Today I’m excited to share my conversation with Andrew Brooks, founder of Contextual, an AI solutions company built around the idea that businesses should build and control their own AI capabilities rather than lock themselves into a single provider. Andrew’s path here runs through decades of entrepreneurship, operations, and innovation, including building and selling a smart home company (SmartThings) to Samsung, and he brings a grounded perspective on where AI genuinely changes how businesses operate versus where the hype outpaces reality.

We get into how smaller and mid-sized companies can compete using AI, how the skills employers are looking for are shifting, and why staying curious and hands-on with these tools matters more than ever.

Couple of teasers for you.

First, Andrew outlines the impetus for starting Contextual as an AI orchestration layer and the value of owning your technology…

Then Andrew and I talk about the balance of intelligent technology and human potential…do you really know what your talent is capable of?

Definitely check out the full conversation, though, as it’s full of real-world lessons and a healthy dose of optimism about what’s ahead.

Thanks, as always, for listening and subscribing to Elevate Your AIQ!

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Summary

Andrew Brooks, founder of Contextual, joins Bob to trace a career that runs from early-internet consulting through three exits (Seven Space to Sun Microsystems, a marketing company to ReachLocal, and SmartThings to Samsung) before landing on AI. Andrew explains Contextual’s “own your AI” philosophy, why businesses should design, build, and operate their own systems rather than lock into a single model provider, and how real transformation comes from deepening a company’s data, process, or relationship moats rather than chasing cost takeout alone. They dig into real client stories, from a commercial refrigeration estimator’s tacit knowledge to a vacation rental company that discovered unexpected revenue recovery through AI-audited work orders. The conversation closes on what’s shifting for engineering talent, why “human in the loop” needs more precision, and why waiting for the perfect model is a losing strategy.

Keywords

Contextual, Andrew Brooks, own your AI, agentic AI, AI orchestration, mid-market businesses, AI moats, model selection, Digital Greg, tacit knowledge, automation vs facilitation, human in the loop, agent sprawl, AI governance, private equity, Southfield Capital, system design, engineering talent, responsible AI by design, SmartThings, Seven Space, MCP, rational optimism

Takeaways

• “Own your AI”: build a system-agnostic layer instead of locking into one model or provider

• Durable AI investments deepen an existing moat, whether data, tacit knowledge, or relationships, not just cut costs

• Automation builds trust and adoption, but resist treating AI as a hammer for every problem

• Well-designed systems surface second and third order value nobody planned for

• Talent is shifting toward system designers who can spot edge cases and challenge AI outputs

• Waiting for a “perfect” model is a losing strategy given the pace of change

Quotes

• “The phrase we use is own your AI. Do not become too embedded in a single provider or a single model, because you need to be able to react to what’s happening in the space.”

• “Not everything’s an AI problem. Some things are process, and some things are just workflow.”

• “You can’t wait for the perfect model. The models are revving every ten, fifteen days. The pace of change is just too fast. You need to get into the river.”

• “AI can be confidently wrong, and very confidently wrong. You’ve got to be able to see that and flag it.”

• “I’m in the rational optimist camp here. AI might change jobs, but we’ve been changing jobs for many, many years.”

Chapters

00:01 Welcome and introducing Andrew Brooks

00:35 From Accenture to entrepreneurship: Seven Space, Reach Local, and SmartThings

03:45 Landing on AI and founding Contextual

04:41 Design, build, operate: how Contextual works with clients

08:33 Choosing the right model without over-committing to one provider

10:04 Beyond chatbots: agentic systems and finding your AI moat

12:36 Automation as an on-ramp to bigger AI thinking, and avoiding the shiny-hammer trap

17:58 Systems thinking, from Smart Things to agentic infrastructure

21:27 Responsible design, client collaboration, and unexpected value from clean data

28:19 Bad data, bad processes, and why waiting for the perfect model is a mistake

30:00 Where humans stay central and what “team superpowers” means

35:51 Vacation rental case study: audits, revenue recovery, and upsell insight

41:50 Getting acquired by a PE firm and what it means for AI adoption

45:20 Tool sprawl, governance, and rethinking “human in the loop”

51:45 Engineering talent, adaptability, and the Stripe MCP lesson in trust

Andrew Brooks

Contextual.io

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You also can find the Elevate Your AIQ podcast, along with tons of other great shows, on the WRKdefined Podcast Network!

Today I’m joined by Katherine von Jan, better known as KVJ, CEO and Co-founder of Tough.Day, an AI workplace advisor that gives employees and managers confidential, personalized coaching to navigate real workplace challenges.

KVJ is a veteran technology executive whose career includes Lotus Development Corp and IBM (where despite both of us being involved in social technologies and the future of work, somehow we never met), and Salesforce, where she was the Chief Strategy Officer for their innovation division, before co-founding Tough Day.

We get into what it actually takes to build AI that reflects an organization’s values, why ethical review and red teaming matter just as much as the innovation itself, and how thoughtful research design can uncover what great management really looks like.

If you care about building AI that earns trust rather than erodes it, this one’s for you, so go give it a listen. I always learn a lot from my conversation with KVJ, and I loved this open dialogue with her. I know you will too.

Here’s a teaser where KVJ outlines the rationale and three core tenets of her human positive framework:

Thanks, as always, for spending time with us on Elevate Your AIQ!

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Summary:
Katherine von Jan (KVJ), CEO and Co-founder of Tough.Day and a longtime innovation leader across Lotus Development, IBM, Salesforce, and multiple startups, joins Bob to trace a career built on one consistent thread: putting culture and human potential at the center of technology. The conversation covers the perils of workforce surveillance AI, why “human in the loop” has become a nearly meaningless phrase without real definition, and how KVJ’s Human Positive Company framework gives organizations a way to evaluate whether their AI and culture choices are actually earning trust. They dig into the ethical review process that killed a risky Salesforce AI project (and the better one that replaced it), how KVJ’s earlier startup RadMatter tackled bias against non-Ivy League candidates, and what her team learned about great management while building the AI behind Tough Day. It’s a wide-ranging, practitioner-level conversation about responsible innovation, moral leadership, and what it actually takes to build AI people can trust.

Keywords: human-centric AI, responsible AI, AI governance, workforce surveillance, human in the loop, AI ethics, Human Positive Company framework, Tough Day, Tuffy, RadMatter, Salesforce, IBM, Lotus Development, Irene Greif, talent acquisition, hiring bias, quality of hire, employee trust, ethical review, red teaming, collective intelligence, workplace culture, moral leadership, AI slop, skills-based hiring, retention

Takeaways:

• KVJ’s path from anthropology and Lotus Development (working for Irene Greif) through IBM, Salesforce, and now Tough Day traces one consistent thread: technology in service of culture and human potential

• “Human in the loop” is losing meaning as a governance concept; every stage of a workflow, like a recruiting funnel, is a decision point that either includes or excludes real human judgment

• Workforce surveillance AI, tools that flag “risk” signals across email, Slack, and HR systems, is a dangerous use case that erodes trust rather than building it

• Responsible innovation requires research and ethical review before deployment, not just fast iteration; Salesforce’s own attrition-prediction AI backfired until it was redesigned into a re-recruiting tool instead

• KVJ’s Human Positive Company framework evaluates organizations across three pillars: workforce ingenuity, positive-sum prosperity, and the ethical and humane use of AI

• RadMatter, her earlier startup, aimed to give overlooked and non-Ivy-League students visibility with employers, a problem that still shapes bias in AI-driven hiring today

• Building AI that reflects an organization’s values starts with defining those values clearly and creating a real process, not just a poster on the wall, for employees to raise concerns

• Great management often looks like curiosity, asking more questions before offering answers, a pattern KVJ observed directly while researching how to train Tough Day’s AI

Quotes:

• “A coalition is designed to go solve something.”

• “You don’t just go build the app. You build the research first.”

• “A lot of organizations have values written on the wall and that’s as far as it goes.”

• “We’re getting AI slop, and we’re getting process slop, and we’re getting application slop.”

• “Every employee is responsible for understanding, what am I complicit in?”

• “Human in the loop is almost meaningless at this point. What is the loop? And where is the human in said loop?” (Bob)

Chapters:

00:01 Welcome and introductions

01:00 KVJ’s path into tech: anthropology, Lotus Development, Irene Greif, and IBM

08:09 The strange LinkedIn deactivation and the leap to Salesforce

12:26 Comparing culture and tools across IBM, Salesforce, and beyond

15:13 Early social network analysis and today’s AI parallels

18:32 Where to draw the line: what AI should do, not just what it can

21:27 Workforce surveillance AI and the danger of thinning out the workforce

25:47 Responsible innovation and human-positive AI

29:34 Inside the Human Positive Company framework

33:32 Measuring what matters: retention, morale, and moral leadership

37:05 Rethinking human in the loop across the recruiting funnel

38:26 RadMatter and surfacing overlooked talent

43:26 Building governance: ethics committees and guardrails

47:06 Training Tough Day’s AI on values, culture, and what research reveals about great management

57:20 Closing thoughts and a call to action

KVJ: https://www.linkedin.com/in/kvonjan

Tough.Day

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Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com

You also can find the Elevate Your AIQ podcast, along with tons of other great shows, on the WRKdefined Podcast Network!

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In this episode, I’m joined by my friend and Human-Centric AI Council collaborator Trent Cotton, for a long overdue and brutally honest conversation about AI in hiring.

Trent is currently the Head of Talent Insights and Analyst Relations at iCIMS, and beyond his day job, Trent is a multi-time author and the host of The Human Capitalist podcast. He brings more than two decades in talent acquisition along with a refreshingly data-grounded, no-excuses take on why hiring feels so broken right now - and why AI tends to be more of a mirror than a culprit.

We get into the real tension between experience and skills, what the latest workforce data is actually telling us, and why the next generation coming out of school may be far more ready for this moment than we give them credit for.

Not one but two teasers for you…

This one is about being much more deliberate in how, where and why you’re using AI for in talent acquisition. Do you have the humility to have your own biases exposed?

This one is about how we think about AI readiness and adaptability in the current and future workforce: divest in early career hiring at your own peril. Also, can we move the resume into an assisted living facility yet?

Give the full episode a listen, and thank you, as always, for being part of the Elevate Your AIQ community.

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Summary

Bob catches up with Trent Cotton, Head of Talent Insights and Analyst Relations at iCIMS, for a data-grounded look at why hiring feels so broken right now. Drawing on iCIMS workforce data, Trent unpacks a widening gap between job openings and actual hires, the rise of “job hugging,” and application volumes falling below last year. The conversation digs into the real culprit behind entry-level frustration: a decades-old habit of confusing years of experience with actual skill, which AI is now exposing and scaling rather than causing. Trent makes the case for blowing up the traditional job ad in favor of a transparent scorecard, and for using AI to surface hidden bias and predict success rather than just automate the old process. They close on an optimistic note about Gen Z teaching themselves AI skills and why it may finally be time to retire the resume.

