Explore every episode of the podcast Elevate Your AIQ

Bob sits down with Erika Oliver, Founder and Managing Director of NewtonHaus and Executive Analyst at Aptitude Research, for a wide ranging look at where AI is really landing in HR and the workforce. Erika shares her non-linear path through executive search and coaching, an unexpected pivot into labor market intelligence, and a moment that reset her priorities and sharpened her focus on the human side of work. The two dig into the shift from the year of the pilot to hard questions about ROI, why AI readiness now includes security and guardrails, the difference between responsible and human-centric AI, and the build versus buy pressure facing HR tech. It is equal parts career wisdom and market analysis, with a part two already in the works. Keywords AI readiness, responsible AI, human-centric AI, AI pilot, AI ROI, HR tech, talent acquisition, talent intelligence, workforce analytics, executive search, executive coaching, career pivot, build versus buy, agentic AI, security, guardrails, candidate experience, veterans hiring, neurodiversity, transformation, IBM Watson, NewtonHaus, Aptitude Research, Erika Oliver, Bob Pulver, Elevate Your AIQ Takeaways The market is shifting from the year of the pilot to a harder reckoning over ROI and where AI truly delivers value. AI readiness now goes beyond willingness to adopt; security, guardrails, and responsible deployment are central to the conversation. Responsible AI and human-centric AI overlap but are not the same, and the onus for human-centric deployment sits largely with buyers, not just vendors. Responsibility starts with the individual, using AI where you should rather than wherever you can, not waiting for a corporate framework or legislation. Build versus buy is a real pressure point for HR tech, and building responsible, enterprise grade solutions is far harder than it looks. Career reinvention is possible amid fear and uncertainty, and the right opportunity is often the one you least expect. Quotes "Sometimes the opportunity that is for you is the one that you least expect, the one that you don't think you're qualified for." "Regardless of the fear, regardless of the unknown, there is a path forward. You just have to be dedicated to seeing that through and what that means for you." "Don't let somebody else tell you solely how to be responsible." "As someone who's come from the vendor side, it's as much the responsibility of the buyer and the enterprise." "The load is greater if it's done responsibly than I think a lot of boards and a lot of C level folks realize." "If you don't invest in people, then it doesn't matter how much you spend on tokens." (Bob) "Hold yourself accountable for using AI where you should, not wherever you can." (Bob) Chapters 00:02 Welcome and introductions 01:08 Erika's winding path through executive search and coaching 06:08 An unexpected pivot into AI powered labor market intelligence 12:01 A health scare that reset her priorities 16:13 Building a portfolio of coaching, advisory, and analyst work 20:23 The year of the pilot and the push to prove ROI 27:57 Readiness, responsible AI, and human centricity 30:08 When agentic AI goes rogue and security takes center stage 32:33 Being responsible by design and accountable builders 38:11 The three pillars and why responsibility starts with us 42:37 Transformation, Watson, and adapting to constant change 44:49 Solving for candidates, veterans, and neurodiversity 54:09 The build versus buy pressure facing HR tech 1:00:13 Responsible AI in the build versus buy calculus 1:04:09 Closing thoughts on pace, people, and part two Erika Oliver: https://www.linkedin.com/in/eoliver Newton Haus: newton-haus.com For AI readiness advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Gerry Crispin, founder of CareerXroads and a five-decade veteran of the talent industry, joins Bob to trace recruiting's evolution from paper resumes and fax machines to today's AI-driven hiring landscape. Gerry reflects on the origins of CareerXroads as a trusted peer community built on open sharing rather than competition, and explains why he sees knowledge hoarding as a losing strategy for the industry. The conversation turns to one of recruiting's most persistent failures, candidate ghosting, and how AI agents could actually make the process fairer and more consistent than overworked human recruiters manage today. Gerry and Bob close by imagining a future of verified digital twins that let candidates and employers build trust on their own terms, and why there is no going back to a pre-technology hiring era, only forward toward something more human-centric. Keywords Gerry Crispin, CareerXroads, talent acquisition, recruiting technology, candidate experience, candidate ghosting, applicant tracking systems, AI agents, AI screening, digital twins, human-centric AI, social capital, responsible AI, candidate feedback, trust and transparency Takeaways Gerry Crispin's five-decade recruiting career and 30 years building CareerXroads trace the industry from paper resumes and fax machines to AI-driven hiring. Real community differs from a network: people who call you back, not just first-degree LinkedIn connections. Knowledge sharing creates a bigger pie for everyone; zero-sum thinking about proprietary recruiting practices holds the industry back. Candidate ghosting remains rampant, and Gerry estimates more than half of US employers intentionally leave applicants without a response, despite ATS tools that could prevent it. AI agents could bring more consistency, and even more humanity, to candidate communication than an overworked recruiter handling hundreds of applicants across dozens of open roles. The best recruiters already give rejected candidates honest, constructive feedback quietly, without their employer's blessing. The goal is to make that the norm. Gerry envisions a future of AI-verified digital twins that let candidates and employers exchange trustworthy information on their own terms, similar to how actors fought to protect their likeness. Going backward to paper resumes and in-person-only interviews isn't realistic. The real work is reimagining recruiting for every stakeholder as trust-building technology matures. Quotes: "I believe and I've always believed that the expertise is in learning." "A lot of people think in terms of zero-sum games: the more I share, the less of the pie I'm going to have. As opposed to the bigger pie we both create for all of us." "A candidate says, 'I want a human to talk to.' It's not a choice between a human or a non-human. It's a choice between a non-human or nothing." "There's an ability with the technology we have today to tell candidates we're not going forward with them... there's just no excuse not to do that." "The question is whether we're doing the wrong things with new technology, or are we reimagining how we could do things more effectively." Chapters: 00:03 Welcome and introduction of Gerry Crispin 01:10 CareerXroads' 30 years and owning your career 05:36 Fax machines, ATS pain points, and the internet's arrival 10:08 Building CareerXroads as a trusted peer community 12:23 Trust, community, and IBM's social computing guidelines 17:52 Working out loud, social capital, and the moving target of expertise 24:26 Ghosting, missing feedback, and a more humane hiring agent 42:02 Algorithms, consistency, and human centricity 46:28 Digital twins, boundaries, and a human-in-the-loop future Gerry Crispin: https://www.linkedin.com/in/gerrycrispin CareerXroads: https://community.cxr.works/home For AI readiness advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Andrew Brooks, CEO and Founder of Contextual.io, joins Bob to trace a career that runs from early-internet consulting through three exits (Seven Space to Sun Microsystems, a marketing company to ReachLocal, and SmartThings to Samsung) before landing on AI. Andrew explains Contextual's "own your AI" philosophy, why businesses should design, build, and operate their own systems rather than lock into a single model provider, and how real transformation comes from deepening a company's data, process, or relationship moats rather than chasing cost takeout alone. They dig into real client stories, from a commercial refrigeration estimator's tacit knowledge to a vacation rental company that discovered unexpected revenue recovery through AI-audited work orders. The conversation closes on what's shifting for engineering talent, why "human in the loop" needs more precision, and why waiting for the perfect model is a losing strategy. Keywords Contextual, Andrew Brooks, own your AI, agentic AI, AI orchestration, mid-market businesses, AI moats, model selection, Digital Greg, tacit knowledge, automation vs facilitation, human in the loop, agent sprawl, AI governance, private equity, Southfield Capital, system design, engineering talent, responsible AI by design, SmartThings, Seven Space, MCP, rational optimism Takeaways "Own your AI": build a system-agnostic layer instead of locking into one model or provider Durable AI investments deepen an existing moat, whether data, tacit knowledge, or relationships, not just cut costs Automation builds trust and adoption, but resist treating AI as a hammer for every problem Well-designed systems surface second and third order value nobody planned for Talent is shifting toward system designers who can spot edge cases and challenge AI outputs Waiting for a "perfect" model is a losing strategy given the pace of change Quotes "The phrase we use is own your AI. Do not become too embedded in a single provider or a single model, because you need to be able to react to what's happening in the space." "Not everything's an AI problem. Some things are process, and some things are just workflow." "You can't wait for the perfect model. The models are revving every ten, fifteen days. The pace of change is just too fast. You need to get into the river." "AI can be confidently wrong, and very confidently wrong. You've got to be able to see that and flag it." "I'm in the rational optimist camp here. AI might change jobs, but we've been changing