Chapters
00:00 Introduction
01:45 From police academy to CHRO
05:50 Why AI will deepen HR expertise instead of replacing it
08:50 The direct deposit lesson
13:55 Starting small with proof of concept
16:35 Inside Toshiba's HR Shark Tank
20:00 Build, borrow, or bot
21:30 The two goalposts and the integration trap
28:55 FOBO and getting people AI ready
32:55 From curious to cautious

Takeaways

-AI will change how HR work looks, not whether it exists. Like computers, direct deposit, and factory robots before it, it enhances the function and creates new work.

-Start small instead of trying to do everything at once. Pick one proof of concept and give it a full cycle, a year to 18 months, before judging whether it worked.

-Cross functional experimentation multiplies value. Toshiba's HR Shark Tank mixed payroll, field HR, business partners, and L&D to build real -Copilot agents on top of their day jobs.

-Every AI decision has to pass two goalposts: little to zero disruption to the business and a genuine improvement to the employee experience.

-Watch the integration trap. HR spent 15 years consolidating eight systems down to three, and bolting on AI point solutions risks recreating the same fragmentation.

Connect with the Guest
LinkedIn: https://www.linkedin.com/in/jason-desentz/
Website: https://www.toshiba.com/

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

Payroll problems were blowing up Slack channels at SurveyMonkey, and employees weren't taking them to finance. In this episode of The WorkOps Podcast, host Jeet Mukerji talks with Becky Cantieri, Chief People Officer at SurveyMonkey, about the decision to hit the blow-up button and move payroll out of finance and into HR, how a willing CFO and one simple question (who is willing to talk to the employee?) made the handoff a slam dunk, and why the defining skill for HR leaders right now is metabolizing change: finding signal in noise fast enough to prioritize, pivot, and keep up in the AI era. A practical listen for people leaders, HR operations teams, and anyone rethinking who should own the employee experience.

Chapters

00:00 Cold open

01:16 From the Nordstrom sales floor to CPO

06:37 Fifteen years of reinvention

09:26 Metabolizing change

14:22 The payroll problem

18:31 Winning over the CFO

21:44 Rebuilding the team

28:03 The AI champion at SurveyMonkey

32:51 Refusing to wait in the AI queue

35:21 Permission, capability, agency

Takeaways

-Employees don't think in org charts. When something breaks, they go to whoever usually answers, so design ownership around the employee experience, not department history.

-The team willing to talk to employees should own the employee-facing function. SurveyMonkey moved payroll into HR when it became clear finance didn't want those conversations.

-Make handoffs safe with data: SurveyMonkey's HR team committed to SLAs and email volume reporting while banking controls stayed with finance.

-The defining HR skill right now is metabolizing change: finding signal in noise fast enough to prioritize, pivot, and keep up with AI.

-Metabolizing is teachable. It starts with curiosity, a growth mindset, and the willingness to throw out a process you built and are proud of.

Connect with the Guest

LinkedIn: https://www.linkedin.com/in/rebecca-cantieri-85654a/

Website: https://www.surveymonkey.com

Sponsor

What happens when a CEO hands unlimited AI access to every employee, and adoption still stalls? In this episode of The WorkOps Podcast, host Jeet Mukerji sits down with Kimberly Nerpouni, Global VP of People & International Operations at Pearl, the parent company of JustAnswer. Kimberly shares why AI transformation is a human enablement problem rather than a technology problem, how her People Ops team built Pearl's AI accelerator and a 1 to 10 adoption scale with no bad scores, and her three levels of work framework for deciding what AI should absorb first. She also explains the feedback facilitator agent her team is building, the hard line she draws (AI never gives feedback, it helps managers facilitate it), and why PIPs don't exist at Pearl. This conversation is for people leaders, HR teams, and anyone navigating AI adoption inside their organization.

Chapters

00:00 Introduction

00:45 From IT manager to people operations

04:25 JustAnswer, Pearl, and human plus AI

06:15 Inside Pearl's AI accelerator

10:55 Build, buy, or borrow

13:15 The three levels of work

17:05 AI that facilitates feedback instead of giving it

19:50 The AI agent hub and transparency

23:35 Why PIPs don't work and what replaces them

35:05 Don't wait, just do

Takeaways

-AI adoption is a human enablement problem, not a technology problem, and people ops is uniquely positioned to lead it.

-Move AI into level one work first, the tasks that don't require your expertise, so your team can focus on level two and level three work.

