— Marc Weinstein

Episode Overview

In this episode I’m joined by Marc Weinstein, an attorney with 27+ years of practice focused on organizations that use tests to make high-stakes decisions about people — licensure, certification, admissions, and employment selection. His work centers on the legal rights of people impacted by AI systems and the organizations that use them.

The legal ground under AI hiring is moving fast. In the past few months a state AI law got sued into a rewrite, a court handed down a ruling that shields bias audit data from view, and a new lawsuit opened a line of attack nobody was watching. If you buy, sell, or use AI hiring tools, this episode helps you stay on top of the shifting sands.

Topics Discussed & Key Insights

1. First, how to roll: governance before headlines.

Marc’s advice starts above the regulations. Your own governance is the key to successful risk mitigation. Get this part right and the rest gets much easier.

• Before you track any law, ask why you want to use AI in the first place. If you have good answers, then ask how

• Set principles for your organization — not for the whole world, for your organization. Let them drive your use cases, your vendor selection, and your evaluation criteria

• Operate by them for real. A policy that lives on paper is worth nothing

• Do this and you’re already in compliance with most of the laws that could touch you. Everything below is what’s shifting underneath that foundation

2. Vendor liability is shifting fast — get ready now.

For a long time, vendors selling hiring tools got a pass. How the customer used the tool was the customer’s problem. That arrangement is breaking down.

• In Mobley v. Workday, the court is letting claims proceed on the theory that the platform acts as an agent of its employer customers — which puts the same federal anti-discrimination laws that apply to employers on the platform

• Plaintiffs go where the deep pockets are. Mega vendors like Workday and 8fold are the deep pockets

• Employers are still independently on the hook. Vendor liability adds to yours, it doesn’t replace it

• Newer legislation points the same direction, and California’s new regulations make bias testing itself evidence in court — more on that below

• Since we recorded: the court refused to dismiss most of the remaining claims against Workday. The case keeps moving, and the core theory keeps surviving

3. Bias audit data can be withheld — and that says a lot.

A discovery ruling in the Workday case protected the company’s bias testing data from the plaintiffs. Worth sitting with what that means.

• The court held the data was privileged because lawyers curated it and the testing was done to provide legal advice — not for business use

• Publicly advertising that you conduct bias testing did not waive the privilege

• So the people alleging harm from the system may never see the data that would show whether the system harmed them

• The bigger question: if the most important evidence about how these tools perform can be shielded, what does accountability actually look like? Proof may have to come from other directions

4. There are new ways to hold these tools accountable.

The Eightfold case shows creative lawyering finding routes into AI hiring that didn’t exist a year ago.

• The claim is built on the Fair Credit Reporting Act and privacy law — the argument is that AI-generated candidate scores are consumer reports, compiled without the required disclosures and consent

• These theories don’t require proving discrimination as a legal element, which makes them easier cases to bring

• The target hasn’t changed. It’s still about how these tools treat people. There are just more ways to get there now

5. Colorado is the cautionary tale: get aggressive, get crushed.

Colorado passed the most rigorous state AI law in the country — mandatory risk management, impact assessments, a real duty of care. Then it got taken apart.

• xAI sued to block the law. The DOJ intervened on xAI’s side — the first time the federal government moved to invalidate a state AI law

• The legislature rewrote the law under pressure. The replacement is a notice-and-transparency framework, delayed to January 2027

• The lesson for everyone else: states that regulate AI aggressively are going to get sued by the federal government and the private sector. Plan around that reality

6. Meanwhile, California quietly became the strictest.

While everyone watched Colorado, California put real requirements on the books — and nobody has sued to stop them.

• Under the FEHA regulations, conducting a documented bias test supports an employer’s defense in discrimination litigation. Not conducting one supports the plaintiff. Records must be kept four years

• The privacy agency’s automated decision-making rules take effect January 2027: pre-use notice to applicants and employees, opt-out rights, and the right to demand how a system reached a decision about you

• That last one matters. Can anyone actually explain how a frontier AI model reached its decision? For most of these tools, the honest answer is no

Final Takeaway

The laws will keep shifting. Colorado got rewritten, California’s deadlines are coming, and the courts are redrawing vendor liability case by case. You can’t chase all of it. Build the governance foundation, make vendors show you their evidence — your data, not just their study — and treat every recommendation a machine makes as the decision it really is. Good governance gets you more than 90% of the way there, as long as it’s not just lip service.

— Nicole Mundy

Episode Overview

In this episode I’m joined by Nicole Mundy, Senior Research Analyst at Talentech Labs — a research and advisory firm that helps its enterprise clients evaluate and procure hiring technology systems.

Nicole brings a valuable perspective to the table because she sees the dynamic between vendor and buyer up close and personal. When combined with my experience in this same realm from the science side- our discussion shines light on the reality of what is happening in AI tech adoption for TA.

Topics Discussed & Key Insights

1. Companies are buying AI hiring tools at scale — and then leaving them switched off.

Among the world’s top enterprise organizations, Nicole estimates a surprisingly small amount are actually using AI to automatically assess active applicants.

Why?

Approvals never come - Companies buy the platform intending to enable the AI, “once the right approvals are in place- but years often pass without any change.

Pilots underperform - Big companies test these tools and often conclude they can’t really use them, or they just didn’t work.

Lack of solid ROI evidence - Despite the best intentions- most companies do not do the follow up work needed to demonstrate the impact of these tools on the bottom line.

Legal ambiguity freezes decisions - With regulations constantly in flux, risk management often takes priority over business needs.

2. Validation is misunderstood and absent.

What vendors without I/O science guidance call validation isn’t what legal compliance actually requires.

• Vendors are quick to speak about the validity of their solution and talk endlessly about validating their AI models — running statistical checks that the model predicts consistently and de-biasing its outputs across groups. This is purely empirical work.

• But that’s IT-style validation. It confirms the system runs as built; it says nothing about whether the tool is fair or job-related

• Validation for legal compliance, and sound science, demands a blend of rational and empirical work to document the job-relatedness of any tool used to make employment decisions

3. “Skills” are everywhere, and nowhere.

Skills-based hiring is the headline everyone wants. The problem is what counts as a skill.

• Most platforms apply the “skill” label with no objective framework to define it. A skill ends up being little more than a tag like “Excel” or “communication”

• The definitions behind these labels are usually poorly organized and loosely constructed.

• There is no connection between the skills a platform claims to measure and any outcome on the job. Without that link, there is nothing for a buyer to trust or defend.

• The companies doing skills-based hiring well are not buying one vendor and flipping a switch. They run multi-year programs: define the skills objectively, inventory what the organization has against what it needs, curate tools carefully on the front end, and collect assessment data at multiple points.

4. Cheating is a zero-sum game, so let’s change the rules

AI-assisted candidate fraud, from AI completing assessments to coaching candidates through interviews, is driving enterprises toward more dynamic evaluation that is harder to game. But chasing detection is largely a losing battle.

• Trying to catch and block AI use is whack-a-mole. It’s a zero-sum game, and it’s frustrating, because the tools keep getting better and the detection never really gets ahead

• There’s a more useful way to think about it. People are going to use AI on the job, so why not let them use it in the application process in a controlled way? The question stops being “did they use AI” and becomes “how well do they use it”

• Most large organizations are still just trying to get visibility on how much cheating is happening and what it looks like, which says how early everyone is on this.

• Across the approaches Nicole sees, one thing tends to hold up whether a process is locked down or fully AI-assisted: competency-based follow-up questions that make candidates explain their own reasoning in their own words.

Final Takeaway

Enterprise isn’t slow on AI in hiring because it doesn’t understand the technology. It’s slow because the tools are bought on vendor claims that were never reviewed against real science, and the danger only becomes clear once the tool is in play. The companies getting it right aren’t chasing tools. They’re building programs, science first. Held to that bar, many of the tools on the market today wouldn’t survive the review, and the ones that would wouldn’t be sitting switched off.

Robert and I (and my AI co-host Mayda Tokens!) dig into one of the most urgent problems in hiring right now: the complete breakdown of traditional hiring signals.

We ponder the question- “How do we find the truth in an age where AI has flooded the top of the funnel, made credentials and resumes unreliable, and put enormous pressure on organizations to find new ways to identify talent?”

And we come up with some pretty good answers!

1. The Top of the Funnel Is in Chaos

The numbers are staggering. Accenture’s global resourcing lead told Robert they’re on pace for 12 million applications this year for roughly 100,000 hires — up from 4 million just three years ago. Same size team. Two and a half times the volume. The culprit isn’t a surge in qualified candidates; it’s AI-powered application tools that let candidates apply to jobs while they sleep. The moral contract between candidates and employers has been broken: candidates assume companies are using AI to screen, so they’re using AI to apply.

“It’s chaos out there. Candidates are using AI to fight AI — and we’re in a no-win scenario.”

