Elevate Your AIQ

By WRKdefined Podcast NetworkBusiness Technology Management

Elevate Your AIQ episodes

October 09Ep 139: Maturing Recruitment and Recognizing AI-Ready Talent with Gary Hanley
Gary Hanley, Senior Vice President of US Talent Strategy and Recruiting at MCS Group USA, joins Bob to explore how AI is reshaping the recruiting profession and the talent market it serves. Drawing on a nonlinear career spanning Sun Microsystems, government, and global location strategy, plus his graduate teaching at Northeastern University, Gary explains why recruiters must move from filling requisitions to true talent advisory. Bob and Gary discuss hiring for roles that may change within 18 months, the erosion of the resume as AI-enhanced applications flood the market, and why assessments of curiosity and critical thinking matter more than ever. They also cover why early-career hiring remains essential, how global location strategy reduces talent risk, and why trust with candidates is built through community and repeated engagement.
Keywords
Gary Hanley, MCS Group, Northeastern University, talent strategy, recruiting, talent advisory, AI readiness, AI engineer, location strategy, skills-based hiring, assessments, AI-generated resumes, early-career talent, responsible AI, EU AI Act, talent pipeline, candidate trust, future of recruiting
Takeaways
AI is automating transactional recruiting tasks like sourcing and scheduling, pushing agencies and TA teams toward advisory work such as market mapping, compensation benchmarking, and role design.

Organizations confident in their AI readiness take a multi-threaded approach, combining recruitment with internal and external training across every role.

Hiring for roles that may look different in 18 to 24 months means prioritizing durable skills, learning mindset, and cultural fit over current job specs.

AI-customized resumes are undermining the resume’s value, so employers are shifting focus to assessments and asking candidates how they use AI as a thought partner.

Pausing early-career hiring is a mistake; graduates adopt AI tools quickly and remain essential to a sustainable talent pipeline.

Concentrating AI hiring in a few competitive cities increases attrition and salary inflation risk, making global location strategy a risk-management tool.

Trust with candidates is built over time through transparency, community events, and passive talent pipelines.
Quotes
“How do you hire for roles that you’re not sure if that role will be there in eighteen to twenty four months.”

“You can use a term like AI engineer and it could mean three different things in three different organizations”

“Many of the candidates we’re working with of course will have multiple offers at any point in time so they’re making a decision why this particular organization fits their career goals.”

“It’s encouraging to hear it’s not…apocalyptic for early stage talent coming out into the market. They certainly have value.”

“Trust comes from repeated engagements. All good things start with conversations”
Chapters
00:02 Welcome and introductions
01:06 Gary’s nonlinear career and MCS Group
04:38 AI shifts recruiting from transactional to advisory
07:14 Assessing AI readiness and in-demand skills
10:37 Teaching consulting and hiring for evolving roles
15:24 Transferable skills and selling candidates on strategy
20:16 Role definitions, compensation, and talent data
25:13 Resumes in the age of AI
29:45 Responsible AI, regulation, and global hiring
36:42 Assessing durable skills beyond the resume
40:02 Why early-career talent still matters
44:46 Building trust and community with candidates
51:16 The future of the AI-augmented recruiter
58:47 Final thoughts

Gary Hanley: https://www.linkedin.com/in/gary-hanley
MCS Group USA: https://mcsgroupusa.com

For AI readiness advisory work and marketing inquiries:
Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠
Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠
Substack: ⁠https://elevateyouraiq.substack.com⁠
58 min
October 02Ep 138: Reframing Leadership and Accountability for the AI Era with Dr. Adrian Wolfberg
Dr. Adrian Wolfberg spent four decades in national security, from flying Navy reconnaissance missions to thirty years at the Defense Intelligence Agency, while researching how people and organizations make decisions. He sits down with Bob Pulver to explain why silos and mismatched language have always undermined shared understanding, and why AI raises the stakes. Rather than reaching for the tool first, Adrian argues leaders must invest time up front to understand the problem, then match the right mix of human and AI to it and keep adjusting as reality changes. They also explore the uniquely human strengths worth protecting, from framing and empathy to accountability and critical thinking, and why reading may be key to developing them.
Keywords
Adrian Wolfberg, Who Leads When AI Thinks, Organizational Insight Consulting, decision making, leadership, national security, defense intelligence, silos, cognitive diversity, problem framing, human-AI balance, complexity, wicked problems, novelty, responsible by design, empathy, accountability, critical thinking, creativity, reading, judgment, decisions, collective intelligence
Takeaways
Where you sit determines what you see, and different organizational languages make shared understanding hard even before AI enters the room.

