Elevate Your AIQ

Phil Kirschner, founder of PK Consulting, creator of The Workline newsletter, and Transformation Partner at Lead with AI, joins Bob to unpack why so many workplace and AI transformations fail to stick. Drawing on a career that runs from cybersecurity at Credit Suisse to workplace strategy at WeWork and McKinsey, Phil explains why change management can’t just be communication, and why AI is a “resistible” change that demands a clear why and a North Star. They explore why leaders must build their own AI fluency, and admit what they don’t know, before guiding others. Phil also makes the case for a Chief of Work, an independent owner of the end-to-end employee experience, and shares how AI could make teams and careers more fluid. Keywords Phil Kirschner, PK Consulting, The Workline, Lead with AI, McKinsey, WeWork, Credit Suisse, change management, AI fluency, AI readiness, North Star, resistible change, Chief of Work, employee experience, workplace strategy, manager behavior, Charter AI Pulse, Parkinson’s Law, forward-deployed black belt, work redesign, flash teams, internal mobility, psychological safety Takeaways Change sticks when leaders define what’s broken, why it matters, and where they’re headed before they start communicating and training. AI adoption is a resistible change: employees can resist it or simply go through the motions, so the groundwork matters even more. Leaders can’t transform their teams until they’ve transformed their own work, and admitting what they don’t know builds trust. Set a personal North Star for the time AI saves, or Parkinson’s Law will fill it with more low-value work. A Chief of Work, independent of HR, IT, and real estate, can read signals across the employee experience and fix friction no single function owns. Organizations need more than forward-deployed engineers; they need independent process experts who redesign the work itself. Real change requires letting go of the old way before the new one is secure, which takes psychological safety. AI could ease the coordination that makes fluid teams, short engagements, and portfolio careers so hard today. Quotes “I don’t think you can get people to transform whatever is in their little empire, which could just be them or my team or my department or my company until they have gone through it personally.” “Everybody thinks of change management is communication.” “It all comes down to individual fluency at every level is the gateway to every other level of transformation, ultimately.” “I believe we need some kind of independent body to act as a little bit of the sort of like Fitbit of the organization to just say, I hoover up all these signals.” “A forward-deployed engineer is only successful if they have something to engineer.” “The person who you know is most likely to eat your lunch is not the person you think of as your peer now. It’s someone who’s gonna do it from the ground up using the kind of operating system that we’re talking about.” Chapters 00:01 Welcome and introductions 01:02 Phil’s path from computer science to workplace strategy 06:40 Why transformations fail to stick 12:37 AI fluency, readiness, and the limits of individual productivity 14:40 Transforming yourself before your team 24:11 Setting a personal North Star for reclaimed time 26:44 The case for a Chief of Work 32:12 AI transformation leaders and the forward-deployed black belt 37:31 The trapeze metaphor and letting go of the old way 40:38 Flash teams, internal mobility, and manager incentives 42:34 Consulting models, fluid teams, and portfolio careers 51:26 Closing thoughts and where to find Phil Phil Kirschner: https://www.linkedin.com/in/philkirschner The Workline: https://walktheworkline.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