Keywords

talent acquisition, skills-based hiring, experience versus skills, job hugging, iCIMS workforce report, three-line report, entry-level hiring, Gen Z, early career, AI in hiring, recruiting bias, responsible AI, AI interviewer, job scorecard, job description, AI sourcing, quality of hire, retention, workforce data, future of work, Trent Cotton, Bob Pulver, Elevate Your AIQ

Takeaways

• Job openings are rising faster than hires while application volume dips below last year, pointing to job hugging and recruiting teams stretched past their limits

• The “experience” bar is often a poor proxy for skill, a problem that predates AI by decades

• Skills-based hiring only works if organizations stop assuming years of experience are directly proportional to ability

• The job ad should be rebuilt as a transparent scorecard that candidates see going in and that drives consistent scoring across every interviewer

• AI does not create hiring bias so much as expose and scale the bias already there, and it can also help detect and coach against it (recency bias, manager patterns, and more)

• AI sourcing can pressure-test unrealistic requirements before a role is ever posted, turning recruiters into advisors rather than order-takers

• Gen Z is teaching itself AI skills and taking ownership of continuous learning, making it an overlooked and ready talent pool

• Fixing retention starts in the hiring process, by confirming candidates are not just qualified but genuinely want the role

Quotes

• “We’ve been looking at experience, assuming that skills are directly proportional to the number of years of experience.”

• “You can be working for 10 years at something and still suck at it.”

• “The only thing that’s different with AI is it’s gonna find them, expose them, and scale them.”

• “You just don’t know until you give people a chance.”

• “The resume needs to be retired. It’s well past its retirement age.”

Chapters

00:02 Welcome and reconnecting

01:29 Trent’s non-linear path from banking to HR

03:46 The unicorn role and the talent insights program

05:26 A new book and five mindsets for HR

06:40 What the market data reveals about hiring

08:53 Job hugging and a cautious candidate market

10:18 The experience trap and five years of LLM experience

14:01 Gen Z and the mid-level experience expectation

16:24 Skills versus experience and the self-taught coder

21:25 Blowing up the job ad and building a scorecard

29:17 The bias conversation AI is not having

36:26 A balanced narrative and smarter sourcing

44:43 Gen Z teaching themselves and the education gap

52:07 Retiring the resume and closing advice

Trent Cotton

iCIMS

Human Capitalist

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠

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Today I am delighted to share my conversation with Diane Weaver, co-founder of Baryons and a seasoned edtech entrepreneur whose career has been defined by one throughline: helping people see themselves and their potential more clearly.

Baryons is a voice-based AI platform designed to support the human dynamics that drive alignment, decision-making, and execution. Diane and I talk about what it actually looks like to build AI that puts human agency at the center, why she believes the real crisis facing today’s workforce is not a technology problem but a human one, and how Baryons is trying to give every employee access to the kind of personalized support that has historically only been available to people at the top.

If you believe that the future of work depends on how well we use AI to maximize human potential, this is a conversation you will not want to miss.

FUN FACT: Diane’s grandfather is Pat Weaver, an Emmy award-winning member of the Television Academy Hall of Fame, credited with founding NBC’s ‘Today’ and ‘The Tonight Show’!

Below is a teaser where Diane outlines a leadership challenge and the six facets of organizational resilience that Baryons works with their customers to address…

Thank you so much for tuning in and being a part of this community!

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Summary

Diane Weaver, co-founder of Baryons, brings a career defined by translation — across languages, disciplines, and roles — to what she describes as the most important problem she has ever worked on: human flourishing in the age of AI. Drawing on her background in edtech, linguistics, and startup leadership, including the founding and acquisition of CourseTune, Diane shares how a post-exit identity crisis and the release of ChatGPT converged to spark the idea for Baryons. The platform is a voice-based AI companion designed to support mental wealth, resilience, and human agency through four modes: daily check-in, checkout, thinking partner, and flourishing partner. Diane and Bob explore the science behind the product, the organizational dysfunction it is built to address, and why Baryons was designed from day one to get people off AI and back into meaningful connection with other human beings.

Keywords

Baryons, human flourishing, mental wealth, human agency, resilience, voice AI, executive coaching, organizational health, edtech, CourseTune, systems thinking, neuroplasticity, burnout, resonance report, team dynamics, responsible AI, technology stewardship, Diane Weaver

Takeaways

• Baryons is a voice-based AI companion that supports individual and organizational health through four modes: check-in, checkout, thinking partner, and flourishing partner

• The platform democratizes access to a daily practice long associated with high performers and executive coaching, making it available to every employee at $20 a month

• Baryons uses a patent-pending approach to memory, allowing it to surface patterns and prior conversations in ways that build continuity and accountability over time

• Weekly resonance reports give individuals and teams insight into energy levels, recurring themes, and early indicators of burnout — without exposing individual conversations

• The product is built on six well-researched domains of organizational health: coordination, shared reality, early risk visibility, decision quality, engagement, and learning velocity

• Diane frames the current moment not as a technology problem but as a human one, and argues that organizations fixated on productivity metrics are missing the signals that actually predict team resilience and long-term performance

• Baryons is intentionally designed to be non-addictive and non-affirming — it is built to help users identify root causes and reconnect with human beings, not keep them talking to an AI

Quotes

• “I really want to be working with people who want to be doing something that was impossible to do before.”

• “We talk about ourselves as being the first AI that is truly built and designed to get people off of AI and back connecting with human beings in the real world.”

• “The interface is more of your inner world than anything else.”

• “It doesn’t matter that an individual resonance — or even a team — is always trending up. When everything’s always trending up, you know as a leader they’ve gamed the system.”

• “Those six functions of the organization have to be repaired — or they may survive all of this tech disruption and still be dysfunctional.”

Chapters

00:02 Welcome and introductions

00:46 Diane’s background: from the family farm to edtech and entrepreneurship

10:11 The paparazzi story: Pat Weaver, the Today Show, and a legacy of democratizing technology

12:41 The genesis of Baryons and the post-ChatGPT moment

15:33 Human agency as the core design principle

18:26 How Baryons works: voice-first design and the check-in mode

21:48 Shifting from technology users to technology stewards

23:38 The checkout mode: cognitive offloading and ending the workday with clarity

26:01 The thinking partner and flourishing partner modes

29:48 Democratizing executive coaching and the value of a non-judgmental AI

36:47 Why voice is the right interface: psychological safety and trust

41:15 A user story: Baryons as a neutral mediator in a fractured friendship

43:29 Mental wealth vs. mental health: resilience as a daily practice

47:34 Resonance reports: individual and team insights, burnout signals, and the limits of productivity metrics

52:00 Choosing the right partners: ethical AI, organizational dysfunction, and the six domains of health

59:51 What’s ahead: Baryons.com, community brain health initiatives, and keeping humans at the center

Diane Weaver: https://www.linkedin.com/in/weaver-diane

Baryons: https://baryons.com/

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com

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Today’s distinguished guest is Daniel Sieberg, co-founder and CEO of ScreenGeni.us, a media discovery-as-a-service platform using AI to help people cut through the noise and find content that actually matches what they’re in the mood for, across streaming, books, retail, and beyond.

Daniel and I met recently in NYC as co-panelists for a human-centric AI event, and I was really intrigued by both his background and his new venture, so of course I wanted to get him in the studio.

Daniel’s varied experiences include journalism as a science and technology correspondent at CNN, CBS, and ABC, to product management, spending nearly a decade at Google building the Google News Lab, to now running an early-stage startup that is putting responsible, human-centric AI to work on one of the most relatable problems out there: the paradox of choice on our screens.

We get into how ScreenGeni.us works, why most recommendation engines have underdelivered despite years of data, what it takes to build AI that respects the user rather than manipulates them, and why Daniel sees this moment not as the AI generation, but as Generation Transition.

If you’ve ever stared at a streaming menu for twenty minutes and thought there must be a way for AI to help, or you’re curious about responsible innovation in the media discovery space, this one’s for you.

Here’s a teaser where Daniel talks about our cross-generational responsibility with data and AI and how seriously his team takes this mission (along with an admission that ‘Gen T’ was not his own concept, but he’ll gladly borrow it)…

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Summary
Daniel Sieberg, co-founder and CEO of ScreenGeni.us, joined the show to discuss how his company is building what he calls a universal navigation layer for human curiosity. Coming from over a decade in broadcast journalism followed by six years at Google, Daniel brings a distinctive perspective on how we search, discover, and consume content. ScreenGeni.us started as a B2C streaming guide and pivoted into a B2B discovery-as-a-service platform, helping companies with large digital catalogs, from books and art to retail and food, surface more relevant recommendations through conversational, intent-driven AI. The conversation covers the gap between what recommendation engines promise and what they actually deliver, the importance of building AI responsibly by design, and the concept of “Gen T,” as in “Generation Transition”, as a framework for shared human responsibility in shaping where AI goes next.

Keywords
Daniel Sieberg, ScreenGenius, discovery as a service, recommendation engines, conversational search, semantic tagging, human-centric AI, responsible AI, paradox of choice, content discovery, B2B middleware, personalization, digital catalogs, Gen T, generation transition, Google News Lab, AI hype cycle

Takeaways

• ScreenGeni.us pivoted from a consumer streaming guide to a B2B discovery-as-a-service platform after recognizing that its recommendation engine had broader value across verticals including books, art, food, and retail

• Most recommendation systems ask users to search like a machine; ScreenGeni.us is building conversational, intent-driven discovery that lets people search more like humans

• The paradox of choice is a core design constraint: once options exceed roughly five, human decision-making breaks down, so narrowing a massive catalog to a meaningful few is the real product

• Enterprise knowledge workers are a second use case: internal discovery tools to help employees navigate large data archives, not just consumer-facing recommendations

• Daniel frames responsible AI not as compliance but as ethos, citing his family history and mission to leave something beneficial to humanity as the throughline behind the company

• “Gen T,” generation transition, reframes the AI debate away from generational blame toward shared responsibility for shaping what AI becomes

Quotes

• “It feels like a rebellious act to fight for humanity these days.”

• “AI is now helping us to search more like a human, which I find fascinating in the discovery evolution of where this is all going.”

• “We like to call ourselves the universal navigation layer for human curiosity.”

• “Business is trust, money is trust, relationships are trust. You’re going to need to talk to a human being.”

• “Gen T is generation transition, and we all have a shared responsibility in thinking that through.”

• “I hope that we champion this responsible AI flag for as long as we’re in existence.”

Chapters

00:02 Welcome and introductions

01:01 Daniel’s career arc from journalism to Google to entrepreneurship

04:53 The origins of ScreenGeni.us and the problem of content overload

08:38 From streaming guide to B2B discovery-as-a-service platform

13:02 Competing with Algolia and moving past the AI hype cycle

15:55 Personalization, intent, and the limits of recommendation engines

20:10 The paradox of choice and narrowing massive digital catalogs

24:14 Breaking down silos and building a universal navigation layer

30:33 Respecting human time and the enterprise knowledge worker use case

40:30 Why human relationships still matter more than vibe coding

43:05 Gen T, generation transition, and shared responsibility for AI’s future

46:32 Responsible by design and the ScreenGeni.us mission

Daniel Sieberg: https://www.linkedin.com/in/danielsieberg/

ScreenGenius: screengeni.us

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com

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I am thrilled to welcome back Noelle London, founder and CEO of Illoominus, an AI-native workforce intelligence platform that helps organizations connect their people data across HR, finance, and operations into a single, secure, governed view.

Noelle originally joined the show just over a year ago, and she is back today to catch up on what’s been happening in the AI in HR Tech space, and to tell us about the exciting agentic workforce intelligence capabilities that Illoominus launched this week.