Unlike past tools, AI lacks transparent inputs, processes, and outputs, so leaders can no longer default to "just get me the tool."

Time spent framing a problem up front prevents costly rework and helps match the right degree of human and AI involvement.

Problems that are novel, complex, and value-laden demand far more human involvement than closed, well-understood systems.

The leader's role shifts from knowing the answer to setting conditions so everyone can spot change and help reframe.

Accountability must stay with humans, no matter how sophisticated the AI becomes.

Reading and mathematics build the creative and critical thinking we can least afford to offload.
Quotes
"Do the words mean the same thing from different organizations? And do we even care about the same things?"

"We can't compete with these aspects of AI and nor do we want to default everything to these aspects of AI."

"The leader's responsibility is not anymore, I know the answer, here's the solution, go forth, implement it via the resources that we have available to us."

"We don't want to just default accountability to AI. I don't think humans will allow that to happen."

"Reading offers the mind an opportunity to wander as one is thinking about these things."

"Preserving critical and creative thinking, that would be the worst thing to give to a machine and offload..."
Chapters
00:01 Welcome and introductions
00:47 From Navy reconnaissance to decades in defense intelligence
05:17 Silos, language, and the struggle for shared understanding
10:59 Why AI is unlike any tool before it
13:21 Leaders making time to understand the problem
15:18 Matching the right mix of human and AI
19:14 Cognitive diversity, bias, and choosing the right AI
23:41 Sizing up problems by novelty, complexity, and values
29:22 Unknown unknowns and being responsible by design
33:20 The risk of handing too much over to AI
35:31 Framing and reframing as uniquely human strengths
39:42 Empathy, setting conditions, and accountability
45:39 Protecting critical and creative thinking
46:42 Preparing the next generation and managing agents
51:03 Reading, smartphones, and AI in schools
57:12 Where to find Adrian and final thoughts on valuing time

Dr. Adrian Wolfberg: https://www.linkedin.com/in/adrianwolfberg
Organizational Insight Consulting: https://www.oicllc.org/
“Who Leads When AI Thinks”: https://www.amazon.com/dp/3032197163

For AI readiness advisory work and marketing inquiries:
Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠
Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠
Substack: ⁠https://elevateyouraiq.substack.com⁠
59 min
September 25Ep 137: Trading AI Hype for Evidence and Education in Talent Tech with Hayley Skivington
Hayley Skivington began her career in recruitment and has come full circle as Head of Product at Oleeo, where she sits at the intersection of product vision, commercial strategy, and the realities recruiters face every day. In this conversation with Bob, she explains why her role has expanded well beyond building features: with fear of job loss, litigation, and getting it wrong still widespread, vendors now have to hold customers' hands as educators and trusted advisors. The two dig into what responsible AI looks like in practice for a company serving government, policing, and financial services, and how Oleeo competes in a crowded market where ERPs, HCM suites, and AI startups all want a piece of recruiting. Hayley also offers an inside look at how Oleeo builds AI fluency across its own workforce, and Bob draws parallels to the early days of corporate social media bans. They close by looking ahead at how recruiters' work and skills will change as their tools become more connected.
Keywords
Hayley Skivington, Oleeo, responsible AI, explainability, talent acquisition, applicant tracking systems, AI literacy, candidate experience, EU AI Act, ISO 42001, vendor evaluation, shadow AI, Police Scotland, interoperability, cultural alignment, recruiter upskilling
Takeaways
Surfacing the evidence behind AI screening decisions gives recruiters confidence and gives candidates feedback to improve future applications.

Buyers adding AI features increasingly need sign-off from security and IT compliance, so vendors should equip them with ready-made documentation.

Integrating with existing systems like Oracle can deliver AI value without the cost and upheaval of replacing an ATS.

Police Scotland went from treating any AI use as cheating to cutting email inquiries by roughly 30% within weeks, freeing time for candidates navigating medicals and vetting.

Lunch hackathons and peer-led sessions spread practical AI know-how faster than formal training alone.

Hayley expects recruiting tools to converge with collaboration and analytics platforms, pulling HR, talent management, and talent acquisition closer together.

