『600s in HRTech by RedThread Research』のカバーアート

600s in HRTech by RedThread Research

600s in HRTech by RedThread Research

著者： Stacia Garr & Dani Johnson
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概要
 600 Seconds in HRTech is for HR leaders who are tired of vendor content that looks like research and want a faster, easier way to stay current.

Hosted by RedThread Research analysts, each episode features a focused conversation with a HRTech leader or product expert, asking the questions buyers actually want answered: What problem does this solve? Who isn't it right for? What did you get wrong before you got it right? What's really behind that acquisition?

Then the analyst unpacks it solo. No vendor present. You get what was genuinely useful, what was marketing, and what to push back on in your own evaluations.

600 Seconds in HRTech covers talent platforms, learning technology, people analytics tools, and more. One episode at a time, you build a clearer picture of the landscape without chasing every press release or conference session yourself.

Want to be part of the conversation? Join our community for free and connect with others shaping the future of work.

Learn more about RedThread Research here: https://redthreadresearch.com/homeRedThread Research 2026 マネジメント マネジメント・リーダーシップ 経済学
エピソード
  • Beyond SuccessFactors: What SAP’s Acquisition of TechWolf Really Means

    Beyond SuccessFactors: What SAP’s Acquisition of TechWolf Really Means
    2026/10/15
    In this episode of 600 Seconds in HRTech, Stacia Garr talks with Jeroen Van Hautte (Jay), co-founder and CTO of TechWolf, about SAP’s agreement to acquire the skills and work data company. Most coverage has focused on SuccessFactors. Stacia wanted to know what it means for the rest of SAP.

    They cover why TechWolf chose SAP, what a context graph does for AI agents, how the deal could lower AI costs for HCM vendors, and whether TechWolf can stay open to every tech stack.

    Note: SAP entered into a definitive agreement to acquire TechWolf, subject to the customary closing conditions. The deal is expected to close in Q4 2026, numbers aren't disclosed

    QUESTIONS ANSWERED
    What is TechWolf, and what is SAP actually buying?
    TechWolf is a headless data layer for skills and work. It figures out what work is happening in a company and what skills people have and need, then feeds that into enterprise systems and AI agents. SAP gets the product, the in-house AI models, and the team.

    What is a context graph, and why do AI agents need one?
    A context graph takes data from systems of record and maps it into one connected picture of skills, work, and the external market. Agents pointed straight at a system of record handle simple lookups fine. On harder questions, like which training moves people into data roles, they read piles of irrelevant data. That drives up cost and raises the risk of bias and hallucination. A context graph puts only the right information into the agent’s context window.

    How does a context graph lower AI costs for HCM vendors?
    Agents don’t have to search through as much data. Jeroen says that at organizations of 50,000 to 100,000 people, the difference is about an order of magnitude. That’s TechWolf’s own estimate, so test it with your own harder questions. Stacia ties this to her HBR piece on HCM vendors passing AI costs to customers through consumption pricing.

    What is the “pancake guy” problem with AI and people data?
    One Slack message about leftover pancakes led an LLM to label an engineer “the pancake guy” in an onboarding game. In promotions or redeployment, one stray data point could pigeonhole someone and keep them from roles they’re qualified for. Grounded, auditable data across a wide range of signals helps prevent this.

    Can TechWolf stay “Switzerland” inside SAP?
    Jeroen says TechWolf will keep serving customers on any tech stack, since closed architecture takes options away from customers. Stacia adds that openness takes more than one side. If competitors pull back integrations, much of the value of a cross-system graph falls apart.

    TIMESTAMPS
    [00:39] – Introduction to Jeroen Van Hauttev and TechWolf
    [01:54] – Why TechWolf chose SAP
    [04:17] – What SAP is buying: product, models, and team
    [05:49] – How a context graph changes what AI agents can do
    [08:07] – How a context graph lowers AI costs
    [08:40] – The pancake guy story and AI reliability
    [12:05] – Can TechWolf stay open inside SAP?
    [13:25] – Stacia’s reflection on the conversation

    RESOURCES & LINKS
    • SAP Buys TechWolf: The Fight for the Skills and Tasks Layer
    • How to Respond to the Coming AI Cost Shock


    Connect with TechWolf
    • TechWolf Official Website: https://www.techwolf.ai/
    • Jeroen Van Hautte on LinkedIn: https://www.linkedin.com/in/jeroenvanhautte/


    Connect with RedThread Research
    • RedThread Research Official Website: https://redthreadresearch.com/
    • RedThread Research on LinkedIn: https://www.linkedin.com/company/redthread-research/
    • Stacia Garr on LinkedIn: https://www.linkedin.com/in/staciashermangarr/
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    19 分
  • The AI Execution Layer: How Amp Is Connecting the HR Tech Stack

    The AI Execution Layer: How Amp Is Connecting the HR Tech Stack
    2026/10/06
    What happens when AI moves beyond individual HR platforms and becomes an execution layer across the entire HR tech stack?

    In this episode of 600 Seconds in HRTech, the conversation explores how Amp is using AI to help frontline employers connect systems, execute workflows, and turn fragmented HR data into actionable intelligence. Somen Mondal, Co-founder and CEO of Amp, explains why the company is focused on high-volume frontline work and why he believes the next generation of HR technology will sit across existing systems rather than replace them.