We get into how decision cycles are compressing across organizations, why the gap between AI experimentation and enterprise-ready deployment is where so many companies are getting stuck right now, and how proactive, agent-driven insights are starting to replace the static dashboard as the primary way leaders stay on top of workforce change.

This is the kind of conversation that reminds me why I do this show: shining well-served light on a brilliant female founder and her team making a real impact. Illoominus is building something that is reshaping how leaders make decisions about their people, and I loved hearing about their forward-thinking customers already taking full advantage of these new capabilities.

Here’s a teaser where Noelle explains what Illoominus means by ‘agentic workforce intelligence’ for all types of talent data and decisions where alignment is crucial…

If you are a people leader, a finance leader, or honestly, any executive trying to make better, data-driven decisions faster, this one is for you. Thanks for listening!

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Summary

Noelle London, founder and CEO of Illoominus, returns to Elevate Your AIQ just over a year after her first appearance to share what has changed and what Illoominus has built in response. The conversation covers how decision cycles inside organizations are compressing, why AI adoption has accelerated but also created new governance risks, and how the gap between individual experimentation and enterprise-ready deployment has become the defining challenge for people leaders today. Noelle details the launch of Illoominus Agentic Workforce Intelligence, a capability already in production with customers that delivers proactive, AI-generated insights directly into executive workflows rather than waiting for someone to go find them in a dashboard. The discussion closes on the importance of governed, secure AI environments as organizations move from pilots to scale, and why data alignment across HR, finance, and operations remains the foundation everything else depends on.

Keywords

Noelle London, Illoominus, workforce intelligence, agentic AI, people analytics, HR data, workforce planning, talent acquisition, data governance, AI adoption, decision support, workforce transformation, future of work, data literacy, AI readiness, responsible AI, executive reporting

Takeaways

• Decision cycles across HR, finance, and operations are compressing rapidly, making real-time workforce data no longer a nice-to-have but a business requirement

• The gap between AI experimentation at the individual level and governed, enterprise-ready deployment is where most organizations are getting stuck right now

• Illoominus Agentic Workforce Intelligence delivers proactive, contextualized insights directly into executive inboxes, shifting the model from reactive dashboarding to continuous intelligence

• Data alignment across functions, getting HR, finance, and ops working from a single trusted source, is the prerequisite for any meaningful workforce analytics initiative

• Governed, secure AI environments are essential as agentic tools scale, particularly around access levels, data privacy, and agent-to-agent communication

• Consultants are increasingly embedding Illoominus as the analytical backbone of engagements, shifting their own value toward change management and strategy

Quotes

• “The puzzle pieces weren’t talking, and so that’s first and foremost, it doesn’t really help to have something very interesting if it’s not connected together.”

• “Every single week, every single person on their executive leadership team are getting AI-driven insights into their inboxes to help them understand what’s going on.”

• “You’re not getting graphics, you’re getting the full understanding on are we good, or is this something that we need to pay attention to.”

• “HR doesn’t have a different version of headcount than finance does. Those are very real examples of where we’ve seen some of these data initiatives stall.”

• “How do you make sure if you’re using AI within the organization, it’s governed properly so that you’re not using these tools as a pass through for people that shouldn’t have access to information.”

• “It’s the end of everything as we’ve known it, and change management with the amount of technology that companies are adopting, that’s a really interesting place for consultants to play.”

Chapters

00:02 Welcome and guest introduction

00:46 Illoominus origin story and the data connectivity problem

04:25 How Illoominus complements rather than competes with consultants

08:07 A year of change: compressed cycles, AI adoption, and new organizational pressures

15:12 Expanding self-serve insights across the leadership team

21:29 Launching Illoominus Agentic Workforce Intelligence

26:17 Accelerating business cases through data alignment across HR and finance

30:22 The full data picture: talent acquisition, skills, engagement, and beyond

36:51 Industry fit and the profile of an Illoominus customer

39:28 How executives interact with agentic insights

43:52 AI readiness, governance, and moving from experimentation to scale

49:52 Closing reflections and what comes next

Noelle London: https://www.linkedin.com/in/noellelondon

Illoominus: illoominus.com

For AI readiness advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com

You also can find the Elevate Your AIQ podcast, along with tons of other great shows, on the WRKdefined Podcast Network!

Today I’m joined by Yurii Lazaruk, digital community consultant and independent talent advocate, who has spent more than a decade building and leading professional communities across Europe, most recently as a core team member behind the Freelance Unlocked conference.

Yurii and I explore the rising freelance economy, the role of community in sustaining independent careers, and why human connection remains the foundation of a thriving professional network even as AI reshapes how and where we work. In fact, I have found communities to be essential for elevating my own AIQ.

If you’re navigating the shift toward more human-centered ways of working across all types of employment and methods of collaboration, this is an insightful conversation. Below is a teaser where Yurii passionately highlights the importance of community and human-to-human connection:

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Summary

Yurii Lazaruk built a decade-long career in community management before he even knew the profession existed, starting with a grassroots SEO forum in Ukraine and scaling to a 700-person sales conference before co-founding the Freelance Unlocked conference in Europe. Bob and Yurii explore how freelancers are using AI to function as one-person teams of twenty, while warning that the same tools are eroding the human connections that make independent careers sustainable. They examine the tension between AI-driven hiring automation and the cultural fit that determines whether a freelancer truly succeeds with a client. Yurii’s throughline is a conviction that human energy is something no tool can replicate or replace.

Keywords

Yurii Lazaruk, Freelance Unlocked, independent talent, freelance economy, community management, digital twins, AI in hiring, human connection, loneliness epidemic, solopreneur, co-opetition, AI literacy, second brain, talent acquisition, future of work

Takeaways

• Freelancers embracing AI literacy are scaling from solo operators to multi-agent teams, but human judgment remains non-negotiable for quality and trust

• The AI arms race in hiring, where both job descriptions and applications are machine-generated, strips out the human signal that determines cultural fit

• Digital twins are already being used by freelancers to handle early-stage client conversations, creating efficiency gains alongside new credential fraud risks

• Community is a structural necessity for independent workers, especially as AI-driven isolation deepens the broader loneliness epidemic

• AI works best when you already understand the domain; without foundational knowledge, tools can mislead as easily as they assist

• Pre-conference rituals including WhatsApp groups, LinkedIn introductions, and short pre-event Zoom meetups drive Freelance Unlocked’s 50-plus percent return rate

Quotes

• “I was doing community [work] for over ten years without knowing it was called community. I was just thinking it was meeting people and having fun together.”

• “There is an AI fight happening. Recruiters go to ChatGPT for job descriptions and applicants go to the same tools, and we are losing the human connection part.”

• “If your second brain is smarter than your first brain, you stop learning and move nowhere. You have to continuously grow.”

• “The more AI tools we have, the more disconnected people become, and the more they need community.”

• “You are not getting energy from your computer. You get energy from other people, and you share yours. It is always an exchange.”

Chapters

00:02 Welcome and guest introduction

01:11 Yurii’s background from risk analyst to community professional

04:50 Community as infrastructure for solopreneurs and freelancers

07:36 Freelance Unlocked and the co-opetition model

09:11 The fragmented freelance platform landscape and the case for a unified profile

13:59 AI in hiring and the arms race crowding out human signal

19:05 Digital credentials, second brains, and freelancer AI agents

22:24 Digital twins: efficiency gains and fraud risks

30:58 How freelancers use AI to scale output and prevent burnout

33:49 Responsible AI use and starting with the problem

43:29 The loneliness epidemic and community as antidote

44:53 In-person energy and the value of physical presence

49:50 Human-first networking and why pitching kills connection

51:17 Pre-conference rituals that build belonging before the event

56:02 Designing events where people come back to meet friends

Yurii Lazaruk: https://www.linkedin.com/in/yurii-lazaruk-community-consultant

Working with Yurii: https://yurii.community/

For AI readiness advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠

You also can find the Elevate Your AIQ podcast, along with tons of other great shows, on the WRKdefined Podcast Network!

Today’s guest is David Kilzer, founder of Strategic Transformation Advisors, a mechanical engineer and MBA with a career spanning General Electric, Digital Equipment Corporation, and decades of entrepreneurial work at the intersection of operations and emerging technology.

David and I explore the convergence of AI and humanoid robotics, how organizations can build a data-first mindset to unlock real AI value, and why the path to sustainable abundance runs through effective human and machine collaboration.

David brings a grounded, hands-on perspective shaped by living through every major technology shift of the last 50 years, and this conversation will challenge you to stop waiting for perfection and start getting your hands on the tools that are reshaping our world and our lives.

Here’s a quick teaser where David emphasizes that despite all the advancements in humanoid robotics and AI, we must aspire to have these technologies augment humans, not replace us, while remaining under our control…

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Customer service award-winning professional help: JP Promotional Products

Summary

Bob Pulver sits down with David Kilzer, founder of Strategic Transformation Advisors, for a wide-ranging conversation on the convergence of AI and humanoid robotics and what it means for the future of work. Drawing on a career that spans GE, Digital Equipment Corporation, and decades of entrepreneurial practice, David traces the arc of technology convergence from integrated circuits to the internet to today’s AI-powered machines. The discussion covers how organizations can responsibly adopt AI by building a data-first foundation, prioritizing high-impact use cases, and keeping humans firmly in control. Both Bob and David share a cautious optimism: the path forward runs through collaboration between humans and machines, not displacement of one by the other.

Keywords

David Kilzer, Strategic Transformation Advisors, technology convergence, humanoid robotics, AI and manufacturing, Boston Dynamics, Tesla Optimus, Figure AI, data-first mindset, AI hallucination, responsible AI, human-centric AI, upskilling, generative AI, TEDx, supply chain AI, blue collar workforce, sustainable abundance

Takeaways

• Technology convergence, not any single innovation, drives the most transformative leaps; AI combined with humanoid robotics may be the most consequential convergence in human history

• Robots are best applied first to work that is dull, dirty, or dangerous, augmenting human capability rather than replacing human judgment

• A data-first mindset is the unglamorous but foundational prerequisite for any organization looking to extract real value from AI

• AI hallucinations are often traceable to bad or incomplete data; human oversight of AI-assisted decisions remains essential

• Generative AI is shifting in 2026 from experimental tool to backbone technology, and individuals and organizations that wait for perfection will fall behind

• The US and China are in an accelerating race for robotics leadership, and maintaining that edge requires cross-sector collaboration and continued investment in AI literacy

Quotes

• “When done right, it’s not humanoid robotics replacing humans. It’s augmenting, collaborating with humans.”

• “This is going to be looked at as the next biggest thing for humankind since fire.”

• “Data drives AI. Make sure that all the data you’ve prepared is highly accurate and then expand from that point.”

• “Humans employ it by looking at what you need to accomplish primarily as a business and look for high-impact use cases.”

• “Don’t be intimidated by it. Get in there. Get that hands-on approach.”

• “I’m an enthusiastic optimist.” / “I am a cautious optimist.”