Recruiters should build skills in managing knowledge bases, evaluating compliance risk, and handling candidate appeals.
Quotes
Whenever anybody comes to us with anything, we always say, what's the problem? What are we trying to fix?

You don't need to rip everything out. We can complement your technology stack, but help you solve that problem that you've got that your current technology can't do.

We run a thing called the AI Forum and that's all about education. I'm not going there to talk about my latest product or why you all need to buy it from me.

Those simple tools... can be the light bulb moments in organizations that are really risk averse because a chat bot feels quite friendly, right?

People become strangely fond of whichever tool they're using the most.

My advice to recruiters is to be AI literate. Think about how you leverage AI within your role and really start to upskill yourself.
Chapters
00:01 Welcome and introductions
00:51 Hayley's path from recruitment to head of product
05:23 Building AI on strong foundations, not band-aids
09:06 Showing recruiters and candidates the evidence behind AI
13:29 Leveling the playing field and learning AI at home
18:43 Regulations, vendor evaluation, and shared responsibility
25:55 Competing in a crowded talent tech market
33:10 AI literacy inside Oleeo and the AI Forum
42:33 Police Scotland's journey from AI ban to adoption
45:20 Why banning AI leads to shadow AI
53:06 The evolving role of the recruiter

Hayley Skivington: https://www.linkedin.com/in/hayley-skivington-63217531/
Oleeo: https://www.oleeo.com/

For AI readiness advisory work and marketing inquiries:
Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠
Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠
Substack: ⁠https://elevateyouraiq.substack.com⁠
59 min
September 18Ep 136: Measuring Belonging to Strengthen Human Infrastructure with Eric Knauf
Bob sits down with Eric Knauf, founder of BelongHQ and author of The 56% Solution: How Belonging Infrastructure Transforms Performance, who traces his path from studying organizational psychology to leading talent through a company's 55% reduction in force and a historically low employee net promoter score (eNPS). That turnaround became the origin of his belonging framework: five measurable pillars, psychological safety, inclusion, support, connection, and purpose, each with a direct, causal tie to business outcomes like innovation, retention, and profitability. The conversation moves from operationalizing belonging inside real organizations to why most AI transformations are already failing before they start, and how the health of an organization's human infrastructure predicts its readiness for change. Eric and Bob dig into what it takes to close the gap between a company's best and worst managers, and why fixing that gap costs commitment rather than money.
Keywords
belonging, psychological safety, organizational health, human infrastructure, employee engagement, reduction in force, eNPS, talent leadership, AI transformation, change management, Deloitte, BetterUp, Amy Edmondson, frontline managers, inclusion, connection, purpose, The 56% Solution, BelongHQ, workforce analytics, retention, M&A due diligence
Takeaways
Filling a role isn't the same as creating value, and talent leaders should map where value is actually created before optimizing headcount

A 55% reduction in force and an eNPS of negative 73 became the origin story for Eric's belonging framework, which pulled that same team's score to positive 8 within six months

Deming's finding that 94% of performance variance sits inside the system, not the individual, reframes culture as an engineering problem rather than a personality problem

Belonging breaks into five measurable pillars, psychological safety, inclusion, support, connection, and purpose, each tied to a specific, causal business outcome

Averages hide the real risk. The gap between an organization's strongest and weakest frontline managers predicts far more than a single companywide engagement score

Psychological safety is the top predictor of whether employees actually use AI tools, according to a 2,250 person study Eric cites in the conversation

Only seven cents of every AI investment dollar reportedly goes toward people, even as 88% of AI initiatives fall short of plan

Strengthening human infrastructure costs commitment and ego, not budget, and pays off in retention, innovation throughput, and even M&A due diligence
Quotes
"It's one thing to fill a role. It's another thing for that human to actually add value."

"It was stated that 88% of AI initiatives are not going as planned."

"Ninety-three cents on the dollar are going to AI to the technology itself. Only seven cents on the dollar are going to the people."

"The number one predictor of whether or not they use AI, psychological safety."

"It requires being more human. It requires five things: psychological safety, inclusion, support, connection, and purpose."