2. Traditional Assessment Is Increasingly Gameable

Arctic Shores’ research from 18 months ago showed what most people didn’t want to admit: AI can ace virtually any traditional assessment format — personality tests, cognitive reasoning, multiple choice — with ease. And it’s not just about having a second screen open. Candidates can now point a phone at their screen, have the AI read the item, and get the answer instantly. Proctoring doesn’t solve this. The old protection mechanisms are obsolete.

3. The Answer Is Better Signal, Not More AI

The solution isn’t to ban AI from the process — it’s to design assessments that AI can’t easily game because they’re rooted in authentic behavior. Robert’s framework: if AI is being used to evaluate signals, those signals have to be grounded in high-fidelity behavioral data — not scraped from job descriptions, not inferred from keyword matching, not built on garbage in.

Job descriptions themselves are often the first failure point, and no amount of downstream AI sophistication fixes a weak foundation.

4. Stop Counting Leaves — Look at the Roots

Robert’s tree analogy is one of the sharpest frameworks in this episode. For decades, hiring has been obsessed with leaves — the skills on a resume, the credentials on a LinkedIn profile. But with the average shelf life of a skill now estimated at two and a half years, leaves are increasingly unreliable.

What matters is the root system: the durable human capabilities that allow someone to grow new skills, adapt to changing roles, and thrive in uncertainty.

5. Skills-Based Hiring Needs a Clearer Definition of “Skill”

Both Robert and I agree: the skills-based hiring movement is directionally right, but conceptually messy. Calling “innovation” or “persistence” a skill conflates what can be learned with what is innate. Durable traits — personality, cognitive style, learning orientation — don’t expire the way technical skills do. Measurement strategy has to account for these differences, or skills-based hiring just becomes the next echo chamber.

Final Takeaway

The hiring signal crisis is real — and it’s accelerating. AI has made it trivially easy to fake credentials, game traditional assessments, and flood the funnel with noise.

The organizations that receive the best signal won’t be the ones that deploy the most AI. They’ll be the ones that invest in the right signal: behavior-based, validated, and rooted in the durable human traits that no machine can fake.

*Claude.ai assisted with the creation of these show notes

— Taylor Sullivan

Episode Overview

In this episode I’m joined by rising I/O rockstar Taylor Sullivan, IO psychologist and the architect of Workera’s assessment strategy. With Taylor’s guidance Workera, a verified skills intelligence platform, is doing something most of the industry is still afraid to do: going all in on using AI to build, deliver, and validate AI-based assessments.

Taylor and I (and my AI co-host Mayda Tokens) dig into how this actually works, why it’s scientifically defensible, and why the industry needs to stop waiting and start moving.

Topics Discussed & Key Insights

1. Traditional Assessment Development Is Already Broken

By the time a traditional assessment clears all the I-dotting and T-crossing, it’s often already out of date. AI changes that — enabling dynamic content generation, richer construct understanding, and real-time iteration that keeps pace with how work actually evolves.

2. Codifying Measurement Science Into a Multi-Agent System

Workera didn’t just bolt AI onto existing processes. They embedded IO psychology’s core principles — evidence-centered design, validity frameworks, job analysis — directly into a multi-agent authoring system. Experts define the standards. Agents execute to those standards. The science drives the machine, not the other way around.

Here’s a brief sketch of how it works in practice

• Define the purpose — Tell the agent what you’re measuring and why. This grounds everything that follows.

• Extract the construct — The agent probes the skill space using critical incident techniques, identifying what great performance actually looks like.

• Design the assessment — The agent selects question formats (multiple choice, drag and drop, voice interaction, sequencing) based on what will best elicit evidence of the skill.

• Automated quality review — Before anything goes live, the system checks for bias, language issues, and content alignment to the original skill definition.

• Monitor and improve — Once deployed, the agent tracks response patterns, flags problems, and learns from score appeals adjudicated by humans.

The skill domain is flexible — it works for cheeseburgers or cybersecurity. The methodology behind it is the same either way.

3. The “Harness” — Why This Is Safe

The key to responsible agentic AI isn’t less autonomy — it’s a well-designed harness (the constrained ecosystem where the agents do their thing). Human experts define what good looks like, set quality thresholds, and build in escalation points. The agents work within those constraints and loop back when they hit uncertainty. As Taylor puts it: “It’s not running completely autonomously unchecked.”

4. This Is About Development, Not Just Hiring

Workera’s primary focus is post-hire — workforce development, upskilling, and learning. Once an assessment identifies verified gaps in a person’s skills, the platform connects those gaps directly to personalized learning plans, curating from an organization’s existing content library. Two people can get the same score on an assessment and walk away with completely different development paths based on their specific pattern of strengths and gaps.

5. Verified Skills Intelligence — What It Actually Means

In a world where AI can write a perfect resume and LinkedIn profile for anyone, credentials are noise. Verified skills intelligence cuts through that — using assessment to generate actual evidence of what someone can do, fit for the stakes of the decision being made.

Final Takeaway

The tools to move beyond multiple choice, beyond static assessments, and beyond slow validation cycles exist today. The bottleneck isn’t technology — it’s the will to trust well-designed systems. When the science is built into the machine from the start, speed and rigor aren’t in conflict. They’re the same thing.

“If you know what you’re doing, AI makes you faster. If you don’t, it just makes you wrong faster.”

–Louis Hickman

In this episode I’m joined by esteemed Psych Tech @ Work, Alumnus and AI research machine, Louis Hickman. Our incredible conversation taps into Louis’ myriad research studies to unpack AI’s direct impact on work, domain expertise, and talent assessment.

And of course, this episode also marks the return of the now new and improved AI podcast co-host Mayda Tokens (2.0).

Besides telling dumb jokes- Mayda’s job is to remind us that AI isn’t just a tool — it’s becoming an active participant in how we think, question, and explore ideas.

In the course of our conversation Mayda and I coax some PROFOUND take aways from our friend Louis as he shares the practical outcomes of his research:

1. AI is not removing the need for expertise — it’s making it more visible.

Scaling intelligence is easy.
Scaling judgment is not.

The organizations that succeed won’t be the ones that adopt AI the fastest.

They’ll be the ones that:

• Understand what they’re measuring

• Use AI to enhance — not replace — that understanding’

• Maintain control over how decisions are made
  

2. AI allows us to scale both good science and bad measurement

Louis pushes back on the idea that recent advances represent a fundamental shift in how we measure people. Instead, what we’re seeing is:

• Better models

• Faster processing

• More scalable systems

But none of that replaces the need for valid, reliable, and job-relevant measurement.

3. AI doesn’t level the playing field — it often rewards those who already understand the game.

One of the most interesting ideas in this episode is how AI interacts with individual differences in expertise.

At a high level:

• For simple tasks, AI helps novices perform closer to experts

• For complex tasks, AI actually widens the gap- allowing experts to perform better
  

  Why?

Because experts know how to ask better questions, recognize when AI is wrong, and refine its outputs—while novices often lack the ability to judge quality, diagnose errors, or course-correct when things go off track.

4. Replicability in LLMs Is Possible — if you know how to set it up right

A major “wow” moment in Louis’ research:

By running the model locally on the same class of hardware, fixing the model and prompt, and turning off sampling/randomness in the settings, you can make the system produce the same output for the same input every time.

5. AI should be used to scale decisions, but those decisions still need to be grounded in clearly defined constructs

At this point, AI adoption isn’t optional—it’s expected. Organizations are being pushed to move faster and scale, while vendors are rapidly building and deploying solutions, often without deep validation.

The resolution isn’t to slow down adoption—it’s to ensure we add and maintaining rigor.

6. AI makes it easy to scale assessment, but if the underlying design is weak, we’re just scaling bad measurement faster.

The resolution is to ensure what gets scaled is built on clear constructs, strong design, and validated measurement, so speed amplifies quality—not noise.

7. Working with AI is no longer just about what you can do—it’s about how effectively you can partner to make what you do better!

The tension is clear: AI can accelerate work, but over-reliance without critical evaluation leads to lower quality, missed errors, and reduced trust.

This shows up in real ways—unchecked outputs, declining attention to detail, and growing skepticism in collaborative work.

The resolution is that AI doesn’t replace accountability—users still need to apply judgment, review outputs, and take ownership of the final result.

Tune in to get the full story on these profound revelations and hear Mayda’s stand up comedy routine.

“The rules haven’t changed. The technology has — but the rules haven’t.”
— Nathan Mondragon

Episode Overview

In this episode, I’m joined by my old friend (and now co-worker!) Nathan Mondragon, an IO psychologist and long-time leader in creating the future at the intersection of assessment science, hiring technology, and applied AI.

Nathan and I have lived through multiple waves of “this will change everything” technology — from early online testing to video interviewing, machine learning, and now generative AI. And the beat goes on!