Chapters

00:02 Welcome and guest introduction

00:41 David’s background, from North Dakota to GE and DEC

04:38 Technology convergence and its historical pattern

06:59 David’s TEDx talk and the AI plus robotics thesis

12:26 Augmenting humans, not replacing them

17:26 US versus China in the robotics race

20:31 Prioritizing use cases, dangerous and drudge work first

25:46 Drones, emergency response, and the road to Rosie

30:34 Blue collar work, trade jobs, and the upskilling imperative

31:54 Responsible AI by design and the first law of robotics

38:49 Ethics, guardrails, and keeping humans in control

43:39 Building a data-first mindset for AI adoption

46:31 AI hallucination, enterprise readiness, and supply chain wins

David Kilzer

Strategic Transformation Advisors

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠

Share Elevate Your AIQ

You also can find the Elevate Your AIQ podcast, along with tons of other great shows, on the WRKdefined Podcast Network!

After hosting more than 120 conversations in the Elevate Your AIQ ® studio about AI, work, and human potential, I thought I would recap some of the highlights and insights we’ve heard so far this year.

I originally started the show because I believed these conversations needed to happen out loud, in public, with practitioners, researchers, educators, and futurists who could provide us with the cognitive diversity and lived experiences that help us put it all in perspective. What I didn’t fully anticipate was how much - or how quickly - the conversations themselves would evolve.

In the first 50 episodes, the dominant energy was anticipation. In episodes 51–100, it was urgency. In 2026 so far, something has shifted again. The guests who joined me in the first (almost) half of 2026 weren’t speculating about what AI would do to organizations. They were reporting back from deployments already underway, decisions already made, and consequences already visible.

The future of work is a strange concept, especially when technology and change are happening so fast it can feel like the future is now. What does it mean to stay human through one of the most disorienting transitions any of us has ever navigated?

Here’s a summation of what I heard.

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Trust Is the Infrastructure Nobody Budgeted For

If I had to name the single most underestimated factor in AI adoption across every industry I’ve covered this year, it would be trust — not AI’s trustworthiness in a technical sense, but the broader web of trust that has to hold for any of this to work. Trust between organizations and their data. Trust between employers and employees. Trust between systems and the humans who are supposed to rely on them. Trust between candidates and the hiring processes that are increasingly mediated by AI on both sides of the table.

Vijay Swami of Draup put it plainly: transparency is about giving people the right to know, even when they don’t ask. His team built traceability and explainability into their talent intelligence platform by design, not as a retrofit — because they understood that trust, once lost, is nearly impossible to rebuild in enterprise relationships.

Adam Gordon at Poetry made a similar argument from a different direction: the “trust barometer” in organizations is dropping, and when that happens, candidate experience and employer brand collapse together. His response was to build guardrails so tight that the platform literally cannot hallucinate — removing variability as a trust liability.

Dave Vu at Ribbon framed trust as a two-sided problem in high-volume hiring: candidates need to trust that AI screening is fair and that their time is respected, while organizations need to trust that AI outputs are consistent enough to act on. His point that transparency about AI usage actually increases candidate adoption — rather than creating resistance — was something I found genuinely counterintuitive and worth thinking more deeply about.

Stephen Messer at Collective[i] and Intelligence.com took the longest view on this. His argument is that trust isn’t just a nice-to-have in AI-powered commerce — it’s a transaction accelerant. Organizations with trusted relationship graphs move faster, close more, and waste less. The friction that most companies accept as normal is, in his framing, a trust deficit masquerading as a process problem.

Jerry Jao at Employ, processing nearly 100 million applications annually, is watching trust erode in real time from a different angle — AI-optimized resumes are degrading signal quality, deepfakes are appearing earlier in hiring funnels, and the volume problem has become so severe that meaningful evaluation is genuinely at risk.

And Laura Maffucci at Globalization Partners raised the sharpest version of a concern I keep hearing: when people turn to unreliable general-purpose AI instead of trusted, expert-grounded data, the consequences in compliance and legal contexts can be severe. Her line about hoping Reddit isn’t anyone’s first stop for a legal question landed hard.

The through-line across all six of these conversations is that trust isn’t a feature you add to an AI system. It’s the condition under which the system gets used at all.

Measuring What Actually Matters — And Most Organizations Aren’t

AI adoption stories that sound anything like “deploy tools, track productivity improvements, declare successful adoption” abound. I have spent much of 2026 talking to people who acknowledge that this is an immature approach, not to mention that it ignores the lessons we should have learned from prior tech-influenced transformations. Most transformations are failing, and that result was entirely predictable where leaders treat AI transformation like a technology deployment.

Russ Fradin at Larridin has built his entire advisory practice around the measurement gap. His observation is blunt: most organizations have no idea which AI tools their employees are actually using, let alone whether those tools are driving outcomes that matter at the enterprise level. Individual efficiency gains — time saved per task, outputs per hour — are real, but they’re also necessary but not sufficient. The companies that will win aren’t the ones that deployed the most tools. They’re the ones that redesigned work around the tools and reinvested the gains into growth rather than just margin.

Paul Rubenstein at Visier brought a framework that I found genuinely clarifying. He tracks three curves for CHROs navigating AI: the human-machine efficiency frontier, the ROI curve on AI investments, and what he calls the humanity index — a composite of talent density, engagement, and culture. Most organizations, he argues, are stuck in what he calls the “gym membership phase” — they distributed the tools, paid the subscription fees, and assumed transformation would follow. It doesn’t. Real returns require intentionally deconstructing jobs and reassembling them around AI capability, which is harder and slower than buying software.

Charlene Li, returning to the show fresh off the release of Winning with AI (co-authored with Dr. Katia Walsh), made the most provocative argument in this cluster: organizations should stop running pilots. Pilots, she argued, are just a socially acceptable way to procrastinate the real decision — yes or no — and they give everyone cover not to commit. The organizations she studied that are actually winning led with business strategy first and asked how AI could serve goals they already had, rather than searching for problems worthy of their new tools.

Melissa Reeve, whose Hyperadaptive framework maps a five-stage journey to becoming AI-native, diagnosed the same problem from the organizational design side. She calls individual productivity gains a vanity metric. The real question is whether AI is unlocking new organizational capabilities and a more ambitious mission — whether it’s expanding what the organization can be, not just how efficiently it can do what it already does.

What strikes me across these four conversations is that the measurement problem is also a courage problem. Knowing you’re in the gym membership phase requires admitting that significant investment hasn’t yet produced significant transformation — and that the path forward involves more disruption, not less.

What Humans Are Actually For

This is the question I keep returning to, episode after episode, because I don’t think most organizations have seriously answered it. They’ve answered the adjacent question — what tasks can AI do? — but not the harder one: given that, what are humans distinctively for?

The most rigorous answer I heard this year came from Jonathan Aberman at Hupside. His Original Intelligence Quotient framework is built on a precise definition: originality equals novelty plus salience. AI can generate novelty — it’s quite good at it. What it cannot do is determine what’s meaningful to the humans consuming its output. That judgment remains a human attribute. Organizations that misunderstand this, that treat AI output as inherently valuable rather than as raw material requiring human curation, are building on a faulty foundation.

Bob Danna, physicist, former Deloitte partner, and author of My Curious Life, lived a version of this question when he and collaborator Joe DiDonato built “Bot-Bob” — a digital twin trained on his memoir, writings, and decades of accumulated experience. What emerged from that experiment wasn’t a replacement for Bob Danna. It was an amplifier — a way of making his judgment, values, and perspective available at scale. His framing stuck with me: the knowledge worker of the future isn’t someone who competes with their digital twin. It’s someone who collaborates with it.

Jacob Bank at Relay.app has been thinking about AI agents longer than almost anyone I’ve talked to, having run the multi-agent systems lab at Stanford before founding an AI calendar company that Google acquired. His current argument is that the humans best positioned for the AI era aren’t necessarily the best coders or the most technically sophisticated. They’re the best managers — people who know how to give clear direction, set expectations, provide feedback, and hold agents accountable. “We’re all managers now” is how he put it, and I think it’s one of the most practically useful framings I’ve encountered this year.

Oded Dubovsky, my former IBM colleague who now runs STRAIX, brought the longest institutional memory to this question. Having spent over two decades at IBM Research building cognitive computing systems before they were called AI, he’s watched multiple waves of “this changes everything” wash through organizations. His contribution was a kind of epistemic humility: slow down to think carefully before prompting or building, because the questions you ask determine the quality of what you get. Einstein’s 55/5 rule — spend 55 minutes on the problem, five on the solution — is more relevant in an age of instant AI output than it has ever been.

The Courage Gap Is Real, and It Lives at the Top

The concept that “leaders need to get on board” has been a recurring theme in AI transformation conversations, but it doesn’t tell us enough. What I heard in 2026 is more specific than that; and perhaps more uncomfortable. The leaders who are getting this wrong aren’t necessarily skeptics or technophobes. Many of them are enthusiastic AI adopters in their personal workflows. What they haven’t done is the harder internal work: genuinely reckoning with what AI means for how they lead, what they model, and whether they’re willing to be visibly uncertain in front of their teams. You can’t ask your people to embrace disruption and self-reinvention while projecting certainty from the top. That gap — between what leaders say about AI transformation and what they’re actually willing to risk personally — is what I kept running into this year. (I should also note that Charlene Li called out a variation of this in her first guest appearance (Episode 19) when she talked about the Knowing-Doing-Leading gap.)

Juan Garcia at Tuio, a digital-native insurer in Spain that has genuinely reimagined how AI works inside an insurance company, named it directly: the real barrier to AI transformation is organizational courage. Not resources, not technology, not talent — courage. The willingness to rework processes and structures around AI in ways that require leaders to take personal risk for uncertain outcomes. His team made a decision I found remarkable in its simplicity: they would never automate negative customer decisions. Not because regulation required it, but because their values did. That kind of clarity requires leadership that has actually thought about what it believes, not just what’s efficient.

Meg Bear, former President of SAP SuccessFactors and one of the sharpest observers of organizational behavior I’ve had on the show, came at the courage question through her critique of what she calls “founder mode” — the belief that a single visionary individual drives organizational success. Her counter-argument is that sustainable transformation requires intellectual humility, collective intelligence, and the willingness to bring your learning self rather than your knowing self. Her line that the only way to preserve your relevance as a worker is to make your own job obsolete is the most honest statement of the individual challenge I’ve heard anywhere this year.

Tim Borys, executive coach and host of the Working Well podcast on the WRKdefined network, located the courage gap in the individual as much as the organization. His framing — that humans have a “human operating system” that most people never optimize — resonated with me because it puts agency back in the hands of individuals rather than waiting for organizations to get it right. Adaptability, in his view, isn’t a soft skill. It’s the master skill that determines whether everything else compounds or decays.

Lisa Cole at 2X brought the marketing leader’s perspective on a specific kind of courage: the willingness to decide what should remain distinctly human even when AI could technically do it. Her argument — that deciding what to say and how to say it should stay human because that’s where competitive differentiation lives — is one I think applies far beyond marketing. Her observation that guardrails are what actually liberate AI adoption, rather than constrain it, reframed a conversation I’ve been having with guests for two years.

The Agentic Era Isn’t Coming. It’s Here.

The final theme from this year’s episodes is the one that will age fastest, because the landscape is moving so quickly that anything I write about specific tools or architectures may already be outdated by the time you read it. What I’ll focus on instead is the pattern.