"To improve those metrics doesn't require a lot. It requires being a better person."
Chapters
00:02 Welcome and introduction of Eric Knauf
00:36 Eric's roots in organizational psychology and path into talent leadership
02:54 Filling roles versus creating value, lessons from lean consulting
05:12 A brutal turnaround, leading a company through a 55% reduction in force
07:40 From negative to positive, the swing that led to writing a book
10:48 Systems versus people, and where the epiphany began
12:12 Discovering belonging, the Deming principle and the BetterUp research
16:30 Defining belonging, five pillars and why CFOs need proof, not emotion
20:31 Culture and engagement as outcomes, not goals in themselves
27:12 Operationalizing belonging infrastructure inside an organization
29:43 Why most AI transformations are already starting on the wrong foot
33:27 Psychological safety as the top predictor of AI adoption
45:14 Psychological safety failures at the C-suite level
49:14 Writing for the CFO, the skeptic, and the human side
59:00 Closing advice, know where you stand before you chase where you're going

Eric Knauf: https://www.linkedin.com/in/eknauf
BelongHQ: https://belonghq.com/
The 56% Solution: https://a.co/d/06xUAVmy

For AI readiness advisory work and marketing inquiries:
Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠
Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠
Substack: ⁠https://elevateyouraiq.substack.com⁠
1 hr
September 11Ep 135: Assessing and Evolving Human-Centric AI Readiness with Tracy St.Dic
Tracy St.Dic, VP of Global Talent at Zapier, joins Bob to talk about what it actually takes to build an AI-fluent workforce, drawing on her fifteen years in education, including a stint leading national recruitment at Teach for America, before joining Zapier. She walks through the origin and evolution of Zapier's AI fluency rubric, the difference between AI adoption and true AI transformation, and why she still rates her own company a four out of ten. The conversation covers how Zapier is reshaping the recruiter role into more of a talent advisor function, freeing people from busywork to focus on coaching and relationship-building, and the internal AI workbench her team is building to support that shift. Tracy and Bob also dig into the risk of companies blaming headcount reductions on AI when the real driver is a search for different skills, and why hiring for trajectory, not a static skill snapshot, matters more than ever.
Keywords
AI fluency, talent transformation, Zapier, Teach for America, AI adoption, AI transformation, citizen development, talent advisors, workforce upskilling, hiring philosophy, agent harness, slope over snapshot, human-centric AI, quality efficiency employee experience, responsible AI
Takeaways
Zapier's AI fluency rubric has four pillars: mindset, strategy, building skills, and accountability, and it applies to both hiring and internal development.

Tracy distinguishes AI adoption (bolting AI onto existing workflows) from AI transformation (redesigning work from the ground up), and rates Zapier a four out of ten on that scale.

A simple test for any AI initiative: does it improve quality, efficiency, and employee experience, not just speed.

Leaders need to define a clear vision for their function before scaling citizen development, or teams end up building in inconsistent directions.

Zapier is shifting recruiters toward a "talent advisor" role, using AI to handle research and reporting so people can focus on coaching and relationship-building.

Blaming headcount reductions solely on AI is often inaccurate; the real driver is companies wanting different, more AI-fluent talent.

Zapier hires for "slope over snapshot," prioritizing a candidate's trajectory and rate of learning over current tool proficiency.

The talent team is building an internal "TA workbench" inside an agent harness (Claude Code) to centralize context and best practices for recruiters.
Quotes
"Brilliance is distributed everywhere and opportunity is not."

"You can delegate the task, but not the accountability."

"Even if the technology isn't there yet, eventually it will be. And then you'll be ready for it."

"We're not hiring people for just what they know today. We want to hire people for the trajectory at which they climb."

"It's a very small percentage of companies that are seeing real ROI with AI right now."

"Their company's philosophy is to keep what you kill."
Chapters
00:02 Welcome and introduction to Tracy St.Dic
00:32 Tracy's path from Teach for America to VP of Global Talent at Zapier
02:06 Why access and democratization shaped her career
05:28 Origins of Zapier's AI fluency rubric and its four pillars
11:59 AI adoption versus AI transformation
16:22 A simple framework: quality, efficiency, and employee experience
19:43 Why leaders need a vision before scaling citizen development
24:02 Updating the rubric as AI fluency rises company-wide
27:14 Turning recruiters into talent advisors
31:27 Keep what you kill: reinvesting time saved
35:23 Why AI headcount narratives are often misleading
37:49 Hiring for slope over snapshot
40:56 Building the TA workbench inside an agent harness
46:06 Using AI for traceability and coaching
48:12 Final advice on building AI fluency

Tracy St.Dic: https://www.linkedin.com/in/tracy-stdic
Zapier: zapier.com
Using AI in Zapier’s hiring process: https://zapier.com/l/jobs/ai-at-zapier