jobs for many, many years." Chapters 00:01 Welcome and introducing Andrew Brooks 00:35 From Accenture to entrepreneurship: Seven Space, Reach Local, and SmartThings 03:45 Landing on AI and founding Contextual 04:41 Design, build, operate: how Contextual works with clients 08:33 Choosing the right model without over-committing to one provider 10:04 Beyond chatbots: agentic systems and finding your AI moat 12:36 Automation as an on-ramp to bigger AI thinking, and avoiding the shiny-hammer trap 17:58 Systems thinking, from Smart Things to agentic infrastructure 21:27 Responsible design, client collaboration, and unexpected value from clean data 28:19 Bad data, bad processes, and why waiting for the perfect model is a mistake 30:00 Where humans stay central and what "team superpowers" means 35:51 Vacation rental case study: audits, revenue recovery, and upsell insight 41:50 Getting acquired by a PE firm and what it means for AI adoption 45:20 Tool sprawl, governance, and rethinking "human in the loop" 51:45 Engineering talent, adaptability, and the Stripe MCP lesson in trust Andrew Brooks: https://www.linkedin.com/in/andrewcarrollbrooks Contextual.io For AI readiness advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Katherine von Jan (KVJ), CEO and Co-founder of Tough Day and a longtime innovation leader across Lotus Development, IBM, Salesforce, and multiple startups, joins Bob to trace a career built on one consistent thread: putting culture and human potential at the center of technology. The conversation covers the perils of workforce surveillance AI, why "human in the loop" has become a nearly meaningless phrase without real definition, and how KVJ's Human Positive Company framework gives organizations a way to evaluate whether their AI and culture choices are actually earning trust. They dig into the ethical review process that killed a risky Salesforce AI project (and the better one that replaced it), how KVJ's earlier startup RadMatter tackled bias against non-Ivy League candidates, and what her team learned about great management while building the AI behind Tough Day. It's a wide-ranging, practitioner-level conversation about responsible innovation, moral leadership, and what it actually takes to build AI people can trust. Keywords: human-centric AI, responsible AI, AI governance, workforce surveillance, human in the loop, AI ethics, Human Positive Company framework, Tough Day, Tuffy, RadMatter, Salesforce, IBM, Lotus Development, Irene Greif, talent acquisition, hiring bias, quality of hire, employee trust, ethical review, red teaming, collective intelligence, workplace culture, moral leadership, AI slop, skills-based hiring, retention Takeaways: KVJ's path from anthropology and Lotus Development (working for Irene Greif) through IBM, Salesforce, and now Tough Day traces one consistent thread: technology in service of culture and human potential "Human in the loop" is losing meaning as a governance concept; every stage of a workflow, like a recruiting funnel, is a decision point that either includes or excludes real human judgment Workforce surveillance AI, tools that flag "risk" signals across email, Slack, and HR systems, is a dangerous use case that erodes trust rather than building it Responsible innovation requires research and ethical review before deployment, not just fast iteration; Salesforce's own attrition-prediction AI backfired until it was redesigned into a re-recruiting tool instead KVJ's Human Positive Company framework evaluates organizations across three pillars: workforce ingenuity, positive-sum prosperity, and the ethical and humane use of AI RadMatter, her earlier startup, aimed to give overlooked and non-Ivy-League students visibility with employers, a problem that still shapes bias in AI-driven hiring today Building AI that reflects an organization's values starts with defining those values clearly and creating a real process, not just a poster on the wall, for employees to raise concerns Great management often looks like curiosity, asking more questions before offering answers, a pattern KVJ observed directly while researching how to train Tough Day's AI Quotes: "A coalition is designed to go solve something." - KVJ "You don't just go build the app. You build the research first." - KVJ "A lot of organizations have values written on the wall and that's as far as it goes." - KVJ "We're getting AI slop, and we're getting process slop, and we're getting application slop." - KVJ "Every employee is responsible for understanding, what am I complicit in?" - KVJ "Human in the loop is almost meaningless at this point. What is the loop? And where is the human in said loop?" - Bob Chapters: 00:01 Welcome and introductions 01:00 KVJ's path into tech: anthropology, Lotus Development, Irene Greif, and IBM 08:09 The strange LinkedIn deactivation and the leap to Salesforce 12:26 Comparing culture and tools across IBM, Salesforce, and beyond 15:13 Early social network analysis and today's AI parallels 18:32 Where to draw the line: what AI should do, not just what it can 21:27 Workforce surveillance AI and the danger of thinning out the workforce 25:47 Responsible innovation and human-positive AI 29:34 Inside the Human Positive Company framework 33:32 Measuring what matters: retention, morale, and moral leadership 37:05 Rethinking human in the loop across the recruiting funnel 38:26 RadMatter and surfacing overlooked talent 43:26 Building governance: ethics committees and guardrails 47:06 Training Tough Day's AI on values, culture, and what research reveals about great management 57:20 Closing thoughts and a call to action KVJ: https://www.linkedin.com/in/kvonjan Tough.Day: https://tough.day 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 catches up with Trent Cotton, Head of Talent Insights and Analyst Relations at iCIMS, for a data-grounded look at why hiring feels so broken right now. Drawing on iCIMS workforce data, Trent unpacks a widening gap between job openings and actual hires, the rise of "job hugging," and application volumes falling below last year. The conversation digs into the real culprit behind entry-level frustration: a decades-old habit of confusing years of experience with actual skill, which AI is now exposing and scaling rather than causing. Trent makes the case for blowing up the traditional job ad in favor of a transparent scorecard, and for using AI to surface hidden bias and predict success rather than just automate the old process. They close on an optimistic note about Gen Z teaching themselves AI skills and why it may finally be time to retire the resume. Keywords talent acquisition, skills-based hiring, experience versus skills, job hugging, iCIMS workforce report, three-line report, entry-level hiring, Gen Z, early career, AI in hiring, recruiting bias, responsible AI, AI interviewer, job scorecard, job description, AI sourcing, quality of hire, retention, workforce data, future of work, Trent Cotton, Bob Pulver, Elevate Your AIQ Takeaways Job openings are rising faster than hires while application volume dips below last year, pointing to job hugging and recruiting teams stretched past their limits The "experience" bar is often a poor proxy for skill, a problem that predates AI by decades Skills-based hiring only works if organizations stop assuming years of experience are directly proportional to ability The job ad should be rebuilt as a transparent scorecard that candidates see going in and that drives consistent scoring across every interviewer AI does not create hiring bias so much as expose and scale the bias already there, and it can also help detect and coach against it (recency bias, manager patterns, and more) AI sourcing can pressure-test unrealistic requirements before a role is ever posted, turning recruiters into advisors rather than order-takers Gen Z is teaching itself AI skills and taking ownership of continuous learning, making it an overlooked and ready talent pool Fixing retention starts in the hiring process, by confirming candidates are not just qualified but genuinely want the role Quotes "We've been looking at experience, assuming that skills are directly proportional to the number of years of experience." "You can be working for 10 years at something and still suck at it." "The only thing that's different with AI is it's gonna find them, expose them, and scale them." "You just don't know until you give people a chance." "The resume needs to be retired. It's well past its retirement age." Chapters 00:02 Welcome and reconnecting 01:29 Trent's non-linear path from banking to HR 03:46 The unicorn role and the talent insights program 05:26 A new book and five mindsets for HR 06:40 What the market data reveals about hiring 08:53 Job hugging and a cautious candidate market 10:18 The experience trap and five years of LLM experience 14:01 Gen Z and the mid-level experience expectation 16:24 Skills versus experience and the self-taught coder 21:25 Blowing up the job ad and building a scorecard 29:17 The bias conversation AI is not having 36:26 A balanced narrative and smarter sourcing 44:43 Gen Z teaching themselves and the education gap 52:07 Retiring the resume and closing advice Trent Cotton: trentcotton.com iCIMS: icims.