In early 2026, the guests who were furthest along in AI deployment weren’t talking about AI as a technology layer sitting on top of existing processes. They were talking about agents as the primary unit of work — systems that operate proactively, in the background, across workflows that used to require sustained human attention. The shift isn’t from human work to AI assistance. It’s from human execution to human orchestration.

David Arnoux, co-founder of AI-native venture studio Humanoidz and community leader of the Gen AI Circle — a global network of nearly 500 heavy AI adopters — gave me the most honest map of where this is actually going. His four-stage framework — co-intelligence, augmentation, automation, redundancy — doesn’t flinch at the uncomfortable end of the spectrum. Some job categories, he said, simply won’t survive. Pretending otherwise isn’t optimism; it’s how you end up unprepared. His practical contribution was the concept of the “skills file” — a simple markdown document that turns any repeated workflow into a reusable, transferable, shareable asset — which struck me as one of the most accessible on-ramps to agent-powered work I’ve seen.

Lance Thompson at VIVI is deploying this in hospitality right now — voice AI agents handling reservations, room service, HR inquiries, and golf tee times across multiple languages in real time. His framing was one of the most human-centric I encountered: “We don’t want to replace Janet in Reservations — we want to scale her.” That distinction — amplification versus replacement — is doing a lot of work in every organization that’s getting this right.

Laura Maffucci at Globalization Partners is living it from the inside, piloting GIA — G-P’s agentic AI platform — as an internal HR agent handling employee inquiries, while simultaneously watching what it can do for clients at scale. Her candor about the perception gap between executives (who believe AI is driving efficiency) and employees (who often feel it’s adding to their workload) was one of the most grounded reality checks in any conversation this year.

What I take from these conversations collectively is that the window for “we’re still figuring it out together” is narrowing, as Meg Bear observed. The organizations that are ahead aren’t necessarily smarter or better resourced. They’re the ones that made a decision to act — and then kept adjusting based on what they learned.

What I Believe After More Than 120 Conversations

I started Elevate Your AIQ because I was convinced that the human dimensions of AI transformation — readiness, responsibility, talent, ethics, culture, leadership — were being systematically underweighted in the public conversation about AI. The technology coverage was relentless. The human coverage was an afterthought.

Two years later, I’m more convinced of that than ever. And I’ve developed some sharper views from sitting across from over 120 people who are actually doing this work.

I believe trust is the real substrate of AI adoption — the condition under which everything else either works or doesn’t — and that most organizations are spending heavily on capability while underinvesting in the conditions that make capability usable and sustainable.

I believe the measurement gap and the courage gap are related, though not identical. You can’t honestly measure transformation without being willing to see what the numbers actually say — and then act on them, even when what they reveal is inconvenient. That second part is where courage comes in, and where most organizations stall.

I believe humans aren’t “in the loop” as a compliance formality. They’re in the loop because judgment, originality, passion, curiosity, and creativity are innately human contributions. The organizations that internalize this, rather than treating human oversight as friction until AI gets good enough, will build fundamentally different and more resilient solutions than the ones that don’t.

And I’ll admit something I think about more than I say publicly: I worry about the pace. Not the pace of AI development per se, but the pace at which the human infrastructure around it — the governance, the literacy, the cultural norms, the honest conversations — is keeping up. These recent conversations gave me real reason for optimism on that front. But optimism and complacency are not the same thing.

As I’ve said countless times, we are all responsible for Responsible AI, so we need to stay vigilant about doing the right thing and holding each other accountable. Which brings us full circle back to trust.

To every guest who joined me this year — Vijay, Adam, Lance, Tim, Dave, Stephen, Russ, Juan, Jonathan, Melissa, Jacob, Oded, Charlene, Paul, Jerry, Laura, Bob, Lisa, David, and Meg — thank you. These conversations are genuinely the best part of what I do. You brought your thinking, your candor, and in many cases your willingness to say things that don’t always get said in polished keynotes or press releases. That authenticity is key to Elevate Your AIQ.

I’ll also say this, because it’s true and I don’t say it enough: the timing of starting this show, while it might look obvious in retrospect, never felt inevitable to me. Everything I did before — the enterprise transformation and innovation work, the 25 years at IBM and NBCUniversal, the research, the perpetual curiosity about where technology and people intersect — it all pointed here eventually. I’m glad I followed where it led. And I’m glad you’re here with me for what comes next.

If any of this connects with challenges you’re working through, I’d love to hear from you. In the meantime, stay human-centric, keep learning, and innovate responsibly.

— Bob

Elevate Your AIQ is available on all major podcast platforms. Subscribe on Substack for written content, video clips, and upcoming live conversations. New episodes weekly.

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My distinguished guest today is Meg Bear, a globally-recognized technology executive, innovator, and operator, former President of SAP SuccessFactors, board advisor at Novaworks and Papaya Global, and co-host of the Meg and Amy Show (with her BFF Amy Wilson) on the WRKdefined Podcast Network.

Meg and I cover a lot of ground in our conversation, from the intellectual humility that separates sustainable leadership from the “founder mode” hype, to the power of recognizing human potential in the AI era, to the challenges of total workforce visibility and what a future of fluid, individualized work could actually look like.

Meg is one of the sharpest, most thoughtful voices in the talent and HR technology space, and this conversation did not disappoint. My virtual recording studio (Riverside.fm) seems to concur, as Meg broke the record for guest with the most clips above 90 on its virality score index.

In honor of the occasion, and props to Meg, I have included not one but two teasers for your viewing pleasure!

Here’s Meg talking about why you should intentionally disrupt your own job rather than playing it safe…

And here’s Meg describing how she personally uses AI to ensure she stays focused and aligned with her tactical and strategic goals…

Are you sponsoring an upcoming event? Need employee recognition ideas?

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Summary

Meg Bear is a senior executive, board member, advisor, investor, podcaster, and keynote speaker on some of the world’s biggest technology stages. As co-host of the Meg and Amy Show and a board advisor at Novaworks and Papaya Global, she brings the rare combination of deep operator experience and forward-looking strategic vision that makes this conversation genuinely worth your time. Meg joins Bob to explore what it truly takes to lead and develop talent in the AI era, opening with a sharp critique of “founder mode” thinking and making the case that intellectual humility and collective intelligence are what sustainable organizations are built on. The discussion spans workforce disruption, the risks of AI-driven headcount cuts without strategic vision, and why psychological safety is the foundation for building genuinely adaptive teams.

Keywords

Meg Bear, SAP SuccessFactors, founder mode, grower mode, intellectual humility, collective intelligence, human potential, skills-based hiring, psychological safety, learning agility, talent marketplace, workforce planning, total talent, NovaWorks, Papaya Global, Meg and Amy Show, AI readiness, workforce disruption, human experience management, agentic AI

Takeaways

• Founder mode thinking trades intellectual humility for hubris, undermining the collective intelligence organizations need to thrive

• Human value is not defined by job titles or past achievements but by the inherent strengths and adaptive capacity each person brings

• Leaders who fail to recognize and invest in their team’s potential also forfeit the organization’s capacity to innovate through disruption

• Disrupting your own job before someone else does is not a threat; it is the only viable strategy for staying relevant in an AI-transformed workforce

• The current AI learning moment is unusually pro-social, but the window to engage while everyone is still figuring it out together is narrowing fast

Quotes

• “The belief that a single person is going to make everything happen is the wrong kind of culture to build a sustainable future.”

• “We have all of the raw materials to thrive in this future state, but it’s not going to work if we only want to bring our knowing selves.”

• “The only way to save your status as a worker is to make your own job obsolete.”

• “Our job as leaders is to manage energy, identify potential, and help individuals see progress in work that really matters.”

• “This is the most pro-social learning environment I’ve ever seen.”

• “How do we marshal the collective intelligence of our customers and our market to unlock new value capture in this world?”

Chapters

00:02 Welcome and guest introduction

02:35 Meg’s background and mission to invent the future

03:26 Founder mode vs. grower mode and the case for intellectual humility

09:04 Cognitive diversity, collective intelligence, and the limits of one-person leadership

12:18 Recognizing human strengths and finding new pathways of excellence

20:10 Human value beyond titles and the importance of bringing your learning self

22:36 Psychological safety as the foundation for adaptive teams

28:58 From human capital to human experience management at SAP SuccessFactors

32:31 Workforce disruption, AI-driven headcount cuts, and the risk of incrementalism

36:27 The pro-social AI learning moment and why the window is closing

43:16 Board-level AI strategy and the risk of ready-fire-aim decisions

56:35 NovaWorks, total talent visibility, and the future of fluid work

01:07:07 Meg’s personal AI journey and building goal-alignment agents

Meg Bear: megbear.com

The Meg & Amy Show: https://wrkdefined.com/podcast/the-meg-and-amy-show

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ https://elevateyouraiq.com

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You also can find the Elevate Your AIQ podcast, along with tons of other great shows, on the WRKdefined Podcast Network!

In this episode, I sit down with David Arnoux, who is currently the co-founder of the AI-native venture studio Humanoidz, community leader of the Gen AI Circle, and GTM strategist at Hey Arnoux. David formerly co-founded a startup called Growth Tribe and in general has been a technology innovator for a long time.

David and I explore what it really means to move beyond augmentation and into the agentic era of work. We discuss the four-stage landscape of how AI is reshaping work, from co-intelligence and augmentation, through full workflow automation, and into the uncomfortable reality that some categories of jobs simply won’t survive the next few years. We also get into responsible AI guardrails for real-world agent deployment, the emerging shift from SaaS to custom-built tools, and how the heaviest AI adopters are running entire businesses with agents doing the work in the background.

If you want an honest, practitioner-level look at where AI and work are actually heading, you won’t want to miss this one. Speaking of honesty, here’s a teaser where David provides some observations, a couple predictions, and a much-needed wake up call for those not paying close enough attention…

Thanks so much for subscribing, for listening, and for being part of the Elevate Your AIQ community!

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Summary

Bob Pulver sits down with David Arnoux, co-founder of AI-native venture studio Humanoidz, fractional GTM strategist at Hey Arnoux, and community leader of the Gen AI Circle, a global network of nearly 500 heavy AI adopters. David shares a clear-eyed framework for understanding where AI is actually taking work, moving from co-intelligence and augmentation through full workflow automation and into the uncomfortable reality of job category redundancy. The conversation covers responsible AI guardrails, the architecture of second brain systems, and the emerging shift from SaaS subscriptions to custom-built agent-powered tools. David draws on patterns he observes across his community, client work, and venture studio to offer practical first steps for individuals and organizations ready to move beyond the chat window.

Keywords

David Arnoux, Humanoidz, Gen AI Circle, HeyArnoux, Growth Tribe, agentic AI, augmentation, automation, redundancy, co-intelligence, responsible AI, second brain, skills files, SaaS disruption, GTM strategy, go-to-market, lethal trifecta, prompt injection, MCP integrations, Claude Code, solopreneur, workflow automation, human in the loop, agent orchestration, buy vs. build, future of work

Takeaways

• The four-stage framework of co-intelligence, augmentation, automation, and redundancy offers a more honest map of where AI is taking work than the comfortable augmentation narrative most organizations have sold themselves on

• Responsible AI is less a philosophy debate and more a practical checklist: confirm before acting externally, maintain audit logs, cap high-frequency tasks, and never allow destructive actions without human approval

• The “lethal trifecta” of private data, untrusted content, and external communication access in a single agent creates serious prompt injection risk, and the architectural answer is isolation by capability

• Heavy AI adopters are productizing every repeated workflow as a skills file, a simple markdown document that turns any process into a reusable, shareable, and transferable asset

• The buy vs. build calculus is shifting fast, with community members replacing multi-tool SaaS stacks costing hundreds per month with custom-built solutions at a fraction of the cost

• Distribution and audience are now the primary moat for any new venture, making community building and direct relationships more valuable than ever

Quotes

• “Smart humans plus better tooling equals crazy results. That’s what we see happening 10x, 100x at the moment.”