Bob sits down with Jess Von Bank, co-founder of Now to Next and author of Mother of Disruption: Reshaping the Human Contract in the Age of AI, fresh off emceeing the Skills stage at Unleash Paris and on the eve of her book launch. Jess argues that the humanization of work is not a new idea, it’s just more urgent, and that organizations should invest at least as much in workforce transformation as in AI tools. They discuss why most companies are using AI to perfect old ways of working, why skills strategies stall without clear use cases, and why only a small share of companies pursuing growth and innovation are seeing real returns. Jess also shares how raising three teenage daughters keeps her grounded, and why she wrote a book about AI that is really about what people should demand and deserve. Keywords Jess Von Bank, Now to Next, Mother of Disruption, human contract, workforce transformation, AI transformation, skills-based organization, skills passport, internal mobility, learning organization, hands heads hearts, human judgment, humans in the lead, AI ROI, augmentation vs. automation, responsible AI, AI governance, systems thinking, Unleash Paris Takeaways Invest as much or more in workforce transformation as in AI tools, and measure capacity and value created, not tool adoption. People are not an obstacle to transformation; they are the transformation. Most organizations are in a “fad diet” phase of AI: green adoption dashboards without real organizational change. Skills architecture only pays off when it serves clear use cases like mobility, redeployment, and succession. The organizations that win will be learning organizations that can move talent to work on demand. Use AI fully for routine work, as a thinking partner for complex work, and keep decisions about people firmly human. Redesign core processes from first principles; legacy steps should earn the right to be carried forward. Governance can enable experimentation through safe pilots instead of blocking it. Quotes “People are not in the way of transformation. They are the transformation.” “You should be investing more in workforce transformation than you are in AI transformation, meaning the tools and the technology.” “I think we have used AI to perfect the way we used to work.” “I like to say if it’s a no-brainer, give it to AI.” “Judgment is what you apply when the rules run out.” “I wrote a whole book about AI without ever talking about AI. I talked about what people need to demand and deserve in this time as we’re designing the future of work.” Chapters 00:02 Welcome and recovering from industry conferences 00:52 From small-town North Dakota to the future of work 03:26 Raising three teenage daughters and seeing the world through their eyes 06:46 Uncertain predictions and holding the steering wheel 09:55 Why the humanization of work is not a new conversation 16:19 Zooming out to the purpose of a business 21:13 Rethinking teams, the employment contract, and skills architecture 27:30 Skills passports, internal mobility, and learning organizations 31:51 Moving past the fad diet phase of AI 36:20 Playing the long game on AI returns 40:29 Hands, heads, and hearts: deciding where AI belongs 45:13 Earning the right to be carried forward 50:18 Governance as a hero and safe ways to start 52:30 Mother of Disruption and what people deserve Jess Von Bank: https://linkedin.com/in/jessvonbank/ Now to Next: nowtonext.ai Jess’s Substack: https://jessvonbank.substack.com Mother of Disruption: https://motherofdisruption.ai/ 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

Sharawn Tipton, Chief People Officer at Greenhouse, joins Bob to discuss how AI can widen access to opportunity in hiring when it’s built with fairness and accountability. Drawing on a career spanning recruiting, compensation, and diversity leadership, Sharawn explains why the resume has become a weak signal, how AI voice interviews can give more candidates a fair shot, and why every hiring decision must trace back to a human owner. They explore cognitive diversity as a driver of innovation, using AI to find and fix pay gaps, and why HR leaders now sit at the center of AI transformation. Sharawn closes with advice for HR peers and job seekers. Keywords Sharawn Tipton, Greenhouse, Chief People Officer, talent acquisition, AI interviews, voice AI, Ezra AI Labs, resume signal, candidate trust, transparency, explainability, structured hiring, responsible AI, inclusion by design, employee resource groups, cognitive diversity, pay equity, AI fluency, AI readiness, change management, learning agility, dream job Takeaways AI-polished resumes have diluted the signal, while AI interviews can bring more candidates into the conversation. Closing the candidate trust gap requires authentic experiences and clarity about where AI is used and where humans decide. Every AI-assisted decision needs a named human owner, and every outcome should be explainable. Testing tools with employee resource groups helps ensure products work for people with different accents and communication styles. Diverse perspectives in decision-making lead to better products, new markets, and stronger innovation. AI can surface pay outliers faster, but it scales existing inequities if no one applies judgment. Leaders must model AI use openly, including their missteps, for adoption to take hold. Job seekers should get clear on what they want, apply early, and keep learning while they look. Quotes “AI has allowed everyone to create an amazing resume, but what it also does is it waters down the signal that you get from that resume.” “Every decision has to have an owner. And every outcome has to be traced back to a human decision.” “Yes, AI is gonna give you the recipe, but we still need you to cook.” “We want to make sure your pay is not based on your ability to negotiate.” “If you’re a good steward of DEI, you’re a good steward of AI because you’re inherently going to know that there could be bias.” “Now you’ve got agents, right? And so it’s also who you managed and what did you build.” Chapters 00:01 Welcome and introductions 00:57 From recruiter to Chief People Officer 03:13 Responsible innovation and Greenhouse as customer zero 05:01 AI voice interviews and the weakening resume signal 07:21 Closing the candidate trust gap 10:59 Explainability, accountability, and inclusion by design 16:28 Building AI fluency and discernment across the company 20:08 Cognitive diversity and innovation 26:13 Using AI to advance pay equity 30:26 Why AI scales existing inequities 33:09 HR’s seat at the AI transformation table 37:05 Leaders modeling AI readiness 41:35 Easing AI anxiety and reinvesting freed capacity 48:28 Advice for HR leaders and job seekers Sharawn Tipton: https://www.linkedin.com/in/sharawnconnors Greenhouse: https://greenhouse.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