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 is joined by Diane Weaver, co-founder and COO of Baryons, who brings a career defined by translation — across languages, disciplines, and roles — to what she describes as the most important problem she has ever worked on: human flourishing in the age of AI. Drawing on her background in EdTech, linguistics, and startup leadership, including the founding and acquisition of CourseTune, Diane shares how a post-exit identity crisis and the release of ChatGPT converged to spark the idea for Baryons. The platform is a voice-based AI companion designed to support mental wealth, resilience, and human agency through four modes: daily check-in, checkout, thinking partner, and flourishing partner. Diane and Bob explore the science behind the product, the organizational dysfunction it is built to address, and why Baryons was designed from day one to get people off AI and back into meaningful connection with other human beings. Keywords Baryons, human flourishing, mental wealth, human agency, resilience, voice AI, executive coaching, organizational health, edtech, CourseTune, systems thinking, neuroplasticity, burnout, resonance report, team dynamics, responsible AI, technology stewardship, Diane Weaver Takeaways Baryons is a voice-based AI companion that supports individual and organizational health through four modes: check-in, checkout, thinking partner, and flourishing partner The platform democratizes access to a daily practice long associated with high performers and executive coaching, making it available to every employee at $20 a month Baryons uses a patent-pending approach to memory, allowing it to surface patterns and prior conversations in ways that build continuity and accountability over time Weekly resonance reports give individuals and teams insight into energy levels, recurring themes, and early indicators of burnout — without exposing individual conversations The product is built on six well-researched domains of organizational health: coordination, shared reality, early risk visibility, decision quality, engagement, and learning velocity Diane frames the current moment not as a technology problem but as a human one, and argues that organizations fixated on productivity metrics are missing the signals that actually predict team resilience and long-term performance Baryons is intentionally designed to be non-addictive and non-affirming — it is built to help users identify root causes and reconnect with human beings, not keep them talking to an AI Quotes "I really want to be working with people who want to be doing something that was impossible to do before." "We talk about ourselves as being the first AI that is truly built and designed to get people off of AI and back connecting with human beings in the real world." "The interface is more of your inner world than anything else." "It doesn't matter that an individual resonance — or even a team — is always trending up. When everything's always trending up, you know as a leader they've gamed the system." "Those six functions of the organization have to be repaired — or they may survive all of this tech disruption and still be dysfunctional." Chapters 00:02 Welcome and introductions 00:46 Diane's background: from the family farm to edtech and entrepreneurship 10:11 The paparazzi story: Pat Weaver, the Today Show, and a legacy of democratizing technology 12:41 The genesis of Baryons and the post-ChatGPT moment 15:33 Human agency as the core design principle 18:26 How Baryons works: voice-first design and the check-in mode 21:48 Shifting from technology users to technology stewards 23:38 The checkout mode: cognitive offloading and ending the workday with clarity 26:01 The thinking partner and flourishing partner modes 29:48 Democratizing executive coaching and the value of a non-judgmental AI 36:47 Why voice is the right interface: psychological safety and trust 41:15 A user story: Baryons as a neutral mediator in a fractured friendship 43:29 Mental wealth vs. mental health: resilience as a daily practice 47:34 Resonance reports: individual and team insights, burnout signals, and the limits of productivity metrics 52:00 Choosing the right partners: ethical AI, organizational dysfunction, and the six domains of health 59:51 What's ahead: Baryons.com, community brain health initiatives, and keeping humans at the center Diane Weaver: https://www.linkedin.com/in/weaver-diane Baryons: https://baryons.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

Daniel Sieberg, co-founder and CEO of Screen Genius, joined the show to discuss how his company is building what he calls a universal navigation layer for human curiosity. Coming from over a decade in broadcast journalism followed by six years at Google, Daniel brings a distinctive perspective on how we search, discover, and consume content. Screen Genius started as a B2C streaming guide and pivoted into a B2B discovery-as-a-service platform, helping companies with large digital catalogs, from books and art to retail and food, surface more relevant recommendations through conversational, intent-driven AI. The conversation covers the gap between what recommendation engines promise and what they actually deliver, the importance of building AI responsibly by design, and the concept of "Gen T," generation transition, as a framework for shared human responsibility in shaping where AI goes next. Keywords Daniel Sieberg, Screen Genius, discovery as a service, recommendation engines, conversational search, semantic tagging, human-centric AI, responsible AI, paradox of choice, content discovery, B2B middleware, personalization, digital catalogs, Gen T, generation transition, Google News Lab, AI hype cycle Takeaways Screen Genius pivoted from a consumer streaming guide to a B2B discovery-as-a-service platform after recognizing that its recommendation engine had broader value across verticals including books, art, food, and retail Most recommendation systems ask users to search like a machine; Screen Genius is building conversational, intent-driven discovery that lets people search more like humans The paradox of choice is a core design constraint: once options exceed roughly five, human decision-making breaks down, so narrowing a massive catalog to a meaningful few is the real product Enterprise knowledge workers are a second use case: internal discovery tools to help employees navigate large data archives, not just consumer-facing recommendations Daniel frames responsible AI not as compliance but as ethos, citing his family history and mission to leave something beneficial to humanity as the throughline behind the company "Gen T," generation transition, reframes the AI debate away from generational blame toward shared responsibility for shaping what AI becomes Quotes "It feels like a rebellious act to fight for humanity these days." "AI is now helping us to search more like a human, which I find fascinating in the discovery evolution of where this is all going." "We like to call ourselves the universal navigation layer for human curiosity." "Business is trust, money is trust, relationships are trust. You're going to need to talk to a human being." "Gen T is generation transition, and we all have a shared responsibility in thinking that through." "I hope that we champion this responsible AI flag for as long as we're in existence." Chapters 00:02 Welcome and introductions 01:01 Daniel's career arc from journalism to Google to entrepreneurship 04:53 The origins of Screen Genius and the problem of content overload 08:38 From streaming guide to B2B discovery-as-a-service platform 13:02 Competing with Algolia and moving past the AI hype cycle 15:55 Personalization, intent, and the limits of recommendation engines 20:10 The paradox of choice and narrowing massive digital catalogs 24:14 Breaking down silos and building a universal navigation layer 30:33 Respecting human time and the enterprise knowledge worker use case 40:30 Why human relationships still matter more than vibe coding 43:05 Gen T, generation transition, and shared responsibility for AI's future 46:32 Responsible by design and the Screen Genius mission Daniel Sieberg: https://www.linkedin.com/in/danielsieberg/ ScreenGenius: screengeni.us For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Noelle London, founder and CEO of Illoominus, returns to Elevate Your AIQ just over a year after her first appearance to chat with Bob about what has changed and what Illoominus has built in response. The conversation covers how decision cycles inside organizations are compressing, why AI adoption has accelerated but also created new governance risks, and how the gap between individual experimentation and enterprise-ready deployment has become the defining challenge for people leaders today. Noelle details the launch of Illoominus Agentic Workforce Intelligence, a capability already in production with customers that delivers proactive, AI-generated insights directly into executive workflows rather than waiting for someone to go find them in a dashboard. The discussion closes on the importance of governed, secure AI environments as organizations move from pilots to scale, and why data alignment across HR, finance, and operations remains the foundation everything else depends on. Keywords Noelle London, Illoominus, workforce intelligence, agentic AI, people analytics, HR data, workforce planning, talent acquisition, data governance, AI adoption, decision support, workforce transformation, future of work, data literacy, AI readiness, responsible AI, executive reporting Takeaways Decision cycles across HR, finance, and operations are compressing rapidly, making real-time workforce data no longer a nice-to-have but a business requirement The gap between AI experimentation at the individual level and governed, enterprise-ready deployment is where most organizations are getting stuck right now Illoominus Agentic Workforce Intelligence delivers proactive, contextualized insights directly into executive inboxes, shifting the model from reactive dashboarding to continuous intelligence Data alignment across functions, getting HR, finance, and ops working from a single trusted source, is the prerequisite for any meaningful workforce analytics initiative Governed, secure AI environments are essential as agentic tools scale, particularly around access levels, data privacy, and agent-to-agent communication Consultants are increasingly embedding Illoominus as the analytical backbone of engagements, shifting their own value toward change management and strategy Quotes "The puzzle pieces weren't talking, and so that's first and foremost, it doesn't really help to have something very interesting if it's not connected together." "Every single week, every single person on their executive leadership team are getting AI-driven insights into their inboxes to help them understand what's going on." "You're not getting graphics, you're getting the full understanding on are we good, or is this something that we need to pay attention to." "HR doesn't have a different version of headcount than finance does. Those are very real examples of where we've seen some of these data initiatives stall." "How do you make sure if you're using AI within the organization, it's governed properly so that you're not using these tools as a pass through for people that shouldn't have access to information." "It's the end of everything as we've known it, and change management with the amount of technology that companies are adopting, that's a really interesting place for consultants to play." Chapters 00:02 Welcome and guest introduction 00:46 Illoominus origin story and the data connectivity problem 04:25 How Illoominus complements rather than competes with consultants 08:07 A year of change: compressed cycles, AI adoption, and new organizational pressures 15:12 Expanding self-serve insights across the leadership team 21:29 Launching Illoominus Agentic Workforce Intelligence 26:17 Accelerating business cases through data alignment across HR and finance 30:22 The full data picture: talent acquisition, skills, engagement, and beyond 36:51 Industry fit and the profile of an Illoominus customer 39:28 How executives interact with agentic insights 43:52 AI readiness, governance, and moving from experimentation to scale 49:52 Closing reflections and what comes next Noelle London: https://www.linkedin.com/in/noellelondon Illoominus: illoominus.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