-Build, buy, or borrow: piloting with AI startups can reveal exactly what's worth building bespoke in-house.

-AI should facilitate feedback conversations by scanning context and prompting managers, but it should never give the feedback itself.

-Front-load clarity with job descriptions, career ladders, and employee-owned development plans so PIPs are never needed.

Connect with the Guest

LinkedIn: https://www.linkedin.com/in/kimberlypignolet/

Website: https://www.pearl.com

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

What happens when your AI pilot works too well to stay a pilot? In this episode of The WorkOps Podcast, host Jeet Mukerji talks with Ivan Nosov, Head of HR Tech and Global Total Rewards Director at Campari Group, about taking AI in HR from proof of concept to production. Ivan shares why he started with Copilot Studio knowing it wouldn't last, where RAG breaks down at enterprise scale, and how his team built a digital twin router app on Claude that loads only the context each question needs. He also lays out the adoption playbook that made it stick: winning over regional directors and COE champions first, running hackathons for awareness, and addressing the work people hate instead of the work they love. A practical conversation for HR, people ops, and workplace technology leaders navigating AI transformation.

Chapters

00:00 Introduction

01:30 From IT engineer to HR leader

03:45 Should AI transformation sit in HR or IT

05:55 How non technical HR can get started

07:20 Copilot Studio as a proof of concept tool

09:30 Harness, routing, and the limits of RAG

15:00 Building the digital twin router app on Claude

17:40 Context engineering and distilling tacit knowledge

19:30 Winning adoption through champions

29:55 The future of junior roles and build versus buy

Takeaways

• Start with the accessible tool to prove the concept, then move on. Copilot Studio validated Campari Group's AI ideas, but production required control over the harness that abstracted platforms can't offer.
• Routing beats RAG at scale. Loading only the relevant context for each question makes AI more targeted, more efficient, and far less likely to hallucinate.
• Campari Group's digital twin runs on Claude with deliberate model selection: Sonnet for efficiency, Haiku for helper queries, and Opus for final artifacts because of its design taste.
• Adoption spreads through champions. Win over regional directors and COE heads first, stay with them until the outputs click, and they will share it further than any rollout plan.
• Context engineering is the real work. Distill tacit knowledge into a compact, unambiguous knowledge base: 250,000 tokens in total, with no session loading more than 50,000.

  
  

Connect with the Guest

LinkedIn: https://www.linkedin.com/in/ivan-nosov/

Website: https://www.camparigroup.com/

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

What happens to careers when AI makes more than half of all roles look fundamentally different within two years? In this episode of The WorkOps Podcast, host Jeet Mukergi sits down with Lisa Sherwell, Chief People Officer at SUSE, to unpack her answer: strive for job security, not role security. Lisa shares how SUSE's Future Selves program transformed engagement for its over 50 workforce, why internal gigs are the new engine of career growth, and why performance management fails not because of broken processes but because leaders avoid honest conversations. A practical, candid conversation for HR leaders, people operations professionals, and executives navigating the AI era.

Chapters

00:00 Introduction

01:15 From sales and call centers to chief people officer

02:30 Building commercial curiosity in HR

04:15 Future Selves: investing in the over 50 workforce

07:00 Job security vs role security

08:55 Hiring for AI potential, not expertise

10:30 How SUSE rolled out AI across the business

16:45 Running HR for 8% less while delivering more

24:30 Performance is a clarity problem, not a process problem

33:00 A new playbook for the AI era

Takeaways

-Strive for job security, not role security: broad competencies and internal mobility protect people as AI reshapes their roles.

-More than half of all roles could look fundamentally different within two years, so career agility is now a core business responsibility.

-SUSE's Future Selves program proved that investing in the over 50 workforce lifts engagement, internal mobility, and community, while preserving invaluable business context.

-Performance is a clarity problem, not a process problem: honest meets, fails, exceeds conversations beat new ratings scales and OKR frameworks.

-Ownership of performance belongs with the individual contributor, because nobody cares more about your career growth than you do.

Connect with the Guest

LinkedIn: https://www.linkedin.com/in/lisasherwell/

Website: https://www.suse.com

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

On this episode of The WorkOps Podcast, host Jeet Mukergi sits down with Natasha Valani, Chief People Officer at Tubi, the number one free ad-supported streaming service in the US. Natasha shares how her people team built a live calibration tool in Replit in just two days, connecting it back to Workday and saving roughly two weeks during year end review season. The conversation goes well beyond one tool: Natasha unpacks how she hires for high agency and low ego, why subtraction is her favorite leadership discipline, and how she sees junior and people roles evolving as AI takes on more of the routine work. It's a practical, candid look at running a lean, builder-minded HR function, ideal for people leaders, HR practitioners, and anyone rethinking how their team operates in the AI age.

