600s in HRTech by RedThread Research

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/

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 

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/

How can HR leaders use AI and workforce data to make better decisions about the future of their organizations? 

In this episode of 600 Seconds in HRTech, Priyanka Mehrotra, Senior Analyst at RedThread Research, sits down with John Barry, Co-CEO and Co-Founder of HRBench, to discuss how AI is changing people analytics, skills intelligence, and workforce planning. 

They explore how HRBench customers are using AI to interact with workforce data, why skills are becoming essential to workforce planning, and how organizations can begin mapping where AI augments or replaces human work. 

QUESTIONS ANSWERED 
How are HRBench customers using AI with workforce data? 
Some early adopting customers are using AI assistants such as Claude to access HRBench data and ask questions about turnover, trends, and future workforce projections. John says adoption varies significantly, with many organizations still figuring out how they want to use AI within HR. 

Why are skills becoming critical to workforce planning? 
HRBench is connecting skills taxonomies with job architecture to help organizations understand the skills and tasks within their workforce. This gives HR leaders a stronger data foundation for identifying where AI can augment work and planning what skills and resources they will need in the future. 


How can organizations account for AI in workforce planning? 
HRBench can represent AI either as its own resource within an organizational chart or as something augmenting an existing employee. This allows organizations to visualise where AI is already being used and plan where it could play a greater role across the workforce. 

TIMESTAMPS 
00:00 – Introduction to John Barry and HRBench 
01:13 – Connecting workforce data with AI assistants
02:12 – AI workforce transformation and HR’s opportunity 
03:37 – Mapping skills and tasks to AI opportunities 
05:18 – Skills and workforce planning 
06:25 – Representing AI within the organizational chart 
08:59 – Priyanka’s reflection on the conversation 

RESOURCES & LINKS 

Connect With HRBench 

• HRBench Official Website: https://www.hrbench.com/ 
• John Barry on LinkedIn: https://www.linkedin.com/in/john-barry-615804/ 

Connect with RedThread Research

• RedThread Research Official Website: https://redthreadresearch.com/ 
• RedThread Research on LinkedIn: https://www.linkedin.com/company/redthread-research 
• Priyanka Mehrotra on LinkedIn: https://ca.linkedin.com/in/priyankamh

In this episode of 600 Seconds in HRTech, Stacia Garr sits down with Ronni Zehavi, CEO and co-founder of HiBob, to unpack Salesforce's $166 million strategic investment in the company and what it signals about the future of people data in an AI-driven HR market.

Salesforce's investment in HiBob is a bet on people data as the context layer every AI agent needs — and HiBob is positioning itself to serve both a governed, in-platform future and a headless, data-flows-everywhere future at once.

The conversation covers why Salesforce chose HiBob specifically, what makes HiBob's "system of engagement" approach different from traditional HCMs, how HiBob is thinking about data permissioning for AI agents, and why build-versus-buy decisions are getting faster in the AI era. As Ronni puts it, "There is only one Salesforce on the planet" — and the deal is as much a market signal as it is capital.

QUESTIONS ANSWERED
Why did HiBob take Salesforce's investment?
HiBob took the investment because it shares Salesforce's vision of humans and AI agents working side by side, and because the two companies had already proven they could integrate quickly — their Slack bot integration took just three weeks to build on a foundation both sides had already invested in.


What is Salesforce getting out of the HiBob deal?
Salesforce is building an ecosystem around data and context, and Ronni argues people data underlies nearly every prompt or question run through an AI model — making HiBob's people data a valuable piece of that ecosystem.

What makes HiBob's data more valuable than other HCMs like Workday, SAP, or ServiceNow?
HiBob was built as a "system of engagement" rather than a system built primarily for HR administrators. Its platform is designed for managers and employees too, which drives higher engagement, more data creation, and more useful context for AI agents. HiBob also focuses on mid-market to SMB companies, a segment with different scaling needs than enterprise.

How does HiBob handle data permissioning for AI agents?
HiBob gives customers two options: extract their data to use with any AI model (with the customer taking on security, privacy, and retention obligations), or run analysis through HiBob's own studio or trusted integrations like Slackbot, where HiBob's governance applies. HiBob recommends the governed path, especially for sensitive HR data.

How is AI changing build-versus-buy decisions in HR tech?
Because building is faster and cheaper than ever, companies are now debating build-versus-buy on nearly every product decision — but Ronni notes that acquiring a team with real market experience and domain expertise still often beats building from scratch.