Yurii Lazaruk built a decade-long career in community management before he even knew the profession existed, starting with a grassroots SEO forum in Ukraine and scaling to a 700-person sales conference before co-founding the Freelance Unlocked Conference in Europe. Bob and Yurii explore how freelancers are using AI to function as one-person teams of twenty, while warning that the same tools are eroding the human connections that make independent careers sustainable. They examine the tension between AI-driven hiring automation and the cultural fit that determines whether a freelancer truly succeeds with a client. Yurii's throughline is a conviction that human energy is something no tool can replicate or replace. Keywords Yurii Lazaruk, Freelance Unlocked, independent talent, freelance economy, community management, digital twins, AI in hiring, human connection, loneliness epidemic, solopreneur, co-opetition, AI literacy, second brain, talent acquisition, future of work Takeaways Freelancers embracing AI literacy are scaling from solo operators to multi-agent teams, but human judgment remains non-negotiable for quality and trust The AI arms race in hiring, where both job descriptions and applications are machine-generated, strips out the human signal that determines cultural fit Digital twins are already being used by freelancers to handle early-stage client conversations, creating efficiency gains alongside new credential fraud risks Community is a structural necessity for independent workers, especially as AI-driven isolation deepens the broader loneliness epidemic AI works best when you already understand the domain; without foundational knowledge, tools can mislead as easily as they assist Pre-conference rituals including WhatsApp groups, LinkedIn introductions, and short pre-event Zoom meetups drive Freelance Unlocked's 50-plus percent return rate Quotes "I was doing community [work] for over ten years without knowing it was called community. I was just thinking it was meeting people and having fun together." "There is an AI fight happening. Recruiters go to ChatGPT for job descriptions and applicants go to the same tools, and we are losing the human connection part." "If your second brain is smarter than your first brain, you stop learning and move nowhere. You have to continuously grow." "The more AI tools we have, the more disconnected people become, and the more they need community." "You are not getting energy from your computer. You get energy from other people, and you share yours. It is always an exchange." Chapters 00:02 Welcome and guest introduction 01:11 Yurii's background from risk analyst to community professional 04:50 Community as infrastructure for solopreneurs and freelancers 07:36 Freelance Unlocked and the co-opetition model 09:11 The fragmented freelance platform landscape and the case for a unified profile 13:59 AI in hiring and the arms race crowding out human signal 19:05 Digital credentials, second brains, and freelancer AI agents 22:24 Digital twins: efficiency gains and fraud risks 30:58 How freelancers use AI to scale output and prevent burnout 33:49 Responsible AI use and starting with the problem 43:29 The loneliness epidemic and community as antidote 44:53 In-person energy and the value of physical presence 49:50 Human-first networking and why pitching kills connection 51:17 Pre-conference rituals that build belonging before the event 56:02 Designing events where people come back to meet friends Yurii Lazaruk: https://www.linkedin.com/in/yurii-lazaruk-community-consultant Working with Yurii: https://yurii.community/ 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 David Kilzer, founder of Strategic Transformation Advisors, for a wide-ranging conversation on the convergence of AI and humanoid robotics and what it means for the future of work. Drawing on a career that spans GE, Digital Equipment Corporation, and decades of entrepreneurial practice, David traces the arc of technology convergence from integrated circuits to the internet to today's AI-powered machines. The discussion covers how organizations can responsibly adopt AI by building a data-first foundation, prioritizing high-impact use cases, and keeping humans firmly in control. Both Bob and David share a cautious optimism: the path forward runs through collaboration between humans and machines, not displacement of one by the other. Keywords David Kilzer, Strategic Transformation Advisors, technology convergence, humanoid robotics, AI and manufacturing, Boston Dynamics, Tesla Optimus, Figure AI, data-first mindset, AI hallucination, responsible AI, human-centric AI, upskilling, generative AI, TEDx, supply chain AI, blue collar workforce, sustainable abundance Takeaways Technology convergence, not any single innovation, drives the most transformative leaps; AI combined with humanoid robotics may be the most consequential convergence in human history Robots are best applied first to work that is dull, dirty, or dangerous, augmenting human capability rather than replacing human judgment A data-first mindset is the unglamorous but foundational prerequisite for any organization looking to extract real value from AI AI hallucinations are often traceable to bad or incomplete data; human oversight of AI-assisted decisions remains essential Generative AI is shifting in 2026 from experimental tool to backbone technology, and individuals and organizations that wait for perfection will fall behind The US and China are in an accelerating race for robotics leadership, and maintaining that edge requires cross-sector collaboration and continued investment in AI literacy Quotes "When done right, it's not humanoid robotics replacing humans. It's augmenting, collaborating with humans." "This is going to be looked at as the next biggest thing for humankind since fire." "Data drives AI. Make sure that all the data you've prepared is highly accurate and then expand from that point." "Humans employ it by looking at what you need to accomplish primarily as a business and look for high-impact use cases." "Don't be intimidated by it. Get in there. Get that hands-on approach." "I'm an enthusiastic optimist." Chapters 00:02 Welcome and guest introduction 00:41 David's background, from North Dakota to GE and DEC 04:38 Technology convergence and its historical pattern 06:59 David's TEDx talk and the AI plus robotics thesis 12:26 Augmenting humans, not replacing them 17:26 US versus China in the robotics race 20:31 Prioritizing use cases, dangerous and drudge work first 25:46 Drones, emergency response, and the road to Rosie 30:34 Blue collar work, trade jobs, and the upskilling imperative 31:54 Responsible AI by design and the first law of robotics 38:49 Ethics, guardrails, and keeping humans in control 43:39 Building a data-first mindset for AI adoption 46:31 AI hallucination, enterprise readiness, and supply chain wins David Kilzer: https://www.linkedin.com/in/david-kilzer-3964688 Strategic Transformation Advisors: https://www.xform.me/ For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠

Meg Bear is an award-winning global executive, board member, advisor, investor, podcaster, and keynote speaker who has taken the stage at TEDx, SXSW, Davos, the World Economic Forum, HR Technology Conference, Unleash, and beyond. As co-host of the Meg and Amy Show and a board advisor at NovaWorks and Papaya Global, she brings the rare combination of deep operator experience and forward-looking strategic vision that makes this conversation genuinely worth your time. Meg joins Bob to explore what it truly takes to lead and develop talent in the AI era, opening with a sharp critique of "founder mode" thinking and making the case that intellectual humility and collective intelligence are what sustainable organizations are built on. The discussion spans workforce disruption, the risks of AI-driven headcount cuts without strategic vision, and why psychological safety is the foundation for building genuinely adaptive teams. Keywords Meg Bear, SAP SuccessFactors, founder mode, grower mode, intellectual humility, collective intelligence, human potential, skills-based hiring, psychological safety, learning agility, talent marketplace, workforce planning, total talent, NovaWorks, Papaya Global, Meg and Amy Show, AI readiness, workforce disruption, human experience management, agentic AI Takeaways Founder mode thinking trades intellectual humility for hubris, undermining the collective intelligence organizations need to thrive Human value is not defined by job titles or past achievements but by the inherent strengths and adaptive capacity each person brings Leaders who fail to recognize and invest