    The discussion covers Amp's approach to cross-system orchestration, AI-powered intelligence, frontline hiring, compliance, and the evolving role of point solutions in an increasingly platform-driven HR technology market. Somen also explains why Amp sees compliance as a core part of its product architecture, including AI governance, data residency, and the risks of deploying AI in HR without sufficient domain expertise.

    QUESTIONS ANSWERED
    What is Amp and what does it do?
    Amp is focused on high-volume frontline work, with the goal of helping move workers from application to their first shift. Somen describes the company as combining technology with human processes to improve the frontline hiring experience.

    How does Amp use AI across multiple HR systems?
    Somen explains that Amp combines data from different systems and applies intelligence across that information. The goal is to identify patterns and take action; for example, using information about hiring and workforce management to improve retention or reduce the likelihood that a new hire fails to show up for their first shift.

    Why is compliance central to Amp's approach?
    Compliance is built into Amp's foundation rather than treated as an additional feature. Somen highlights AI governance, AI compliance, data residency, and other enterprise requirements as central considerations when building AI technology for HR.

    He also distinguishes between AI compliance and broader IT compliance, including questions around where employee and applicant data is stored and how organizations respond to requests to delete data.

    Why can't organizations simply "vibe code" their own HR technology?
    Somen argues that enterprise HR technology requires significant domain expertise. Building an application that can manage complex HR processes at enterprise scale is different from creating a small internal tool.

    Compliance is another major barrier. AI systems used for hiring, payroll, and other HR processes can create significant risks if they are not designed and operated within the appropriate regulatory and compliance frameworks.

    TIMESTAMPS
    00:00 – Introduction to Amp and Somen Mondal
    01:28 – Why the market is ready for Amp
    02:00 – Somen's journey through HR tech, AI, and governance
    03:29 – Cross-system orchestration across HR technology
    04:08 – Using AI intelligence across HR systems
    06:55 – Using intelligence to improve frontline hiring and retention
    07:28 – Where should HR data and intelligence live?
    09:36 – Why systems of record may not move as quickly as innovation
    11:40 – Can organizations simply "vibe code" their own HR technology?
    14:08 – Dani’s Reflection

    RESOURCES & LINKS
    Connect With Amp
    • Amp Official Website: https://runamp.com/
    • Somen Mondal on LinkedIn: https://ca.linkedin.com/in/somenmondal

    Connect With RedThread Research
    • RedThread Research Official Website: http://www.redthreadresearch.com
    • RedThread Research on LinkedIn: https://www.linkedin.com/company/redthread-research
    • Dani Johnson on LinkedIn: https://www.linkedin.com/in/dani-johnson


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    17 分
  • The Power of Access To External Workforce Data with Revelio Labs

    The Power of Access To External Workforce Data with Revelio Labs
    2026/09/22
    What can HR leaders learn from the same workforce data used by investors and financial analysts?

    In this episode of 600 Seconds in HRTech, Stacia Garr sits down with Paulina Tilly, Data Scientist at Revelio Labs, to explore how external workforce data can help HR teams make faster, better-informed decisions.

    They discuss Revelio Labs’ new Terminal, how organizations can benchmark attrition, hiring, sentiment, and skills against competitors, and how AI and MCP connectors are making complex workforce data more accessible.

    QUESTIONS ANSWERED
    Why do HR leaders need external workforce data?
    External data can help HR teams understand how their workforce compares with the wider market. Revelio Labs brings together publicly available data on employment histories, job postings, sentiment, layoffs, and more to answer questions around attrition, compensation, talent flows, and skills gaps.

    How is AI changing access to workforce data?
    Revelio Labs offers multiple ways to access its data, including raw data feeds, its Terminal, and an MCP connector. The MCP allows organizations to bring Revelio’s data into their own AI tools, helping teams analyze workforce questions without needing the same level of dedicated data resources.

    What separates organizations that get real value from workforce data?
    Paulina says the organizations getting the most value come to the data with hypotheses they are ready to test. Access to more data alone isn’t enough. HR teams need to ask the right questions and understand how the insights can inform actual workforce decisions.

    TIMESTAMPS
    00:00 – Introduction to Paulina Tilly and Revelio Labs
    01:04 – Why HR leaders need the same data as financial analysts
    03:13 – Inside the Revelio Labs Terminal
    04:19 – Where Revelio Labs gets its workforce data
    06:34 – Connecting workforce data to AI through MCP
    10:21 – Skills, activities, and standardizing workforce data
    13:19 – Turning workforce data into meaningful decisions
    17:40 – Stacia’s reflection on the conversation

    RESOURCES & LINKS
    Connect with Revelio Labs
    • Revelio Labs: https://www.reveliolabs.com/
    • Paulina Tilly on LinkedIn: https://www.linkedin.com/in/paulinatilly/

    Connect with RedThread Research
    • RedThread Research: https://redthreadresearch.com/
    • RedThread Research on LinkedIn: https://www.linkedin.com/company/redthread-research
    • Stacia Garr on LinkedIn: https://www.linkedin.com/in/staciashermangarr/
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    24 分
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