HR Data Labs

About HR Data Labs

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!

Publisher

WRKdefined Podcast Network

Category

business

Language

en

Explicit

No

First episode

30 Sept 2020

Latest episode

15 Oct 2026

Latest episodes

291 episodes in the feed.

1. 

   15 Oct 2026

   Jay Polaki - HR technology in 2027: AI readiness, trust, and the data problem

   This episode looks at where HR technology is today and where it may be heading by 2027, with a focus on AI’s real impact on HR teams. David Turetsky is joined by Jay Polaki, CEO of HR Geckos, to unpack why so many organizations have access to AI tools but still lack the data, governance, and skills to use them well.We discuss the growing gap between AI enthusiasm and operational readiness, why HR is still unclear on its role in AI strategy, and why the conversation needs to shift from hype to use cases, trust, and measurable outcomes. Key topics Jay Polaki, CEO of HR Geckos, explains that HR Geckos is a people-centric co-pilot for HR operations, built by HR for HR. The episode opens with a lighter note about Jay’s unexpected appearance on MTV during a Russell Peters event clip. The central theme is that AI access is no longer the main problem — scaffolding is. Jay shares findings from HR Geckos’ “AI Ready or Not” roadshow, based on conversations with about 55 HR and business leaders across multiple sessions. Many organizations have AI tools, but data quality, governance, ownership, and funding remain unclear or incomplete. David raises concerns about security risks, especially employees using consumer AI tools with sensitive HR and PII data. The discussion highlights a split in AI maturity: Some HR teams have no access and no training. Others are forced to use AI with little or no education. ROI is a major sticking point, with both speakers arguing that most organizations are measuring the wrong things or measuring too early. Jay says the best way to deploy AI in HR is by starting with a specific use case, not a broad enterprise rollout. HR service delivery and the HR service desk are identified as one of the most likely near-term AI investment areas. The conversation repeatedly returns to trust, especially around governance, role clarity, and employee anxiety about job loss. David and Jay agree that AI is more likely to augment workers than replace them, and that people who know how to use AI will outpace those who do not. Jay argues that HR should stay central to reskilling, learning and development, recruiting, and re-engineering processes. The episode ends with a strong warning against using AI for sensitive compliance work without human oversight. Timestamps (00:00) Opening the HR technology conversation with Jay Polaki (01:12) Jay introduces HR Geckos as a people-centric HR ops co-pilot (02:17) Jay’s unexpected MTV cameo and the Russell Peters story (04:42) Why 2026 feels chaotic for HR technology and AI (05:23) Roadshow findings from 55 HR and business leaders (06:21) AI access is widespread, but scaffolding is missing (07:47) Why data, ownership, and governance are still unresolved (09:18) Security concerns around employees using consumer AI tools (10:29) Two extremes of AI readiness in HR teams (11:26) Large organizations vs. smaller firms in AI adoption (12:23) Trust is the biggest barrier to AI deployment (13:14) Why AI desktop tools raise privacy and control concerns (14:11) Why AI was often used as a cost-cutting tool before HR was ready (15:22) Why ROI is hard to define for AI in HR (16:46) The real friction is data readiness and skills readiness (18:09) Why the best AI deployments start with a single use case (18:34) Why the HR service desk is a likely next AI investment (20:30) Start with why, then move to how (21:57) Why AI should be deployed in baby steps (22:56) Centralizing siloed HR data before scaling AI (24:21) Why leaders are unsure about HR’s role in AI strategy (26:08) Mixed views on HR’s AI future: curious, transformational, undefined (27:29) Where HR can lead: skills, learning, recruiting, and re-engineering (28:32) AI in talent management and the future of interviewing (29:30) Governance gaps, risk levels, and access controls (30:28) HR’s role in managing worker anxiety about AI (31:47) Trust and governance as urgent CEO, CHRO, and CTO issues (33:27) AI as a tool, not a replacement for people (34:04) Why the doomsday narrative is overstated (35:12) Why AI is still “pretty dumb” without human training (36:04) Augmented intelligence and human judgment in tandem (38:12) Why AI still cannot handle HR compliance on its own (39:12) Government scrutiny and accountability risks (39:50) Wrap-up and HR Tech conference frustrations for service providers Notable quotes “Technology should elevate people, not replace them.” “We’ve solved the AI access issue — ownership, funding, data, governance, all of that is still in flux.” “People who know how to use AI will replace people who don’t know how to use AI.”

2. 

   8 Oct 2026

   Gabriella Garcia - Build vs Buy in People Technology: AI, Guardrails, and the New Role of HR