in their team's potential also forfeit the organization's capacity to innovate through disruption Disrupting your own job before someone else does is not a threat; it is the only viable strategy for staying relevant in an AI-transformed workforce The current AI learning moment is unusually pro-social, but the window to engage while everyone is still figuring it out together is narrowing fast Quotes "The belief that a single person is going to make everything happen is the wrong kind of culture to build a sustainable future." "We have all of the raw materials to thrive in this future state, but it's not going to work if we only want to bring our knowing selves." "The only way to save your status as a worker is to make your own job obsolete." "Our job as leaders is to manage energy, identify potential, and help individuals see progress in work that really matters." "This is the most pro-social learning environment I've ever seen." "How do we marshal the collective intelligence of our customers and our market to unlock new value capture in this world?" Chapters 00:02 Welcome and guest introduction  02:35 Meg's background and mission to invent the future  03:26 Founder mode vs. grower mode and the case for intellectual humility  09:04 Cognitive diversity, collective intelligence, and the limits of one-person leadership  12:18 Recognizing human strengths and finding new pathways of excellence  20:10 Human value beyond titles and the importance of bringing your learning self  22:36 Psychological safety as the foundation for adaptive teams  28:58 From human capital to human experience management at SAP SuccessFactors  32:31 Workforce disruption, AI-driven headcount cuts, and the risk of incrementalism  36:27 The pro-social AI learning moment and why the window is closing  43:16 Board-level AI strategy and the risk of ready-fire-aim decisions  56:35 NovaWorks, total talent visibility, and the future of fluid work  01:07:07 Meg's personal AI journey and building goal-alignment agents Meg Bear: megbear.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 David Arnoux, co-founder of AI-native venture studio Humanoidz, fractional GTM strategist at HeyArnoux, and community leader of the Gen AI Circle, a global network of nearly 500 heavy AI adopters. David shares a clear-eyed framework for understanding where AI is actually taking work, moving from co-intelligence and augmentation through full workflow automation and into the uncomfortable reality of job category redundancy. The conversation covers responsible AI guardrails, the architecture of second brain systems, and the emerging shift from SaaS subscriptions to custom-built agent-powered tools. David draws on patterns he observes across his community, client work, and venture studio to offer practical first steps for individuals and organizations ready to move beyond the chat window. Keywords David Arnoux, Humanoidz, Gen AI Circle, HeyArnoux, Growth Tribe, agentic AI, augmentation, automation, redundancy, co-intelligence, responsible AI, second brain, skills files, SaaS disruption, GTM strategy, go-to-market, lethal trifecta, prompt injection, MCP integrations, Claude Code, solopreneur, workflow automation, human in the loop, agent orchestration, buy vs. build, future of work Takeaways The four-stage framework of co-intelligence, augmentation, automation, and redundancy offers a more honest map of where AI is taking work than the comfortable augmentation narrative most organizations have sold themselves on Responsible AI is less a philosophy debate and more a practical checklist: confirm before acting externally, maintain audit logs, cap high-frequency tasks, and never allow destructive actions without human approval The "lethal trifecta" of private data, untrusted content, and external communication access in a single agent creates serious prompt injection risk, and the architectural answer is isolation by capability Heavy AI adopters are productizing every repeated workflow as a skills file, a simple markdown document that turns any process into a reusable, shareable, and transferable asset The buy vs. build calculus is shifting fast, with community members replacing multi-tool SaaS stacks costing hundreds per month with custom-built solutions at a fraction of the cost Distribution and audience are now the primary moat for any new venture, making community building and direct relationships more valuable than ever Quotes "Smart humans plus better tooling equals crazy results. That's what we see happening 10x, 100x at the moment." "Just pretending it's all about augmentation is how you and I end up unprepared." "Responsible AI is not a philosophy debate. It can actually be a checklist." "Learning is a markdown file. You download their thinking directly into the system." "I posted that I would never purchase a CRM ever again. It got the most engagement of anything I've ever written in months because people felt it." "Distribution is everything nowadays and audience is more important than ever." Chapters 00:03 Welcome and guest introduction 01:03 David's background, Growth Tribe, and the three-entity flywheel 04:52 Why distribution and audience matter more than ideas 09:36 Co-intelligence, augmentation, automation, and redundancy 16:38 Human centricity, responsible AI, and finding your personal line 19:59 Practical guardrails for agentic systems 23:41 How heavy adopters are actually working today 31:32 Digital literacy, learning habits, and the mindset that compounds 36:19 Bob's second brain challenges and the ethics of AI-powered outreach 41:40 The lethal trifecta and agent isolation architecture 48:11 First steps: Claude Code, MCP integrations, and skills files 51:32 SaaS disruption, buy vs. build, and the future of software pricing 58:19 Career paths, entrepreneurship, and building your audience David Arnoux: https://www.linkedin.com/in/davidarnoux HeyArnoux: heyarnoux.com Humanoidz: humanoidz.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

Lisa Cole, Chief Marketing, Product, and AI Officer at 2X and three-time author, joins Bob to explore how AI is fundamentally reshaping the B2B marketing function. Lisa shares how 2X, a global marketing-as-a-service firm with 1,400 marketers worldwide, is navigating the shift toward generalist talent, AI-native roles, and human-centered AI adoption. The conversation covers her framework for "brand gravity," the case for keeping strategic thinking and brand voice uniquely human, and how AI enables the scale needed to be findable and chosen by buyers long before they raise their hand. Lisa also discusses her new book, The Limitless CMO, which offers a practical operating model for scaling marketing impact without skyrocketing costs. Keywords Lisa Cole, 2X, brand gravity, B2B marketing, marketing as a service, AI adoption, generalist marketers, AI-native roles, human centricity, prompt engineering, knowledge layer, synthetic personas, mock focus groups, omnipresence, The Limitless CMO, Brand Gravity, The Revenue RAMP, responsible AI, content at scale, buyer journey Takeaways AI is shifting marketing toward generalists who are adaptable, curious, and comfortable with continuous change, while also creating entirely new roles like AI automation specialists and prompt engineers Organizations fall into three categories of AI readiness: AI-forward with clear strategy, still figuring it out, and fully resistant; 2X leads with full disclosure and follows each client's lead The deciding of what to say and how to say it should remain uniquely human, as it is the source of competitive differentiation and brand trust Brand gravity is built by accumulating digital mass across all the places buyers research anonymously, making a brand findable and chosen before any sales conversation begins AI enables the scale needed to repurpose core thought leadership into derivative assets across channels, without outsourcing the underlying thinking Synthetic personas and mock focus groups offer a faster, lower-cost path to messaging development, though high-stakes repositioning decisions still warrant real human input Building a knowledge layer from unstructured organizational data, call transcripts, emails, and more, is the key unlock for eliminating AI slop and generating reliable, contextual output Quotes "Deciding what to say and how to say it, those points of view that you're putting out in the market, that should be uniquely human. That's your secret sauce." "It's the absence of guardrails that people are so afraid of. The guardrails are what's actually unleashing it." "We recruit about 80 to 100 marketers a month, and we now have to really focus on soft skills: are they open to an ever-changing environment?" "I used AI when I was writing my book, not to write the book, but to interview me." "If you actually know your workflows and can taskify it in such a way that you can explain it to an intern, then it's very easy to apply AI to accelerate it." Chapters 00:03 Welcome and guest introduction 03:46 AI's role across a 1,400-person marketing organization 06:12 Evolving roles and the rise of the generalist marketer 10:06 Client AI readiness and 2X's full-disclosure approach 13:56 Defining what should remain uniquely human 18:21 Brand voice, storytelling, and competitive differentiation 21:21 Brand Gravity and the anonymous buyer journey 23:41 The Limitless CMO and scaling without skyrocketing costs 28:40 Building the knowledge layer from unstructured data 31:31 Synthetic personas and mock focus groups 36:13 Good enough as a framework for AI use case decisions 41:34 Voice-first workflows and AI-assisted book writing 45:43 Global operations, offshore teams, and cultural dynamics 50:28 Closing thoughts and book resources  Lisa Cole: https://www.linkedin.com/in/lisacole01 2X: https://2x.marketing “The Limitless CMO”: https://lisacole.ai/ For advisory work and marketing inquiries: Bob Pulver:⁠⁠ https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ https://elevateyouraiq.com⁠⁠⁠Substack: ⁠https://elevateyouraiq.substack.com⁠

