Psych Tech @ Work

“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 WallvoordIn 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.SummaryThis 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 Themes1. Building Skills Infrastructure at ScaleAshley 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 SystemAI 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 TransparencyVerizon’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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

Quote:“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 LaurenoIn 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 ExtinctMadeline 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 HiringMadeline 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 BuyingMadeline 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 ExperienceMadeline 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! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

“Most firms that are using AI are saving two to four hours per week per employee. That’s not transformative. That’s just doing the same thing faster.”-Alexis FinkIntroductionIn 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 EraTrue progress requires rethinking workflows so humans and machines complement each other rather than compete.3. Data Quality and Human-Centered DesignMost 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 AdoptionThe hardest barriers to AI adoption aren’t technical but human — fear, resistance, and behavior change.6. Looking to 2035: The Next-Gen I-O PsychologistFuture I-Os will master AI as a partner, using simulation and immersive tools while keeping work human-centered.ConclusionOur 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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

“The problem with AI adoption isn’t just technical—it’s emotional. Creativity lowers the barrier of fear, and that opens the door to skill building.”– Jimmy Lepore HaganNewsflash!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.SummaryCreative 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.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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

My creative experience building an AI podcast co-host says it all. Hear all about it on the next episode of the Psych Tech @ Work Podcast - coming soon!AI skills are essential but dauntingAI 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 skillsI 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 allIt 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 skillsLearn how we’re helping companies build fearless, AI-ready teams.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 feedbackBy 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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

Guest: Christine Boyce, Global Innovation Leader at ManpowerGroup/Right Management“We have to stress-test innovation in the messiness of real-world hiring, not just ideal lab conditions.”-Christine BoyceIn 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.SummaryAt 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 FairnessAt 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 FacingDespite 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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

"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 PulverMy 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.  * Human-Centric AI * AI Adoption and Readiness * AI Regulation and GovernanceThe 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 AIChange 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 capabilitiesWhat to Expect in the Next Year:* Increased experience with human machine partnerships* Increased opportunities to build superpowers* Increased adoption of human centric tools by employers2. AI Adoption and ReadinessChange 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 GovernanceChange 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 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

“We have to move beyond the idea that a skills-based job description is enough—there needs to be validation, assessment, and a clear pathway for job seekers to prove their abilities.”-Jason TyszkoIn 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.* 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.* 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.* 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 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

In our recent LinkedIn Live session my esteemed colleague, Neil Morelli, founder of Workplace Labs, and I present a philosophical but practical approach to the adoption of HR Tech tools.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 experienceTalent leaders are feeling the pressure to executeModern 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 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

"The hiring industry is at a breaking point—AI is putting pressure on old systems that were never designed for this level of automation."–Jeff TaylorIn this episode of Psych Tech @ Work, I am joined by Jeff Taylor, serial entrepreneur and founder of Monster.com, & Boomband a revolutionary new platform that is looking to turn hiring on its ear.Few people have shaped the hiring industry as profoundly as Jeff, whose vision transformed job search from a niche experiment into an industry standard. Jeff’s journey—from building the first large-scale job board to continuously innovating in the talent acquisition space—gives him a unique perspective on where hiring technology has been and where it’s headed, making him the perfect guest to explore the next big disruptions in talent acquisition and how AI is reshaping the hiring process..  In our time together we reminisce about the story behind Monster’s memorable Superbowl ads. (Who can forget the kid saying:  “I want to claw my way up to middle management!” ) and the formative impact my job at Monster (circa 2000) has had on my career.  But enough about me!  Our conversation explores the rapid acceleration of AI in recruiting, from automating sourcing and matching to the potential risks of AI-generated applications flooding hiring systems. Jeff happily shares his candid thoughts on why hiring technology has stagnated, how AI is creating new challenges for recruiters, and what companies must do to stay ahead in an increasingly automated hiring landscape. We also discuss the core concepts behind Boomband, Jeff’s new social hiring platform.Topics Covered:* Monster.com’s origin story and how it transformed hiring and created the “job board” industry.* The shift from traditional job search to AI-driven sourcing and candidate matching and what this means for the future of hiring.* The pros and cons of AI-generated resumes and job applications—are we heading toward an overload of unqualified applicants?* The failure of legacy hiring systems to keep up with modern job-seeker behavior.* The potential for AI to create more personalized and predictive hiring experiences and Boomband Jeff’s new venture that is focused on creating a new paradigm for hiring (again!).Takeaways:* Job boards revolutionized hiring—but they haven’t evolved fast enough. The core concept of posting jobs and waiting for applications hasn’t fundamentally changed in decades.* AI is making job search more efficient but also more chaotic. Automated resume generation and mass applications are overwhelming recruiters and breaking traditional applicant tracking systems.* Legacy hiring technology is struggling to adapt. The demand for AI-powered sourcing and skills-based hiring is exposing the limitations of old-school job posting and resume-matching platforms.* The next frontier of hiring is predictive and personalized. Jeff envisions AI-driven career pathing, real-time job market intelligence, and new ways to match candidates based on abilities, not just experience.Jeff’s perspective on AI-driven hiring, the changing nature of job search, and where hiring technology must go next makes this conversation a must-listen for anyone interested in the future of work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com