Épisodes

  • Ep 90: Exploring How AI Shifts Our Approach to Content and Authenticity with William Tincup

    Ep 90: Exploring How AI Shifts Our Approach to Content and Authenticity with William Tincup
    Oct 24 2025
    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
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    59 min
  • Ep 89: Navigating the AI Doom Loop to Improve Hiring Outcomes with Dan Chait

    Ep 89: Navigating the AI Doom Loop to Improve Hiring Outcomes with Dan Chait
    Oct 17 2025
    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
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    56 min
  • Ep 88: Advancing the Human-AI Relationship to Redesign Work with Agi Garaba

    Ep 88: Advancing the Human-AI Relationship to Redesign Work with Agi Garaba
    Oct 10 2025
    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
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    56 min
  • Ep 87: Reimagining Learning Experiences in the AI Era with Lisa Yokana

    Ep 87: Reimagining Learning Experiences in the AI Era with Lisa Yokana
    Oct 3 2025
    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
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    55 min
  • Ep 86: Architecting the Future of Workforce Intelligence with Ben Zweig

    Ep 86: Architecting the Future of Workforce Intelligence with Ben Zweig
    Sep 26 2025
    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
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    55 min
  • Ep 85: Navigating AI Hiring Risks to Mitigate Adverse Impact with Emily Scace

    Ep 85: Navigating AI Hiring Risks to Mitigate Adverse Impact with Emily Scace
    Sep 19 2025
    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
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    58 min
  • Ep 84: Orchestrating Responsible AI Transformation at Scale with Brandon Roberts

    Ep 84: Orchestrating Responsible AI Transformation at Scale with Brandon Roberts
    Sep 12 2025
    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
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    55 min
  • Ep 83: Recalibrating Workforce Decisions via People Analytics and Gen AI with Cole Napper

    Ep 83: Recalibrating Workforce Decisions via People Analytics and Gen AI with Cole Napper
    Sep 5 2025
    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
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    57 min