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Directionally Correct, A People Intelligence Podcast

Directionally Correct, A People Intelligence Podcast

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Directionally Correct is the #1 people intelligence podcast in the world. Hosted by Cole Napper, the podcast dives into people intelligence, workforce planning, behavioral science, and talent analytics, helping leaders navigate the future of AI in the workplace with insight and a dash of fun. To find out more, check out colenapper.comAll rights reserved by WRKdefined Management & Leadership Wissenschaft Ökonomie
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  • Intelligence at Databricks Where the Future is Now - Marina Pearce - #197

    Intelligence at Databricks Where the Future is Now - Marina Pearce - #197
    Nov 2 2026
    Thanks to HRBench for powering this episode. To find out more about the company building the future of people intelligence, reach out to book a demo at hrbench.com/directionallycorrect! Check out this episode of the #1 people intelligence podcast with special guest, Marina Pearce, Senior Director of Workforce Intelligence at Databricks! In this episode Cole Napper talks with Marina Pearce, Senior Director of Workforce Intelligence at Databricks, about what it really means to create an intelligence layer and how that differs from people analytics of just a few years ago. Marina describes a shift from analysts as reporters who alone held the keys to data and definitions to architects who build foundational ecosystems that let everyone ask and answer questions. The work centers on unifying information, securing and governing it, keeping gold tables reliable, and certifying dashboards, agents, and apps so users trust the outputs without needing a human check. She emphasizes democratizing capability rather than raw data, then moving beyond interaction into helping people automate their own work. Building the team looks different in a lean, high-velocity data and AI company. Instead of traditional specialist roles (data engineer, data scientist, analytics partner), Marina prefers full-stack generalists with high G, deep curiosity, and a strong “figure it out” orientation. Many on her current team did not come from people analytics backgrounds. Context matching matters more than any cookie-cutter playbook: understand the company’s operating rhythms and constraints first, then design the function to remove friction. Once the platform exists, HR partners and recruiters begin building their own models and apps on certified assets; the central team’s job expands to support that speed rather than gatekeep it. Marina introduces “radiating intent”—openly publishing roadmaps, dependencies, and vision so others can poke holes, align, or help. Value shows up in system-level indicators such as operating efficiency per employee rather than isolated study ROIs. Rolling out a company-wide intelligence layer required deliberate change management: clear expectations, delightful tools, and reinforcers, plus a framework of mindset, skills, usage, and impact. An AI champions network, hypercare support, and leadership modeling accelerated adoption once people experienced the platform and became “hooked.” Technical foundations and security remain essential, yet the harder work is changing habits so people prefer the new tools. Looking past intelligence, Marina sees the next horizon as workflow automation that individuals and teams own themselves through job crafting, not something done to them by a central hub. She also reflects on career paths: business exposure (HRBP-style work or rotations) is often the missing piece for people-intelligence professionals who aspire to broader people leadership roles. The conversation covers three pieces: an article on whether workplaces drain curiosity (and the risk of isolation when AI becomes the middleman), Paul Spector’s critique of formulaic academic papers and the incentives that reward complexity over clear findings, and a large tech-worker survey showing burnout, ambivalence about recommending the field, and the enduring importance of managers. Cole and Marina note the gap between apocalyptic public narratives and the day-to-day experience of many practitioners who already feel amplified by the tools. Throughout, Marina models the philosophy of being loud and opinionated internally so ideas improve, while remaining practical about technical foundations, security, and human habits. The episode paints a concrete picture of what is already possible today when a people team treats itself as infrastructure and enables the rest of the organization to build. If you like this episode, you’d also love exploring prior episodes—visit colenapper.com for the full archive and show links.
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    1 Std. und 37 Min.
  • Building a People Intelligence Product at HRBench - Jason Murphy & Matt Maguire - #196