Nathan and I have recently joined forces at ProboTalent where we are creating defensible AI based assessment tools.

We talk about where AI has genuinely moved the field forward, where it hasn’t, and why so many of the debates we’re having today are versions of conversations we’ve been having for decades. Along the way, we unpack Nathan’s paradigm busting work at HireVue’, and why the fundamentals of good measurement haven’t changed — even as the tools have.

Topics Discussed & Key Insights

1. The Rules of Good Assessment Haven’t Changed — We Just Keep Forgetting Them

Nathan makes a point that anchors the entire episode: while technology has advanced dramatically, the core rules of good assessment — validity, relevance, interpretability, and fairness — are exactly the same.

AI doesn’t get a pass on methodology. If anything, it raises the bar for rigor, because mistakes scale faster.

2. Early Hiring Tech Was Built to Solve Operational Problems, Not Measurement Problems

We talk about the early days of online hiring and assessment, where the primary goal was digitization, not insight. Systems were designed to move paper processes online, not to improve how well we understand people.

That legacy still shapes today’s platforms — and explains why so many tools feel efficient but shallow.

3. HireVue Was a Real Paradigm Shift — and It Required Scientific Courage

Nathan reflects on the early days of HireVue and why it was genuinely revolutionary at the time. The breakthrough wasn’t just video — it was the larger shift toward digitizing and scaling structured assessment experiences in a way the field hadn’t seen before.

What made this moment interesting from an IO psychology standpoint is that it required a different mindset as a scientist: being willing to engage with a new modality, even when the measurement implications weren’t fully understood yet. Innovation in assessment has always involved tension — between rigor and experimentation, between what’s proven and what’s possible.

Nathan shares what it was like to help lead through that transition, and why thoughtful scientists have to be able to sit with uncertainty long enough to shape new approaches responsibly, rather than rejecting them outright.

4. AI Didn’t Create Bad Measurement — It Made It Easier to Scale

A recurring theme: AI doesn’t magically improve weak constructs. If you feed it noisy proxies, you just get faster, more confident noise.

We discuss why generative AI and machine learning don’t eliminate the need for careful construct definition — and why “it correlates” is not the same thing as “it measures something useful.”

5. Interactivity Matters More Than Modality

One of the most important takeaways: the future of assessment isn’t about whether something is text, video, or simulation-based — it’s about how interactive and information-rich the experience is.

Nathan explains why dynamic interaction reveals far more about decision-making, reasoning, and capability than static prompts ever will.

6. Native AI vs. Embedded AI Is a False Debate

We unpack the difference between “AI-native” products and traditional tools with AI layered on top — and why this distinction often misses the point.

What matters isn’t where AI lives in the stack, but whether it’s being used to improve interpretation, not just automate scoring or classification.

7. Skills and Knowledge Are Still Hard to Measure — and AI Has to Be Used Carefully

We close by confronting a reality the market often underestimates: skills and knowledge testing have always been difficult to do well, and scaling them without losing rigor is even harder.

We connect this directly to the work we’re doing at Probo Talent, where the focus is on a more responsible alternative: using AI to scale the parts of assessment that have historically been hardest to scale, while staying within safe, established modalities and an explainable, scientifically grounded wrapper. The goal is not novelty for its own sake, but a practical example of how AI can be used carefully to solve long-standing problems in skills-based hiring without sacrificing defensibility or trust

Final Takeaway

AI changes how we can build hiring and assessment systems — but it doesn’t change what makes them good.

If we ignore decades of psychological science in favor of speed, novelty, or convenience, AI will simply help us make the same mistakes faster. But if we use it to deepen interaction, improve interpretation, and stay disciplined about what we measure, it has the potential to finally move the field forward in meaningful ways.

–Doug Berg

In this episode, I’m joined by Doug Berg, head matcher and big kahuna at Match2, a longtime builder and operator in the talent technology/recruitment space and the only guy I know that wears flip flops to HR Tech..

Doug has lived and hacked nearly every iteration of online hiring — from fax machines and early internet job fairs to today’s AI-powered recruiting chaos.

Doug and I have lived parallel lives in some sense. We have both been on the scene as recruitment went on-line and have continued to wage war against the barriers that are blocking successful hiring. But Doug’s unique experience building recruiting focused tech helps us take a very well rounded perspective.

Doug and I trace the psychology of hiring systems, why most recruiting technology still fails both candidates and employers, and how efficiency-driven design has quietly stripped humanity out of the process.

We talk about what broke, why AI is making some problems worse before it makes them better, and what a candidate-centered future could actually look like if we stop designing hiring like a transactional funnel and start designing it like a relationship.

Topics Discussed & Key Insights

1. Hiring Has Always Been Psychological — Ignoring That Is Why It Breaks

Doug shares early recruiting stories that reveal a core truth: people don’t make job decisions based solely on skills or titles. They’re driven by values, aspirations, lifestyle preferences, and identity. Yet most hiring systems still treat people as static records instead of dynamic humans.

Music to the ears of a psychologist like me!

2. Applicant Tracking Systems Were Built for Control, Not for Candidates

We unpack how applicant tracking systems were designed for compliance and efficiency — not engagement.
The result:

• One-way transactions

• Forced applications

• Zero room for curiosity, context, or conversation

Doug explains why this original design choice still haunts modern hiring.

3. AI Isn’t Breaking Hiring — It is Amplifying the Broken Parts

AI didn’t invent hiring dysfunction — it amplified it.
Candidates now apply to dozens of jobs at once using bots.
Employers respond with more screening, more filters, more automation.

The outcome?
More noise. Less signal. Worse experiences on both sides.

4. Real Hiring Happens Through Interaction, Not “Efficiency”

Doug tells stories about simple interventions — like proactive chat on career sites — that led to real hires for impossible-to-fill roles.
The lesson is clear: when candidates are allowed to participate instead of comply, hiring actually works.

5. Hiring Will Stay Broken Until Candidates Control Their Side of the System

One of the central ideas in the episode: candidates have never been given real agency.
Doug explains the structural imbalance:

• Companies control the systems

• Candidates adapt or disappear

We explore what changes when candidates control their own data, preferences, and relationships — and why that shift matters.

6. The Resume Is a Dead Artifact — Identity Needs to Be Portable

Resumes are outdated snapshots.
Doug makes the case for living profiles, portable personalization, and persistent relationships that move with the candidate across employers.

AI finally makes this possible — not by enforcing rigid taxonomies, but by interpreting relevance across skills, experience, and context.

7. The Future of Hiring Should Feel Like Reconnection, Not Rejection

We close by zooming out.
Doug shares a simple but radical vision: if someone gets laid off on Friday, they shouldn’t start from zero.

They should already know:

• Who wants them

• What they’re worth

• Where they fit

Hiring shouldn’t feel like rejection roulette.
It should feel like an intelligent market reconnecting human supply and demand.

Final Takeaway

Hiring doesn’t fail because people are hard to assess.
It fails because we designed systems that ignore how people actually choose, trust, and engage.

AI won’t fix that on its own.
But used thoughtfully — with psychology, agency, and dignity baked in — it might finally help us build hiring systems that work for humans again.

AI literacy is becoming a baseline skill. This episode explores how organizations and individuals are actually building AI capability at work, with a focus on:

• Self-directed learning and AI education at scale
  

• Personalized learning journeys versus one-size-fits-all training
  

• The shift from basic AI use to agentic workflows
  

• The role of human strengths—creativity, judgment, and adaptability—in an AI-driven workplace

In this episode, I’m joined by Erica Salm Rench, an AI educator and leader at Sidecar AI.

Sidecar is an AI education platform and learning management system (LMS) designed to help organizations educate their employees on AI through self-directed learning.

It combines structured courses, role-based learning paths, and hands-on use cases so individuals can build AI capability at their own pace while organizations raise overall AI fluency.

Our conversation explores what AI education actually looks like beyond hype—how people are learning it, how organizations are rolling it out, and why understanding AI is quickly becoming a career differentiator rather than a technical specialty.

AI Education Has Shifted from “What Is It?” to “How Do I Use It?”

Erica explains that the conversation around AI in associations has changed dramatically over the last several years. Early on, organizations were hesitant to even talk about AI. Today, the question is no longer what is AI? but how can we use it to advance our mission, improve operations, and better serve our members?

That shift brings a new challenge: helping people move from curiosity to competence in a way that feels approachable rather than overwhelming.

Meeting People Where They Are

One of the strongest themes in our discussion is the importance of meeting learners at their current level of comfort and knowledge. AI education isn’t one-size-fits-all.

This means combining:

• Foundational AI concepts
  

• Role-specific applications (marketing, events, operations)
  

• A growing library of real-world use cases
  

• Ongoing updates as tools evolve
  

The goal isn’t to turn everyone into a AI engineer—it’s to help people understand what’s possible and apply AI meaningfully in their day-to-day work.