For AI readiness advisory work and marketing inquiries:
Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠
Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠
Substack: ⁠https://elevateyouraiq.substack.com⁠
51 min
September 04Ep 134: Unpacking the Psychology Behind AI Success with Dr. Gleb
Bob is joined by Dr. Gleb Tsipursky, better known as Dr. Gleb, CEO and founder of Disaster Avoidance Experts and author of the new book “The Psychology of AI Adoption at Work: From Resistance to Results”. Drawing on over a hundred consulting projects and thousands of survey responses, Dr. Gleb explains why roughly 95 percent of AI pilots fail to show ROI, arguing the real obstacle is not the technology but three psychological profiles driving resistance: fear of job loss, threats to professional identity, and shame around quietly using AI in the shadows. They discuss how forced AI mandates can backfire into deliberately sloppy output, why untrained junior employees produce polished but poorly reasoned work, and the widening gap between how executives and everyday employees experience AI adoption. Dr. Gleb closes with practical fixes, from training people on tasks they hate first to having leaders model and reward AI usage openly.
Keywords
AI adoption, cognitive biases, decision science, psychology of AI adoption at work, change management, AI alarmists, pragmatic resistors, reluctant adopters, shadow AI use, AI slop, malicious compliance, psychographic profiles, identity threat, Office Whisperer, Disaster Avoidance Experts, MIT pilot study, Pew Research, leadership communication, AI training
Takeaways
95 percent of AI pilots fail to show ROI because leaders treat a psychological challenge as a technical one

Three psychographic profiles drive resistance: AI alarmists who fear job loss, pragmatic resistors who feel identity threat, and reluctant adopters who hide shameful shadow AI use

Forced AI mandates can trigger malicious compliance, where resistant employees deliberately produce sloppy output to prove the tool does not work

Untrained junior employees produce polished looking but poorly reasoned AI output, widening a generational skills gap

Executives and rank and file employees experience AI adoption very differently, a split reality that hides the fear driving resistance

Leaders reduce resistance by modeling their own AI usage publicly and rewarding employees who share new use cases
Quotes
It's not the technology that's a challenge. It's psychology.

The technology is great. But the social stigma is high.

You go slow to go fast.

The leaders need to model AI usage. They need to talk about it.

The crucial thing is leaders know that most people aren't engaged in their work.
Chapters
00:03 Welcome and introductions
00:54 Dr. Gleb's background and the new book
07:00 Why 95 percent of AI pilots fail to show ROI
08:58 The real barrier is psychology, and the three resistance profiles
12:57 A real world story of shadow AI use and hidden shame
18:30 AI slop, malicious compliance, and untrained junior employees
27:37 The split reality between executives and employees
31:48 Reaching the AI alarmists and pragmatic resistors
38:47 Reaching the reluctant adopters through modeling and reward
52:01 Workshops, the DIY approach, and closing thoughts

Dr. Gleb: https://www.linkedin.com/in/dr-gleb-tsipursky
Disaster Avoidance Experts: http://disasteravoidanceexperts.com/
The Psychology of AI Adoption at Work: https://a.co/d/0cdkVi76

For AI readiness advisory work and marketing inquiries:
Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠
Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠
Substack: ⁠https://elevateyouraiq.substack.com⁠
1 hr 2 min
August 28Ep 133: Empowering Communities and Unlocking Self-Awareness with Anne Descalzo and Rachel Zillner
Bob sits down with Anne Descalzo and Rachel Zillner, co-founders of The RADZ Group, to trace their path from banking colleagues to serial entrepreneurs building a diverse portfolio of companies. Anne and Rachel share what it felt like to step back from the CEO seat at Clutch, the company they built from scratch, and how they grew a venture fund that invests in historically underserved founders. The conversation turns to LQ: Listening Intelligence, their behavioral AI platform built on cognitive science, and how it helps people uncover blind spots and communicate more effectively at work and at home. They also discuss how AI shows up across their portfolio and why they want more founders to share their stories.
Keywords
The RADZ Group, Anne Descalzo, Rachel Zillner, Clutch, Minerva Fund, LQ Listening Intelligence, behavioral AI, cognitive listening assessment, venture capital, underserved founders, CEO transition, self-awareness, communication, coaching, Rancho Cordova, AI ecosystem, candidate fraud, cybersecurity, workforce development
Takeaways
Anne and Rachel built Clutch together before growing it into The RADZ Group, a portfolio spanning government consulting, co-working, venture capital, marketing, and technology.

