『Elevate Your AIQ』のカバーアート

Elevate Your AIQ

Elevate Your AIQ

著者： WRKdefined Podcast Network
無料で聴く

【Amazonプライム会員限定】今ならプレミアムプランが4か月 月額99円。

10月19日まで。※適用条件あり
Bob Pulver is helping each of us navigate our respective journeys with artificial intelligence (AI) effectively and responsibly. Bob chats with AI and Future of Work experts, talent and transformation leaders, and practitioners who provide diverse perspectives on how AI is solving real-world challenges and driving responsible innovation.All rights reserved by WRKdefined マネジメント マネジメント・リーダーシップ 経済学
エピソード
  • Ep 137: Trading AI Hype for Evidence and Education in Talent Tech with Hayley Skivington

    Ep 137: Trading AI Hype for Evidence and Education in Talent Tech with Hayley Skivington
    2026/09/25
    Hayley Skivington began her career in recruitment and has come full circle as Head of Product at Oleeo, where she sits at the intersection of product vision, commercial strategy, and the realities recruiters face every day. In this conversation with Bob, she explains why her role has expanded well beyond building features: with fear of job loss, litigation, and getting it wrong still widespread, vendors now have to hold customers' hands as educators and trusted advisors. The two dig into what responsible AI looks like in practice for a company serving government, policing, and financial services, and how Oleeo competes in a crowded market where ERPs, HCM suites, and AI startups all want a piece of recruiting. Hayley also offers an inside look at how Oleeo builds AI fluency across its own workforce, and Bob draws parallels to the early days of corporate social media bans. They close by looking ahead at how recruiters' work and skills will change as their tools become more connected. Keywords Hayley Skivington, Oleeo, responsible AI, explainability, talent acquisition, applicant tracking systems, AI literacy, candidate experience, EU AI Act, ISO 42001, vendor evaluation, shadow AI, Police Scotland, interoperability, cultural alignment, recruiter upskilling Takeaways Surfacing the evidence behind AI screening decisions gives recruiters confidence and gives candidates feedback to improve future applications. Buyers adding AI features increasingly need sign-off from security and IT compliance, so vendors should equip them with ready-made documentation. Integrating with existing systems like Oracle can deliver AI value without the cost and upheaval of replacing an ATS. Police Scotland went from treating any AI use as cheating to cutting email inquiries by roughly 30% within weeks, freeing time for candidates navigating medicals and vetting. Lunch hackathons and peer-led sessions spread practical AI know-how faster than formal training alone. Hayley expects recruiting tools to converge with collaboration and analytics platforms, pulling HR, talent management, and talent acquisition closer together. Recruiters should build skills in managing knowledge bases, evaluating compliance risk, and handling candidate appeals. Quotes Whenever anybody comes to us with anything, we always say, what's the problem? What are we trying to fix? You don't need to rip everything out. We can complement your technology stack, but help you solve that problem that you've got that your current technology can't do. We run a thing called the AI Forum and that's all about education. I'm not going there to talk about my latest product or why you all need to buy it from me. Those simple tools... can be the light bulb moments in organizations that are really risk averse because a chat bot feels quite friendly, right? People become strangely fond of whichever tool they're using the most. My advice to recruiters is to be AI literate. Think about how you leverage AI within your role and really start to upskill yourself. Chapters 00:01 Welcome and introductions 00:51 Hayley's path from recruitment to head of product 05:23 Building AI on strong foundations, not band-aids 09:06 Showing recruiters and candidates the evidence behind AI 13:29 Leveling the playing field and learning AI at home 18:43 Regulations, vendor evaluation, and shared responsibility 25:55 Competing in a crowded talent tech market 33:10 AI literacy inside Oleeo and the AI Forum 42:33 Police Scotland's journey from AI ban to adoption 45:20 Why banning AI leads to shadow AI 53:06 The evolving role of the recruiter Hayley Skivington: https://www.linkedin.com/in/hayley-skivington-63217531/ Oleeo: https://www.oleeo.com/ For AI readiness advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠
    続きを読む 一部表示
    59 分
  • Ep 136: Measuring Belonging to Strengthen Human Infrastructure with Eric Knauf