Chapters

0:45 Welcome and Natasha's story

1:45 From 26 schools to belonging and the people space

3:45 Building a culture where everyone builds

6:45 Hiring for high agency and low ego

9:45 The year end calibration problem

10:45 The two day tool built in Replit

15:45 Why build instead of buy

19:45 Trade-offs and the power of subtraction

21:45 The honest gap, headcount management

26:45 The future of junior roles in the AI age

Takeaways

-You don't need a vendor RFP to solve an HR problem. Someone who knows the guts of the process built a live calibration tool in Replit in two days and connected it to Workday, saving roughly two weeks.

-High agency is the differentiator in the AI age. Natasha screens for it with one question: tell me about a time you built a solution nobody asked you to.

-Pair high agency with low ego. Stay curious and ask questions, but don't assume nothing good existed before you.

-Subtraction is a strategy. Force-rank priorities, openly showcase what you removed, and protect your team's bandwidth by refusing to do everything.

-Junior roles won't disappear, they'll shift. The people closest to the guts of a workflow become operational advisors and orchestrators, and AI still needs a human eye to catch the slop.

Connect with the Guest

LinkedIn: https://www.linkedin.com/in/natasha-valani-1758a418/

Website: https://tubitv.com

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

Carmel Smith, Director of People Operations and Programs at Tenstorrent, joins host Jeet Mukerji on The WorkOps Podcast to make the case that the AI moment is really a human one. Fresh off building people operations inside a company that grew headcount 43% past 1,300 employees, Carmel explains why she's rebranding her team from "people operations" to system architects and experience creators, why she refuses to mandate AI or track tokens and agents, and what she means when she says she's leading a human revolution rather than an AI one. It's a refreshingly optimistic, practical conversation for HR and people leaders, operations teams, and anyone trying to bring humans along through rapid change.

Chapters

00:00 Cold open, the scary part of getting AI right

00:45 Meet Carmel Smith and Tenstorrent

03:45 From people operations to system architects

06:45 Why she never mandates AI

07:45 The painting lesson and psychological safety

11:45 Leading a human revolution

12:45 People data as an open source foundation

20:45 Redefining productivity beyond AI ROI

30:45 The monthly hackathon her leader defends

Takeaways

-HR's real identity shift is internal: help your team see themselves as system architects and experience creators, not process executors, and their confidence follows.

-Mandating AI backfires. Counting tokens, hours, and agents produces box checking, not rethinking. Curiosity, safety, and a genuinely excited leader scale far better.

-AI rewards the most human skills. Having strong opinions and articulating them clearly matters more than technical knowledge, because the systems part is the easy part.

-Don't track ROI on AI directly. If it's implemented well it bleeds into everything, so measure faster processes, redesigned workflows, and reduced fear instead.

-Protect creative time on purpose. A defended monthly hackathon, even when the output fails, changes how a team works by redefining what productivity means.

Connect with the Guest

LinkedIn: https://www.linkedin.com/in/carmelmoyal/

Website: https://tenstorrent.com

Disclaimer: The views and opinions expressed here are my own and do not reflect the official policy or position of Tenstorrent.

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

On this episode of the WorkOps Podcast, host Jeet Mukergi talks with Kate Stewart, Staff People Operations, AI and Automations Lead at Horizon3.ai, about what really happens when you put an AI agent into a live people-operations workflow. Kate shares the story of the offer-validation agent she built in Slack to check every job offer against approved comp bands and job architecture, how a Slack update broke it after two months, and the two weeks she spent secretly becoming the agent herself to keep the quality bar from slipping. Along the way she unpacks why a hallucinating agent is more dangerous than a silent one, why automation raises the bar instead of lowering it, and how to ship, break, and maintain automations without burning out. It's a candid, practical listen for anyone in people ops, HR tech, or operations moving from curiosity about AI to actually running it in production.