• “Just pretending it’s all about augmentation is how you and I end up unprepared.”

• “Responsible AI is not a philosophy debate. It can actually be a checklist.”

• “Learning is a markdown file. You download their thinking directly into the system.”

• “I posted that I would never purchase a CRM ever again. It got the most engagement of anything I’ve ever written in months because people felt it.”

• “Distribution is everything nowadays and audience is more important than ever.”

Chapters

00:03 Welcome and guest introduction

01:03 David’s background, Growth Tribe, and the three-entity flywheel

04:52 Why distribution and audience matter more than ideas

09:36 Co-intelligence, augmentation, automation, and redundancy

16:38 Human centricity, responsible AI, and finding your personal line

19:59 Practical guardrails for agentic systems

23:41 How heavy adopters are actually working today

31:32 Digital literacy, learning habits, and the mindset that compounds

36:19 Bob’s second brain challenges and the ethics of AI-powered outreach

41:40 The lethal trifecta and agent isolation architecture

48:11 First steps: Claude Code, MCP integrations, and skills files

51:32 SaaS disruption, buy vs. build, and the future of software pricing

58:19 Career paths, entrepreneurship, and building your audience

David Arnoux: https://www.linkedin.com/in/davidarnoux

HeyArnoux: heyarnoux.com

Humanoidz: humanoidz.ai

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ https://linkedin.com/in/bobpulver

Elevate Your AIQ:⁠⁠ https://elevateyouraiq.com⁠⁠⁠

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Today I’m excited to share my insightful conversation with Lisa Cole, Chief Marketing, Product, and AI Officer at 2X, a global B2B marketing-as-a-service firm, and a three-time author whose just-released The Limitless CMO tackles how marketing leaders can scale impact in the age of AI.

Lisa and I explored how AI is fundamentally reshaping the marketing function, from the emergence of generalist marketers and new AI-native roles to the concept of brand gravity and what it means to be discoverable and selected by both humans and machines. We also got into the nuances of when to use AI at scale versus when to keep things uniquely human, and how organizations at every stage of AI adoption can find the right approach without losing their competitive edge.

I think you’re really going to enjoy this conversation. Lisa is super smart and there are lots of important lessons well beyond the marketing domain. Below is a teaser where Lisa talks about the advantages that AI-native companies have over legacy (or at least legacy-minded) businesses:

Thanks so much for subscribing, for listening, and for being part of the Elevate Your AIQ community!

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Summary

Lisa Cole, Chief Marketing, Product, and AI Officer at 2X and three-time author, joins the show to explore how AI is fundamentally reshaping the B2B marketing function. Lisa shares how 2X, a global marketing-as-a-service firm with 1,400 marketers worldwide, is navigating the shift toward generalist talent, AI-native roles, and human-centered AI adoption. The conversation covers her framework for “brand gravity,” the case for keeping strategic thinking and brand voice uniquely human, and how AI enables the scale needed to be findable and chosen by buyers long before they raise their hand. Lisa also discusses her new book, The Limitless CMO, which offers a practical operating model for scaling marketing impact without skyrocketing costs.

Keywords

Lisa Cole, 2X, brand gravity, B2B marketing, marketing as a service, AI adoption, generalist marketers, AI-native roles, human centricity, prompt engineering, knowledge layer, synthetic personas, mock focus groups, omnipresence, The Limitless CMO, Brand Gravity, The Revenue RAMP, responsible AI, content at scale, buyer journey

Takeaways

• AI is shifting marketing toward generalists who are adaptable, curious, and comfortable with continuous change, while also creating entirely new roles like AI automation specialists and prompt engineers

• Organizations fall into three categories of AI readiness: AI-forward with clear strategy, still figuring it out, and fully resistant; 2X leads with full disclosure and follows each client’s lead

• The deciding of what to say and how to say it should remain uniquely human, as it is the source of competitive differentiation and brand trust

• Brand gravity is built by accumulating digital mass across all the places buyers research anonymously, making a brand findable and chosen before any sales conversation begins

• AI enables the scale needed to repurpose core thought leadership into derivative assets across channels, without outsourcing the underlying thinking

• Synthetic personas and mock focus groups offer a faster, lower-cost path to messaging development, though high-stakes repositioning decisions still warrant real human input

• Building a knowledge layer from unstructured organizational data, call transcripts, emails, and more, is the key unlock for eliminating AI slop and generating reliable, contextual output

Quotes

• “Deciding what to say and how to say it, those points of view that you’re putting out in the market, that should be uniquely human. That’s your secret sauce.”

• “It’s the absence of guardrails that people are so afraid of. The guardrails are what’s actually unleashing it.”

• “We recruit about 80 to 100 marketers a month, and we now have to really focus on soft skills: are they open to an ever-changing environment?”

• “I used AI when I was writing my book, not to write the book, but to interview me.”

• “If you actually know your workflows and can taskify it in such a way that you can explain it to an intern, then it’s very easy to apply AI to accelerate it.”

Chapters

00:03 Welcome and guest introduction

03:46 AI’s role across a 1,400-person marketing organization

06:12 Evolving roles and the rise of the generalist marketer

10:06 Client AI readiness and 2X’s full-disclosure approach

13:56 Defining what should remain uniquely human

18:21 Brand voice, storytelling, and competitive differentiation

21:21 Brand Gravity and the anonymous buyer journey

23:41 The Limitless CMO and scaling without skyrocketing costs

28:40 Building the knowledge layer from unstructured data

31:31 Synthetic personas and mock focus groups

36:13 Good enough as a framework for AI use case decisions

41:34 Voice-first workflows and AI-assisted book writing

45:43 Global operations, offshore teams, and cultural dynamics

50:28 Closing thoughts and book resources

Lisa Cole: https://www.linkedin.com/in/lisacole01

2X: https://2x.marketing

“The Limitless CMO”: https://lisacole.ai/

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ https://elevateyouraiq.com

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Today I’m thrilled to share a conversation with my friend Bob Danna, a physicist, naval officer, former Senior Managing Director at Deloitte Consulting, and author of the memoir “My Curious Life.” Bob brings a rare perspective spanning nearly six decades of technological evolution, and he’s got the scars and the stories to prove it. You’ll also quickly appreciate the book title, as Bob is one of the most curious people you’ll ever meet.

We get into some truly thought-provoking territory, including the creation of digital twins as both a legacy preservation tool and a force multiplier for knowledge workers, the power of AI mastermind panels where multiple digital twins collaborate in real time, and why the human guardrails we build into these systems may be the most important design decision we make.

I have such admiration for Bob, and we always have amazing and insightful conversations, so I was excited to record one of them for your listening pleasure.

If you’re wondering what the future of knowledge work really looks like when human wisdom and artificial intelligence start to converge, I know you’ll enjoy this one as much as I did.

Here’s a teaser where Bob talks about the evolution of work and human potential. Teaser for the teaser: we’ve seen this movie before…

Thanks so much for subscribing, for listening, and for being part of the Elevate Your AIQ community!

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Summary

Bob Danna, physicist, naval officer, former Senior Managing Director at Deloitte Consulting and Bersin by Deloitte, and author of the memoir “My Curious Life,” joins host Bob Pulver for a wide-ranging conversation about a lifetime at the frontier of science and technology. Bob traces his journey from slide rules and nuclear reactors to agentic AI, sharing how he and collaborator Joe DiDonato built “Bot-Bob,” a digital twin trained on his memoir, writings, and decades of experience. The conversation explores what digital twins can mean for knowledge workers, legacy building, and collective intelligence, including a live mastermind experiment where multiple digital twins, plus a digital Mark Twain, fielded questions from a live audience. Bob closes with an urgent call to bring more diverse human voices into AI development before the decisions that shape civilization get made without them.

Keywords

Bob Danna, digital twin, agentic AI, Bot-Bob, Joe DiDonato, My Curious Life, knowledge worker, collective intelligence, co-intelligence, legacy, mastermind, ElevenLabs, nuclear warfare, responsible AI, human centricity, future of work, STEM, Deloitte, memoir, Substack

Takeaways

• Curiosity is the connective tissue of Bob’s entire career, from nuclear physics and naval service to Deloitte consulting and digital twins, and he positions it as the essential human quality that AI can amplify but never replicate

• A digital twin is far more than a knowledge repository; it encodes values, judgment, and personality, making it a genuine extension of a person’s thinking

• The “mastermind” format, where multiple digital twins deliberate together in real time, opens new possibilities for accessing cognitive diversity without scheduling constraints

• When AI models are trained by a narrow group (such as military strategists), the outputs reflect that bias, making diverse human representation in AI development a matter of consequence

• Knowledge workers who collaborate with their own digital twins can operate at dramatically higher capacity and quality, not by being replaced, but by being amplified

• A responsibly built digital twin can preserve the wisdom, voice, and values of an individual for future generations

Quotes

• “I’m just a curious guy. No matter what I’m into, I’m always looking at other things.”

• “When we free up tasks that human beings were doing, I think that is very, very positive. The real question is, what does the human being step up to do that only a human being can do?”

• “The definition of a knowledge worker is going to change. It’s going to be that human being collaborating with the digital twin of that person.”

• “It’s very timely right now that we really start to have human conversations before we go down the path too far.”

Chapters

00:03 Welcome and introductions

01:21 Bob Danna’s career journey, from physics to Deloitte

03:27 Early encounters with AI and neural networks

07:21 What makes us human, the evolution of calculators and computers

10:26 Joe DiDonato, soul-sinking, and the origin of Bot-Bob

14:41 Building Bot-Bob, memoir, voice, and guardrails

17:17 From chatbots to agents to digital twins, a practical framework

25:27 Brainstorming mode and collaborating with your own twin

28:16 Digital twins in consulting and the future of knowledge work

35:20 The mastermind experiment, Bot-Bob, Robo Laci (Laci Loew), and digital Mark Twain

41:14 AI, nuclear war scenarios, and the dangers of narrow training data

51:31 The workforce of 2030 and what it means to be a knowledge worker

58:55 Closing thoughts and how to connect with Bob Danna

Bob Danna’s Substack:

“My Curious Life”: https://mycuriouslife.net/

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ https://elevateyouraiq.com

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For this episode I’m delighted to be joined by Laura Maffucci, Head of HR at Globalization Partners (G-P), a pioneer in global employment and employer of record (EOR) solutions. (In fact, G-P created the EOR category.)

Laura brings nearly three decades of HR leadership experience and is truly leaning into her crucial role spearheading AI-fueled transformation at G-P.

I was fortunate to meet Laura last Fall for an in-person chat at the HR Tech conference in Las Vegas, but I invited her into the studio to give us the chance to go much deeper on how AI is reshaping the world of work.