    Ep 136: Measuring Belonging to Strengthen Human Infrastructure with Eric Knauf
    2026/09/18
    Bob sits down with Eric Knauf, founder of BelongHQ and author of The 56% Solution: How Belonging Infrastructure Transforms Performance, who traces his path from studying organizational psychology to leading talent through a company's 55% reduction in force and a historically low employee net promoter score (eNPS). That turnaround became the origin of his belonging framework: five measurable pillars, psychological safety, inclusion, support, connection, and purpose, each with a direct, causal tie to business outcomes like innovation, retention, and profitability. The conversation moves from operationalizing belonging inside real organizations to why most AI transformations are already failing before they start, and how the health of an organization's human infrastructure predicts its readiness for change. Eric and Bob dig into what it takes to close the gap between a company's best and worst managers, and why fixing that gap costs commitment rather than money. Keywords belonging, psychological safety, organizational health, human infrastructure, employee engagement, reduction in force, eNPS, talent leadership, AI transformation, change management, Deloitte, BetterUp, Amy Edmondson, frontline managers, inclusion, connection, purpose, The 56% Solution, BelongHQ, workforce analytics, retention, M&A due diligence Takeaways Filling a role isn't the same as creating value, and talent leaders should map where value is actually created before optimizing headcount A 55% reduction in force and an eNPS of negative 73 became the origin story for Eric's belonging framework, which pulled that same team's score to positive 8 within six months Deming's finding that 94% of performance variance sits inside the system, not the individual, reframes culture as an engineering problem rather than a personality problem Belonging breaks into five measurable pillars, psychological safety, inclusion, support, connection, and purpose, each tied to a specific, causal business outcome Averages hide the real risk. The gap between an organization's strongest and weakest frontline managers predicts far more than a single companywide engagement score Psychological safety is the top predictor of whether employees actually use AI tools, according to a 2,250 person study Eric cites in the conversation Only seven cents of every AI investment dollar reportedly goes toward people, even as 88% of AI initiatives fall short of plan Strengthening human infrastructure costs commitment and ego, not budget, and pays off in retention, innovation throughput, and even M&A due diligence Quotes "It's one thing to fill a role. It's another thing for that human to actually add value." "It was stated that 88% of AI initiatives are not going as planned." "Ninety-three cents on the dollar are going to AI to the technology itself. Only seven cents on the dollar are going to the people." "The number one predictor of whether or not they use AI, psychological safety." "It requires being more human. It requires five things: psychological safety, inclusion, support, connection, and purpose." "To improve those metrics doesn't require a lot. It requires being a better person." Chapters 00:02 Welcome and introduction of Eric Knauf 00:36 Eric's roots in organizational psychology and path into talent leadership 02:54 Filling roles versus creating value, lessons from lean consulting 05:12 A brutal turnaround, leading a company through a 55% reduction in force 07:40 From negative to positive, the swing that led to writing a book 10:48 Systems versus people, and where the epiphany began 12:12 Discovering belonging, the Deming principle and the BetterUp research 16:30 Defining belonging, five pillars and why CFOs need proof, not emotion 20:31 Culture and engagement as outcomes, not goals in themselves 27:12 Operationalizing belonging infrastructure inside an organization 29:43 Why most AI transformations are already starting on the wrong foot 33:27 Psychological safety as the top predictor of AI adoption 45:14 Psychological safety failures at the C-suite level 49:14 Writing for the CFO, the skeptic, and the human side 59:00 Closing advice, know where you stand before you chase where you're going Eric Knauf: https://www.linkedin.com/in/eknauf BelongHQ: https://belonghq.com/ The 56% Solution: https://a.co/d/06xUAVmy For AI readiness advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠
    続きを読む 一部表示
    1 時間
  • Ep 135: Assessing and Evolving Human-Centric AI Readiness with Tracy St.Dic