    Building a People Intelligence Product at HRBench - Jason Murphy & Matt Maguire - #196
    Oct 26 2026
    Thanks to HRBench for powering this episode. To find out more about the company building the future of people intelligence, reach out to book a demo at hrbench.com/directionallycorrect! Check out this HR Tech Voices Series episode of the #1 people intelligence podcast with special guests, ⁠Jason Murphy, Chief Product Officer and Matt Maguire, Chief Revenue Officer at HRBench! In this episode Cole Napper sits down with Jason Murphy, Chief Product Officer, and Matt Maguire, Chief Revenue Officer at HRBench to unpack what it takes to build a people intelligence product and why people analytics must move from a transactional function to a product-based one. Jason defines the product role as understanding customer problems and delivering business value so users accomplish their work more efficiently. Matt describes the collapsed feedback loop: client conversations that once required weeks of mockups can now produce interactive prototypes in hours or days. Jason takes real feedback, builds code-based prototypes matching the application, gathers reactions the next day, and hands them to engineering—compressing timelines dramatically while improving accuracy. The discussion centers on the architecture of people intelligence. After dashboards fade, only an intelligence layer and a data layer remain. HRBench acts as the extensible middle layer that cleans data from disparate sources, applies methodology and health checks, and securely passes it via MCP connectors into other AI tools. Organizations seek to eliminate dashboard maintenance, surface engagement insights, and score AI impact by mapping company jobs to a proprietary taxonomy that includes tasks, skills, and predicted effects. Jason details reskilling and redeployment tools so leaders identify impacted employees and move them into valuable roles. Matt notes that even advanced teams lack clear answers, creating opportunity for AI-native platforms free of legacy technical debt. Practical examples include headcount planning workflow approvals that replace spreadsheet processes, expanded performance features such as stack ranking and nine-box, richer employee listening, and org charts that combine open requisitions with planning. Tokenomics is rising: capturing agent token spend, assigning annualized costs, and reflecting agents in the org chart so plans show human and machine costs plus hours returned. Jason explains how AI tools transformed product work itself by automating QA test cases and documentation that follows a custom house style, collapsing weeks of effort into hours. Looking a year ahead, barriers between applications dissolve. Users will work inside a vendor interface or pull data through conversational layers; specialized point solutions will fade as large organizations rely on an intelligence middle layer while smaller ones still need accessible platforms. Cole adds that systems of record must rebuild AI-natively and that former products become features inside broader intelligence and workflow layers. The episode reviews Jack Costl’s argument for portfolio management so teams choose high-value problems rather than polishing every request; Ludic Stella’s analysis linking culture signals such as transparency to return on assets; and a HumRRO paper on indexing AI impact within O*NET through task- and KSAO-level scores. Jason highlights the difficulty of tracking current AI capabilities given rapid model improvement. In Cole’s Corner the guests address rapid-fire questions and the near-term future of intelligence products. The conversation stays grounded in real customer problems, the speed of AI-native tooling, and the shift from dashboards to solutions that redesign work. Supporting platforms and leaders who deliver this practical progress is essential as years of roadmap compress into months. If you like this episode, you’d also love exploring prior episodes—visit colenapper.com for the full archive and show links.
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    1 Std. und 8 Min.
  • Are We Taking Care of the Leaders of Our Field Enough? - Yuyan Sun - #195

    Are We Taking Care of the Leaders of Our Field Enough? - Yuyan Sun - #195
    Oct 19 2026
    Thanks to HRBench for powering this episode. To find out more about the company building the future of people intelligence, reach out to book a demo at hrbench.com/directionallycorrect! Check out this episode of the #1 people intelligence podcast with special guest, ⁠Yuyan Sun, Former Head of Workforce Analytics, AI Transformation and Technology at Motive and Co-Founder at The Zeroth! In this episode Cole Napper and Yuyan Sun explore the future of people analytics and how practitioners can position the practice inside AI-forward organizations. Yuyan argues the field has leaned too long on altruistic voices without rewarding those who give back. Community leaders who open calendars after layoffs, write freely, and share research build the profession, yet vendors often capture the value. She and Cole stress the need to pay it back as well as forward by thanking those who helped you and supporting them when they commercialize expertise so the community can sustain itself. Yuyan left her role as head of workforce analytics, AI transformation, and technology at Motive to co-found The Zeroth with Dan Riley. The Zeroth gives senior AI transformation leaders a quiet peer space to exchange authentic insights on real challenges free from vendor hype and investor noise. At Motive she applied tools through rapid experimentation such as benefits chatbots that scaled into broader solutions. They address AI economics head-on. The technology is expensive and most organizations will lose money in the first six to twelve months of serious experimentation, just as earlier digital transformations required more specialized staff rather than fewer. Real value requires redesigning work, structures, skills, and learning programs while preserving human judgment and creativity that differentiate a business when competitors use the same models. Dashboard and report work is already disappearing or being automated. Practitioners will serve as AI ambassadors inside HR and then help organizations measure capacity and intelligence as AI becomes part of organizational capability. Yuyan and Colby Nesbitt called this a midlife crisis because field growth was tied to headcount expansion during easy-capital booms that are now slowing. Cole contrasts early trailblazers who wrote the playbook with later button-pushers. As AI automates the commodity side, a second renaissance of trailblazers is needed across HR. The function must decide whether to remain a risk-mitigation shop or become a value-creation engine willing to ruffle feathers. They also examine risks. Personality assessments that are not strictly job-related can create monocultures, and AI-generated resumes screened by AI systems amplify the problem. Assaf Shahar’s ideas on cultural flattening highlight the danger of outsourcing judgment to LLMs, producing quick but unaccountable outputs and cultural debt. Cole and Yuyan note the field’s origins in talent analytics were always about performance through people. The sadness is real, yet the answer is a compelling vision of people intelligence rather than nostalgia. In Cole’s Corner rapid-fire questions reveal Yuyan’s entrepreneurial drive, her wish to visit an outer-planet rock, and her expectation that the typical analytics professional three years from now will have automated most data wrangling and reporting with agents then focus on defining the right problems and metrics. The episode closes with a personal exchange about the value of authentic conversation and the hard work of producing content while the field struggles to commercialize the contributions of its community leaders. Supporting those leaders and sponsors like HRBench is essential if the profession is to mature. If you like this episode, you’d also love exploring prior episodes—visit colenapper.com for the full archive and show links.
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    1 Std. und 34 Min.
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