathan Aberman: https://www.linkedin.com/in/jonathanaberman Hupside: hupside.com For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠Substack: ⁠https://elevateyouraiq.substack.com⁠

Juan Garcia, co-founder of Tuio, a fully digital insurance company based in Spain, joins Bob to discuss how Tuio is reimagining personal lines insurance for digitally-native consumers long underserved by traditional carriers. Juan shares how Tuio evolved its AI strategy from chasing operational efficiency to making smarter decisions across marketing, underwriting, and claims. Tuio built a proprietary AI claims agent that surfaces next-best-action recommendations with confidence scores, always with a human in the loop. The conversation also explores Tuio's grassroots approach to AI literacy, responsible design, and the organizational courage required to fundamentally rethink how a company works. Keywords  Juan Garcia, Tuio, insurtech, digital insurance, personal lines, Spain, AI strategy, claims automation, Watson, human in the loop, AI literacy, responsible AI, subscription insurance, underwriting, organizational transformation, vertical AI, bottom-up innovation Takeaways Tuio identified a digitally-native consumer segment structurally unprofitable for traditional insurers and built a model around serving them through simplicity and transparency Most AI pilots focus on the wrong 10%: cost-to-serve efficiencies. Real value lies in improving decisions across marketing and claims, which represent ~85% of an insurer's cost base Watson processes multimodal inputs and generates next-best-action suggestions with confidence scores — routing complex ones to human reviewers Tuio never automates negative customer decisions — not just due to EU regulation, but because human empathy is irreplaceable in those moments By subsidizing any AI tools employees want to explore, Tuio unlocked bottom-up innovation — including a veterinarian who independently proto-built Watson's logic for pet health claims The real barrier to enterprise AI transformation is organizational courage: reworking processes and structures around AI requires strong leadership Quotes "AI is something that makes you rethink the way you do your whatever you do — and that's going to be different industry per industry, even company per company." "We switched from chasing cost-to-serve efficiencies to using AI to make better decisions — growing efficiently, underwriting smarter, and managing claims more effectively." "We will never automate negative decisions. If you start from the standpoint that your customers are your most valuable resource, you want to give them the most humane treatment you can." "If you don't give people these tools, you'll miss all the bottom-up ideas from the people actually in the trenches every day." "Even if you can build it, it doesn't mean you should. Just because AI can do something doesn't mean you should deploy it there." Chapters 00:02 Welcome and introductions  00:44 Juan's background: from telecom engineer to insurtech co-founder  03:31 Horizontal vs. vertical AI value — where the real opportunity lies  06:41 Tuio's target market and the underserved digitally-native consumer  12:54 Rethinking insurance: digital simplicity as competitive advantage  16:03 Tuio's AI evolution: from chatbot to decision intelligence  20:54 Watson: Tuio's AI claims agent and the shift to next-best-action  23:24 Human in the loop: why some decisions will never be automated  28:53 Building AI literacy through empowerment, not training mandates  32:52 Bottom-up innovation and the veterinarian who built Watson's prototype  40:31 AI readiness, responsible design, and knowing what not to build  45:15 Organizational courage and why AI transformation is harder than those before it  53:30 Closing reflections and what's next for Tuio Juan Garcia: https://www.linkedin.com/in/juanga2/ Tuio: https://tuio.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 sits down with Russ Fradin, Founder and CEO of Larridin, to explore what it really takes for organizations to move from AI experimentation to measurable impact. They unpack the tension between AI excitement and enterprise reality, focusing on ROI, workforce readiness, responsible adoption, and the cultural shifts required to unlock productivity gains. Russ outlines why measurement and visibility are the missing pieces in most AI strategies and makes the case that high-agency professionals who embrace AI will shape the future of work. The conversation reframes AI not as a job eliminator, but as a force multiplier—if leaders build the right scaffolding to support their people. Keywords Russ Fradin, Larridin, AI ROI, AI readiness, AI maturity, workforce transformation, CIO strategy, CHRO strategy, CFO decision-making, productivity measurement, high-agency professionals, AI adoption, responsible AI, enterprise AI, organizational change Takeaways AI adoption without measurement leads to experimentation without accountability. CIOs, CFOs, and CHROs need visibility into what tools are actually being used—and whether they drive real productivity. The future of knowledge work is humans working with AI tools alongside agents. High-agency professionals who embrace AI will dramatically amplify their output and career trajectory. Organizations must move beyond individual productivity metrics toward team and enterprise-level effectiveness. Responsible AI adoption requires training, policy scaffolding, and clarity around secure, enterprise-grade usage. Companies that reinvest AI-driven productivity into growth will outperform those focused solely on short-term margin gains. Quotes “You can’t possibly understand the ROI of these tools without understanding what’s being used in your organization.” “Having great technology is necessary, but not sufficient to drive change.” “The future of work is humans using AI tools, working alongside agents.” “There’s no such thing as a knowledge worker five years from today who isn’t using AI in some part of their job.” “We’re effectively redefining what it takes to succeed in a lot of these roles—in real time.” “The companies that don’t partner with their employees on this transformation will get left behind.” Chapters 00:02 Welcome and Introduction 00:31 Russ’s Background and the Vision Behind Larridin 01:32 Why AI Is a Generational Technology Shift 03:34 The Measurement Gap in Enterprise AI Adoption 06:17 Workforce Anxiety and AI Upskilling 10:33 The ROI Question and Productivity Metrics 15:10 Global Talent, Competition, and AI Parallels 20:17 Responsible AI and Security Considerations 26:20 Building the Scaffolding for Adoption 30:48 Understanding What “Great” Looks Like 34:55 Who Captures the Productivity Gains? 