Chapters

00:00 From the retail floor to people ops

06:30 Staying curious without a second job

07:45 The broken process behind every offer

09:45 Building the offer validator in slack

11:45 Two months in, the agent breaks

12:45 Becoming the human agent

13:45 Why a hallucinating agent is a liability

14:55 Automation raises the bar

15:45 From perfectionism to shipping V1

28:45 Build it, let it break, protect the time

Takeaways

-When an automation breaks, you don't fall back to your old baseline, you fall below it, because automation raises the bar for what your team considers acceptable

-A silent agent is confusing, but a hallucinating agent is a liability, a confident wrong answer is far more dangerous than no answer at all

-Job architecture is not a data hygiene problem, it is an IT provisioning problem, the wrong title in the HRIS means the wrong system access on day one

-Build one agent to do one job at the right moment, rather than spreading the same check across three people and three separate touchpoints

-Ship V1, let it break, and protect the time to maintain it, the build teaches you the problem and the break teaches you what you actually built

Connect with the Guest

LinkedIn: https://www.linkedin.com/in/kate-stewart00/
Website: https://www.horizon3.ai

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

In this episode of The WorkOps Podcast, host Jeet sits down with Colleen McCreary, Chief People Officer and Head of Internal Systems at Confluent, for a candid conversation about people, productivity, and what AI is really doing to the workforce. Colleen makes the contrarian case that early-career talent is a company's edge in the AI era, while the "murky middle" faces the hardest reinvention. She explains why she's torn out the performance review everywhere she's worked, how she compressed a five-month review cycle to three and a half weeks, and how a company-wide "find the bullshit" campaign cut hundreds of meeting hours. Along the way she shares her first-principles approach to choosing tools, her "tasty, not wastey" philosophy on spending, and her definition of the people officer as the product manager of how a company actually runs. It's a sharp, practical listen for HR and people leaders, founders, and anyone rethinking how work gets done.

Chapters

00:45 Welcome and Colleen's path into HR

02:45 Leaving venture capital to operate again

05:30 Keeping humans at the heart of AI

07:15 The murky middle and betting on early career talent

12:15 Why HR and internal systems belong together

17:30 Swim teams versus soccer teams

22:15 The bureaucratic misery index

28:25 Why performance reviews are broken

34:35 Tasty, not wastey, and hiring for taste

37:15 First principles and the people officer as product manager

Takeaways 

The people most at risk from AI aren't juniors, they're the "murky middle" six to twelve years in, whose old playbook is being flipped on its head.

Performance ratings don't predict performance: 90% of the people managed out at Confluent had been rated successful or exceptional.

You can cut a five-month review cycle to a few weeks by subtracting, fewer rating tiers, fewer questions, and a real deadline as a forcing function.

Solve for the problem first, then pick the tool. Choosing the tool first is how good teams get stuck.

In an age where AI can build almost anything, taste (good judgment about what's worth building and spending on) is the scarcest skill.

Connect with the Guest 

LinkedIn: https://www.linkedin.com/in/colleenmccrearychiefpplofficer/

Website: https://confluent.io

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

Chapters
00:00 Cold open: the risk of over-indexing on automation
01:45 Meet Nancy: why HR operations is the center of everything
04:15 The story: onboarding amid reorgs, attrition, and the push to automate
06:15 Notification black holes and "enabling the automation"
08:45 PagerDuty's new HR Infrastructure & Operations function
10:15 End-to-end process owners: one person, the whole experience
12:15 Moving fast enough: rebuilding every time an owner leaves
13:45 Manual work, tool stack decisions, and source of truth vs. agents
18:15 AI as the starting point: process maps, drafts, and the legal persona trick
22:45 The ideal onboarding experience: intuitive, customized, human
27:45 Measuring onboarding success
29:05 Final thoughts: come find me and talk to me

Takeaways
- You can build the perfect automation, but if the people in it don't know their step or lack context, the experience fails — design automation and the human touch in tandem.
- Automation is point-in-time and adoption isn't: managers who hire rarely experience onboarding as brand-new, so one-size-fits-all workflows rarely work and continuous re-enablement is mandatory.
- Cross-functional processes need an end-to-end owner — one person accountable for the experience from start to finish, not just a collection of contributors.
- Slow down on tool-stack decisions to avoid tech debt: assess current state, define owners, and improve incrementally rather than in one big release.
- AI accelerates the starting point — process maps, drafts, persona critiques — but it doesn't do the hard part: incorporating stakeholder feedback, testing, and iterating with a product mindset.

