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Unlock the future of HR, today with the HR Data Labs podcast! Dive into transformative insights, expert interviews, and cutting-edge practices that empower organizations to harness their workforce’s potential. Join us for engaging discussions that will inspire you to innovate, strategize, and lead with confidence! Tune in now!

  1. Unlocking the Future of HR Tech with AI, Innovation, and Trust

    VOR 1 TAG

    Unlocking the Future of HR Tech with AI, Innovation, and Trust

    Are you ready to feel the heartbeat of what's next in HR technology? In this episode, Oz Khan from ADP Ventures pulls back the curtain on how AI is transforming workflows, building trust, and reshaping the very fabric of work. This isn’t just talk — it’s a revolution happening right now, and you need to hear it. In this episode: The evolution from AI assistance to autonomous workflow execution Why HR is shifting from co-pilot to control tower — and what that means The critical importance of trust, compliance, and risk management in AI adoption How enterprise complexity and trust influence product development and investment The overlooked power of judgment — why human experience remains priceless The strategic focus of investors and founders navigating AI’s wild waters Real-world examples: ADP’s scale, the impact of startups like Naya and Emma The role of people, process, and tech — and how they coexist in solving real HR problems Timestamps: 00:00 - The pulse of HR innovation — what’s coming next 02:10 - How ADP’s labs culture sparked real growth 05:10 - Oz shares a fun, surprising fact about himself 06:10 - The seismic shift from AI tools to workflow automators 08:25 - How stability and proven value shape HR tech adoption 09:42 - The co-pilot becomes the control tower — deep dive 11:00 - APIs, data connectivity, and solving enterprise complexity 12:44 - Navigating compliance, risk, and the law in HR AI 13:51 - Why generative AI isn’t ready to replace humans yet 15:54 - The real challenge of trust: transparency, training, guardrails 17:46 - The importance of judgment and experience in AI-driven decision making 20:40 - Building confidence with high-fidelity, deterministic AI solutions 23:38 - The last mile decision — where human judgment still rules 26:11 - The dangers of overhyping AI’s potential — and the truth 29:52 - Education, skills, and the demographic shifts AI will bring 33:53 - The art of flexible, configurable HR tech — how founders navigate ‘craft’ 37:16 - From $1M to scale — what it takes to grow AI-driven HR solutions 42:56 - Real-world impact — ADP’s investments in Naya, Emma, and beyond 43:38 - The human side: solving emotional and organizational problems, not just tech 44:22 - The future is OpenClaw and beyond — what’s next in AI bots Resources & Links: Naya — Transforming benefits decisions at scale Emma AI — Agentic platform powering workflows ADP Ventures — Driving innovation in HR tech Data Cloud Stanford Research — Insights on entry-level hiring and labor shifts ADP — Join the leaders in HR and payroll solutions Connect with Oz Khan: LinkedIn Twitter This isn’t just a conversation — it’s a call to action. Whether you’re in HR, investing, or building the future, you’ve got to understand the truth about AI’s power, limits, and the human judgment that will always be king. Don’t get left behind. Your next big move is waiting — listen now, and be part of the revolution!

    47 Min.
  2. Trust in the Age of AI: Tackling Candidate Fraud While Preparing for Next-Gen Hiring

    5. MÄRZ

    Trust in the Age of AI: Tackling Candidate Fraud While Preparing for Next-Gen Hiring