Bob Danna, physicist, naval officer, former Senior Managing Director at Deloitte Consulting and Bersin by Deloitte, and author of the memoir "My Curious Life," joins host Bob Pulver for a wide-ranging conversation about a lifetime at the frontier of science and technology. Bob traces his journey from slide rules and nuclear reactors to agentic AI, sharing how he and collaborator Joe DiDonato built "Bot-Bob," a digital twin trained on his memoir, writings, and decades of experience. The conversation explores what digital twins can mean for knowledge workers, legacy building, and collective intelligence, including a live mastermind experiment where multiple digital twins, plus a digital Mark Twain, fielded questions from a live audience. Bob closes with an urgent call to bring more diverse human voices into AI development before the decisions that shape civilization get made without them. Keywords Bob Danna, digital twin, agentic AI, Bot-Bob, Joe DiDonato, My Curious Life, knowledge worker, collective intelligence, co-intelligence, legacy, mastermind, ElevenLabs, nuclear warfare, responsible AI, human centricity, future of work, STEM, Deloitte, memoir, Substack Takeaways Curiosity is the connective tissue of Bob's entire career, from nuclear physics and naval service to Deloitte consulting and digital twins, and he positions it as the essential human quality that AI can amplify but never replicate  A digital twin is far more than a knowledge repository; it encodes values, judgment, and personality, making it a genuine extension of a person's thinking The "mastermind" format, where multiple digital twins deliberate together in real time, opens new possibilities for accessing cognitive diversity without scheduling constraints When AI models are trained by a narrow group (such as military strategists), the outputs reflect that bias, making diverse human representation in AI development a matter of consequence Knowledge workers who collaborate with their own digital twins can operate at dramatically higher capacity and quality, not by being replaced, but by being amplified A responsibly built digital twin can preserve the wisdom, voice, and values of an individual for future generations Quotes "I'm just a curious guy. No matter what I'm into, I'm always looking at other things."  "When we free up tasks that human beings were doing, I think that is very, very positive. The real question is, what does the human being step up to do that only a human being can do?" "The definition of a knowledge worker is going to change. It's going to be that human being collaborating with the digital twin of that person." "It's very timely right now that we really start to have human conversations before we go down the path too far." Chapters 00:03 Welcome and introductions 01:21 Bob Danna's fascinating career journey 03:27 Early encounters with AI and neural networks 07:21 What makes us human, the evolution of calculators and computers 10:26 Joe DiDonato, soul-sinking, and the origin of Bot-Bob 14:41 Building Bot-Bob, memoir, voice, and guardrails 17:17 From chatbots to agents to digital twins, a practical framework 25:27 Brainstorming mode and collaborating with your own twin 28:16 Digital twins in consulting and the future of knowledge work 35:20 The mastermind experiment, Bot-Bob, Robo Lacey, and digital Mark Twain 41:14 AI, nuclear war scenarios, and the dangers of narrow training data 51:31 The workforce of 2030 and what it means to be a knowledge worker 58:55 Closing thoughts and how to connect with Bob Danna Bob Danna: https://bobdanna.substack.com/ “My Curious Life”: https://mycuriouslife.net/ 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 Laura Maffucci, Head of HR at Globalization Partners, for a wide-ranging conversation on AI adoption, global workforce trends, and the evolving role of HR. Laura shares how G-P is deploying its agentic AI product, GIA, both externally for global employment compliance and internally as a pilot HR agent, while emphasizing the importance of grounding AI in trusted, expert-sourced data. They explore the growing disconnect between executive optimism and employee sentiment around AI, the durable human skills that matter most in an AI-augmented workplace, and why AI adoption without a clear problem to solve is a recipe for costly confusion. Laura also shares candid reflections on her own AI learning journey and what she sees ahead for the HR function. Keywords Laura Maffucci, Globalization Partners, GIA, employer of record, EOR, agentic AI, global employment, AI compliance, HR transformation, AI adoption, employee sentiment, AI literacy, early career roles, talent acquisition, deep fakes, AI governance, critical thinking, human skills, learning agility, shadow AI, AI Awesomeness Awards, internal mobility, cognitive diversity, compensation analytics, AI readiness, Gemini, NotebookLM Takeaways GIA has evolved from a compliance Q&A tool into an agentic platform that can generate contracts, audit company policies, and is now being piloted as an internal HR agent handling employee ticket inquiries The data behind GIA is grounded in over a decade of G-P's global employment expertise, offering a trusted alternative to general-purpose LLMs drawing from unverified internet sources A significant perception gap exists between executives who believe AI is driving efficiency and employees who feel it is actually adding to their workload or generating unreliable output they must clean up Entry-level roles are shifting toward managing and directing AI agents rather than executing tasks directly, with "taste" (the ability to evaluate AI output) emerging as a critical early-career skill Workforce hiring criteria must increasingly prioritize unteachable human attributes such as curiosity, learning agility, courage, and the willingness to relinquish control, because technical AI skills can be taught but these cannot AI adoption mandates without a clearly defined problem to solve create fragmented, siloed "shadow AI" that can undermine organizational strategy rather than advance it HR functions are being asked to lead organizational AI transformation without adequate resources, technical support, or direction, making the role both high-opportunity and genuinely demanding Assessing real AI usage requires creative mechanisms: GP uses a Slack sharing channel, quarterly performance check-in questions, and monthly AI Awesomeness Awards to surface how people are actually applying the technology Quotes "I know that when these things come out, whether you like them or not, you had best learn them and learn how to work with them." "If you have a legal or compliance question, I hope Reddit's not your first stop to get that answer." "You can't embrace AI and be a control freak. You have to be willing to let something go and let something do something." "The pitfall that I can see so many companies falling into is, we don't need people because we've got the AI to do this." "Being in HR right now is not for the weak. That I will say, for sure." "It should always be: what problem are we trying to solve? Because that's actually one of the biggest issues with AI." Chapters 00:02 Welcome and guest introduction 01:40 Employer of record explained 03:03 GIA overview and new agentic capabilities 05:27 Responsible AI and the importance of trusted data sources 08:47 GP research findings on AI adoption and executive-employee sentiment gap 11:50 AI as added burden vs. efficiency driver 13:45 Redefining early career roles in an agentic world 15:42 The human cost of replacing workers instead of augmenting them 19:05 The problem with AI mandates that skip the "why" 22:44 Human skills that matter most when hiring for an AI-augmented workforce 26:37 The AI-versus-AI problem in talent acquisition 27:39 Deep fakes, virtual interview integrity, and human oversight in TA 31:53 The evolving role of HR as a strategic function 37:13 C-suite dynamics and running HR as a pilot for GIA 39:27 Building an AI council, sharing culture, and identifying shadow AI 42:15 Measuring AI fluency through awards, check-ins, and community 45:14 Laura's personal AI journey and closing thoughts Laura Maffucci: https://www.linkedin.com/in/laura-maffucci G-P: https://www.globalization-partners.com/ 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 is joined by Jerry Jao, CEO of Employ, who brings a perspective shaped by years of building AI-driven consumer personalization before turning that lens on hiring. Jerry shares how he is restructuring Employ for greater agility, how Pillar's interview and screening companions are reducing friction for job seekers and recruiters alike, and why the surge in AI-assisted applications is complicating matching on both sides. He also addresses candidate fraud and deepfakes, and explains how Employ's IBM partnership helps reduce bias and hallucinations across nearly 100 million applications processed annually. Keywords Jerry Jao, Employ, Lever, JazzHR, Pillar, interview intelligence, screening companion, talent acquisition, candidate experience, AI bias, responsible AI, IBM, deepfakes, candidate fraud, AI literacy, two-sided marketplace, organizational design, hiring technology Takeaways Jerry's background in AI-powered personalization at Retention Science informs his approach at Employ, viewing both job seekers and hiring managers as people deserving a more thoughtful, personalized process Employ processed nearly 100 million applications last year, with some roles receiving two to three thousand submissions, making meaningful evaluation a serious operational challenge Pillar's interview and screening companions are being integrated platform-wide to improve TA accuracy and give recruiters measurable time back in their day Responsible AI is a strategic priority, with IBM as a thought partner on model bias, hallucinations, and protecting candidate data at scale Candidate fraud, including deepfakes and multiple identity submissions, is an emerging risk Employ is working to detect earlier in the funnel AI-optimized resumes are eroding the signal value of traditional screening, making interview intelligence increasingly critical Quotes "What I'm most excited about is creating a more effective process for people to provide for their loved ones by getting to their dream jobs." "We want to help our TA team get home a little sooner, or take a 30-minute mental break if AI can help get that time back in their day." "Hiring managers are telling us people sound incredibly amazing, but once they get on the call, it's a little different." "We're all people at the end of the day, so how do we personalize the experience so no one feels overlooked?" "It's almost as big a change as when the internet first arrived. We're in a very uncertain and unprecedented time." Chapters 00:02 Welcome and introductions 02:03 From consumer personalization to talent acquisition 04:55 Building a human-centered hiring marketplace 07:16 AI on both sides: the cat-and-mouse dynamic in recruiting 09:24 Restructuring Employ for agility and accountability 14:12 Screening companion, talent fit, and processing 100 million applications 20:40 Candidate fraud, deepfakes, and emerging hiring risks 22:07 Responsible AI and the IBM partnership 30:38 AI literacy in job descriptions and skills assessment 34:54 Jerry's new podcast and the future of TA storytelling 39:02 Navigating workforce uncertainty in the AI era 41:12 Closing reflections and Employ research reports Jerry Jao: https://www.linkedin.com/in/jerryjao Employ: http://www.employinc.com For advisory work and marketing inquiries: Bob Pulver:⁠⁠ https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ https://elevateyouraiq.com⁠⁠⁠Substack: ⁠https://elevateyouraiq.substack.com⁠