Connect with the Guest

LinkedIn: https://www.linkedin.com/in/nancy-luschkowski-pmp-shrm-cp-1a955665
Website: https://www.pagerduty.com

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

In this episode of The WorkOps Podcast, Jeet sits down with Ann Watson, Chief People Officer at Cover Genius, to unpack why pay-for-performance creates a structural integrity problem that no amount of manager training can fix. Ann argues that the annual raise has always been a budget decision dressed in performance language, and that pay transparency didn't create the breakdown — it just made it undeniable. She shares how Cover Genius moved to anniversary-based automatic raises, what happened when managers were freed from the comp conversation entirely, and why she still gives low performers their raise every year.

Chapters

00:00 Ann Watson's path to CPO (starting at Starbucks)
02:30 The three routes into people leadership and which one dominates right now
08:00 AI as a workforce topic, not just a tooling decision
13:30 The dysfunctional process Ann identified at every job she's ever had
15:30 The breakdown of integrity inside every review cycle
19:30 What the research actually says about pay-for-performance
23:00 Pay transparency and the structural problem it exposed
27:00 How Cover Genius inherited a comp quirk and leaned into it
29:30 Building the anniversary raise system for 700 global employees
32:00 The low-performer objection and loading the seat 
35:00 AI in compensation: the accidental flight risk catch

Takeaways

• Pay-for-performance is a budget mechanism, and calling it a performance signal is where the integrity breakdown starts.
• Managers who can't explain the comp process aren't failing — they've been handed something structurally unexplainable.
• Anniversary-based raises remove the manager from a conversation they never should have owned.
• Raising the low performer's salary maintains the market rate of the seat, not the person — so you can hire well when you're ready.
• When a manager objects to a low performer's raise, that objection is often a performance conversation that's overdue.

Connect with the Guest
LinkedIn: https://www.linkedin.com/in/ann-watson-5404a48/
Website: https://covergenius.com/

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

On The WorkOps Podcast, Jean Parchewsky, VP of People Operations at Vendasta, makes a case most AI conversations miss: whether AI takes hold in your company is decided at the hiring table, not in the tooling budget. She traces it back to a training binder that optimized for terminations over hiring, the "hire slow, fire fast" principle she built in response, and the behavior-first "ideal employee profile" her team uses today. Then she shows how that same hiring discipline is what made AI adoption stick, through a citizen developer program, a searchable build board, and a culture where sharing your failures out loud is the norm. Essential listening for any People leader who has been asked to "roll out AI."

Chapters

00:00 Why Jean never planned a career in HR

03:50 The binder that optimized for firing, not hiring

06:00 Hire slow, fire fast

07:30 The bar raiser: never interview hungry

11:00 The ideal employee profile: hiring for behavior

13:20 Why AI adoption is a culture problem

14:10 Citizen developers and the build board

18:30 Putting AI enablement in People Ops, not IT

23:00 Pepper and the rise of AI "employees"

26:00 One piece of advice: just jump in

Takeaways

Optimizing HR for legal risk instead of the team can quietly cost you your best people.

Hire slow and fire fast: spend your effort choosing the right person, and be honest quickly when it isn't working.

Hiring for behaviors rather than skills builds the culture everything else depends on.

Stalled AI adoption is usually a culture problem, not a tool problem.

AI enablement belongs close to the work, in People Ops, where it becomes workflow change instead of better emails.

Connect with the Guest
LinkedIn: https://www.linkedin.com/in/jean-parchewsky/
Website: https://www.vendasta.com/

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

Chapters
00:00 William West's path from nonprofit HR to VP People at Wrapbook
04:00 How HR changes across sectors: pace, complexity, and scale
07:30 The calibration problem: 20-plus hours and still unclear
12:30 The fix: reading instead of explaining
16:30 Separating calibration from development, permanently
21:00 AI and human connection: what the technology is actually for
25:00 Who leads AI adoption, and why HR owns the why
29:00 Vulnerability as a change management strategy

Takeaways

1. Calibration and development are two different conversations with different goals — combining them makes both worse.
2. Switching from verbal summaries to a read-and-discuss format cut Wrapbook's calibration from 20-plus hours to around six.
3. HR's distinct role in an AI transformation is owning the why, not governing the tooling; technical teams are better positioned for the what.
4. Late adopters don't move without context-specific reasons; the leader closest to people in each function is best positioned to provide them.
5. Naming openly that you feel behind on AI creates space for others to start learning instead of waiting for certainty.