Boris Khazin, AI governance and digital risk advisor and author of 'The Robopsychologist's Problem: Governing AI in the Room not the Machine', joins Bob to explore why AI that finds the most efficient path to a goal demands a new approach to governance. Drawing on behavioral economics and decades in financial services, Boris explains why oversight must shift from one-time audits to continuous monitoring, and why controls belong in the environment around AI rather than inside it. They discuss shadow AI, the limits of compliance training in an era of cloned voices, biased training data, and where humans should sit in the loop. Boris closes with three things every risk and compliance leader should do tomorrow morning. Keywords Boris Khazin, The Robopsychologist's Problem, AI governance, governance risk and compliance, behavioral economics, game theory, agentic AI, continuous monitoring, guardrails, shadow AI, shadow IT, AI literacy, voice cloning, human in the loop, flash crash, training data bias, incident disclosure, AI regulation, value at risk, Isaac Asimov Takeaways Governing AI agents can't be a one-time audit; it requires continuous assessment and monitoring. AI optimizes for the task it's given without the moral and social pressures that restrain people, so "cheating" is a logical outcome, not intent. Effective controls live in the environment, not the model: limit access, block development capabilities, and make any exception temporary and approved. Shadow AI is a signal. Ask why people go around sanctioned tools and give them a non-punitive way to request alternatives. Training reduces risk but won't stop a convincing cloned voice; processes must let junior staff verify and say no without consequences. Removing human checkpoints eliminates the pause that once caught errors, so place humans where judgment matters, not on hundreds of routine approvals. Translating AI risk into value at risk lets leaders make real business decisions instead of reacting to color-coded scores. Quotes "Because its goal is to achieve the task. If you're turning it off, it cannot achieve the task. Turning it off is bad." "Requiring people to disclose events in no way is it gonna nerf the development of AI or anything like that." "Because look, if two out of a thousand people want a tool, totally understandable. If four hundred out of a thousand people want the tool, something is wrong." "Make sure your agent doesn't have development capabilities. Because even though you put guardrails on it, it can create an agent that doesn't have those guardrails." "Cybersecurity, especially around the AI companies, is designed to protect from outside coming in." "Look, we're gonna sit down and we're gonna figure out what is the value at risk if you don't close this risk. And then it becomes a business decision." Chapters 00:01 Welcome and introductions 00:21 From behavioral economics to AI governance 04:00 How regulated industries stack up on AI readiness 06:01 Why agents demand continuous governance 09:12 Behavioral economics and AI that cheats the test 15:55 Incident disclosure and the case for regulation 17:27 Inside The Robopsychologist's Problem 21:48 Guardrails, escalation, and the limits of human review 24:41 Shadow AI and asking why people go around tools 30:33 AI literacy, cloned voices, and verification 36:33 Biased training data in regulated industries 42:00 Agent limits, human in the loop, and restoring the pause 48:56 Why AI incidents go unreported 56:38 Value at risk versus red, yellow, and green scores 59:24 Three steps for compliance leaders tomorrow Boris Khazin: https://www.linkedin.com/in/boris-khazin-0391931/ The Robopsychologist’s Problem: https://www.amazon.com/dp/B0HKD1632C 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