Stepping back from a CEO role you built from scratch can bring genuine grief, even alongside pride and trust in new leadership.

Their Minerva venture fund intentionally invests in historically underserved founders, with most portfolio companies female founded or led by a founder of color.

LQ: Listening Intelligence draws on 15 years of cognitive science research to help people understand their own listening habits and adapt how they communicate.

Behavioral AI built on that research can prep people for difficult conversations and reframe misunderstood behaviors, like mistaking reflective listening for indecisiveness.

New AI risks are emerging too, including candidate fraud and cybersecurity threats tied to remote work.

Anne and Rachel see in-person community as essential to how their region and portfolio companies keep pace with AI.

They're on a mission to help more founders and leaders feel comfortable sharing their stories instead of staying under the radar.
Quotes
We think of projects like Play-Doh and everything has, you know, like a new shape that it can take.

I came downstairs, very dramatically draped myself across to Anne's desk and just, if I could ever leave this place, would you go with me? And I now call that my proposal.

Grief is associated a lot of times with death, but grief can also show up with transition and change.

We have 15 years of scientific research. It's the only scientifically backed cognitive listening assessment in the world.

We decided to start a venture capital fund to support other folks who don't always get the type of lending or support that they need.

Imagine what could happen in the workplace when folks show up with that understanding.
Chapters
00:02 Welcome and introductions
00:56 Meet Anne and Rachel
02:54 From banking colleagues to co-founders
04:15 The Clutch origin story
05:38 Building The RADZ Group portfolio
10:04 Stepping back as CEO and navigating grief
15:03 Introducing LQ Listening Intelligence
19:51 Behavioral AI for difficult conversations
22:34 Bringing the tool into everyday work
26:08 Uncovering hidden biases and rethinking labels
35:02 The future of listening as a standardized skill
37:15 Portable assessments and coaching applications
41:21 AI across the portfolio and the Rancho Cordova ecosystem
44:53 AI risks including candidate fraud and cybersecurity
48:16 What's next and encouraging founders to share their story

Anne Descalzo: https://www.linkedin.com/in/anne-descalzo
Rachel Zillner: https://www.linkedin.com/in/rachel-zillner
The RADZ Group: https://www.theradzgroup.com/

For AI readiness advisory work and marketing inquiries:
Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠
Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠
Substack: ⁠https://elevateyouraiq.substack.com⁠
56 min
August 21Ep 132: Untangling AI Readiness and Prioritizing Workforce Data with Olivier Vidal
Bob sits down with Olivier Vidal, founder of Sightline and a longtime HR tech product leader, for an overdue conversation on AI readiness. They explore why enterprise ambitions for AI so often outpace the underlying data and organizational maturity needed to support them, and how the workforce dataset is becoming an increasingly strategic asset. The conversation turns to how AI evaluation differs from traditional software testing, the risks of vibe coding sensitive HR processes, and the many, sometimes conflicting, definitions of AI readiness circulating in the industry. Bob and Olivier also dig into explainability, using analogies from mapping apps and self-driving cars, and close with a candid look at how much of the substantive decision-making has already shifted from humans to AI systems.
Keywords
AI readiness, workforce data, HR tech, Talent Intelligence Collective, Sightline, data maturity, AI evaluation, vibe coding, responsible AI, explainability, agentic AI, WPP, Adecco, human-AI teams
Takeaways
Enterprise AI ambitions routinely outpace the data and organizational readiness needed to support them, a gap Olivier sees at companies of every size

Workforce data is poised to become a top-tier strategic asset as agentic AI needs much higher-fidelity information to orchestrate human and AI work

Traditional HRIS systems and fragmented tool stacks miss the unstructured, contextual data AI systems actually need

AI evaluation is a distinct discipline from traditional software QA, requiring specialized expertise to stress-test models and guardrails

Olivier cautions against vibe coding AI solutions for sensitive HR use cases without proper evaluation and governance

AI readiness spans individual skills, technical model controls, and organizational information flows, and conflating them creates confusion

As AI takes on more decision-making in workforce tools, human oversight risks becoming a rubber stamp unless systems are genuinely explainable

