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

Bob sits down with Colette Mason, a tech veteran with 40 years of experience in computing and a deep understanding of human behavior through her work in coaching and neuro-linguistic programming. Together, they explore the hype and reality around AI adoption, automation myths, and why “responsible by design” is more than just a catchphrase. Colette shares her perspectives on human-centric design, AI literacy, and how to keep authenticity intact in an AI-powered world. With warmth, humor, and real-world wisdom, this conversation brings clarity to an often-confusing landscape—and reminds us that technology should augment rather than replace what only humans can and should do. Keywords AI literacy, human-centric design, responsible AI, automation, digital assistants, content generation, neuro-linguistic programming, human-AI collaboration, ethical AI, digital tools, Colette Mason, trusted AI Takeaways AI ≠ Automation: Many tasks called "AI" are really just workflow automation. It's important to distinguish between the two. Human-Centered Design Matters: AI tools should reflect human needs, limitations, and behaviors, especially when used in sensitive areas like hiring. The Hype Is Real—and Misleading: Over-promising on AI capabilities can hurt trust and morale. Colette urges a more grounded, realistic view. Use AI Where It Helps, Not Where It Hurts: Delegate the boring stuff, but don’t let AI speak in your voice without oversight. Authenticity Still Wins: Whether it's writing, speaking, or building a personal brand, being transparent about AI involvement builds trust. Responsible Use Is Everyone’s Job: From solo entrepreneurs to large enterprises, we all have a role in building and using trustworthy AI. Design for Real People: Most users aren’t tech-savvy. Tools need to be intuitive, safe, and aware of different user needs—including neurodiversity. Top Quotes “I model people’s brains because I’m a hypnotherapist—and that’s actually a superpower in tech.” “There’s a lot of AI that isn’t really AI. It’s just automation with lipstick.” “The system has to read the room—it can’t just say ‘you didn’t give me all the info, mate.’” “Regular people need AI that helps them make it to their kids’ school play—not impress YouTube bros.” “Don’t replace yourself with AI. Do less, but make it more you.” “We’re not in the early innings—we’re still in warmups when it comes to AI literacy.” Chapters 00:00 – Intro and Colette's Background 02:00 – AI Hype vs. Reality: What’s Really Happening 06:00 – Automation ≠ AI: Breaking the Misconceptions 10:30 – Building Human-Centered Tools and Workflows 17:00 – Responsible AI and “Designing for Safety” 24:00 – Fairness in Hiring and Interviewing with AI 30:00 – The Quality of AI-Generated Content 38:00 – Being Transparent About AI Use 44:00 – Ethics, Reputation, and the Court of Public Opinion 50:00 – Global Perspectives on AI Regulation 54:30 – Favorite Tools and Real-World Applications 01:00:00 – The Future of Personality in AI Models 01:03:30 – Closing Thoughts Colette Mason: https://www.linkedin.com/in/colettemason Clever Clogs AI: https://www.cleverclogsai.com/ Ditch Rework, Build Teamwork: https://www.amazon.com/Ditch-Rework-Build-Teamwork-Principles-ebook/dp/B0FBL4C6ZP 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 Dan Stradtman, Chief Marketing Officer at Bloomfire, to explore the evolving landscape of knowledge management (KM) in the age of AI. Dan brings a wealth of experience from Fortune 500 giants like Walmart, GE, and Lubrizol (Berkshire Hathaway). They discuss how often tacit and institutional knowledge is undervalued and underutilized. Bob and Dan unpack Bloomfire’s concept of “Enterprise Intelligence” and its new framework for treating knowledge as a measurable, strategic asset. They also cover the risks of overlooking tacit knowledge, how AI adoption is changing who leads knowledge initiatives, and the crucial role of ethics, trust, culture, and human-centricity in the enterprise AI journey. Keywords Enterprise Intelligence, Knowledge Management, Tacit Knowledge, Bloomfire, Enterprise AI, Digital Assistants, Leadership, Strategic Workforce Planning, Culture, Cognitive Diversity, Collective Intelligence, Human-Centricity, Trust, Future of Work, Ethical AI Key Takeaways Knowledge is an asset: Companies often fail to treat knowledge—especially tacit knowledge—as a formal asset on the balance sheet. AI elevates knowledge management: The rise of AI has pushed KM into the C-suite, with a growing emphasis on enterprise-wide integration. Tacit knowledge loss is costly: Orgs lose significant institutional knowledge without