From Prompting to Agentic Work

We spend time talking about the evolution from simple AI use cases—like writing emails or summarizing content—to agentic AI, where systems take action on a user’s behalf.

This shift matters because it fundamentally changes how work gets done. Instead of just assisting with tasks,

AI begins to:

• Automate multi-step workflows
  

• Scale work that previously required human labor
  

• Act as a force multiplier rather than a one-off tool
  
  We agree that while much of this is still clunky today, the direction is clear: agents are becoming a core part of how work will be organized.

Personalized Learning Is the Future of Education

A major insight from the episode is that personalized learning journeys will define the next phase of education—especially in fast-moving domains like AI.

Erica describes how Sidecar uses AI within its learning environment to:

• Act as a learning assistant
  

• Answer questions in real time
  

• Reinforce concepts
  

• Help learners connect theory to application
  

This mirrors a broader trend: education becoming less about static courses and more about continuous, adaptive support.

The Psychology of Learning AI at Work

We talk openly about fear—fear of job loss, fear of falling behind, fear of not being “technical enough.” Erica makes the case that leaders have a responsibility to educate their teams, not just for organizational performance, but for people’s long-term career resilience.

From a psychological perspective, AI education:

• Reduces anxiety by replacing uncertainty with understanding
  

• Increases confidence and autonomy
  

• Helps people see AI as a collaborator, not a threat
  

Spending even 20–30 minutes a day learning AI can quickly change how people see their own future at work.

Human Strengths Still Matter More Than Ever

One of my favorite parts of the conversation is where we zoom out to the human side of all this. As AI removes technical barriers, the differentiator becomes human qualities—creativity, resilience, judgment, adaptability, and the ability to ask good questions.

AI doesn’t replace these traits. It amplifies them.

Used well, AI allows people to overcome past limitations, work around weaknesses, and bring their ideas to life faster than ever before.

What Listeners Should Take Away

AI literacy is becoming a baseline skill. The people who thrive won’t be the most technical, but the most curious, adaptable, and intentional about learning how to work alongside intelligent systems.

Education—done thoughtfully and continuously—is the bridge between fear and opportunity.

Where to Find Erica

Erica is highly active on LinkedIn and can be found through Sidecar AI, where she and her team are building education-first pathways into AI for associations, nonprofits, and mission-driven organizations.

— Conrad Shaw

Conrad is perhaps the most unique guest I have had in the 5 year history of this show and he is on to talk about Universal Basic Income (UBI) , a very unique topic that is growing in exposure.

For almost a decade Conrad has dedicated his life and career to furthering the cause of Universal Basic Income (UBI).

In 2016 he and his wife started a documentary called Bootstraps which focuses on following families who lived through the experience of a basic income.

Since then, he has:

• Fundraised for and operated a nationwide basic income pilot
  

• Filmed a multi-year docuseries currently in post-production
  

• Co-founded Commingle, a mutual-aid platform enabling communities to self-fund their own grassroots basic income systems
  

• Worked extensively on messaging, outreach, and public education around income, stability, and societal transformation
  

I learned a lot from Conrad and our conversation debunked my own myths about UBI. So a really important part of this episode is the truth about what Universal Basic Income (UBI) actually is — and what it is not.

What Universal Basic Income (UBI) Is — And What It Isn’t

UBI is the idea that every person receives a recurring, unconditional, baseline income — a financial floor that ensures no one starts the month at zero. It is not meant to replace work or equalize everybody’s income. Instead, it shifts the starting point so people can make decisions from stability rather than desperation.

What UBI is:

• A stable, universal base-level income for all
  

• A platform for economic mobility and personal freedom
  

• A modernized, simplified social safety net
  

• A tool for reducing the survival-based pressure in the labor market
  

What UBI is not:

• It does not eliminate jobs
  

• It does not cap how much people can earn
  

• It does not remove incentives to work
  

• It is not a socialist equal-wealth system

UBI reframes the labor market so people compete for work based on interest, alignment, and ability, not raw financial need.

Practical Ways UBI Could Work

Conrad’s work goes beyond speculation. He has spent nearly a decade building practical UBI experiments, including the national pilot documented in Bootstraps (2016) and his current role with the Income To Support All Foundation and Commingle, a new community-driven model.

He explains that UBI can be implemented through several pathways—government programs, private pilots, or community-level mutual aid—but none are simple. A government-led UBI requires political will and rethinking how we allocate resources. Philanthropic pilots can demonstrate impact, but they’re temporary. Community models like Commingle allow people to pool and redistribute resources now, without waiting for legislation, but scaling them is challenging.

What’s clear is that executing UBI at any level is difficult, requiring trust, infrastructure, and cultural acceptance. Yet the difficulty doesn’t diminish the need. Instead, it underscores why experimentation and new models matter.

Individual Differences: Why UBI Supports People Doing What They’re Meant to Do

One of the deepest connections between Conrad’s work and mine is the concept of individual differences—the idea that every person brings a unique constellation of strengths, traits, interests, and abilities that make them naturally better suited to certain kinds of work.

When people are trapped in survival mode, those natural gifts often go unused. They pick jobs they can get, not jobs that reflect who they are. Freedom from this paradigm reshapes careers in ways that benefit both individuals and employers, allowing people to walk away from toxic or exploitative conditions and take jobs they genuinely care about, leading to better performance and engagement.

With a secure foundation, people have the psychological and financial freedom to make career decisions based on fit, not fear. This supports:

• Better alignment between person and role
  

• Higher engagement and intrinsic motivation
  

• Better workforce outcomes because people choose work that matches their abilities
  

• Greater societal value, as more people apply their genuine talents instead of defaulting to whatever job pays immediately
  

From Conrad’s perspective, this alignment is one of the most compelling aspects of UBI. When people are free to choose work that resonates with their abilities, the labor market becomes more efficient and more human. Employers gain workers who actually want to be there. Individuals gain a sense of purpose rooted in their authentic strengths.

In a world where AI, automation, and job volatility make career paths uncertain, helping people express their natural abilities becomes more important—not less.

How AI Fits Into the UBI Conversation

AI enters this conversation as both a catalyst and a complicating force. As Conrad points out, technological change is accelerating so quickly that we can no longer predict which jobs will exist, which skills will matter, or how stable any given career path will be. This uncertainty puts enormous pressure on individuals—especially those who don’t have the luxury to retrain, take risks, or weather employment gaps. UBI provides a stabilizing infrastructure in that landscape, giving people the freedom to adapt as work evolves rather than being overwhelmed by it.

AI serves the UBI concept well because it highlights the importance of individual differences:

as routine tasks get automated, the value of uniquely human abilities—creativity, empathy, problem-solving, and deep domain expertise—rises.

UBI supports people in discovering and developing those strengths, while also offering society a buffer as AI reshapes industries faster than institutions can respond. In this way, AI doesn’t replace the need for UBI—it makes the case for it even stronger.

Why Making UBI Work Matters in an Uncertain Future

We must acknowledge the reality: we are entering a period defined by instability—rapid technological change, unpredictable job markets, and widening gaps between opportunity and access. In such an environment, the old assumptions about steady careers, stable industries, and predictable pathways no longer hold.

UBI becomes a tool for resilience. It doesn’t solve every problem, but it gives people the space to adapt, learn, and navigate a chaotic future without falling into crisis. It creates room for people to pursue what they’re best suited for, rather than what pays the most simply out of need.

The conversation frames UBI not as a political ideology but as a human-centered adaptation strategy—a way to strengthen psychological well-being, improve labor market alignment, and provide society with a more stable foundation as the world accelerates around us.

The truth is that UBI isn’t easy; it’s a fight against gravity in a system not built for change, but we are entering into an unprecedented level of uncertainty in all aspects of our lives- so we need to have creative and idealistic solutions

“You can’t implement skills-based hiring by flipping a switch. It’s about changing mindsets, systems, and the language your organization uses to describe talent.”

-Ashley Wallvoord

In this episode of Psych Tech @ Work, me and my AI co-host, Mayda Tokens, welcome fellow I/O psychologist (and LSU Tiger!) Ashley Walvoord, Senior Vice President of Talent at Verizon.

We are joined by my AI co-host Mayda Tokens who continues to impress at times and but showing a tendency to be pretty boring at other times and always telling really bad jokes (I think the API to Chat-GPT 5o gets a very different sense of humor than the consumer version).

I reached out to Ashley after seeing her SIOP presentation about Verizon’s skills based hiring (and organizational transformation) program. Her and her fellow presenters-

Max McDaniel (Verizon)

Christina-Norris Watts (J & J)

Ruth Imose (J & J)

Jason Frizel (Walmart)

provided amazing insights into their company’s’ amazing and inspiring skills based hiring programs.