We explore how agentic AI tools like GIA are transforming global hiring, compliance, and HR operations, and what it really means to lead HR from within an HR tech organization—where you’re not just supporting transformation, but actively piloting it in real time. Laura also shares candid perspectives on the gap between executive expectations and employee experiences with AI, and the evolving human skills that matter most.

Below is a teaser where Laura provides her perspective on how the HR function itself is evolving to meet the needs of both the workforce and the business.

Thanks so much for subscribing, for listening, and for being part of the Elevate Your AIQ community!

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Summary

Bob Pulver sits down with Laura Maffucci, Head of HR at Globalization Partners, for a wide-ranging conversation on AI adoption, global workforce trends, and the evolving role of HR. Laura shares how G-P is deploying its agentic AI product, GIA, both externally for global employment compliance and internally as a pilot HR agent, while emphasizing the importance of grounding AI in trusted, expert-sourced data. They explore the growing disconnect between executive optimism and employee sentiment around AI, the durable human skills that matter most in an AI-augmented workplace, and why AI adoption without a clear problem to solve is a recipe for costly confusion. Laura also shares candid reflections on her own AI learning journey and what she sees ahead for the HR function.

Keywords

Laura Maffucci, Globalization Partners, GIA, employer of record, EOR, agentic AI, global employment, AI compliance, HR transformation, AI adoption, employee sentiment, AI literacy, early career roles, talent acquisition, deep fakes, AI governance, critical thinking, human skills, learning agility, shadow AI, AI Awesomeness Awards, internal mobility, cognitive diversity, compensation analytics, AI readiness, Gemini, NotebookLM

Takeaways

• GIA has evolved from a compliance Q&A tool into an agentic platform that can generate contracts, audit company policies, and is now being piloted as an internal HR agent handling employee ticket inquiries

• The data behind GIA is grounded in over a decade of G-P’s global employment expertise, offering a trusted alternative to general-purpose LLMs drawing from unverified internet sources

• A significant perception gap exists between executives who believe AI is driving efficiency and employees who feel it is actually adding to their workload or generating unreliable output they must clean up

• Entry-level roles are shifting toward managing and directing AI agents rather than executing tasks directly, with “taste” (the ability to evaluate AI output) emerging as a critical early-career skill

• Workforce hiring criteria must increasingly prioritize unteachable human attributes such as curiosity, learning agility, courage, and the willingness to relinquish control, because technical AI skills can be taught but these cannot

• AI adoption mandates without a clearly defined problem to solve create fragmented, siloed “shadow AI” that can undermine organizational strategy rather than advance it

• HR functions are being asked to lead organizational AI transformation without adequate resources, technical support, or direction, making the role both high-opportunity and genuinely demanding

• Assessing real AI usage requires creative mechanisms: GP uses a Slack sharing channel, quarterly performance check-in questions, and monthly AI Awesomeness Awards to surface how people are actually applying the technology

Quotes

• “I know that when these things come out, whether you like them or not, you had best learn them and learn how to work with them.”

• “If you have a legal or compliance question, I hope Reddit’s not your first stop to get that answer.”

• “You can’t embrace AI and be a control freak. You have to be willing to let something go and let something do something.”

• “The pitfall that I can see so many companies falling into is, we don’t need people because we’ve got the AI to do this.”

• “Being in HR right now is not for the weak. That I will say, for sure.”

• “It should always be: what problem are we trying to solve? Because that’s actually one of the biggest issues with AI.”

Chapters

00:02 Welcome and guest introduction

01:40 Employer of record explained

03:03 GIA overview and new agentic capabilities

05:27 Responsible AI and the importance of trusted data sources

08:47 GP research findings on AI adoption and executive-employee sentiment gap

11:50 AI as added burden vs. efficiency driver

13:45 Redefining early career roles in an agentic world

15:42 The human cost of replacing workers instead of augmenting them

19:05 The problem with AI mandates that skip the “why”

22:44 Human skills that matter most when hiring for an AI-augmented workforce

26:37 The AI-versus-AI problem in talent acquisition

27:39 Deep fakes, virtual interview integrity, and human oversight in TA

31:53 The evolving role of HR as a strategic function

37:13 C-suite dynamics and running HR as a pilot for GIA

39:27 Building an AI council, sharing culture, and identifying shadow AI

42:15 Measuring AI fluency through awards, check-ins, and community

45:14 Laura’s personal AI journey and closing thoughts

Laura Maffucci: https://www.linkedin.com/in/laura-maffucci

G-P: https://www.globalization-partners.com

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ https://elevateyouraiq.com

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Today I’m joined by Jerry Jao, CEO of Employ, a leading talent technology platform that brings together powerful hiring solutions including Lever, JazzHR, Jobvite and a suite of integrated AI companions for sourcing, screening, and interviewing.

Jerry shares how lessons from his entrepreneurial background in AI-driven consumer personalization are now shaping his vision for a more human, more effective hiring experience for all stakeholders.

We get into how Employ is using AI to streamline the candidate journey, to reduce the burden on TA teams, and to address some of the unintended consequences that emerge when AI gets deployed on both sides of the recruiting process.

If you believe that finding the right job, or the right person for it, should feel less like a numbers game and more like a meaningful connection, this is a conversation worth your time. Below is a teaser where Jerry outlines the personalized and streamlined candidate experience he plans to deliver; one that every candidate deserves…

Thanks so much for subscribing, for listening, and for being part of the Elevate Your AIQ community!

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Summary

Jerry Jao, CEO of Employ, brings a perspective shaped by years of building AI-driven consumer personalization before turning that lens on hiring. He shares how he is restructuring Employ for greater agility, how Pillar’s interview and screening companions are reducing friction for job seekers and recruiters alike, and why the surge in AI-assisted applications is complicating matching on both sides. He also addresses candidate fraud and deepfakes, and explains how Employ’s IBM partnership helps reduce bias and hallucinations across nearly 100 million applications processed annually.

Keywords

Jerry Jao, Employ, Lever, JazzHR, Pillar, interview intelligence, screening companion, talent acquisition, candidate experience, AI bias, responsible AI, IBM, deepfakes, candidate fraud, AI literacy, two-sided marketplace, organizational design, hiring technology

Takeaways

• Jerry’s background in AI-powered personalization at Retention Science informs his approach at Employ, viewing both job seekers and hiring managers as people deserving a more thoughtful, personalized process

• Employ processed nearly 100 million applications last year, with some roles receiving two to three thousand submissions, making meaningful evaluation a serious operational challenge

• Pillar’s interview and screening companions are being integrated platform-wide to improve TA accuracy and give recruiters measurable time back in their day

• Responsible AI is a strategic priority, with IBM as a thought partner on model bias, hallucinations, and protecting candidate data at scale

• Candidate fraud, including deepfakes and multiple identity submissions, is an emerging risk Employ is working to detect earlier in the funnel

• AI-optimized resumes are eroding the signal value of traditional screening, making interview intelligence increasingly critical

Quotes

• “What I’m most excited about is creating a more effective process for people to provide for their loved ones by getting to their dream jobs.”

• “We want to help our TA team get home a little sooner, or take a 30-minute mental break if AI can help get that time back in their day.”

• “Hiring managers are telling us people sound incredibly amazing, but once they get on the call, it’s a little different.”

• “We’re all people at the end of the day, so how do we personalize the experience so no one feels overlooked?”

• “It’s almost as big a change as when the internet first arrived. We’re in a very uncertain and unprecedented time.”

Chapters

00:02 Welcome and introductions

02:03 From consumer personalization to talent acquisition

04:55 Building a human-centered hiring marketplace

07:16 AI on both sides: the cat-and-mouse dynamic in recruiting

09:24 Restructuring Employ for agility and accountability

14:12 Screening companion, talent fit, and processing 100 million applications

20:40 Candidate fraud, deepfakes, and emerging hiring risks

22:07 Responsible AI and the IBM partnership

30:38 AI literacy in job descriptions and skills assessment

34:54 Jerry’s new podcast and the future of TA storytelling

39:02 Navigating workforce uncertainty in the AI era

41:12 Closing reflections and Employ research reports

Jerry Jao: https://www.linkedin.com/in/jerryjao

Employ: http://www.employinc.com

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ https://elevateyouraiq.com

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My guest today is Paul Rubenstein, a veteran executive at Visier whose current role is Chief Evangelist and Talent Strategist after previous tours of duty as their Chief People Officer and Chief Customer Officer. I’ve had the pleasure of knowing Paul for a few years, and he is one of those rare people who is as fun to talk to as he is insightful. Paul also loves to challenge your thinking, and in a world where it’s easy to stay in your echo chamber, that keeps me honest and on my toes.

In this conversation, we get into Paul’s “three curves” framework for CHROs navigating AI — the human-machine efficiency frontier, the ROI curve, and the humanity index — along with why workforce planning is finally having its moment, and what it actually takes to keep humans at the center of your AI-powered transformation. Paul brings a ton of insight, some great advice, and of course, a healthy dose of humor to this overdue conversation.

Here’s a teaser where Paul outlines those three curves. But I highly, highly encourage you to listen to the full episode. The term ‘masterclass’ get overused but I honestly think it’s appropriate in Paul’s case.

Thanks so much for subscribing and listening!

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Summary

Paul Rubenstein, Chief Evangelist and Talent Strategist at Visier, brings deep expertise in people analytics and workforce strategy to this wide-ranging conversation. He introduces a three-curves framework for CHROs navigating AI: the human-machine efficiency frontier, the ROI curve, and the humanity index. The discussion explores why workforce planning is having a long-overdue resurgence, and how HR can use AI-powered analytics to reach managers proactively rather than waiting for them to come to HR. Bob and Paul also examine the courage gap in AI adoption, the governance tension between restricting data and enabling better AI answers, and the design-plan-operate mindset required to truly transform work.

Keywords

Paul Rubenstein, Visier, people analytics, workforce planning, CHRO, human-machine efficiency frontier, ROI curve, humanity index, agentic AI, MCP server, intelligent service delivery, governance, data strategy, organizational design, courage gap, talent strategy, future of work

Takeaways

• CHROs should track three curves: the human-machine efficiency frontier, the ROI curve on AI investments, and a humanity index covering talent density, engagement, and culture.

• Most organizations are stuck in the “gym membership phase” — distributing AI tools without redesigning work — and real returns require intentional deconstruction and reassembly of jobs.

• The courage gap is real: employees need to see self-disrupting behaviors modeled and rewarded before they willingly give up tacit knowledge to train agents.

• MCP servers enable systems-to-systems intelligence that can give managers contextual, proactive insights in the flow of their work — without them ever having to engage HR directly.

• Workforce planning is entering a golden age, requiring continuous, real-time, multi-dimensional design that mirrors how finance operates with FP&A.

• AI governance needs to shift from restricting data by default to securing personal accountability for use — otherwise AI answers will remain narrow and biased.

• When all companies have access to the same agents, people and culture will again be the differentiator — making the humanity index a strategic, not just a moral, priority.

Quotes

• “The floor for the tools we expect at work has just risen. AI is one.”

• “You can’t lay off a hand or an arm to recover your technology investment.”

• “I want my workforce plan to be as easy as Google Maps — give me traffic updates and help me reroute.”

• “Sameness does not yield greatness in a talent strategy.”

• “Don’t rely on your company for your career. You are responsible for staying relevant.”