Real transformation requires redesigning workflows and roles around AI, not just layering AI onto existing jobs
Quotes
“In an awful lot of the projects I've been involved in, the hopes and dreams of senior management have been miles ahead of the actual preparedness of a business to feed a given system with the information it needs to make decisions”

“If you follow the logic through to its sort of maturity, ultimately, the company's own data set is the product”

“I'm really cautious about vibe coding anything frankly that touches sensitive data. It's a different club, a different mindset. I'm not in it”

“There are a lot of people building ‘agents’ for things that could just be basically automated rules”

“We're beyond the point where the humans are actually making the substance of the decision. They are just acting as a fail safe on have we done anything monumentally unfair or monumentally stupid”

“Sightline is a new AI readiness practice for workforce products, we look at all of the client side data and knowledge that feeds systems and makes them work”
Chapters
00:02 Welcome and introductions
01:15 Olivier's HR tech backstory
03:40 Readiness gaps across big and small companies
06:19 Trust and the rising value of workforce data
12:18 Human-AI teams, data quality, and tool sprawl
16:10 Talent intelligence and the data as product
22:18 AI evaluation, vibe coding, and where the caution lies
29:22 Untangling AI literacy, fluency, and readiness
33:14 Defining organizational AI readiness
38:53 Accountability, explainability, and the Google Maps analogy
49:10 WPP's value chain and disrupting your own role
52:46 Fear of change, adoption, and Sightline's parting words

Olivier Vidal: https://linkedin.com/in/ojvidal
Sightline: sightline-ai.co

For AI readiness advisory work and marketing inquiries:
Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠
Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠
Substack: ⁠https://elevateyouraiq.substack.com⁠
57 min
August 14Ep 131: Scaling Human-Centric AI in Government and Higher Ed with James Regan
James Regan, CEO of Clutch, joins Bob to unpack what he learned leading some of the earliest generative AI deployments inside California state government under Governor Newsom's 2023 executive order. James traces his path from public health in Health and Human Services to Deputy Secretary for Workforce Development, and explains how procurement and change management had to be rebuilt to keep pace with AI. He and Bob discuss why reducing employee fear of AI starts with human centered design, and why the real opportunity in workforce AI is skills matching tools built for job seekers, not just recruiters. They also cover California's Career Passport initiative, Clutch's change management method built on the human trauma curve, and how universities are rethinking AI literacy for the future workforce.
Keywords
James Regan, Clutch, California state government, Governor Newsom, generative AI, workforce development, Google Public Sector, AI governance, procurement policy, change management, human centered design, human trauma curve, skills based hiring, skills matching, career mapping, veterans, Career Passport, AI literacy, higher education, AI readiness, job displacement fear
Takeaways:
Early generative AI pilots in California state government spanned transportation, health and human services, and tax, proving out real production use cases under Governor Newsom's 2023 executive order.

Sustainable AI adoption in government required rebuilding procurement, since traditional buy once, freeze code IT purchasing does not fit generative AI's constant evolution.

Human centered design and consistent, repeated communication, not just tooling, are what actually reduce employee fear of AI driven job displacement.

The bigger opportunity in workforce AI is not recruiter facing tools, it is skills matching tools that help job seekers, including veterans, translate existing skills into new job qualifications.

Skills based hiring is gaining ground as employers move away from defaulting to a four year degree, especially with AI driving demand for skills learned through certifications.

California's Career Passport initiative aims to create a portable, verified record so job seekers do not have to repeatedly prove the same credentials.

Clutch is launching a change management method built on the cognitive science of the human trauma curve, designed to quantify and reduce individual resistance to workplace AI rollouts.
Quotes:
"One of the things that drove our philosophy was creating a safe space to learn by doing."

"It's not something happening to them. It's something that is happening with them and with their input and support."

"The post and pray method does not work. It does not work."

"I think one of the biggest fears that we're hearing in sentiment across the state among students is not knowing which degree program or which education track to pick."