    Ep 135: Assessing and Evolving Human-Centric AI Readiness with Tracy St.Dic
    2026/09/11
    Tracy St.Dic, VP of Global Talent at Zapier, joins Bob to talk about what it actually takes to build an AI-fluent workforce, drawing on her fifteen years in education, including a stint leading national recruitment at Teach for America, before joining Zapier. She walks through the origin and evolution of Zapier's AI fluency rubric, the difference between AI adoption and true AI transformation, and why she still rates her own company a four out of ten. The conversation covers how Zapier is reshaping the recruiter role into more of a talent advisor function, freeing people from busywork to focus on coaching and relationship-building, and the internal AI workbench her team is building to support that shift. Tracy and Bob also dig into the risk of companies blaming headcount reductions on AI when the real driver is a search for different skills, and why hiring for trajectory, not a static skill snapshot, matters more than ever. Keywords AI fluency, talent transformation, Zapier, Teach for America, AI adoption, AI transformation, citizen development, talent advisors, workforce upskilling, hiring philosophy, agent harness, slope over snapshot, human-centric AI, quality efficiency employee experience, responsible AI Takeaways Zapier's AI fluency rubric has four pillars: mindset, strategy, building skills, and accountability, and it applies to both hiring and internal development. Tracy distinguishes AI adoption (bolting AI onto existing workflows) from AI transformation (redesigning work from the ground up), and rates Zapier a four out of ten on that scale. A simple test for any AI initiative: does it improve quality, efficiency, and employee experience, not just speed. Leaders need to define a clear vision for their function before scaling citizen development, or teams end up building in inconsistent directions. Zapier is shifting recruiters toward a "talent advisor" role, using AI to handle research and reporting so people can focus on coaching and relationship-building. Blaming headcount reductions solely on AI is often inaccurate; the real driver is companies wanting different, more AI-fluent talent. Zapier hires for "slope over snapshot," prioritizing a candidate's trajectory and rate of learning over current tool proficiency. The talent team is building an internal "TA workbench" inside an agent harness (Claude Code) to centralize context and best practices for recruiters. Quotes "Brilliance is distributed everywhere and opportunity is not." "You can delegate the task, but not the accountability." "Even if the technology isn't there yet, eventually it will be. And then you'll be ready for it." "We're not hiring people for just what they know today. We want to hire people for the trajectory at which they climb." "It's a very small percentage of companies that are seeing real ROI with AI right now." "Their company's philosophy is to keep what you kill." Chapters 00:02 Welcome and introduction to Tracy St.Dic 00:32 Tracy's path from Teach for America to VP of Global Talent at Zapier 02:06 Why access and democratization shaped her career 05:28 Origins of Zapier's AI fluency rubric and its four pillars 11:59 AI adoption versus AI transformation 16:22 A simple framework: quality, efficiency, and employee experience 19:43 Why leaders need a vision before scaling citizen development 24:02 Updating the rubric as AI fluency rises company-wide 27:14 Turning recruiters into talent advisors 31:27 Keep what you kill: reinvesting time saved 35:23 Why AI headcount narratives are often misleading 37:49 Hiring for slope over snapshot 40:56 Building the TA workbench inside an agent harness 46:06 Using AI for traceability and coaching 48:12 Final advice on building AI fluency Tracy St.Dic: https://www.linkedin.com/in/tracy-stdic Zapier: zapier.com Using AI in Zapier’s hiring process: https://zapier.com/l/jobs/ai-at-zapier For AI readiness advisory work and marketing inquiries: Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠ Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠ Substack: ⁠https://elevateyouraiq.substack.com⁠
    続きを読む 一部表示
    51 分
adbl_web_anon_alc_button_suppression_t1
まだレビューはありません