Paul Rubenstein, Chief Evangelist and Talent Strategist at Visier, brings deep expertise in people analytics and workforce strategy to this wide-ranging conversation with Bob Pulver. He introduces a three-curves framework for CHROs navigating AI: the human-machine efficiency frontier, the ROI curve, and the humanity index. The discussion explores why workforce planning is having a long-overdue resurgence, and how HR can use AI-powered analytics to reach managers proactively rather than waiting for them to come to HR. Bob and Paul also examine the courage gap in AI adoption, the governance tension between restricting data and enabling better AI answers, and the design-plan-operate mindset required to truly transform work. Keywords  Paul Rubenstein, Visier, people analytics, workforce planning, CHRO, human-machine efficiency frontier, ROI curve, humanity index, agentic AI, MCP server, intelligent service delivery, governance, data strategy, organizational design, courage gap, talent strategy, future of work Takeaways  • CHROs should track three curves: the human-machine efficiency frontier, the ROI curve on AI investments, and a humanity index covering talent density, engagement, and culture.  • Most organizations are stuck in the "gym membership phase" — distributing AI tools without redesigning work — and real returns require intentional deconstruction and reassembly of jobs.  • The courage gap is real: employees need to see self-disrupting behaviors modeled and rewarded before they willingly give up tacit knowledge to train agents.  • MCP servers enable systems-to-systems intelligence that can give managers contextual, proactive insights in the flow of their work — without them ever having to engage HR directly.  • Workforce planning is entering a golden age, requiring continuous, real-time, multi-dimensional design that mirrors how finance operates with FP&A.  • AI governance needs to shift from restricting data by default to securing personal accountability for use — otherwise AI answers will remain narrow and biased.  • When all companies have access to the same agents, people and culture will again be the differentiator — making the humanity index a strategic, not just a moral, priority. Quotes  • "The floor for the tools we expect at work has just risen. AI is one."  • "You can't lay off a hand or an arm to recover your technology investment."  • "I want my workforce plan to be as easy as Google Maps — give me traffic updates and help me reroute."  • "Sameness does not yield greatness in a talent strategy."  • "Don't rely on your company for your career. You are responsible for staying relevant."  • "Just because you can automate something doesn't mean you should." Chapters 00:02 Welcome and introductions 00:53 Paul's career journey and obsession with HR's untapped potential 05:05 How AI is changing the analyst role and collapsing distance to insight 06:50 The three curves framework for CHROs navigating AI adoption 10:46 Strategic work planning vs. workforce planning and the agentic org chart 14:11 Manager evolution in a human-agent workforce 16:33 The gym membership phase and why job redesign is the real unlock 19:39 The courage gap and cultural conditions for AI adoption 23:29 Protecting durable human skills and doing the hard things 26:50 AI governance and the tension between data restriction and answer quality 31:38 MCP servers and the future of intelligent HR service delivery 38:54 Orchestration layers and proactive manager engagement 45:50 How analytics builds HR's strategic credibility 47:03 AI as first mate and the case for continuous workforce planning 51:52 Closing thoughts on staying human-centric and owning your career Paul Rubenstein: https://www.linkedin.com/in/paulrubensteinhr Visier: https://visier.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

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

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

Bob speaks with Brandon Roberts, VP of Global People Product, Analytics, and AI at ServiceNow. Brandon shares how ServiceNow is navigating AI transformation from within its HR organization, balancing internal experimentation with client-informed innovation. They dive deep into responsible AI practices, strategic reskilling, and cross-functional collaboration, while unpacking key frameworks. Brandon also offers a preview of forthcoming research on the future impact of agentic AI on the workforce and shares actionable insights for HR and business leaders on how to lead with confidence, empathy, and clarity in a rapidly evolving landscape. Keywords Responsible AI, Agentic AI, HR transformation, AI Playbook, AI readiness, AI literacy, reskilling, upskilling, internal mobility, ServiceNow, people analytics, AI enablement, human-centric, HR-IT collaboration, future of work, AI governance, workforce planning Takeaways ServiceNow’s HR team is leading internal AI adoption while helping shape product development through real-world use and feedback. The AI Playbook for HR Leaders provides a practical framework that blends vision with tactical execution. Responsible AI isn’t just a compliance exercise—it's a continuous process requiring monitoring, iteration, and cross-functional governance. ServiceNow’s AI Control Tower centralizes use case tracking, governance status, adoption metrics, and value realization. The AI Heat Map approach helps identify which tasks are most ripe for AI augmentation and where reskilling efforts should focus. Strategic reskilling efforts, like transitioning HR operations roles into people partner roles, show how AI can enable—not replace—human potential. HR-IT collaboration is essential to enabling governance, product experimentation, and sustained transformation. Upcoming research from ServiceNow estimates 8 million U.S. roles will be transformed by agentic AI in the next five years. Quotes “This is a human transformation, not just a tech transformation.” “Responsible AI isn’t finished at launch—it needs to be continuously monitored.” “We call it the AI Heat Map—breaking down roles into tasks to see where AI can really help.” “Strategic workforce planning needs to evolve into strategic work planning.” “If AI doubles productivity, it should also unlock opportunities—not eliminate people.” “We want employees to feel safe using AI and know we’re committed to reskilling, not replacing them.” Chapters 00:00 – Intro and Brandon’s background 02:00 – Brandon’s unique role in HR and product feedback loops 03:20 – Internal vs. customer-led innovation 04:24 – AI solution inventory and governance 07:18 – AI readiness, literacy, and cultural change 10:00 – Role-based skill development 12:00 – Embedding Responsible AI across the enterprise 14:36 – Balancing innovation with ethical oversight 17:50 – HR and IT collaboration at ServiceNow 20:45 – Agentic AI and workforce planning 23:47 – Case study: reskilling HR ops into people partners 29:03 – Why internal talent is often overlooked 33:21 – The evolving value of analytics in the AI era 36:58 – Importance of data quality and governance 40:32 – How AI will transform every role and industry 46:03 – Banking and reinvesting AI-driven time savings 48:27 – How ServiceNow filters and prioritizes AI ideas 49:18 – Teaser: upcoming research on agentic AI’s impact 51:06 – Personal AI tools and what’s exciting (or scary) 54:04 – Final thoughts and call to action Brandon Roberts: https://www.linkedin.com/in/brandon-roberts-50796ba AI Playbook for HR Leaders: https://www.servicenow.com/content/dam/servicenow-assets/public/en-us/doc-type/resource-center/ebook/eb-hr-role-in-ai-transformation.pdf For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠ What’s Your AIQ?⁠ Assessment interest form