• “Just because you can automate something doesn’t mean you should.”

Chapters

00:02 Welcome and introductions

00:53 Paul’s career journey and obsession with HR’s untapped potential

05:05 How AI is changing the analyst role and collapsing distance to insight

06:50 The three curves framework for CHROs navigating AI adoption

10:46 Strategic work planning vs. workforce planning and the agentic org chart

14:11 Manager evolution in a human-agent workforce

16:33 The gym membership phase and why job redesign is the real unlock

19:39 The courage gap and cultural conditions for AI adoption

23:29 Protecting durable human skills and doing the hard things

26:50 AI governance and the tension between data restriction and answer quality

31:38 MCP servers and the future of intelligent HR service delivery

38:54 Orchestration layers and proactive manager engagement

45:50 How analytics builds HR’s strategic credibility

47:03 AI as first mate and the case for continuous workforce planning

51:52 Closing thoughts on staying human-centric and owning your career

Paul Rubenstein: https://www.linkedin.com/in/paulrubensteinhr

Visier: https://visier.com

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ https://elevateyouraiq.com

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You also can find the Elevate Your AIQ podcast, along with tons of other great shows, on the WRKdefined Podcast Network!

At this stage, most organizations know AI matters. Some don’t yet realize how much, or even how best to use it. They run a bunch of pilots, debate strategy, form committees, and perhaps wait for regulations to catch up. But the gap between them and their competitors quietly (and quickly) widens. The problem isn't the technology, it's the leadership.

I’m thrilled to welcome back to the show Charlene Li — longtime analyst, best-selling author, and one of the most respected global voices on disruptive leadership and organizational transformation. Charlene is joining me fresh off the release of her brand new book, Winning with AI: The 90-Day Blueprint for Success, co-authored with Dr. Katia Walsh, one of the world’s first Chief AI Officers.

Charlene was last on the show in Fall 2024, when we explored the ‘knowing-doing-leading’ gap, how to use AI responsibly, and the human-AI partnership, and in this episode we revisit how that landscape has shifted — and dig deep into her 90-day blueprint for creating real enterprise value with AI, the AI fluency imperative, the concept of integrated intelligence and the rise of the superhuman worker. We also hit on why responsible, values-driven AI governance isn’t just good ethics — it’s good strategy.

Not every AI investment is a technology-focused one. If you believe that organizations which treat their people as strategic assets will have the highest likelihood of long-term AI success, this is a must-listen conversation.

Here’s a teaser where Charlene shares the story of a forward-thinking leadership team that clearly recognizes the strategic value of human-centric AI readiness, and already their organization is reaping the benefits. Music to my ears!

Thanks so much for subscribing and listening!

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Summary

Charlene Li, analyst, author, and disruptive leadership expert, returns to Elevate Your AIQ to discuss her newly released book, Winning with AI, co-authored with Dr. Katia Walsh. Charlene makes the case that most organizations are failing with AI because they treat it as a technology initiative rather than a strategic one — and lays out a 90-day, 12-step framework for building a foundation that creates real enterprise value.

The conversation revisits themes from her Fall 2024 appearance, including responsible AI and the human-AI partnership, and explores how the landscape has evolved. Key topics include AI fluency as an organizational imperative, workforce reinvestment over workforce reduction, and the emerging concept of integrated intelligence — where human and AI capabilities combine to create something genuinely superhuman.

Keywords

Charlene Li, Winning with AI, Katia Walsh, AI strategy, AI fluency, AI literacy, integrated intelligence, superhuman worker, workforce planning, reskilling, pilot purgatory, responsible AI, ethical AI, governance, human centricity, talent transformation, future of work, organizational disruption, values-based AI, co-intelligence

Takeaways

• Lead with business strategy, not AI technology — the question is never “what can we do with AI?” but “how can AI help us accomplish what we’re already trying to do?”

• AI fluency, not just literacy, is the goal — fluency means reaching for AI naturally, trusting it, and using it to learn how to use it better, like chopsticks becoming second nature

• Organizations stuck in pilot purgatory are procrastinating real decisions — pilots give everyone an excuse not to commit, and that dooms projects from the start

• The Konecta example shows a better path: use AI to raise workforce quality first, then expand customer value, then reinvent the business entirely

• Reskilling requires both organizational imagination and honest values — the IKEA story turned 8,500 displaced service reps into a $1B design business

• Integrated intelligence combines AI’s speed and scale with uniquely human traits — empathy, judgment, intuition, self-reflection, and wisdom — to create superhuman capability

• AI fluency in hiring is shifting from a red flag to a baseline expectation — how candidates use AI reveals curiosity, creativity, and adaptability far better than traditional interviews

• Responsible AI governance done right isn’t a compliance burden — a gold-standard internal policy means regulation becomes a checkbox, not a crisis

Quotes

• “You don’t need an AI strategy — you already have a business strategy. Figure out what of your business strategy could really be impacted with AI.”

• “Automating a broken process is the definition of madness. Because of AI, could we do this in a completely different way?”

• “AI can only be as creative as your questions are. It can only be as empathetic as you are.”

• “We should stop doing pilots. It’s just another way to procrastinate having to say yes or no.”

• “The first thing they said was, we are not going to use AI to cut people. That is not the intent going in.”

• “You aim for a higher level than any regulation would ever want. You go for the gold standard and whatever they ask of you, of course you do those things.”

Chapters

00:03 Welcome and guest introduction

01:27 Catching up since Fall 2024 and the impetus for Winning with AI

02:45 The 90-day framework and leading with business strategy

05:46 Reimagining work versus automating broken processes

09:22 AI fluency as an organizational imperative

14:06 Making AI practice habitual and learning in community

17:54 Embedding AI in the flow of work and escaping pilot purgatory

20:07 Workforce reinvestment and the Konecta case study

26:35 Reskilling, redeployment, and the IKEA story

29:54 Getting C-suite and boards to embrace a human-centric approach

33:38 Starting with customers and thinking beyond efficiency

38:30 Building AI fluency fast and making the investment

41:38 AI fluency in recruiting and hiring for AI capability

47:52 Integrated intelligence and the rise of the superhuman worker

50:42 From individual productivity to team and organizational impact

52:14 Values-based AI and imbuing organizational values into AI systems

55:53 Responsible and ethical AI as a strategic advantage

59:38 Goldilocks governance and the 90-day blueprint

01:00:21 Closing thoughts and book information

Charlene Li: https://www.linkedin.com/in/charleneli

“Winning With AI”: https://winningwithaibook.com/

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ https://elevateyouraiq.com

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Today I’m reconnecting with an old friend and former IBM colleague, Oded Dubovsky, founder of STRAIX — which stands for Strategy for AI Execution — an advisory practice helping organizations navigate their AI adoption journey from foundational infrastructure all the way to intelligent automation.

Oded brings a fascinating career arc spanning over two decades at IBM Research’s Haifa Lab, where he pioneered early computer vision and cognitive computing solutions, through applied AI work at Intel, and now guiding companies on building AI solutions aligned to their business strategy.

My last role at IBM was leading the ‘Cognitive Build’ intrapreneurial community, where cross-functional teams created novel solutions using the initial set of IBM Watson APIs (aka ‘cognitive services’). Oded led the development of one of the most popular solutions (out of thousands), titled “What Did I Eat?,” where his research team used computer vision to tell you the items and calories on your plate. (“What Am I About to Eat?” would probably have been more appropriate, but I digress. In 2016 this was quite novel and cool, trust me.)

Oded and I dig into why most organizations are still searching for the ROI of their AI initiatives, the importance of developing solutions responsibly, and why the human roles of architect, innovator, and decision-maker aren’t going anywhere.

So get ready for a candid, wide-ranging conversation between two IBM alumni who’ve been living at the intersection of technology and organizational transformation for a long time. I think you’re going to enjoy this one.

Below is a short teaser where Oded explains the value of AI-assisted coding tools and the importance of designing and building solutions responsibly…

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Summary

Bob Pulver reconnects with former IBM colleague Oded Dubovsky, founder of STRAIX (Strategy for AI Execution), an advisory practice helping organizations adopt AI thoughtfully and effectively. Oded shares a career journey spanning over two decades at IBM Research’s Haifa Lab — where he led pioneering cognitive computing and computer vision projects — through applied AI work at Intel, and into independent consulting. The conversation explores why 95% of organizations struggle to move beyond AI aspiration to real execution, and what it takes to build a solid foundation before layering in AI. Bob and Oded also reflect on the enduring value of human ingenuity, originality, and orchestration in an increasingly AI-assisted world.

Keywords

Oded Dubovsky, STRAIX, AI strategy, AI execution, AI adoption, cognitive computing, computer vision, IBM Research, Haifa Lab, Watson, automation, generative AI, vibe coding, AI-assisted coding, responsible AI, human centricity, AI readiness, orchestration, innovation, shadow AI

Takeaways

• Only about 5% of companies successfully adopt AI — most struggle with where to start, what tools to use, and how to build the right foundation before scaling

• AI is the “penthouse” built on top of decades of IT, software engineering, and automation experience — that foundational knowledge remains critical

• The human role is shifting from execution to orchestration and architecture — developers and knowledge workers are becoming “team leads” directing AI agents

• Responsible AI development means thinking through security, data, scalability, and governance from the start — not as an afterthought

• Slowing down to think carefully before prompting or building — echoing Einstein’s 55/5 rule — leads to better, more scalable outcomes

• Early cognitive computing projects at IBM (food recognition, augmented reality for remote guidance) were ahead of their time, foreshadowing capabilities now taken for granted

• Human originality and the ability to generate truly novel ideas remain a distinctly human trait that AI has not replicated

Quotes

• “AI is kind of the top level, like the penthouse on top of all of that.”

• “95% are just saying we need AI — they kind of don’t know how to absorb that, how to start using it.”

• “Once I crossed the line, I couldn’t go back.”

• “Think about it — you just got a promotion. You’re a team lead now. You don’t micromanage. You give them the bigger picture.”

• “If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and five minutes thinking about the solution.” — Einstein, as quoted by Oded

• “Slow down to speed up.”

Chapters

00:02 Welcome and introductions

01:04 Oded’s background and career journey from IBM to Intel to STRAIX

08:08 Early cognitive computing at IBM — the Watson era and the “What Did I Eat?” project

13:01 From research to product — augmented reality, 3D cameras, and lessons learned

17:54 How AI adoption is accelerating and compressing what once took a decade

20:14 Why 95% of organizations struggle to execute on AI

24:54 How STRAIX works — mapping pain points, building a heat map, and guiding implementation

29:47 Automation tools, vibe coding, and the value of foundational experience

33:13 Human readiness and the mindset shift required to embrace AI

37:22 AI agents, social networks, and the human as orchestrator

44:20 Responsible AI development — building with guardrails from the start

51:26 Asking better questions and thinking architecturally before building

53:31 Closing thoughts and how to connect with Oded

Oded Dubovsky: https://www.linkedin.com/in/odeddubovsky

STRAIX: www.straix.biz

For advisory work and marketing inquiries:

Bob Pulver:⁠⁠ https://linkedin.com/in/bobpulver⁠⁠⁠

Elevate Your AIQ:⁠⁠ https://elevateyouraiq.com⁠

Thanks so much for subscribing and listening!

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