The hype around skills based hiring these days makes it seem easy. But talk is cheap- and doing skills based hiring right takes a total ALL IN approach. - one that is rooted in the commitment to become a true skills based organization.

Ashley has lived this life and her experience provides an awesome preview of how one of the world’s largest organizations is reimagining hiring and development through skills and AI. We are all lucky to have her on the show!

Verizon’s transformation provides a rare look at how enterprise-scale companies operationalize skills-based hiring while navigating the practical realities of change management, technology integration, and workforce readiness.

Summary

This conversation bridges strategy and execution, offering a clear-eyed view of how a Fortune 50 company is aligning people, process, and technology around skills. Ashley shares the lessons learned from Verizon’s commitment to a multi-year, organization wide transformation. A journey with many whistlestops along the way— from defining skills frameworks to embedding them in hiring and internal mobility.

Key Themes

1. Building Skills Infrastructure at Scale
Ashley explains how skills-based hiring starts long before implementation — requiring shared language, governance, and validation across the enterprise. Verizon’s approach focuses on sustainability and integration rather than one-off pilots.

2. Human Oversight in an AI-Driven System
AI plays a growing role in matching and mobility, but Ashley underscores that human judgment remains central. The goal isn’t automation for its own sake, but augmentation — using technology to help people make better, more equitable decisions.

3. Culture Change Through Data Transparency
Verizon’s success depends on building trust with employees and leaders by showing the “why” behind skills data and AI insights. Visibility into how skills are used for development and promotion helps drive adoption.

4. Enterprise Challenges and Lessons Learned
Ashley shares the realities of scaling change: aligning functions, managing vendor relationships, and ensuring consistency across geographies. Her advice is practical — start small, demonstrate impact, and scale what works.

5. Future Vision for Skills and AI in Talent
Ashley envisions a future where skills become the connective tissue between learning, mobility, and performance — and where AI acts as a trusted partner in enabling opportunity at every level.

Takeaways

• Enterprise-scale transformation requires governance, not just technology.
  

• AI can accelerate fairness and insight, but must remain transparent and human-centered.
  

• Data visibility is the key to cultural adoption — employees must see personal benefit.
  

• Scaling skills frameworks demands partnership between HR, technology, and business leadership.
  

The future of work will depend on how we align AI, human judgment, and purpose at scale. And a commitment to verifying and managing skills at scale.

“When this all started (generative AI for the masses), the fear was ‘this is cheating.’ Now we’re flipping the conversation and saying, no — this is actually a skill set you need to develop.”

-Madeline Laureno

In this episode I welcome Madeline Laurano, Founder of Aptitude Research and one of the most trusted voices in HR and TA technology.

With more than 20 years of research and advisory experience, Madeline’s body of work has has tracked the evolution of all things mixing hiring, business, and tech.

We have known one another for a long time and are quite simpatico in our thoughts on talent acquisition, assessment, and skills based hiring.

And we prove it in this show - as we discuss the ins and outs of these crazy times for HR tech, hiring, and of course- AI.

So listen in and take a look into the crystal ball while staying grounded in the truth!

Topics discussed and wisdom dropped include:

1. Why ATS Are Going to Become Extinct

Madeline explained that ATS systems in their current form are not built for the way talent acquisition is evolving. Recruiters are frustrated because ATSs don’t support the workflows or user experience they need, and they will eventually be replaced by more dynamic, integrated platforms that actually match how hiring happens today. Hello AI!

2. What Her Research Says About Skills-Based Hiring

Madeline points out that skills-based hiring is more aspirational than real for most organizations. Aptitude Research has found that companies often treat skills like the old competency models — static, outdated, and resource-intensive — or via an over reliance on AI. Both make it hard to translate into practice without validated frameworks and clean, usable data. The path fwd requires a commitment to strategy, clarity, and validation.

3. How the Fast-Moving Nature of AI Impacts HR Tech Buying

Madeline notes that AI has changed how companies buy HR tech because the market is moving so quickly. In the past, companies would take years to build strategies before investing in technology, but now AI allows them to start much faster — sometimes adopting before they fully understand how to implement, which creates both opportunity and risk. Beware of AI FOMO!

4. Agentic AI and Hiring — What Will the Impact Be?

She described “agentic AI” as a coming wave where AI systems won’t just provide insights but will take autonomous actions. In hiring, this could mean systems that source, screen, and even interact with candidates automatically — raising big questions about oversight, fairness, and how much decision-making organizations are comfortable handing off to machines. Get ready because the rise of autonomous hiring agents is upon us.

5. The Impact of AI on Candidate Experience

Madeline stresses that AI can either improve or damage the candidate experience depending on how it’s implemented. Candidates expect personalization, transparency, and fairness, and if AI-driven processes feel opaque or impersonal, trust will erode quickly — but if designed well, AI can actually enhance communication and responsiveness. We must not villainize AI for this- there is a lot we can do enhance candidate experience and it can actually include the use of AI if done thougthfully.

6. What Will This Look Like 20 Years From Now?

Looking ahead, Madeline predicts that hiring will look radically different in 20 years, with skills-based approaches fully realized and AI deeply embedded into every step of the talent lifecycle. The key difference will be that technology will finally deliver on the vision of matching people to opportunities more accurately, quickly, and fairly at scale.

AMEN- let’s just make sure that people remain in charge!

Check out the episode and learn about the trends from two of the best!

& do yourself a favor and visit Aptitude Research’s website where you can find free access to all of their amazing research!

-Alexis Fink

Introduction

In this episode of Psych Tech @ Work, Mayda Tokens (my AI co-host) and I sit down with Alexis Fink, I-O psychologist, long-time HR tech leader at Microsoft, Intel, and Meta, longtime friend and president of The Society for Industrial-Organizational Psychology (aka SIOP)!

Alexis brings decades of experience at the intersection of people, organizations, and technology to the studio, offering a holistic and integrated perspective on the opportunities and challenges of AI in the workplace that is based on reality- not pure philosophy.

We challenge Mayda to hang with us as we talk about all things people, technology, and the future of work. Alexis rocks it. You be the judge of how well Mayda meets the challenge. Hint: like all AI, Mayda is still a work in progress that fails sometimes, while still feeling miraculous IMHO. I mean come on- she speaks in emoji!!!

Alexis leads the charge with her take on these great highlight topics:

1. The Transformation of Knowledge Work
AI is reshaping not just factory tasks, but the decision-making and knowledge roles once thought safe from automation.

2. Organizational Design in an AI Era
True progress requires rethinking workflows so humans and machines complement each other rather than compete.

3. Data Quality and Human-Centered Design
Most raw HR data isn’t fit for AI, making richer, cleaner, and more contextual data essential for real impact.

4. Risk, Accountability, and Quality Control
As AI takes on more autonomy, organizations must adapt proven quality management and governance principles to keep it accountable.

5. The Human Problem of AI Adoption
The hardest barriers to AI adoption aren’t technical but human — fear, resistance, and behavior change.

6. Looking to 2035: The Next-Gen I-O Psychologist
Future I-Os will master AI as a partner, using simulation and immersive tools while keeping work human-centered.

Conclusion

Our conversation underscores a central theme: AI is not even close to perfect and we need to recognize this (Mayda’s responses to our questions are proof of AI gone whack!)

AI’s future in work won’t be defined by algorithms alone, but by how organizations redesign processes, manage risk, and support people through change. For I-O psychologists, HR leaders, and technologists alike, the task ahead is clear — ensure AI is not just bolted onto old systems, but opens opportunities for true collaboration with we humans.

– Jimmy Lepore Hagan

Newsflash!

After a much needed hiatus- Psych Tech @ Work is back with a vengeance! During the break I have been heads down in my lab- experimenting and playing with AI.

SHE’S ALIVE!

This episode marks the debut of my self-created AI podcast co-host Mayda Tokens.  It took me three weeks to make her and during this process I explored the human side of effectively collaborating with AI.  Making Mayda required me to flex my creativity, critical thinking, flexibility and perseverance.  

My Mayda experience prepared me firsthand for a great conversation with Jimmy about creativity, AI, and the human psyche.

In this episode of Psych Tech @ Work, I welcome my new friend and fellow New Orleanian Jimmy Lepore Hagan.  Together we explore why

creativity is the missing link in many corporate AI readiness programs — and how it can be leveraged to help individuals and teams move from fear to fluency in a rapidly transforming world.

Jimmy brings his bold, experience-driven perspective to the conversation, making the case that creative courage is not a soft skill — it's a strategic asset.

Together, we discuss Jimmy’s new framework for enabling AI adoption through creativity — and my addition to the delivery of his hands-on workshop designed to help HR teams, L&D leaders, and talent professionals build AI fluency through creative exploration.