    Are we truly prepared for the dark side of AI in talent acquisition? In this episode, my great friend, Dara Brenner from Employ drops truth bombs about candidate fraud, the evolution of AI governance, and how organizations can stay ahead in this high-stakes game. If you're a leader in HR or recruiting, this is your wake-up call—trust me, you’ll want to hear this. Here is the link to the fund set up for Dara's late husband: Please click here. In this episode: The shift from traditional to AI-native hiring tech—what it really means How candidate fraud costs businesses $600 billion—yes, billion Layers of fraud detection: Swiss cheese analogy explained The role of IBM Watson in transparency and compliance The future of governance—code as the new rulebook Balancing trust and talent pool access in a hyper-suspicious world Why proactive planning for societal shifts is vital in workforce strategy Dara’s personal story: turning tragedy into impact with a brain tumor foundation Resources & Links: IBM Watson X Governance Learn about the brain tumor foundation Book: "AI Superpowers" by Kai-Fu Lee Dara Brenner on LinkedIn Connect with Dara: LinkedIn Twitter Timestamps: 00:00 - Introduction: The urgent need for trust in AI-driven hiring 02:08 - Dara Brenner’s background and journey through HR tech 03:20 - The fun side: Dara’s unexpected past as a restaurateur 05:45 - The impact of personal tragedy: Creating a foundation for brain tumor support 08:34 - The hidden threat: Candidate fraud in the age of AI 09:49 - How AI can be used nefariously in recruitment 10:56 - Layers of fraud detection: From resumes to deep fakes 12:01 - The staggering cost: $600 billion from resume fraud 14:26 - Organizations’ vulnerabilities: North Korea, bad actors, and real risks 15:36 - Balancing security with talent access: Where to draw the line 22:34 - Governance and compliance: IBM Watson’s role in transparency 23:46 - Constant AI audits: Like having a doctor in your pocket 28:31 - Future of recruitment: From ATS to AI-native systems 29:55 - Building AI into the DNA from day one—code as policy 32:29 - The evolving role of recruiters: proactive, strategic, data-driven 33:53 - Societal impacts: Demographics, immigration, and big-picture workforce planning 36:10 - Dara’s parting wisdom: Evolving trust, governance, and human oversight Final thought:The future of hiring is not just about smarter algorithms—it's about building a trustworthy system where technology and humanity work hand in hand. Stay vigilant, stay proactive, and never stop asking: are we doing this right? Because the stakes couldn’t be higher.

    38 Min.
  3. The Evolution of Recruitment Metrics & The Future of AI in HR

    26. FEB.

    The Evolution of Recruitment Metrics & The Future of AI in HR

    Are you tired of dealing with outdated recruiting strategies? Wondering if AI can truly revolutionize hiring? This episode dives into the gritty truth about how modern metrics and emerging AI tools are reshaping talent acquisition—sometimes for better, sometimes for worse.Join us as we unpack decades of recruiting wisdom, the pitfalls of cost-per-click, and what’s next in HR tech. In this episode: The history of traditional job advertising and its limitations How cost-per-application models are changing the game The messy reality of defining "qualified" applications and why subjectivity matters The myth of AI as an unbiased recruiter and its legal risks Why transparency in pay and job descriptions can fix much of the hiring chaos The dangers of scaling bad processes and how to avoid scaling mistakes Practical insights on choosing the right metrics for your hiring needs The future of AI in recruitment and the importance of process over technology Useful links: College Recruiter Work Defined Podcast Network Ultimate Guide to Recruitment Metrics Artificial Intelligence in Hiring – Industry Report The Future of Pay Transparency Reach out to Steven Rothberg: LinkedIn Twitter Timestamps: 0:00 - Welcome and episode overview: Are recruitment metrics still holding up?00:42 - Introducing Stephen Rothberg and college recruiter’s journey01:14 - The roots of traditional recruiting: From flyers to newspapers02:25 - Starting a business from a summer job and risk-taking mentality03:52 - The Winnipeg Jets story: Childhood risks and lessons in boldness05:23 - The shift from inefficient to performance-based ad models06:49 - The real meaning of "click" and the confusion across platforms08:13 - The rise of cost per application and its emotional impact on employers09:54 - Defining "qualified applications"—subjectivity and its pitfalls11:35 - Why scaling bad processes makes them worse13:08 - The evolution of pay-per-click and undisclosed definitions14:54 - The deadly cycle of bad job descriptions and reposting16:23 - How hiring for "fit" often misses the mark and costs real money17:10 - Qualification in hiring: Who judges? The subjectivity of recruiters18:29 - Scaling broken processes: The airplane manufacturing example19:54 - The quiet cost of bad candidate sourcing: The ATS funnel22:17 - The dangerous hype around AI and its current limitations in hiring23:12 - The black hole of AI decision-making and lack of transparency26:24 - The importance of honest job descriptions and pay transparency28:43 - Do employers prefer CPC, duration-based, or new models?30:01 - The chase for quality candidates and the costs involved31:27 - How to effectively invest in recruitment—long-term wins over shortcuts32:28 - The impact of AI on assessments and candidate matching35:55 - The legal peril of AI-driven hiring decisions and audit trail challenges37:25 - Final thoughts: Embracing process, transparency, and smart metrics

