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Elevate Your AIQ

Elevate Your AIQ

By: WRKdefined Podcast Network
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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 Economics Management Management & Leadership
Episodes
  • Ep 140: Shining Light on Shadow AI and Setting Boundaries for Autonomous Agents with Boris Khazin

    Ep 140: Shining Light on Shadow AI and Setting Boundaries for Autonomous Agents with Boris Khazin
    16 Oct 2026
    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
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    58 min
  • Ep 139: Maturing Recruitment and Recognizing AI-Ready Talent with Gary Hanley

    Ep 139: Maturing Recruitment and Recognizing AI-Ready Talent with Gary Hanley
    9 Oct 2026
    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
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    58 min
  • Ep 138: Reframing Leadership and Accountability for the AI Era with Dr. Adrian Wolfberg

    Ep 138: Reframing Leadership and Accountability for the AI Era with Dr. Adrian Wolfberg
    2 Oct 2026
    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
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    58 min
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