Connect with the Guest
LinkedIn: https://www.linkedin.com/in/williamcwest/
Website: https://www.wrapbook.com/

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

This is one of the cleanest diagnostics we've heard for whether a comp process is really working. And it's the same logic Michael applies to AI, engagement, and the build-versus-buy questions every HR leader is wrestling with right now. The unifying argument: context, not better tools, is the layer that separates the HR teams winning with AI from the ones spinning cycles.

Timestamps
01:00 Michael's path from sales comp to head of people at Qualtrics to COO at Recharge
03:00 The Qualtrics dysfunction: forced curves and 18-month time-in-seat gates
04:00 The "double their salary" diagnostic signal for a broken comp process
06:30 Recharge's fix: 6-month cash cycle, no forced curve, multi-level calibration
10:00 Process transparency versus salary transparency
13:00 Nectar's anonymous follow-up and the context thesis for AI in HR
14:30 Why Anthropic's engagement score doesn't matter to Recharge
16:00 Build versus buy on the people side: when trust outweighs context
21:30 The CEO move that put Recharge's exec team on terminals
23:00 Audit the workflow before you prompt the model

Takeaways
- The fastest test that your comp process is broken: a leaving employee getting offered double their current salary at the next job.
- Forced curves and time-in-seat promotion gates work at 250 employees. They quietly stop working at 2,500.
- AI value in HR shows up in context-gathering, not dashboards. Anonymous follow-up conversations beat static survey scores.
- Internal-historical engagement trends beat external benchmarks. Anthropic's engagement score doesn't tell you what's happening on your team.
- Audit the workflow before you prompt the model. Most failed AI projects in HR are unmapped-workflow problems, not tooling problems.

Connect with the Guest
LinkedIn: https://www.linkedin.com/in/mimcarthur/
Recharge: https://getrecharge.com/about/

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

In this episode of The WorkOps Podcast, Jeet sits down with Weston Fillman, Director of People Operations and Employee Relations at 1Password, to unpack what it actually takes to get an HR transformation funded, and what changes when AI enters the room. Wes describes the months of pre-work that won him more executive budget than he asked for at a 10,000-person enterprise tech company, the reframe he'd apply to the same project today (people systems as infrastructure, not engagement), and the role-redesign conversations he's having on his team at 1Password as AI starts to automate the operational layer of HR ops. A field guide for any People leader heading into their next budget cycle.

Timestamps

00:00 Welcome and Wes's career path (TFA kindergarten to 1Password)

02:45 Inheriting a broken hiring process at a 10,000-person company

07:00 "You can build a new house, but the floor plan's wrong"

08:00 The structured plan, executive buy-in, and a budget bigger than expected

11:00 Sideways socialization: leading without authority across HR peers

13:30 1Password rolls out org-wide agent-building, and modeling AI as a leader

17:30 Build vs. buy in 2026 (and why it's really build AND buy)

22:30 The role redesign conversation: AI rewrites the JD, doesn't cut the role

26:00 The reframe Wes would apply today: people systems as infrastructure

30:00 Where People leaders should start their own AI journey

Takeaways

- HR transformation budgets are won in the months of pre-work before the e-staff ask, not in the room itself.

- The reframe from "employee experience" to "business infrastructure" is the single change that turns the same HR project from nice-to-have into board-level fundable.

- AI lands well when leaders treat it as a role-redesign conversation, not a layoff conversation. Most operational work was never on the JD anyway.

- Cross-functional buy-in (sideways across peer HR leaders) matters as much as executive sponsorship for any large People transformation.

- The best AI on-ramp for an individual contributor is to start with the work they're not great at, not the work they already excel at.

Connect with the Guest
Weston Fillman on LinkedIn: https://www.linkedin.com/in/westonfillman/
1Password: https://1password.com/

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

What looks like a warm, boutique onboarding experience on the outside is often powered by something much less glamorous on the inside: a person copy-pasting LinkedIn headshots into slides at midnight. 

In this episode of the WorkOps Podcast, host Jeet sits down with Amie Taylor, Senior Director of People Operations, Rewards and Technology, for a refreshingly honest conversation about the hidden cost of "human API" processes. 

Amie walks through a three-year saga at a previous hyper-growth tech consulting company where the entire day-zero-to-day-one new hire experience ran on Google Forms, manual IT tickets, and one very overworked TA coordinator hunting down headshots for the CMO's town hall slides. She shares how she eventually built the business case, the internal politics she had to navigate, why it took a team member filing overtime to finally break through, and the bittersweet twist at the end when she left right after getting the project approved. 