KEY TAKEAWAYS

• Salesforce's investment in HiBob is fundamentally a data and context play, not just a capital play
• HiBob's "system of engagement" model — built for managers and employees, not just HR admins — is its core differentiator against larger HCMs
• HiBob offers customers two paths for AI agent data access: governed (within HiBob) or open (customer-managed), and recommends the governed path for sensitive data
• The tension between headless data systems and traditional governed SaaS applications is unresolved — and HiBob is building for both futures at once
• Strategy timelines have compressed; a clear six-month view now counts as a strong position, and build-vs-buy decisions are increasingly made deal-by-deal

TIMESTAMPS
 00:00 – Introduction: Salesforce's $166M investment in HiBob
 01:00 – Why HiBob took the strategic investment, and why from Salesforce
 03:00 – What Salesforce gets out of the deal
 05:00 – What makes HiBob's context more valuable than other HCMs
 08:00 – How HiBob thinks about data permissioning for AI agents
 11:00 – The headless systems tension vs. traditional SaaS
 15:00 – Stacia's analysis


RESOURCES & LINKS
Connect with HiBob

• HiBob Official Website: https://www.hibob.com/
• Ronni Zehavi on LinkedIn: https://www.linkedin.com/in/ronnizehavi/

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/

How can organizations turn workforce data into usable, actionable insights?

In this episode of 600 Seconds in HRTech, Priyanka Mehrotra, Senior Analyst at RedThread Research, sits down with Bill Bokoff, Enterprise Customer Success at TechWolf, to explore how skills intelligence, workforce data, and AI agents are changing talent decisions.

They discuss how TechWolf identifies and validates employee skills, why its technology integrates directly into existing HR systems, and how AI agents could make workforce insights more accessible across organizations.

QUESTIONS ANSWERED
How does TechWolf approach skills intelligence?
TechWolf connects data about the skills employees have today with the skills organizations will need in the future. It can also use workplace activity to help infer and validate how employees are applying those skills in practice.

How does TechWolf fit into an existing HR tech stack?
TechWolf takes a “headless” approach, integrating its workforce intelligence into systems such as Workday, SAP, ServiceNow, Slack, and Microsoft Teams rather than requiring employees to adopt another standalone platform.

What could AI agents mean for workforce analytics?
TechWolf’s analyst agent is designed to make workforce insights easier to access without relying on people analytics teams to manually pull every answer. Priyanka highlights the importance of accountability, context, and connecting AI-generated insights with the actions and outcomes that follow.

TIMESTAMPS
00:00 – Introduction to Bill Bokoff and TechWolf
00:50 – Why workforce data is becoming strategic infrastructure
02:16 – TechWolf’s approach to skills intelligence
03:11 – Measuring skills proficiency through workplace data
03:59 – TechWolf’s headless approach to HR technology
05:56 – Introducing TechWolf’s analyst agent
08:57 – Priyanka’s reflection on TechWolf
10:32 – Accountability and AI agents
11:50 – Connecting insights to actions and outcomes
12:18 – Labor market data and workforce strategy

RESOURCES & LINKS
Connect with TechWolf

• TechWolf Official Website: https://www.techwolf.ai/
• Bill Bokoff on LinkedIn: https://www.linkedin.com/in/billbokoff

Connect with RedThread

• RedThread Research: https://redthreadresearch.com/
• RedThread Research on LinkedIn: https://www.linkedin.com/company/redthread-research
• Priyanka Mehrotra on LinkedIn: https://ca.linkedin.com/in/priyankamh

In this episode of 600 Seconds in HRTech, Priyanka Mehrotra sits down with Jay Henderson, GVP of Products at UKG, live from the RedThread Research Summit at Snowbird, Utah, to talk through two of UKG's newest releases — Workforce Intelligence Hub and Dynamic Workforce Operations — and what agent-to-agent orchestration means for the future of HRTech.

The conversation covers why UKG is betting on cross-system AI coordination, how real-time data is reshaping workforce planning, and why a unified data layer might be the real differentiator in an increasingly crowded AI market. As Jay puts it, "having a strong foundational data layer really allows for the AI to produce more interesting and better results."

QUESTIONS ANSWERED
What is UKG's Workforce Intelligence Hub?
It's a repository that brings HR, pay, and workforce data together in one place, layered with reporting and industry benchmarking, so companies can see how they compare to peers and turn that insight into action.

What is Dynamic Workforce Operations?
A system that takes a company's existing labor plan and adjusts it in real time as conditions change — for example, shifting staff from outdoor to indoor roles when weather changes. It feeds in live data, recommends adjustments, and lets teams implement them on the spot.

How is UKG approaching agent orchestration?
Rather than just building more agent capabilities inside its own platform, UKG is focused on getting its agents to communicate directly with third-party agents (like ITSM systems) and with larger orchestration platforms from Microsoft and Google — aiming for end-to-end processes like onboarding without manual handoffs between tools.

What's coming with Bright 2.0?
UKG's next AI release includes faster response times, better memory management, and a voice-first interface for frontline workers — including integrations with Siri and Google Actions so employees can clock in, check PTO, or request time off without opening the app.