realizing its overall impact. Trust drives knowledge sharing: Cultural factors, psychological safety, and leadership behavior directly impact how willing employees are to share knowledge. Remote work challenges knowledge flow: For early-career professionals, the hybrid environment can inhibit mentorship and exposure to institutional wisdom. Digital advisors & AI agents are rising: As digital personas and assistants become more advanced, organizations must consider the ethical implications. SWP evolution: Strategic workforce planning should evolve into strategic work planning, balancing both digital and human contributions. Measuring value requires new KPIs: Bloomfire’s framework ties knowledge value to tangible outcomes like revenue per employee, onboarding speed, and OKR attainment. Cognitive diversity is crucial: Varied perspectives and experiences within teams lead to better problem-solving and innovation. AI is integral to the future of work: It will require a blend of human and AI capabilities and should remain human-centric. “Tacit knowledge is going out the door, and companies are underestimating how consequential that is.” “AI systems are only as good as the quality of the knowledge you feed them. It’s still garbage in, garbage out.” “Organizations need to think of themselves as ecosystems, where people and digital agents work together.” “Cognitive diversity is going to be critical—otherwise everyone’s just prompting the same chatbot.” Chapters 00:00 – Welcome and Guest Introduction 02:00 – Dan’s Career Journey and Road to Bloomfire 05:00 – What Bloomfire Does and the Rise of Enterprise Intelligence 08:30 – The Evolution of KM 12:00 – AI’s Role in Driving KM to the C-Suite 15:00 – Tacit Knowledge: The Hidden Asset 18:30 – The Value of Human-Centric Design in AI Strategy 24:00 – Skills Atrophy and the Impact of Remote Work 27:30 – Cognitive Diversity in the Age of AI 30:00 – Capturing Institutional Knowledge Through Tech 35:00 – Lessons from Early Expertise Discovery Tools 38:00 – Digital Advisors and the Risk of Redundancy 44:00 – Meeting Intelligence and Ethical Knowledge Capture 47:00 – Trust, Culture, and the Role of Leadership 55:00 – Experimentation, Risk, and AI Governance 59:00 – Innovation, Strategy, and the Future of Work Dan Stradtman: https://www.linkedin.com/in/danstradtman Bloomfire: https://bloomfire.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

Bob Pulver and Casey Fox discuss the evolution of Tekletics, a company focused on bridging the gap between untapped talent and technology careers. Casey shares his journey from a business major to the CTO of Tekletics, emphasizing the importance of work ethic, innate human skills, and the role of AI in talent acquisition and development. They explore the challenges and opportunities presented by AI in the workforce, the need for a culture of responsibility, and the importance of human potential in the age of automation and AI. Keywords Tekletics, AI, workforce development, talent acquisition, future of work, technology, coding bootcamp, human potential, automation, career transition Takeaways Tekletics aims to bridge the gap between untapped talent and technology careers. The evolution of Tekletics reflects the changing landscape of work and technology. Work ethic and a strong interest in technology are crucial for success in tech roles. AI is transforming talent acquisition and development processes. Organizations need to foster a culture of AI responsibility and ethical use. The future of work will involve collaboration between humans and AI. There are untapped talent pools that organizations can explore for hiring. Training programs should focus on real projects rather than traditional boot camps. AI tools can enhance productivity but must be used with caution. Building a diverse and skilled workforce is essential for the future.  Sound bites "We need to tap into untapped human potential." "We want to build a culture of AI responsibility." "We have to help build the next generation of SMEs." Chapters 00:00 Introduction to Tekletics and Casey Fox's Journey 03:23 The Evolution of Tekletics and Its Mission 08:46 Understanding the Future of Work and Career Pivots 16:02 Identifying Talent and Building Skills for the Future 20:00 Adapting to Changing Client Demands and AI Integration 25:09 Navigating the Talent Ecosystem and Future Opportunities 33:27 Navigating the Dystopian Path of AI 34:20 Fostering Curiosity in the Age of AI 36:22 The Evolution of Learning: Libraries vs. AI 38:32 Empowering Employees with AI: Trust vs. Control 40:33 The Human Element in AI Adoption 42:01 Building Trust in AI: Data Privacy Concerns 44:41 The Role of AI in Coding: A Double-Edged Sword 47:50 The Future of Junior Roles in a Tech-Driven World 51:17 Building a Foundation for Future Generations 55:28 AI Literacy: Understanding Risks and Opportunities 59:25 The Future of Work: Humans and AI Collaboration 01:02:55 Tekletics: Bridging the Gap for Future Talent Casey Fox: https://www.linkedin.com/in/foxcase Tekletics: https://www.tekletics.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