40:22 The High-Agency Advantage in the AI Era 46:09 Why Smart Companies Invest in Their People 52:04 What’s Next for Larridin 53:09 Closing Remarks Russ Fradin: https://www.linkedin.com/in/rfradin Larridin: https://larridin.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 is joined by Stephen Messer, serial entrepreneur and co-founder of Collective[i] and Intelligence.com, to explore how collective intelligence, social analytics, and contextual AI are reshaping how business gets done. Stephen challenges the limitations of traditional SaaS and language models, arguing that true AI value comes from modeling real-world systems — especially how trust, relationships, and buying decisions actually unfold. The conversation dives into economic foundation models, the hidden power of relationship graphs, and why activating trusted networks may be the missing link in sales, hiring, and enterprise decision-making. Together, they unpack how removing friction and restoring context can unlock warp-speed productivity and more human-centered outcomes. Keywords Stephen Messer, Collective[i], Intelligence.com, collective intelligence, economic foundation model, relationship graphs, trust networks, contextual AI, sales productivity, forecasting, CRM transformation, go-to-market strategy, weak ties, network intelligence, AI agents, decision-making Takeaways Collective intelligence enables AI to model real-world business systems, not just generate language or automate workflows. Context — including relationships, timing, incentives, and market conditions — is the missing ingredient in most AI-driven decision-making. Traditional SaaS stacks create “silos of intelligence,” limiting visibility and reducing the effectiveness of AI tools layered on top. Relationship graphs built from verified interactions unlock faster, higher-trust introductions and better business outcomes. Trust acts as an accelerator in commerce, reducing friction and enabling decisions at “warp speed.” Economic foundation models can forecast deal outcomes and market shifts by observing patterns across organizations. AI should remove internal friction so humans can focus on value creation, not administrative workflows. The future of work depends on combining contextual intelligence with trusted human networks. Quotes “To the man with a hammer, the world looks like a nail.” “You’re not modeling words — you’re modeling a system.” “If I don’t understand the context, I can’t understand the outcome.” “Trust enables transactions at warp speed.” “Most AI today is predicting the next best word — not the next best decision.” “The friction to leverage your own network is far too high.” Chapters 00:01 Introduction and Stephen’s Entrepreneurial Journey 00:40 Founding Collective[i] and the Vision Behind It 02:22 Replacing the Traditional Sales Stack with Contextual AI 05:46 Why Context Matters More Than Prompt Engineering 09:18 Systems of Record vs. Systems of Understanding 16:01 The Limits of LinkedIn and Relationship Context 23:24 Introducing Intelligence.com and Verified Networks 36:39 The Origins of Collective Intelligence and Economic Modeling 48:20 Trust Networks, Hiring, and Weak Ties 55:52 Forecast Series and the Power of Long-Form Dialogue 1:00:58 Closing Thoughts and What’s Next Stephen Messer: https://www.linkedin.com/in/stephenmesser Collective[i]: https://collectivei.com/ Intelligence.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 sits down with Dave Vu, Co-founder of Ribbon, to explore how AI is reshaping high-volume hiring and the candidate experience. Drawing on his background in recruiting, venture capital, and scaling AI startups, Dave shares why the hiring funnel is breaking under application volume—and how AI interviews can help close the gap. They discuss human-in-the-loop design, responsible AI, regulatory trends, bias mitigation, and why transparency and feedback are critical to building trust in the future of work. Keywords Dave Vu, Ribbon.ai, AI interviews, high-volume hiring, candidate experience, responsible AI, human-in-the-loop, talent acquisition, hiring automation, bias mitigation, AI regulation, recruiter efficiency, quality of hire, generative AI Takeaways Application volume has grown exponentially while recruiter headcount has remained relatively flat, creating a widening efficiency gap. AI interviews can reduce screening time by 50% or more while improving consistency and fairness. Candidate experience improves when applicants receive timely engagement, flexibility, and meaningful feedback. Human-in-the-loop design ensures AI handles repetitive tasks while recruiters retain decision-making authority. Transparency about AI usage builds trust and increases candidate adoption. Regulatory clarity will accelerate enterprise adoption of AI in hiring. Responsible AI implementation requires balancing innovation with bias mitigation and compliance guardrails. Generative AI advancements are reshaping not only hiring, but content creation and digital trust more broadly. Quotes “Our long-term mission is to hire within 24 hours and make hiring faster and fairer.” “Human-centricity doesn’t equate to anti-automation.” “The recruiter and hiring manager are always in the driver’s seat.” “It’s not about replacing humans—it’s about amplifying their capacity.” “Great candidate experience comes down to respect for their time.” “Regulations create certainty—and certainty accelerates adoption.” Chapters 00:02 Introduction and Dave’s career journey in talent 02:55 Scaling an AI startup and identifying hiring challenges 05:02 The high-volume hiring problem and Ribbon’s mission 10:40 Designing a better candidate experience with AI 15:16 Rethinking resumes and screening inefficiencies 22:41 Human-in-the-loop and responsible AI principles 24:40 Regulation, transparency, and enterprise adoption 28:57 Candidate acceptance and AI interview adoption trends 34:28 Integration with ATS platforms and workflow evolution 43:01 Personal reflections on generative AI and digital trust 49:29 AI literacy, workforce disruption, and the future of hiring Dave Vu: https://www.linkedin.com/in/dave-vu Ribbon: https://ribbon.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 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