Bob sits down with Cole Napper, VP of Research, Innovation & Talent Insights at Lightcast, to unpack the complex and rapidly evolving world of people analytics. From his eclectic career across industries to his recent book release and his co-hosting role on the very popular people analytics podcast, Directionally Correct, Cole shares practical insights and hard-earned wisdom on topics like AI readiness, org network analysis, and the intersection of data, influence, and leadership. Bob and Cole explore the paradoxes of the HR tech ecosystem, the stubborn persistence of unsolved problems, and why storytelling with data is really about persuasion. Cole also gets candid about the ethical responsibilities facing those who wield data, and why the future of workforce planning demands a complete rethink of how we study work itself. Keywords people analytics, talent intelligence, workforce planning, organizational network analysis, Lightcast, HR tech, Gen AI, quality of hire, job analysis, data storytelling, ethical AI, talent metrics, innovation, influence and persuasion, data infrastructure, Directionally Correct podcast Takeaways People analytics is only valuable when it influences decisions. Evolution of HR tech is moving from digitization to “value-first” intelligence. Effective storytelling with data is about persuasion and influence, not charts. Despite its maturity, organizational network analysis (ONA) remains underutilized. Most companies are underinvesting in data infrastructure, even as they chase AI initiatives. A flexible framework for measuring quality of hire is more useful than a rigid definition. Job analysis is having a renaissance as AI demands a deeper understanding of work. Ethics in people analytics isn't just about governance — it's about virtue and trust. Quotes “People analytics that doesn't influence decision-making is just overhead.” “We’re still digitizing HR — we haven’t even started to optimize it.” “Smart people assume their conclusions are self-evident, but that’s not how decisions are made.” “We need storytelling with data, but what we really need is persuasion with data.” “AI’s biggest challenge in HR isn’t capability — it’s data infrastructure and context.” “There’s no one watching the watchmen — ethics starts with the person in the seat.” “The study of work isn’t sexy, but it’s suddenly essential again.” Chapters 00:02 - Welcome and Intro to Cole Napper 00:55 - Cole’s Career Journey 03:29 - Patterns Across Industries and the Illusion of Uniqueness 06:51 - Community, Knowledge Sharing, and Power of Consortiums 08:57 - Why Smart People Still Struggle to Influence with Data 11:33 - From HR Tech to People Analytics: Digitization vs. Value Creation 13:51 - Data vs. Self-Interest: Why Decisions Get Blocked 15:49 - Untapped Potential of Org Network Analysis 18:54 - Use Cases: Building Teams, Referrals, and AI-Enhanced Sourcing 25:17 - Cole’s Book: Why Now, and What It’s About 28:13 - Shifting from Cost Center to Profit Center in People Analytics 32:22 - People Analytics Leading AI Adoption in HR 35:31 - Probabilistic Thinking, Determinism, and Predictive Pitfalls 36:55 - Measuring Quality of Hire: Frameworks vs. Definitions 40:41 - AI Assistants, Prescriptive Insights, and Reinforcement Learning 44:26 - Data Infrastructure as the Real AI Unlock 48:25 - Strategic Work Planning in an AI-Enabled World 52:25 - Who Will Watch the Watchmen? Ethics and Virtue in Analytics 55:28 - Predictions vs. Deductions and Parting Thoughts Cole Napper: https://www.linkedin.com/in/colenapper Directionally Correct: https://wrkdefined.com/podcast/directionally-correct "People Analytics": https://www.colenapper.com/book For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠ What’s Your AIQ?⁠ Assessment interest form

In this wide-ranging and thought-provoking conversation, Bob Pulver sits down with Steve Levy — recruiting veteran, technologist, and self-proclaimed “truth-teller” — to explore how talent, technology, and transformation intersect in today’s world of work. From the early days of expert systems and green-screen mainframes to the complexities of generative AI, Steve brings a rare blend of historical context, critical thinking, and humor. Together, they tackle topics like the ethics of candidate AI, bias in hiring platforms, skills-based hiring, the need for AI literacy, and why every recruiter needs to be more curious — and more human. Steve also shares lessons from his decades as a lifeguard at Jones Beach, and how that role shaped his instincts for protecting and empowering people — a theme that carries through everything he does in talent acquisition. Keywords AI in recruiting, expert systems, generative AI, candidate experience, skills-based hiring, talent ethics, AI literacy, job applications, bias in hiring, strategic workforce planning, Jones Beach lifeguard, recruiting tech, AI governance, human-centered design, talent intelligence, responsible AI Takeaways AI isn't new — it's just louder now: Steve recalls early experiences with AI-like systems in the 1980s and draws parallels to today’s hype and fear cycles. Recruiters need more curiosity, less fear: Avoiding AI won’t make it go away — recruiters must engage, experiment, and understand where AI fits. The real problem? Poor inputs: Most job descriptions and resumes are terrible — AI can’t solve for that without better human collaboration. Bias goes both ways: If employers can use AI to screen resumes, candidates can use it to write them — the key is transparency and integrity. Quality of hire starts with better intake: Steve emphasizes the importance of understanding real business problems, not just scanning for keywords. Candidate AI vs Employer AI: The current debate needs to move past gut reactions and toward practical, equitable frameworks. We need new roles and metrics: From TA ethicists to agentic governance leads, the future workforce demands new capabilities. Recruiting is about inclusion, not gatekeeping: Steve’s philosophy centers on humanizing the process and finding reasons to say “yes.” Quotes “If you can't audit it, don't automate it.” “The real challenge is working to include someone rather than exclude them.” “We're seeing artificial stupidity — not artificial intelligence.” “Being afraid of the ocean because of sharks is like avoiding AI because of hallucinations. You’ve got to get in the water.” “You can fight this, or you can plan for it. That’s it.” “Most people don't write good resumes. Most recruiters don't write good job descriptions. AI's not going to save us from that.” Chapters 00:00 – Opening & Reconnecting with Steve Levy 03:01 – Recruiting Before Computers & the Rise of Expert Systems 08:12 – What AI Is (and Isn’t): Fear, Hype & Progress 13:17 – Strategic TA in an Agentic Era 21:07 – AI Literacy, Education & Workforce Readiness 28:11 – Candidates Using AI vs. Employers Using AI 36:45 – Problems with Job Descriptions, Resumes & Gatekeeping 45:24 – Ethics, Transparency & Legal Implications in Hiring AI 54:10 – Talent Intelligence & Strategic Workforce Planning 1:05:33 – The SiriusXM Lawsuit & Candidate Frustration 1:15:57 – Lifeguard Lessons for the AI Age 1:20:12 – Final Thoughts on What Comes Next Steve Levy: https://www.linkedin.com/in/levyrecruits Steve’s Blog: https://recruitinginferno.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 What’s Your AIQ?⁠ Assessment interest form

In this lively and thought-provoking episode of Elevate Your AIQ, Bob Pulver reconnects with former collaborator and pioneering technologist Marshall Kirkpatrick. From their early work intersecting social data and influence to Marshall's latest AI-driven workflows, the conversation explores how human insight and machine intelligence are converging. Marshall shares real-world examples of using synthetic personas, market monitoring systems, and creative prompting strategies to uncover early signals, amplify strategic decisions, and reimagine everything from talent acquisition to environmental policy tracking. It's a conversation that navigates the emergence of machine learning for social insights to the frontier of AI innovation. Keywords AI-powered market monitoring, synthetic personas, talent acquisition, influencer marketing, social analytics, Claude, Perplexity, scenario planning, digital twins, quality of hire, Obsidian, strategic planning, generative AI, Delphi method, social capital Takeaways Marshall’s Journey: Marshall has spent his career identifying experts and building tools to surface valuable insights from social data. Synthetic Personas in Action: Using tools like Claude to create synthetic expert panels that evaluate documents, surface perspectives, and even challenge his own thinking. AI-Augmented Talent Scenarios: AI to simulate team compositions, evaluate candidates’ social behaviors, and even model potential collaboration outcomes. Monitoring the Market with AI: Building systems that detect early signals in markets — including environmental policy — using a mix of RSS, generative AI, and good old-fashioned curiosity. Digital Twins and Ownership: Exploring who owns the knowledge embedded in a “digital twin” of an employee — and how organizations might leverage them responsibly. Strategic Planning Reimagined: Using AI to model outcomes based on actions and strategies offers new ways to engage in scenario planning — not just in workforce contexts, but in grantmaking and innovation networks. Counterargument Workflows: Marshall shares his custom-built browser tool that generates counterarguments to online content using ChatGPT, promoting critical thinking and cognitive diversity. Quotes “I try to eat my own dog food — or drink my own champagne — when it comes to market monitoring.” “There’s gold in that data. We just have to figure out how to mine it responsibly and effectively.” “Synthetic personas are fast, cheap, and good enough to get the conversation started.” “What’s the strategy, what’s the output — and what’s the outcome? That’s where AI can help us model the messy middle.” “You can’t just look at someone’s codebase or resume — you need context, behavior, and communication patterns.” “I built a ‘counterargument bookmarklet’ to challenge the assumptions in what I’m reading online.”Chapters 00:00 – Welcome & Reconnection: Marshall’s Background and Journey 03:12 – AI Systems for Market Monitoring and Early Signal Detection 10:58 – The Evolution of Social Analytics and Social Capital 16:39 – Talent Acquisition, AI, and the Value of Social Footprints 24:57 – Scenario Planning with Synthetic Personas 32:05 – Driving Innovation through Grant Monitoring and Project Pairing 40:41 – From Digital Twins to Ethical Implications of AI in the Workforce 50:15 – Counterargument Workflows and Critical Thinking with AI 58:21 – Closing Thoughts: Responsible AI, Community, and the Road Ahead Marshall Kirkpatrick: https://www.linkedin.com/in/marshallkirkpatrick Earth Catalyst: https://www.earthcatalyst.co/ For advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠ What’s Your AIQ?⁠ Assessment interest form