    39 Min.
  4. The Future of AI in HR: Privacy, Security, and Transformation Opportunities

    19. FEB.

    The Future of AI in HR: Privacy, Security, and Transformation Opportunities

    In this episode, we explore how artificial intelligence is revolutionizing HR, with a focus on building trust through data privacy and security. Join us as we discuss practical steps, emerging challenges, and the evolving role of HR professionals in the AI era. Key Topics: The importance of foundational data quality before implementing AI in HR Securing sensitive employee data and managing privacy concerns The role of semantic layers and data organization for effective AI use How AI impacts HR workflows and transforms knowledge work Practical approaches to integrating AI responsibly and securely Education needs for HR to understand AI risks and opportunities Future trends: AI's potential to reinvent HR practices, not just automate Resources & Links: Fuel 50 - Workforce Mobility and Talent Pipelines Amazon - Book: Data Privacy and Security in the Cloud Flurry - Official Website Amazon Bedrock - AI Model Service Anthropic - AI Safety and Privacy Guarantees OpenAI - Responsible AI Use Connect with Brian Platts: LinkedIn Twitter Timestamps:  00:30 - Welcome and introduction to the episode 01:15 - Brian Platts’ background in HR and software 02:08 - Flurry’s mission to make data meaningful for HR 03:26 - Fun fact: starting career driving a semi truck 04:44 - AI in HR: privacy, security, and data foundations 05:53 - Preparing your HR data for AI adoption 06:08 - Challenges with data quality and use cases 07:08 - Security considerations: private vs. public data 08:22 - Trusting AI vendors and data-sharing risks 09:15 - Teaching AI to query data securely 10:07 - Data organization and semantic layers 11:29 - Improving chatbots and avoiding misinformation 12:26 - Ensuring process accuracy and data integrity 13:14 - Sharing vs. protecting employee data 14:05 - Re-implementing permissions in AI-driven systems 15:01 - Education and awareness around AI security 16:13 - Learning from SaaS security issues during early cloud adoption 17:18 - HR’s role in AI education and safeguarding IP 18:14 - Balancing productivity gains with security controls 19:06 - AI’s impact on HR future: automation and new workforce roles 20:16 - The concept of the “Meat Layer” and human-AI collaboration 21:02 - Will AI replace HR jobs or empower them? 22:16 - The limits of current AI technology and future innovations 23:03 - Analogies: AI as a horse and the importance of tooling 24:06 - Embracing AI to enhance human work rather than replace it 25:16 - Reinventing HR processes beyond IT-led automation 26:18 - Regulatory challenges and incremental HR AI adoption 27:30 - How HR can lead responsible AI integration 28:03 - Final advice for HR professionals: think broadly and connect the dots

    30 Min.
  5. Data Governance in HR is NOT Optional!

    12. FEB.

    Data Governance in HR is NOT Optional!

    In this episode, we dive deep into the challenges and opportunities of HR data governance, exploring how organizations can improve data quality, ownership, and usability in a rapidly evolving AI landscape. Join us for practical insights from seasoned HR analytics experts on building a data-driven culture that supports strategic decision-making. Key Topics: Why HR data is often unreliable and the impact on decision-making The role of ROI and cultural mindset in improving HR data quality The importance of ownership, stewardship, and clear definitions in data governance How AI and machine learning magnify data quality issues if governance is lacking Practical steps to start building your HR data governance framework The critical role of documentation, data catalogs, and system integration Common pitfalls: managing multi-system data consistency and avoiding errors Quick wins: focusing on key metrics and stakeholder collaboration Timestamps: 00:00 - Introduction: Why HR data governance matters today 02:30 - Challenges HR faces with data quality and accuracy 06:15 - Why organizations struggle to demonstrate ROI from HR data 09:00 - Cultural and mindset barriers to effective data management 11:00 - The impact of AI and machine learning on HR data quality 12:30 - Context and system integration challenges across HR tech stack 15:11 - Defining HR data governance: Ownership, stewardship, and quality 17:00 - Creating a data glossary and system of record for HR data 19:05 - Real-world examples of poor HR data visibility and audit issues 21:00 - Using chatbots and AI: risks, benefits, and data consistency 24:00 - The importance of documentation and version control in AI applications 27:40 - Practical steps to start your HR data governance journey 30:00 - The significance of aligning metrics and defining owners 33:00 - Building a culture of data excellence and quick wins 36:00 - Addressing expectations for pristine data and managing realities 37:00 - Final recommendations for HR leaders to improve data governance Connect with Guests: Raswinder Singh - LinkedIn | Twitter Ankit Abrol - LinkedIn | Twitter