   David Turetsky interviews Gabriella Garcia, COO and Chief People Officer at Warp, about how AI is reshaping the build versus buy decision in HR and people operations. They dig into why this question is changing now, what guardrails matter for sensitive data, and why people leaders need to take a more strategic role in technology decisions. Gabby shares how her engineering background informs her view of modern people tech, while David adds real-world context from years of hacking together internal systems. Together, they explore how AI lowers the barrier to building, but also raises new questions around governance, cost, and organizational design. Key topics In this episode, Gabby Garcia explains Warp’s mission as an AI native employee management platform handling payroll, benefits, state tax compliance, and HR systems for more than 1,200 customers. David and Gabby compare today’s AI-enabled build culture with older eras of custom tooling, including early internal dashboards and Access database work. Gabby says the build versus buy equation has changed because non-engineers are now more technical and because tools like Claude Code and Lovable reduce the activation energy for prototyping. The conversation centers on the risk of rogue projects, especially when people teams handle PII, PHI, SSNs, birthdays, payroll data, and other sensitive information. Gabby points to Shopify as an example of a company encouraging broad internal experimentation, while also surfacing the downside of AI slop, access issues, and repetitive low-value tools. She argues for cultural guardrails, approval rules, and manager review processes rather than blanket bureaucracy. The discussion shifts to the engineering function, where Gabby says engineers are increasingly becoming prompt-driven problem solvers and thought partners rather than manual code producers. David and Gabby agree that this changes organizational DNA, especially when non-engineering teams can build internal tools, bots, and workflows that still need governance. Gabby makes the case that people leaders, not just CIOs or CTOs, should lead AI adoption strategy because they own talent, enablement, growth, and role evolution. The episode closes on token spend, cost control, and the need for finance and people teams to understand model selection, usage caps, and the accounting implications of AI spend. Timestamps 00:00 - Introductions and Gabriella Garcia’s background at Warp 01:35 - From Colombia and Miami to MIT and engineering at Facebook, Apple, and Google 02:38 - What Warp does as an AI native employee management platform 03:38 - Fun fact: Gabby’s unusual sense of balance 05:28 - Why build versus buy is changing in HR and people tech 06:10 - More technical people are now building prototypes themselves 07:37 - Low-code and AI tools lower the barrier to entry 09:45 - Why PII, PHI, and compliance make people tech risky 10:11 - Shopify’s “build whatever you want” AI experiment 12:28 - How long old internal tool-building used to take 13:48 - Why innovation needs risk awareness, not reckless experimentation 14:40 - Borrowing engineering-style PR reviews and testing for people tools 16:44 - The future of engineering in an AI-native workplace 17:41 - Engineers, designers, and operators as prompt-based builders 18:10 - AI as a thought partner, chief of staff, and first-pass reviewer 19:38 - Internal tools can be pushed to managers, but customer-facing work still needs engineers 20:22 - How AI changes the DNA of an organization 22:17 - Why the people function should drive adoption strategy 23:11 - OpenAI’s people leadership and the future of every role 24:55 - Why technical depth in a CPO matters for AI transformation 26:14 - People teams have been implementing tech for years, but AI is different 27:39 - Technology adoption takes time, and the AI curve is still early 28:45 - Bad HR data and weak products still block mobile adoption 29:11 - Skipping the messy middle of legacy systems in emerging markets 30:35 - How chat-based tools and image-based workflows improve accessibility 32:14 - Serving small businesses and frontline teams with practical AI 33:40 - AI may make customization accessible beyond large enterprises 34:38 - Token costs, model selection, and the ROI of AI tools 35:55 - One sales rep’s token bill as a cautionary tale 37:18 - Education gaps around model choice, data access, and rollout strategy 38:13 - Token spend as a new cost per employee problem 39:09 - Why accounting and capitalization rules matter for AI spend 40:09 - Tracking utilization and setting token caps 41:00 - Hardware choices, open source models, and reducing model spend 42:11 - A future conversation on tokenomics and people leader skills 43:08 - Moving from experimentation to pragmatic implementation 44:24 - Why every organization needs its own AI point of view Notable quotes “The people function is in service of the people.” “There are ways in which we can absorb and implement a lot of the learning from other organizations.” “My recommendations are likely not a great fit for your business.”

3. 

   2 Oct 2026

   IBEJesus Prince - What We Learned From The National Association of Counties Annual Conference

   David Turetsky speaks with IBEJesus Prince from HR Data Labs Consulting about their time at the NACO conference and what stood out most. The conversation is short but clear: the event brought together people who were focused on finding practical solutions, and even attendees outside the immediate decision-making circle were willing to help connect the dots. Key topics In this episode, David Turetsky introduces the conversation from the HR Data Labs Podcast and frames it around the team’s experience at the NACO conference. IBEJesus Prince, a principal consultant at HR Data Labs Consulting, shares a broad conference takeaway: the people at NACO were genuinely solution-driven. A major theme is the value of openness at the conference — even people who were not directly responsible for making decisions still cared about moving a solution forward. The guest notes that asking questions often led to the right person, which made the event feel collaborative rather than blocked by hierarchy. Commissioners and other officials were mentioned as people the team met, reinforcing that the event brought together stakeholders with influence and interest. The conversation highlights a contrast between environments where non-experts become blockers and this conference, where people helped route questions and support progress. IBEJesus emphasizes that the organization and the people they met left a strong positive impression. The episode closes, suggesting this was a brief conference check-in rather than a full-length interview. Timestamps 00:00 - Testing the audio and getting set up 01:41 - David introduces the HR Data Labs Podcast and the guest 01:57 - Why the NACO conference was the focus of the conversation 02:08 - A quick personal icebreaker before the main topic 03:41 - First conference takeaway: people were solution-driven 04:06 - Commissioners and officials were open to connecting people to answers 04:33 - Why the event felt collaborative instead of blocked by hierarchy 04:52 - The conversation wraps up early

Who has been a guest on HR Data Labs

Names identified in recent episode analyses. Showing up to five guests.

• Alison Campbell
• Tim Sackett
• Chris Russell
• Helena Almeida
• Mark Stelzner

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Books guests

Based on episode analysis; this does not confirm that the show is currently accepting guests.

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