Summary

Creative thinking isn’t just about making art — it’s about rewiring our brains to embrace ambiguity, take risks, and explore the unknown. In this episode, we discuss how cultivating creativity can de-risk the AI learning curve, helping professionals feel more confident engaging with emerging tools.

In an era of automation, the ability to experiment, play, and fail safely is what separates those who adapt from those who resist.

These traits are not innate — they can be developed, and doing so can radically change how individuals approach new technology.

The episode also highlights a workshop experience that puts this theory into action: a fun, safe, and high-impact program designed to build creative fluency first — and then apply it to AI. This approach helps teams lower psychological barriers to AI experimentation and open the door to real skills development.

Learn More About Our Workshop

I have to give a direct and shameless plug for our workshop. Our workshop — combines science, storytelling, and hands-on exercises to help teams build the mindsets and skills needed for the future of work.

Learn More About Our Workshop

Hear all about it on the next episode of the Psych Tech @ Work Podcast - coming soon!

AI skills are essential but daunting

AI adoption is accelerating—over 70% of companies report they’re actively integrating AI tools into their workflows. But for the people expected to use those tools, it’s a different story.

Most professionals say they feel unprepared or even anxious about using AI on the job. Traditional training often falls short with AI skills because it focuses on tools, not mindset.

And the stakes are high: as AI becomes embedded in everyday work, careers will increasingly rely on comfort and expertise with AI.

This gap and the demand for innovative strategies to close it has been top of mind for me. Good news - my fascination with AI led me to a solution! (more on this later)

Creativity unlocks AI skills

I recently gave a talk at a meeting of the New Orleans AI Philosopher’s group (AKA NOAI), on AI and the future of our local economy.

At this event I saw a talk by Jimmy Lepore Hagan—an artist, designer and educator—who shared a fascinating approach to AI adoption that is fresh, unique, and noteworthy.

Jimmy’s talk was about the value of creativity in lowering fear of AI. He demonstrated some concepts from a workshop series he has developed featuring a series of low stakes, creative exercises grounded in design thinking to help people build comfort, confidence, and curiosity when working with AI.

As a workplace psychologist I immediately saw the potential for a collaboration - applying Jimmy’s hands-on educational model to my world to help people leaders solve a difficult problem.

As someone who’s spent decades applying psychological science to the development and measurement of human traits in the workplace, I have experience understanding the impact of creativity on outcomes that are directly related to work performance.

As I processed this stuff- I took a step back and reviewed foundational research that shaped my earlier work—this time, through the lens of AI. The connections stood out immediately. Traits like divergent thinking, cognitive flexibility, and creative self-efficacy have long been linked to performance, but they also play a critical role in how people approach new, uncertain technologies. The evidence is clear: creativity and experiential learning do more than build skills—they tap into deeply human strengths that make people more open, adaptable, and ready to thrive in the face of change.

My dance with AI says it all

It became pretty clear to me that a collaboration with Jimmy could really have some legs.

To get the ball rolling I invited Jimmy to be a guest on my Podcast “Psych Tech @ Work”.

To prepare I wanted to gain some first hand experience with using creativity to help me sharpen my AI skills.

I suck at coding and the requirement to use Python for this definitely gave me some anxiety, but I knew ChatGPT could somehow have my back.

Thus came the idea to challenge myself (and have some fun) building an AI podcast co-host, Mayda Tokens.

Mapping out and executing a workflow to bring Mayda to life threw me plenty of curveballs. Some of ChatGPT’s more noteworthy and frustrating shenanigans included:

• Multiple times ChatGPT relentlessly tried, and continually failed, to solve technical issues; but would not give up until I suggested that we were going in circles in a blind alley and maybe we should explore alternative methods. This prompt led immediately to a set of viable alternatives that would never have been explored if I hadn't decided to pull the plug.

• When I backed ChatGPT into a corner I was flabbergasted when, instead of hallucinating a solution or looking for another option, it simply refused to help me. This was a head scratching result that must have exposed a ghost in the machine because its prime directive is NEVER to say NO!

• As I explored different options for Mayda’s voice, my text to speech output randomly switched to Japanese and then to emoji

• As we hit dead ends trying to figure out how to bring Mayda into my podcast studio, I stupidly followed its instructions to run to Best Buy and Guitar Center to buy unnecessary hardware that neither place actually sold.

In the three weeks it took to bring Mayda to life, I became hyper-focused—borderline obsessed—with working through many obstacles. The dopamine hits I got each time we solved a challenge together reminds me that my brain chemistry is essential for accessing and applying uniquely human traits like creativity, critical thinking, resilience, and tolerance for ambiguity.

The interplay between my human biology and psychology was essential for winning the day, and my experience building Mayda really hammered home the value of creative collaboration with AI.

Our workshop is the gateway to fearless AI skills

Learn how we’re helping companies build fearless, AI-ready teams.

Visit our Workshop Landing Page

Viewing AI as a dance partner is the paradigm that serves as the foundation of our workshop. Instead of lectures, videos, and formulaic exercises; we use creative, hands-on activities that help people relate to AI in a way that feels playful, safe, and real.

In our workshop participants explore AI through:

• Improvised dialogue with generative models
  

• Creative prompt challenges
  

• Group problem-solving sprints
  

• Human-AI art collaborations
  

• Guided reflection and peer feedback

By mapping each of these design thinking centric, hands-on exercises to psychological principles—like creative self-efficacy, openness to experience, and experiential learning—the workshop becomes more than fun. It becomes a stealth learning experience where participants not only gain essential AI skills, they undergo cognitive changes that empower them to believe in the value of partnering with AI.

We believe our workshop can be a difference-maker for companies navigating AI transformation—and a real competitive advantage for those that are bold enough to think differently about AI adoption.

To learn more about our workshop, the collaborative ideas behind it, and meet Mayda Tokens Visit our workshop page and be sure to listen to our conversation about it on the next edition of my Psych Tech @ Work podcast.

Subscribe Now!

Stay ahead of the curve on AI and the future of talent. Every week I explore how technology and psychology intersect at work — with thought-provoking guests and my new AI co-host, Mayda Tokens.

We cover:

• AI for Talent Acquisition

• AI talent transformation

• Talent assessment & predictive hiring

• Skills-based hiring

• Psychology in the workplace

• …and more!

This blog offers a preview of the “AI for Recruiting Master Class” that I will be leading as part of my collaboration with Workplace Labs’ AI for HR Master Class series.

This isn’t a vendor showcase or a hype cycle summary. The course covers:

• Why now?: the systemic shifts behind AI’s rise in TA

• What matters?: how AI is being used across the hiring funnel

• What’s real?: how to map the current vendor landscape

• What’s risky?: how to evaluate and implement tools responsibly

• What do we do next?: strategies for getting started with the right foot forward

Pre-order bonuses (available until Friday, May 23 at midnight EST!):

BONUS 1: A 50% discount off the future price ($99 value)

BONUS 2: A free 1:1 call with Charles* ($299 value)

* Limited to the first 20 pre-orders.

If you pre-order now, the AI for Recruiting Masterclass will only be $99.

So, if you’ve been quietly trying to catch up without spinning your wheels—or you’re responsible for helping others make these decisions—this course is for you.

Learn More & Enroll

And now for a preview of Module Two of my course:

AI, Skills Based Hiring, & Recruitment Strategy

AI is a confusing and intimidating subject to begin with, but add in the vendor hype cycle and making the right moves becomes even harder.

The goal of my course is to demystify the use of AI in the recruiting tech stack. Separating the good from the bad through straight facts and credible knowledge.

Skills based hiring offers a good example. It is definitely a worthwhile cause that offers tremendous rewards when done properly. But execution requires a significant amount of dedicated strategy and planning. The hype cycle around skills based hiring offers the perception that waving the magic AI wand will allow you to instantly transform your recruiting process into a skills based masterpiece.

Really??

The straight facts on what skills based hiring is, what it takes to implement it properly and the role of AI doing so is a thread that is woven throughout my course.

Problem #1: Mistaking Skills for Labels

When thinking about the role of AI in skills based hiring, we must understand that:

-Skills without validation are just assumptions.
-Skills without purpose are just noise.

Everyone’s talking about skills-based hiring — but the truth is, most organizations are doing it incompletely. Fueled by buzzword-heavy vendor pitches and algorithmic shortcuts, “skills” are often reduced to a mismatched list of labels pulled from resumes and job descriptions. Without a way to objectively define and verify skills - skills based hiring becomes nothing more than “empty calories.”

Most organizations haven’t actually defined what they mean by “skills.”

Instead, what gets called a “skills-based” process is often just an AI-based cosmetic relabeling of the resume-and-job-description model. Vendors scrape resumes, tag candidates with generic terms, and infer “skills” from job titles or past experience — with no organizing structure, objective definitions or clarity on whether those skills are current, relevant, or tied to actual performance.