    40 Min.
  6. Season 10 Recap

    22. JAN.

    Season 10 Recap

    🚀 Dive into the future (at least the next 6 months) of HR with the latest episode of the HR Data Labs podcast! In the Season 10 recap, host David Turetsky explores the evolving landscape of compensation, strategy, and people analytics. We recap the current Season 10, where we discuss addressing training gaps, the impact of AI in HR, and other cool episode topics. Then we review our prediction for the past 6 months and make bold predictions for first 6 months of 2026. Join us for a thought-provoking discussion that prepares you for the challenges and opportunities ahead. #HRInnovation #FutureOfWork #HRDataLabs

    25 Min.
  7. What's to come in Compensation in 2026

    15. JAN.

    What's to come in Compensation in 2026

    Join host David Turetsky and guest Paul Reiman from Novo Insights as they delve into the dynamic world of compensation strategies and pay transparency. Discover how regulatory changes in the US and Europe are shaping the landscape, the critical role of job documentation, and the impact of technology in fostering transparency. Tune in for insightful predictions for 2026 and learn how political climates may influence future pay transparency laws. Chapters Introduction and Guest Introduction Compensation Strategies and Pay Transparency Regulatory Changes in the US and Europe The Role of Technology in Transparency Predictions for 2026 Conclusion and Final Thoughts Quotes: "Job documentation is crucial for pay transparency." "Regulatory changes are reshaping the compensation landscape." "Expect a shift in compensation survey methodologies by 2026." "Technology plays a key role in enabling transparency." "Implementing pay transparency presents unique challenges." "2026 will bring significant changes to HR practices." "Adapting to regulatory changes is essential for success."

    42 Min.
  8. How Skills Data is Transforming HR into True Business Partners

    8. JAN.

    How Skills Data is Transforming HR into True Business Partners

    Craig Friedman, Talent and Skills Transformation Leader at St. Charles Consulting Group and author of Enterprise Skills Unlocked, joins us this week to discuss the shift toward skills-based organizations. He breaks down how data-driven transformations allow companies to move from simple headcount management to true capability management. Craig also shares practical advice on how to prioritize skills projects to ensure they solve real business problems and deliver ROI. [0:00] Introduction Welcome, Craig! Today’s Topic: How Skills Data is Transforming HR into True Business Partners [5:05] What does a skills-based transformation look like in practice? Shifting the talent process from an exercise in headcount management to an exercise in capability management. Moving away from static "boxes on an org chart" to using granular data that supports the entire talent infrastructure. Leveraging skills data that lives in both business systems (capabilities) and people systems (individual skills) to better align with business functions. [11:57] How different teams leverage skills data differently Why L&D teams need granular skill details, while staffing teams prioritize context on scope and scale for compensation purposes. The importance of creating an enterprise data taxonomy where different departments can agree on a skill but append their own metadata. Using machine learning to handle the searches, connections, and adjacencies required to make the data useful across teams. [26:14] The impact on Learning and Development (L&D) How real-time skills gap analysis simplifies curriculum redesign when jobs or organizational structures change. The growing need for assessment and validation to verify skills learned through informal methods like coaching or on-the-job experience. Identifying business cases where skills can make a clear difference and prioritizing them based on value and risk. [36:20] Closing Thanks for listening! Quick Quote “A lot of the reason we're doing this now when we couldn't do it before is because of these more advanced tools in data analytics and AI and machine learning that actually help us manage data at that scale.” Link to Craig's book: https://a.co/d/0naqmvh

    39 Min.
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Unlock the future of HR, today with the HR Data Labs podcast! Dive into transformative insights, expert interviews, and cutting-edge practices that empower organizations to harness their workforce’s potential. Join us for engaging discussions that will inspire you to innovate, strategize, and lead with confidence! Tune in now!

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