Gary Hanley, Senior Vice President of US Talent Strategy and Recruiting at MCS Group USA, joins Bob to explore how AI is reshaping the recruiting profession and the talent market it serves. Drawing on a nonlinear career spanning Sun Microsystems, government, and global location strategy, plus his graduate teaching at Northeastern University, Gary explains why recruiters must move from filling requisitions to true talent advisory. Bob and Gary discuss hiring for roles that may change within 18 months, the erosion of the resume as AI-enhanced applications flood the market, and why assessments of curiosity and critical thinking matter more than ever. They also cover why early-career hiring remains essential, how global location strategy reduces talent risk, and why trust with candidates is built through community and repeated engagement. Keywords Gary Hanley, MCS Group, Northeastern University, talent strategy, recruiting, talent advisory, AI readiness, AI engineer, location strategy, skills-based hiring, assessments, AI-generated resumes, early-career talent, responsible AI, EU AI Act, talent pipeline, candidate trust, future of recruiting Takeaways AI is automating transactional recruiting tasks like sourcing and scheduling, pushing agencies and TA teams toward advisory work such as market mapping, compensation benchmarking, and role design. Organizations confident in their AI readiness take a multi-threaded approach, combining recruitment with internal and external training across every role. Hiring for roles that may look different in 18 to 24 months means prioritizing durable skills, learning mindset, and cultural fit over current job specs. AI-customized resumes are undermining the resume’s value, so employers are shifting focus to assessments and asking candidates how they use AI as a thought partner. Pausing early-career hiring is a mistake; graduates adopt AI tools quickly and remain essential to a sustainable talent pipeline. Concentrating AI hiring in a few competitive cities increases attrition and salary inflation risk, making global location strategy a risk-management tool. Trust with candidates is built over time through transparency, community events, and passive talent pipelines. Quotes “How do you hire for roles that you’re not sure if that role will be there in eighteen to twenty four months.” “You can use a term like AI engineer and it could mean three different things in three different organizations” “Many of the candidates we’re working with of course will have multiple offers at any point in time so they’re making a decision why this particular organization fits their career goals.” “It’s encouraging to hear it’s not…apocalyptic for early stage talent coming out into the market. They certainly have value.” “Trust comes from repeated engagements. All good things start with conversations” Chapters 00:02 Welcome and introductions 01:06 Gary’s nonlinear career and MCS Group 04:38 AI shifts recruiting from transactional to advisory 07:14 Assessing AI readiness and in-demand skills 10:37 Teaching consulting and hiring for evolving roles 15:24 Transferable skills and selling candidates on strategy 20:16 Role definitions, compensation, and talent data 25:13 Resumes in the age of AI 29:45 Responsible AI, regulation, and global hiring 36:42 Assessing durable skills beyond the resume 40:02 Why early-career talent still matters 44:46 Building trust and community with candidates 51:16 The future of the AI-augmented recruiter 58:47 Final thoughts Gary Hanley: https://www.linkedin.com/in/gary-hanley MCS Group USA: https://mcsgroupusa.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

Dr. Adrian Wolfberg spent four decades in national security, from flying Navy reconnaissance missions to thirty years at the Defense Intelligence Agency, while researching how people and organizations make decisions. He sits down with Bob Pulver to explain why silos and mismatched language have always undermined shared understanding, and why AI raises the stakes. Rather than reaching for the tool first, Adrian argues leaders must invest time up front to understand the problem, then match the right mix of human and AI to it and keep adjusting as reality changes. They also explore the uniquely human strengths worth protecting, from framing and empathy to accountability and critical thinking, and why reading may be key to developing them. Keywords Adrian Wolfberg, Who Leads When AI Thinks, Organizational Insight Consulting, decision making, leadership, national security, defense intelligence, silos, cognitive diversity, problem framing, human-AI balance, complexity, wicked problems, novelty, responsible by design, empathy, accountability, critical thinking, creativity, reading, judgment, decisions, collective intelligence Takeaways Where you sit determines what you see, and different organizational languages make shared understanding hard even before AI enters the room. Unlike past tools, AI lacks transparent inputs, processes, and outputs, so leaders can no longer default to "just get me the tool." Time spent framing a problem up front prevents costly rework and helps match the right degree of human and AI involvement. Problems that are novel, complex, and value-laden demand far more human involvement than closed, well-understood systems. The leader's role shifts from knowing the answer to setting conditions so everyone can spot change and help reframe. Accountability must stay with humans, no matter how sophisticated the AI becomes. Reading and mathematics build the creative and critical thinking we can least afford to offload. Quotes "Do the words mean the same thing from different organizations? And do we even care about the same things?" "We can't compete with these aspects of AI and nor do we want to default everything to these aspects of AI." "The leader's responsibility is not anymore, I know the answer, here's the solution, go forth, implement it via the resources that we have available to us." "We don't want to just default accountability to AI. I don't think humans will allow that to happen." "Reading offers the mind an opportunity to wander as one is thinking about these things." "Preserving critical and creative thinking, that would be the worst thing to give to a machine and offload..." Chapters 00:01 Welcome and introductions 00:47 From Navy reconnaissance to decades in defense intelligence 05:17 Silos, language, and the struggle for shared understanding 10:59 Why AI is unlike any tool before it 13:21 Leaders making time to understand the problem 15:18 Matching the right mix of human and AI 19:14 Cognitive diversity, bias, and choosing the right AI 23:41 Sizing up problems by novelty, complexity, and values 29:22 Unknown unknowns and being responsible by design 33:20 The risk of handing too much over to AI 35:31 Framing and reframing as uniquely human strengths 39:42 Empathy, setting conditions, and accountability 45:39 Protecting critical and creative thinking 46:42 Preparing the next generation and managing agents  51:03 Reading, smartphones, and AI in schools 57:12 Where to find Adrian and final thoughts on valuing time Dr. Adrian Wolfberg: https://www.linkedin.com/in/adrianwolfberg Organizational Insight Consulting: https://www.oicllc.org/ “Who Leads When AI Thinks”: https://www.amazon.com/dp/3032197163 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