How does UKG differentiate its AI from competitors?
Jay points to the combined HR, pay, and workforce data foundation — including data pulled from third-party systems — as the core differentiator, since stronger underlying data lets UKG fine-tune frontier models for better results.

KEY TAKEAWAYS

• UKG is investing heavily in agent-to-agent orchestration across vendors, not just within its own platform
• Dynamic Workforce Operations shifts workforce planning from a static, periodic process to a real-time, self-adjusting one
• Bright 2.0 introduces voice-first capabilities built specifically for frontline, deskless workers
• A unified, cross-system data layer is positioned as UKG's key AI differentiator
• Data is increasingly treated as strategic infrastructure — a core theme from RedThread's 2026 Megatrends research

TIMESTAMPS
00:07 – Introduction to Jay Henderson and UKG
00:38 – Workforce Intelligence Hub: bringing HR, pay, and workforce data together
01:22 – Dynamic Workforce Operations and real-time labor adjustments
02:41 – UKG’s focus on agent orchestration across systems
03:40 – Bright 2.0: faster responses, memory management, and agent coordination
04:18 – Voice-first tools for frontline workers
06:09 – How UKG differentiates its AI approach through unified data
07:50 – Why data is becoming strategic infrastructure
08:35 – Reflections on cross-vendor agent orchestration
09:39 – Workforce planning that responds to real-time conditions
10:30 – The importance of a shared data model beyond UKG
11:17 – Three takeaways: connected agents, dynamic planning, and broader data infrastructure

RESOURCES & LINKS
UKG Official Website: https://www.ukg.com
Jay Henderson on LinkedIn: https://www.linkedin.com/in/hendersonjay/

CONNECT WITH REDTHREAD RESEARCH
RedThread Research Official Website: https://redthreadresearch.com/
RedThread Research on LinkedIn: https://www.linkedin.com/company/redthread-research/
Priyanka Mehrotra on LinkedIn: https://www.linkedin.com/in/priyanka-mehrotra-9ab2574/

How well can large language models interpret employee feedback, and what does this mean for HR and people analytics leaders? 

In this episode of 600 Seconds in HRTech, Stacia Garr sits down with Joseph Freed, Chief Product Officer at Perceptyx, to discuss PYX Labs, a public benchmark designed to evaluate how well large language models interpret employee experience data. 

The conversation explores how organizations are using tools such as ChatGPT, Claude, and Microsoft Copilot to analyze employee listening data, and how effectively these models can identify themes, interpret employee sentiment, and recommend actions for leaders. Stacia and Joe also take a closer look at what this means for the roles of people analytics professionals in the future. 

QUESTIONS ANSWERED
How well do large language models analyze employee feedback? 
LLMs can perform well when retrieving information and identifying common employee experience themes such as well-being and performance management. 

The discussion also examines how models handle themes that are specific to an individual organization, including areas such as change management and future vision. Organizational context can play an important role in interpreting this type of employee feedback. 

Can AI turn employee listening insights into action? 
The episode explores what Joseph describes as the “insight to action gap.” 

PYX Labs tested whether AI could take employee experience findings and recommend what leaders should do next. The discussion highlights the importance of behavioral science, organizational context, and expert judgment when translating employee feedback into recommendations. 

How can AI change the role of people analytics professionals?
AI can give people analytics professionals new ways to explore employee data, ask questions, and investigate patterns across different datasets. 

This creates opportunities for HR and people analytics experts to spend more time interpreting complex findings, connecting information across employee listening programs, and helping organizations determine appropriate actions. 

TIMESTAMPS
00:00 – Introduction to Joseph Freed and PYX Labs 
01:22 – Why Perceptyx created PYX Labs 
03:36 – The criteria used to evaluate LLM performance 
04:58 – Where AI performs well: common employee experience themes 06:00 – Where AI struggles: organization-specific themes 
07:22 – How the PYX Labs benchmark works 
10:54 – Testing AI's ability to move from insight to action 
12:00 – Why AI struggles to connect the dots across employee datasets 14:50 – Advice for HR leaders using LLMs with employee data 
16:28 – Stacia’s Analysis 

RESOURCES & LINKS 
Learn more about Perceptyx
Perceptyx Official Website: https://www.perceptyx.com/ 
Joseph Freed on LinkedIn: https://www.linkedin.com/in/joefreed/

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/ 

How is AI changing work at the task level, and what does that mean for workforce planning, skills development, and the future of work? 

In this episode of 600 Seconds in HRTech, Divya Iyer, Senior Analyst at RedThread Research, sits down with Anne Fulton, CEO and founder of Fuel50, at the RedThread Summit in Snowbird, Utah, to discuss Fuel50’s latest approach to workforce intelligence and the launch of its Task Intelligence capability. 