Bob Pulver sits down with Siadhal Magos, CEO and Co-founder of Metaview, to explore how AI can unlock a more structured, scalable, and insight-rich approach to hiring. Siadhal brings deep experience from the world of product and people to unpack why interviews—despite being central to business success—remain one of the most inconsistent and intuition-driven processes in organizations. The conversation spans the origins of Metaview, the real cost of poor hiring decisions, and the gap between what hiring teams think they’re evaluating versus what they’re actually reacting to. They also discuss the difference between feedback and insight, the value of AI as an interview companion rather than a replacement, and why structured processes don’t have to come at the expense of candidate experience. Keywords Siadhal Magos, Metaview, interview intelligence, hiring decisions, quality of hire, feedback loops, AI in recruiting, structured interviews, candidate experience, decision-making, hiring bias, interview analytics, talent strategy, hiring intelligence, decision intelligence, summarization Key Takeaways Hiring is high-stakes—but under-instrumented. Most teams still rely on memory, gut feel, and incomplete notes. AI can elevate—not replace—human judgment. Metaview focuses on supporting better decisions, not automating them away.Interview feedback ≠ insight. Capturing what was said and how it was evaluated creates a far more useful learning loop.Consistency doesn’t mean rigidity. Structured interviews can still be candidate-friendly and personalized. Good hiring mirrors good product thinking. Siadhal shares how tight feedback loops, data, and clarity fuel both. Curiosity is a superpower in early-stage building. The Metaview journey is a case study in iterating with empathy. Top Quotes “The way most interviews are run is far too fragile for the importance of the decisions being made.” “We’re not trying to replace human judgment—we’re trying to give it better inputs.” “Hiring is one of the most strategic things a company does, but it’s often the least measured.” “It’s not just about the candidate’s answers—it’s about how the interviewer responded to them.” “Great teams are built through consistent, reflective decision-making—not just instincts.” Chapters 00:00 – Opening and Siadhal’s early career in product and people 06:45 – Why interviews are broken and how Metaview began 13:10 – Feedback vs. insight: a new lens on interview data 20:00 – The ethics and implications of recording interviews 26:35 – Human judgment + AI: striking the right balance 33:20 – Structured interviewing and candidate experience 40:50 – Building with curiosity: lessons from Metaview’s journey 47:00 – Final thoughts on quality of hire, trust, and team growth Siadhal Magos: https://linkedin.com/in/siadhal Metaview: https://metaview.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 What’s Your AIQ? Assessment interest form

Bob Pulver talks with Kristi Broom, Co-founder of Rising Tide Cooperative and a seasoned transformation leader. Kristi's career has spanned EdTech, L&D, operations, and HR. She shares her journey from building early learning platforms to leading organizational change at scale—all while staying grounded in her passion for helping people grow. The conversation explores what it means to be a generalist in an era of specialization, how to design systems that support behavior change, and the role of curiosity, structure, and storytelling in navigating innovation. Whether you’re working in HR, technology, transformation, or operations, this episode offers a fresh perspective on what it really takes to lead through complexity and build a future-ready organization. Keywords Kristi Broom, transformation, L&D, EdTech, generalist career, operational leadership, organizational change, innovation, behavior change, people development, systems thinking, storytelling in business, AI readiness, future of work Key Takeaways Generalists are wired for transformation. Kristi explains how her generalist background allowed her to connect across silos and take on high-stakes change. EdTech roots shaped a systems mindset. Building early online learning systems taught her how to think structurally while staying flexible. Curiosity