If you don’t start with your own definition of what skills mean in your organization — how they show up in the work, how they connect to success, and how they can be reliably observed or measured — then skills-based hiring becomes little more than a buzzword.

Replacing resumes with assessments is a step in the right direction, especially when done thoughtfully. But assessment alone is not execution. True skills-based hiring requires intention, alignment, and verification — not just tools that move fast and sound smart.

Problem # 2: AI Tools aren’t a Shortcut to Real Insight.

Just because a tool generates a “skills match score” doesn’t mean it knows what success looks like in your company.

• You can't trust what you can't verify.

• You can't hire vs. subjective criteria.

When evaluating AI tools for hiring, it’s tempting to believe that technology can shortcut the hard work of measuring skills.

Many vendors promise to “infer” skills from resumes, social profiles, or behavioral signals. These systems rely on algorithms trained to detect patterns — not to understand context. They often map candidate data to generic skill libraries or apply opaque scoring methods that offer little visibility into what’s behind a skill or why it was chosen.

The result? You’re building a house on garbage.

Without validation — a reliable, repeatable way to verify and confirm that candidates actually possess the skills that you have identified as important — skills-based hiring is left toothless.

AI can definitely help this work. But if you're not starting from a solid foundation, no algorithm will save you

Problem #3: Tools (& AI) Don’t Replace Strategy

AI can help scale and accelerate decisions. But only if you’ve already defined what good decisions look like.

No technology — not even AI — can make up for an inverted decision making process.

Best-in-class companies understand this. The most effective skills-based hiring programs are not the product of a single tool or a quick fix — they are the result of years of internal research, cross-functional collaboration, and disciplined execution. These organizations invest in defining what success looks like in their roles, aligning those definitions to measurable skills, and building frameworks to assess and manage those skills across the employee lifecycle.

In that context, AI tools can play a valuable role — but only when they are chosen intentionally, and only when they fit into a well-defined process.

When evaluating AI vendors, the question is not just, “What can this tool do?”
It’s:

• “How does this tool support the framework we've built?”

• “What is the evidence behind its claims?”

• “Can we verify the outputs in ways that align with our success model?”
  

AI tools can accelerate what’s already working. But they won’t design the system for you.

In Module 2 of the AI for Recruiting Masterclass, we’ll break down exactly how to approach vendor evaluation with clarity — so you can spot which tools add real value, which ones don’t, and how to fit them into a strategy you actually control.

Learn More & Enroll

“We have to stress-test innovation in the messiness of real-world hiring, not just ideal lab conditions.”

-Christine Boyce

In this episode of Psych Tech @ Work, I’m joined by my longtime friend Christine Boyce, Global Innovation Leader at ManpowerGroup/Right Management, to explore how innovation — especially around AI — is reshaping hiring and talent development at scale, and why solving for trust, transparency, and operational realities matters more than ever.

Summary

At the heart of this conversation is the reality that scaling AI innovation in hiring brings massive complexity. While AI offers incredible promise, solving for accuracy, fairness, and operational reality becomes exponentially harder when you're dealing with a large number of unique clients.

Christine Boyce, through her work at ManpowerGroup & Right Management, operates at the intersection of these challenges every day. Unlike internal talent acquisition leaders who focus on one organization's needs, Christine must help innovate across a vast client portfolio. Each client presents different barriers — from data limitations, to ethical concerns, to regulatory pressures — and innovation must be modular, defensible, and adaptable to succeed.

This vantage point gives Christine a unique, big-picture view of how AI adoption really plays out across industries and markets.

We dive into the practical challenges of innovating responsibly: earning trust, scaling solutions across diverse environments, and balancing speed with fairness. Christine’s work at ManpowerGroup & Right Management highlights how innovation must be deeply disciplined if it is to achieve true scale and impact.

The Core Challenge: Scaling Accuracy and Fairness

At the heart of using AI for hiring lies the challenge of achieving accuracy and fairness at scale. AI’s true value isn’t just its ability to make individual decisions — it’s in processing vast amounts of data and automating judgment across thousands of candidates. However, scale magnifies both strengths and weaknesses: minor biases can grow into systemic problems, and small inefficiencies can snowball into major failures.

Staffing firms like ManpowerGroup offer critical real-world lessons:

• Scale forces discipline — Every AI tool must be rigorously vetted for fairness, transparency, and defensibility before deployment.
  

• Real-world variation stresses the system for the better — Tools must flexibly adapt to diverse jobs, industries, and candidate pools. This makes the overall path of innovation better and drives great learnings across the board.
  

• Speed must not erode trust — Productivity gains must still respect ethical standards and candidate experience.
  

• External accountability keeps AI honest — Clients demand transparency, validation, and explainability before adoption.
  

Real Barriers to AI Adoption: What Clients Are Facing

Despite AI's potential, Christine identifies several persistent hurdles that she faces when serving her diverse slate of clients:

• Resistance to Behavior Change: Even demonstrably valuable AI tools often struggle against entrenched workflows and distrust of automation.
  

• Ethical and Trust Concerns: Clients demand AI systems that are transparent, explainable, and defensible, fearing reputational or regulatory risks.
  

• Vendor Noise Overload: Saturation by "AI-washed" vendors makes it hard to differentiate true innovation from hype.
  

• Mismatch Between Hype and Practical Needs: Clients need tools that solve today’s operational problems — not just futuristic visions disconnected from reality.
  

• Fear of Creeping AI Adoption: Organizations worry about AI capabilities being embedded into systems without visibility or intentionality.
  

• Compliance and Regulation Anxiety: Global and local regulations (like the EU AI Act or pending US laws) create urgency for proven, compliant AI solutions.
  

• Talent Data Readiness: Without clean, structured internal data, even the best AI solutions struggle to deliver meaningful results.
  

These challenges aren't isolated — they reveal the broader realities companies must manage when trying to adopt AI responsibly at scale.

Ultimately, client concerns have a hand in AI innovation because they are critical for the adoption of these technologies, shaping how staffing firms and vendors must design, validate, and deploy solutions.

There’s an inherent tension between the drive for scale and the need for trust, fairness, and operational reality.

Christine’s experience demonstrates that true innovation in AI for hiring isn't just about introducing new tools — it’s about creating resilient, transparent systems that can adapt to real-world complexity. Managing the tension between speed, scale, trust, and fairness represents the path to a bright future.

"Part of putting an AI strategy together is understanding the limitations and where unintended consequences could occur, which is why you need diversity of thought within committees created to guide AI governance and ethics." 

– Bob Pulver

My guest for this episode is my friend in ethical/responsible AI, Bob Pulver, the founder of CognitivePath.io and host of the podcast "Elevate Your AIQ." 

Bob specializes in helping organizations navigate the complexities of responsible AI, from strategic adoption to effective governance practices.  

Bob was my guest about a year ago and in this episode he drops back in to discuss what has changed in the faced paced world of AI across three pillars of responsible AI usage.  

1. Human-Centric AI 

2. AI Adoption and Readiness 

3. AI Regulation and Governance

The past year’s progress explained through three pillars that are shaping ethical AI:

These are the themes that we explore in our conversation and our thoughts on what has changed/evolved in the past year.

1. Human-Centric AI

Change from Last Year:

• Shift from compliance-driven AI towards a more holistic, human-focused perspective, emphasizing AI's potential to enhance human capabilities and fairness.

Reasons for Change:

• Increasing comfort level with AI and experience with the benefits that it brings to our work

• Continued exploration and development of low stakes, low friction use cases

• AI continues to be seen as a partner and magnifier of human capabilities

What to Expect in the Next Year:

• Increased experience with human machine partnerships

• Increased opportunities to build superpowers

• Increased adoption of human centric tools by employers

2. AI Adoption and Readiness

Change from Last Year:

• Organizations have moved from cautious, fragmented adoption to structured, strategic readiness and literacy initiatives.

• Significant growth in AI educational resources and adoption within teams, rather than just individuals.

Reasons for Change:

• Improved understanding of AI's benefits and limitations, reducing fears and resistance.

• Availability of targeted AI literacy programs, promoting organization-wide AI understanding and capability building.

What to Expect in the Next Year:

• More systematic frameworks for AI adoption across entire organizations.

• Increased demand for formal AI proficiency assessments to ensure responsible and effective usage.

3. AI Regulation and Governance

Change from Last Year:

• Transition from broad discussions about potential regulations towards concrete legislative actions, particularly at state and international levels (e.g., EU AI Act, California laws).

• Momentum to hold vendors of AI increasingly accountable for ethical AI use.

Reasons for Change:

• Growing awareness of risks associated with unchecked AI deployment.

• Increased push to stay on the right side of AI via legislative activity at state and global levels addressing transparency, accountability, and fairness.

What to Expect in the Next Year:

• Implementation of stricter AI audits and compliance standards.

• Clearer responsibilities for vendors and organizations regarding ethical AI practices.