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

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

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

Bob is joined by Dr. Gleb Tsipursky, better known as Dr. Gleb, CEO and founder of Disaster Avoidance Experts and author of the new book “The Psychology of AI Adoption at Work: From Resistance to Results”. Drawing on over a hundred consulting projects and thousands of survey responses, Dr. Gleb explains why roughly 95 percent of AI pilots fail to show ROI, arguing the real obstacle is not the technology but three psychological profiles driving resistance: fear of job loss, threats to professional identity, and shame around quietly using AI in the shadows. They discuss how forced AI mandates can backfire into deliberately sloppy output, why untrained junior employees produce polished but poorly reasoned work, and the widening gap between how executives and everyday employees experience AI adoption. Dr. Gleb closes with practical fixes, from training people on tasks they hate first to having leaders model and reward AI usage openly. Keywords AI adoption, cognitive biases, decision science, psychology of AI adoption at work, change management, AI alarmists, pragmatic resistors, reluctant adopters, shadow AI use, AI slop, malicious compliance, psychographic profiles, identity threat, Office Whisperer, Disaster Avoidance Experts, MIT pilot study, Pew Research, leadership communication, AI training Takeaways 95 percent of AI pilots fail to show ROI because leaders treat a psychological challenge as a technical one Three psychographic profiles drive resistance: AI alarmists who fear job loss, pragmatic resistors who feel identity threat, and reluctant adopters who hide shameful shadow AI use Forced AI mandates can trigger malicious compliance, where resistant employees deliberately produce sloppy output to prove the tool does not work Untrained junior employees produce polished looking but poorly reasoned AI output, widening a generational skills gap Executives and rank and file employees experience AI adoption very differently, a split reality that hides the fear driving resistance Leaders reduce resistance by modeling their own AI usage publicly and rewarding employees who share new use cases Quotes It's not the technology that's a challenge. It's psychology. The technology is great. But the social stigma is high. You go slow to go fast. The leaders need to model AI usage. They need to talk about it. The crucial thing is leaders know that most people aren't engaged in their work. Chapters 00:03 Welcome and introductions 00:54 Dr. Gleb's background and the new book 07:00 Why 95 percent of AI pilots fail to show ROI 08:58 The real barrier is psychology, and the three resistance profiles 12:57 A real world story of shadow AI use and hidden shame 18:30 AI slop, malicious compliance, and untrained junior employees 27:37 The split reality between executives and employees 31:48 Reaching the AI alarmists and pragmatic resistors 38:47 Reaching the reluctant adopters through modeling and reward 52:01 Workshops, the DIY approach, and closing thoughts Dr. Gleb: https://www.linkedin.com/in/dr-gleb-tsipursky Disaster Avoidance Experts: http://disasteravoidanceexperts.com/ The Psychology of AI Adoption at Work: https://a.co/d/0cdkVi76 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