drives innovation. Kristi shares how being a “possibility thinker” has helped her evolve with each new challenge. Real transformation requires structure and story. Without storytelling, even well-designed systems fail to resonate. Growth is personal. Her work has always centered on helping people grow—whether through development programs, leadership models, or building intentional cultures. Pacing matters. When leading transformation, knowing when to accelerate—and when to pause—is a crucial leadership skill. Top Quotes “I’ve always been a generalist—wired to think across disciplines, across people, across possibilities.” “Structure is important, but story is the engine that helps people move.” “I’m obsessed with seeing people grow. That’s where the energy comes from for me.” “You can't automate your way out of transformation—you still need leadership.” “My curiosity is a superpower, and I’ve learned how to use it to help people and organizations evolve.” Chapters 00:00 – Opening and Kristi’s origin story in education and technology 08:15 – Becoming a generalist: curiosity, complexity, and change 14:30 – Early EdTech and learning platform design 20:45 – Making growth personal: L&D as a human imperative 27:10 – Leading transformation: structure, pacing, and storytelling 36:00 – Building future-ready systems without losing your people 42:55 – Final reflections on innovation, possibility, and what’s next Kristi Broom: https://www.linkedin.com/in/kristibroom Rising Tide Cooperative: https://risingtidecooperative.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

Bob Pulver welcomes Andrew Gadomski, Founder and Managing Director of Aspen Analytics, to talk about how AI and data science are reshaping the future of HR, talent, and organizational decision-making. Andrew is a veteran workforce data strategist who shares candid, practical insights on what it really takes for companies to evolve their data maturity, why LLMs can’t be treated like magic wands or oracles, and how to make AI work with your people, not instead of them. From “decision gravity” to the fallacy of talent pipeline management, this episode is a masterclass in balancing technological possibility with human nuance. Keywords Andrew Gadomski, Aspen Analytics, workforce analytics, decision intelligence, data maturity, talent strategy, HR transformation, responsible AI, talent pipeline, future of work Key Takeaways The difference between using AI as a prediction tool vs. a decision-making tool—and why that matters “Decision gravity” and how influence travels through an organization Why most organizations aren’t “data mature” and how to assess where you really are LLMs (like ChatGPT) aren’t ready to make decisions—they need guardrails, oversight, and smart humans The myth of a linear talent pipeline and how hiring should actually work Data-informed != data-driven: what smart decision-making really looks like How to frame AI adoption around people, not just tools Sound Bites “Data is a tool for influence—not control.” “If you don't trust the decision, you won't trust the data.” “AI will tell you what it would do. It won't tell you what you should do.” Chapters 00:00 – Welcome and Guest Intro Overview of Andrew’s role at Aspen Analytics and his approach to data-driven transformation. 05:10 – What “Data Maturity” Really Means Why most organizations overestimate their data capabilities—and what a mature approach actually involves. 12:40 – Decision Gravity and Influence Mapping How organizational decisions really get made and why influence—not hierarchy—is what drives outcomes. 21:25 – Prediction vs. Decision: The Role of AI Understanding how AI fits into human workflows, and why relying on LLMs for decisions is risky. 31:00 – The Limits of Large Language Models (LLMs) Where LLMs can be helpful, where they hallucinate, and how to set trust boundaries around their output. 40:30 – Hiring Myths and the Talent Pipeline Fallacy Why treating hiring like a “pipeline” misses the mark, and what a better model could look like. 52:15 – Building Trust Through Responsible AI How trust, transparency, and cultural readiness shape whether AI is embraced—or ignored. 63:00 – Reframing Success: Learning, Not Just Automation Closing reflections on how organizations can prioritize adaptability, curiosity, and practical value in the AI era. 72:30 – Final Takeaways and Where to Learn More Andrew’s parting thoughts on decision support, ethical data use, and leading with intentionality. Andrew Gadomski: https://www.linkedin.com/in/andrewgadomski Aspen Analytics: https://www.aspenanalytics.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 What’s Your AIQ? Assessment interest form