• Finally some concrete standards that will require fundamental changes in oversight and create messy situations.

Practical Takeaways:

What should I/we be doing to move the ball fwd and realize AI’s full potential while limiting collateral damage?

Prioritize Human-Centric AI Design

• Define Clear Use Cases: Ensure AI is solving a genuine human-centered problem rather than just introducing technology for technology’s sake.
  

• Promote Transparency and Trust: Clearly communicate how and why AI is being used, ensuring it enhances rather than replaces human judgment and involvement.
  

Build Robust AI Literacy and Education Programs

• Develop Organizational AI Literacy: Implement structured training initiatives that educate employees about fundamental AI concepts, the practical implications of AI use, and ethical considerations.
  

• Create Role-Specific Training: Provide tailored AI skill-building programs based on roles and responsibilities, moving beyond individual productivity to team-based effectiveness.
  

Strengthen AI Governance and Oversight

• Adopt Proactive Compliance Practices: Align internal policies with rigorous standards such as the EU AI Act to preemptively prepare for emerging local and global legislation.
  

• Vendor Accountability: Develop clear guidelines and rigorous vetting processes for vendors to ensure transparency and responsible use, preparing your organization for upcoming regulatory audits.
  

Monitor AI Effectiveness and Impact

• Continuous Monitoring: Shift from periodic audits to continuous monitoring of AI tools to ensure fairness, transparency, and functionality.
  

• Evaluate Human Impact Regularly: Regularly assess the human impact of AI tools on employee experience, fairness in decision-making, and organizational trust.

Email Bob- bob@cognitivepath.io 

Listen to Bob’s awesome podcast - Elevate you AIQ

-Jason Tyszko

In this episode of Psych Tech @ Work, I sit down with Jason Tyszko, Senior Vice President of the U.S. Chamber of Commerce Foundation, to discuss what it really takes to make skills-based hiring a reality. 

Jason oversees the Foundation’s T3 Innovation Network, a public-private initiative aimed at creating a more equitable and inclusive job market. T-3 focuses on using digital tools to improve communication between different parts of the job market, ensuring that all learning is recognized and valued.  T-3’s mission to bridge gaps between employers and workers via the advancement of skills-based hiring makes Jason one of the  world’s foremost authorities on the subject.

Our conversation is a must for anyone interested in understanding the REALITIES required for true skills-based hiring.  Most conversations on the subject are more hype than substance, but not this one!  Jason takes us deeper into the reality of what it will take to make skills based hiring more than just an empty buzzword.

To ground our conversation in a dose of reality, Jason boils success with skills based hiring into these three pillars.

1. Interoperable Skills Data
   

   • To make skills-based hiring a reality, we need standardized, structured, and widely accepted skills data that flows seamlessly across education providers, employers, and workforce systems.

   • Without interoperability, skills data remains fragmented, making it difficult for employers to assess candidates meaningfully.

2. Employer Engagement and Adoption
   

   • Employers must align job descriptions, hiring processes, and internal mobility pathways around skills rather than degrees or traditional credentials.

   • Many organizations support skills-based hiring in theory but fail to implement it fully due to ingrained legacy practices.

3. Technology Infrastructure and Ecosystem Readiness
   

   • AI, job-matching platforms, and hiring tools must be built to recognize and evaluate skills accurately, rather than simply filtering candidates based on outdated proxies like job titles or degrees.

   • Systems should support skills validation, assessment, and transparent career pathways to ensure fair and effective hiring decisions.

Jason explains how these pillars support and enable five critical but often overlooked elements that are essential to making skills-based hiring work:

 1. Learning and Employment Records (LERs) & The LER Resume Standard

• What it is: LERs are digital, verifiable records of a person’s skills, training, certifications, and work experience. Instead of relying on traditional resumes or self-reported skills, LERs allow employers to see a structured, validated record of a candidate’s capabilities.

• Why it matters: Today’s hiring systems don’t talk to each other. Skills data is trapped in different platforms (learning management systems, certifications, HR software). LERs allow skills-based hiring to function at scale by ensuring a candidate’s credentials are portable and universally recognized.

• LER Resume Standard: This is a newly developed resume format built to process LERs, ensuring HR tech systems can read, compare, and use skills-based data more effectively.

2. Durable Skills

• What it is: Unlike technical skills (which can quickly become outdated), durable skills are long-lasting, transferable skills like critical thinking, adaptability, leadership, and collaboration.

• Why it matters: Most AI-driven hiring tools over-prioritize technical skills, but durable skills are what truly drive career success. Without a way to assess and validate them, companies risk hiring for short-term needs instead of long-term potential.

3. The Interoperability Layer

• What it is: A technical framework that allows skills data from different platforms to connect and work together—like an API that helps job boards, HR systems, and learning platforms “speak the same language.”

• Why it matters: Right now, skills-based hiring is fragmented because every company and HR tech provider uses different skills taxonomies and formats. An interoperability layer standardizes how skills data is shared, making it easier for employers to evaluate candidates based on a common skills framework.

4. Employer-Led Recognition

• What it is: A system where workers’ skills are validated by their employers and colleagues, not just through certifications or formal education. This could involve peer endorsements, manager assessments, or internal training validations.

• Why it matters: Most skills-based hiring focuses on externally validated credentials (e.g., certificates, degrees), but many people develop critical skills on the job. Without a structured way to recognize and verify these skills, businesses overlook talent that is already in their workforce.

5. Skills Wallets

• What it is: A digital, user-controlled repository where individuals can store, manage, and share verified records of their skills, credentials, and learning experiences.

• Why it matters: Unlike traditional resumes or degree transcripts, Skills Wallets give workers full ownership of their skills data, making it portable across jobs, industries, and learning platforms. This enables lifelong learning and career mobility in ways that existing hiring systems do not support.

Skills-based hiring has the potential to transform the workforce, but it won’t succeed without system-wide changes in HR technology, workforce data, and employer incentives. Jason’s insights reveal the often-ignored challenges and solutions that can make this shift truly scalable and effective. If you’re in talent strategy, workforce development, or HR technology, this episode provides a realistic roadmap for making skills-first hiring work.

• Learn more about the T3 Innovation Network: t3networkhub.org

• Contact Jason

Check out the full video of the presentation attached to this post and our accompanying slides (found at the bottom of the post).

Here is a quick overview of the ideas that form the foundation of the presentation.

“The highest-level goal of the talent acquisition (TA) function is to ensure that an organization has the right people, in the right roles, at the right time, to drive business success.”

-Chat GPT 4o & your hosts’ combined 50 years of experience

Talent leaders are feeling the pressure to execute

Modern hiring problems such as resource constraints, candidate scarcity and overload, the move to skills based hiring, and avoiding bias have talent leaders feeling the pressure to find fast solutions!

Relieving these pressures often create a temptation to put tools before strategy. AI is a great example of this.

The stakes are high, and AI offers a compelling solution- or does it?

AI is complex and making decisions about it requires a strong foundation of knowledge and careful planning.

In this presentation we discuss 4 common mistakes in the adoption of HR tech, with a focus on AI tools (are there any other types these days?).

We discuss how a tools first mentality is often the root cause of these four common mistakes and offer guidance on how to avoid them.

1. Missing AI’s ‘creeping normality':

As technology becomes more entrenched in your processes and vendors add new functionalities that are accessible, adoption often occurs with little oversight or consideration. When it comes to solving problems related to talent supply or overload, AI recruitment platforms are increasingly embedding “talent matching” functionalities that create risk without any substantial rewards.

2. Chasing Skills Without Definition or Direction:

We can all agree that skills based hiring has merit. But it requires alignment on what a skill means to your organization and a holistic view of where they matter and why. Merely removing resumes from the evaluation process or adopting tools, AI or otherwise, that claim to support skills based hiring without a holistic strategy is a dead end street.

3. Failing to evaluate your firm’s culture and climate for adopting AI based tools:

There is a maturity required for the successful adoption of AI based tools. Understanding your firm’s readiness for AI based tools, and ensuring that you are ready to go all in is essential. Education on, and knowledge of, AI across the entire organization is a big part of successful adoption.

4. Letting vendors dictate strategy and adoption:

Most vendors do offer products that can have an impact, and their messages make it tempting to jump right in. Before biting on a shiny new object, adoption of any AI based tool should be pre-empted by a house made strategy. Vendors must be held to a standard evaluated by domain experts using a framework built on the principles of ethical and effective use of AI.

At the end of the presentation we provide a case study that probably feels pretty relatable to any talent acquisition professional. Here we tell a story of how mistakes are made and provide insights to help create the awareness needed to avoid them.

No one is perfect - but AI alone will not create perfection. Keeping things in perspective and a thoughtful and methodical process that is not driven by fear is essential to the successful adoption of AI technologies.

Download our slides here