The conversation explores why tasks are becoming an important new layer of workforce data, how organizations can identify which tasks should be automated or augmented, and why connecting tasks, skill, people, and job intelligence can help leaders make better decisions about AI adoption and workforce transformation. 


QUESTIONS ANSWERED 
What is Fuel50’s Task Intelligence? 
Fuel50’s Task Intelligence is a new layer of its workforce intelligence platform that sits alongside its existing people, skills, and job intelligence capabilities. 

The approach focuses on how AI is changing work at the task level. It helps organizations understand which tasks can be automated, where human effort can be augmented, and which activities remain strategically human. 

How can organizations help employees adapt to AI-driven change? 
Fuel50's approach emphasizes bringing employees into the transformation journey rather than treating AI adoption as something that happens solely at the organizational level. 

Employees can gain visibility into how their roles and tasks are evolving, what skills they will need, which skills they currently lack, and what learning or development activities can help them prepare for the future. 

Can mentoring and development activities be connected to business outcomes?
According to Anne, Fuel50 has seen a strong correlation between mentoring and outcomes such as promotion rates, lateral mobility, internal mobility, and skill acquisition. 

The goal is to give HR and business leaders greater visibility into which development activities are actually moving the needle, rather than simply measuring participation in learning programs. 


TIMESTAMPS 
00:00 – Introduction to Anne Fulton and Fuel50 
00:38 – Fuel50 introduces Task Intelligence 
01:20 – Why tasks are the new atomic unit of work 
02:10 – Using task intelligence to understand workforce transformation 
03:16 – Fuel50’s Predictive Skills model 
04:35 – How mentoring, mobility, and skill acquisition connect 
05:27 – Who gets access to workforce intelligence? 
06:33 – Fuel50’s approach to AI 
08:36 – Creating futures for employees through AI transformation 
09:31 – Divya’s analysis 


RESOURCES & LINKS

Learn More About Fuel50

• Fuel50 Official Website: https://fuel50.com/ 
• Anne Fulton on LinkedIn: https://www.linkedin.com/in/annefultonfuel50/

Connect with RedThread Research 

• RedThread Research Official Website: https://redthreadresearch.com/  
• RedThread Research on LinkedIn: https://www.linkedin.com/company/redthread-research/ 
• Divya Iyer on LinkedIn: https://www.linkedin.com/in/divya-iyer-63813214 

How is AI changing strategic workforce planning, and what does it mean for how organizations design work?

In this episode of 600 Seconds in HRTech, Stacia Garr sits down with Marcus Mossberger, Chief Market Strategy Officer at LYTIQS, to explore how AI is changing strategic workforce planning and why organizations need to move beyond traditional annual headcount planning.

The conversation explores LYTIQS’s IQ Assist, an AI-enabled human-guided workforce planning accelerator designed to turn a traditionally months-long process into a starting point in minutes. Marcus explains how the technology brings together workforce, financial, and operational data, while keeping humans at the helm of the planning process.

They also discuss why workforce planning needs to connect business strategy with technology strategy, how validated skills and task-level analysis could change the way organizations think about jobs, and why HR may need to take greater ownership of work redesign as AI changes how work gets done.

As Marcus puts it, the goal is no longer simply to be “right” about the future. It is to be ready for it.

QUESTIONS ANSWERED
Why is strategic workforce planning shifting from being right to being ready?
Marcus argues that organizations can no longer reliably predict the future well enough to build a single “right” workforce plan. Instead, workforce planning needs to account for multiple possible scenarios and help organizations adapt as conditions change. He suggests that organizations should track scenarios at least quarterly, and potentially monthly, rather than relying solely on annual planning cycles.

How should workforce strategy connect with technology strategy?
As AI and automation change which tasks require human workers, workforce strategy can no longer operate separately from technology strategy. Marcus highlights the need for HR, IT, and Finance to work together when deciding how work should be performed and how organizations should structure their workforce.

Who should own work redesign?
The conversation argues that work redesign is becoming an important opportunity for HR. As AI changes how tasks are performed, HR may need to take greater responsibility for deciding how work is recombined and how humans and technology should work together, while collaborating closely with Finance and IT.

TIMESTAMPS
00:00 – Introduction to strategic workforce planning and LYTIQS
03:33 – How AI accelerates workforce planning
04:29 – Connecting workforce planning to business strategy
05:07 – Why workforce strategy also needs to connect to technology strategy
06:53 – Moving from manual planning to AI-assisted workforce planning
11:34 – Validated skills and AI-driven skills assessment
13:24 – Who owns work redesign?
14:45 – Stacia’s reflections on the conversation

RESOURCES & LINKS
Learn More About LYTIQS

• LYTIQS Official Website: https://www.lytiqs.com/home 
• Marcus Mossberger on LinkedIn: https://www.linkedin.com/in/mmossberger/

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/