"A lot of AI tools are being deployed in a way that reinforces the fear and doubt of its effectiveness. We're here to shatter that problem."
Chapters:
00:02 Welcome and introductions
00:42 James's path from public health to California state government
02:34 Early generative AI pilots launched under Governor Newsom
06:04 Procurement, governance, and vendor partnerships in early AI rollouts
10:32 AI readiness, job displacement fears, and human centered design
19:36 Rapid AI deployment and balancing stakeholders in the process
23:12 Skills matching and skills based hiring for job seekers
35:41 California's Career Passport and verified learning records
40:17 Clutch's new change management method built on the human trauma curve
46:50 University partnerships and the future workforce
51:00 Closing thoughts and where to find Clutch

James Regan: https://www.linkedin.com/in/james-regan-jr
Clutch: https://www.clutchgov.com/

For AI readiness advisory work and marketing inquiries:
Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠
Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠
Substack: ⁠https://elevateyouraiq.substack.com⁠
54 min
August 07Ep 130: Charting Career Reinvention and Prioritizing Responsible AI with Erika Oliver
Bob sits down with Erika Oliver, Founder and Managing Director of NewtonHaus and Executive Analyst at Aptitude Research, for a wide ranging look at where AI is really landing in HR and the workforce. Erika shares her non-linear path through executive search and coaching, an unexpected pivot into labor market intelligence, and a moment that reset her priorities and sharpened her focus on the human side of work. The two dig into the shift from the year of the pilot to hard questions about ROI, why AI readiness now includes security and guardrails, the difference between responsible and human-centric AI, and the build versus buy pressure facing HR tech. It is equal parts career wisdom and market analysis, with a part two already in the works.
Keywords
AI readiness, responsible AI, human-centric AI, AI pilot, AI ROI, HR tech, talent acquisition, talent intelligence, workforce analytics, executive search, executive coaching, career pivot, build versus buy, agentic AI, security, guardrails, candidate experience, veterans hiring, neurodiversity, transformation, IBM Watson, NewtonHaus, Aptitude Research, Erika Oliver, Bob Pulver, Elevate Your AIQ
Takeaways
The market is shifting from the year of the pilot to a harder reckoning over ROI and where AI truly delivers value.

AI readiness now goes beyond willingness to adopt; security, guardrails, and responsible deployment are central to the conversation.

Responsible AI and human-centric AI overlap but are not the same, and the onus for human-centric deployment sits largely with buyers, not just vendors.

Responsibility starts with the individual, using AI where you should rather than wherever you can, not waiting for a corporate framework or legislation.

Build versus buy is a real pressure point for HR tech, and building responsible, enterprise grade solutions is far harder than it looks.

Career reinvention is possible amid fear and uncertainty, and the right opportunity is often the one you least expect.
Quotes
"Sometimes the opportunity that is for you is the one that you least expect, the one that you don't think you're qualified for."

"Regardless of the fear, regardless of the unknown, there is a path forward. You just have to be dedicated to seeing that through and what that means for you."

"Don't let somebody else tell you solely how to be responsible."

"As someone who's come from the vendor side, it's as much the responsibility of the buyer and the enterprise."

"The load is greater if it's done responsibly than I think a lot of boards and a lot of C level folks realize."

"If you don't invest in people, then it doesn't matter how much you spend on tokens." (Bob)

"Hold yourself accountable for using AI where you should, not wherever you can." (Bob)
Chapters
00:02 Welcome and introductions
01:08 Erika's winding path through executive search and coaching
06:08 An unexpected pivot into AI powered labor market intelligence
12:01 A health scare that reset her priorities
16:13 Building a portfolio of coaching, advisory, and analyst work
20:23 The year of the pilot and the push to prove ROI
27:57 Readiness, responsible AI, and human centricity
30:08 When agentic AI goes rogue and security takes center stage
32:33 Being responsible by design and accountable builders
38:11 The three pillars and why responsibility starts with us
42:37 Transformation, Watson, and adapting to constant change
44:49 Solving for candidates, veterans, and neurodiversity
54:09 The build versus buy pressure facing HR tech
1:00:13 Responsible AI in the build versus buy calculus
1:04:09 Closing thoughts on pace, people, and part two

Erika Oliver: https://www.linkedin.com/in/eoliver
Newton Haus: newton-haus.com
For AI readiness advisory work and marketing inquiries:
Bob Pulver:⁠⁠ ⁠https://linkedin.com/in/bobpulver⁠⁠⁠
Elevate Your AIQ:⁠⁠ ⁠https://elevateyouraiq.com⁠⁠⁠
Substack: ⁠https://elevateyouraiq.substack.com⁠
1 hr 6 min

About Elevate Your AIQ

From the publisher's feed

Bob Pulver is helping each of us navigate our respective journeys with artificial intelligence (AI) effectively and responsibly. Bob chats with AI and Future of Work experts, talent and transformation…