She and Jeet also get into how she's applying those lessons today—consolidating HR systems at her current company, using critical thought as the test for what to automate, and why some processes (like leave for someone facing a serious diagnosis) should stay stubbornly human. If you've ever inherited a process held together by goodwill and overtime, this one will hit close to home.

Timestamps

• 00:23 Amie's path from psychology to global payroll
• 04:46 Inheriting a high-touch onboarding process powered by "human APIs"
• 06:12 Google Forms, missed steps, and a candidate experience built on anxiety
• 08:23 Hours spent hunting down headshots for town hall slides
• 11:00 The three-year fight to get buy-in to automate
• 13:00 Getting the project approved, then resigning right after
• 15:13 Rebuilding similar processes today with full stakeholder buy-in
• 19:47 The "critical thought vs. machine work" test for what to automate

Takeaways

• Audit the hidden labor inside "high-touch" processes before you call them culture
• Quantify manual work in overtime and bottom-line impact, not just employee experience
• Build stakeholder buy-in by making the decision feel like theirs, not yours
• Use "critical thought vs. machine work" as your test for what AI and automation should touch
• Protect the human moments—leave processes, serious diagnoses, tough conversations—no matter how advanced your tooling gets
• Remember: if the process doesn't scale, it's not your culture, it's tech debt

Guest LinkedIn: https://www.linkedin.com/in/amie-taylor-5b99b810/

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

What if the most honest feedback about why people leave your company isn't in the exit survey at all—it's in the Slack threads, Zoom transcripts, and emails already happening every day? 

In this episode of the WorkOps Podcast, host Jeet sits down with David Hanrahan, SVP of People Success at SolarWinds, for a candid conversation about one of the most broken processes in HR: the exit survey. 

David walks through his years-long love-hate relationship with exit surveys, including a moment at Zendesk where he got so frustrated with the bad signal that he actually told the team to stop running them altogether. He and Jeet get into why exit data is so unreliable, how bias from both departing employees and their managers rewrites the real story, and why he believes the future is in predictive signals from real conversations rather than post-hoc questionnaires. 

David also shares a contractor agent pilot that SolarWinds recently shelved, what it taught them about where AI belongs and where it doesn't, and how he's coaching his business partner team through the shift from tactical request-handling to strategic HR. And he closes with a line that stuck with me: in this era, people leaders need to think of themselves a little more as technologists and a little less as psychologists. If you lead people ops, build HR systems, or are just trying to figure out where AI fits, this one's worth an hour.

Timestamps

• 00:33 From accidental psychology major to SVP of People Success
• 02:13 Why SolarWinds calls it "People Success" instead of HR
• 03:02 The exit survey problem at Zendesk and why David shut them down
• 06:16 How bias from both employees and managers corrupts exit data
• 12:34 Why employees are more comfortable asking AI sensitive questions
• 16:16 The contractor agent pilot SolarWinds shelved and what it taught them
• 21:05 Freeing up business partners for strategic HR through automation
• 23:33 Flipping exit surveys on their head with predictive signals

Takeaways

• Interrogate the data you're treating as gospel; exit surveys carry more bias than most HR teams admit
• Start with one high-impact area when adopting AI instead of trying to flip everything at once
• Understand where a human needs to stay in the loop before building an agent—narrow scope without judgment design usually fails
• Be explicit with your team about why you're automating; relinquishing work requires trust in what comes next
• Look for retention signals in the conversations already happening, not in surveys you have to force people to fill out
• Think of yourself a little more as a technologist and a little less as a psychologist in this era of HR

Guest LinkedIn: https://www.linkedin.com/in/davidhanrahan/

Company website: https://www.solarwinds.com

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

A CEO signs off on a new compensation philosophy. Two months later, when HR starts rolling it out, that same CEO tells the executive team it was never approved—and blames the HR leader for pushing it. 

Sound impossible? It happened. 

In this episode of the WorkOps Podcast, host Jeet sits down with Kelsey Browning, VP of People Operations, for a candid and detailed story about what happens when leadership alignment breaks down in the middle of a comp cycle at a Series B-to-C startup. 

Kelsey was the first professional HR hire at the company and had to navigate a conflict-averse CEO, an emotionally flooded VP, and a compensation philosophy that was suddenly orphaned—all while protecting employees from the chaos happening above them. She walks through how she de-escalated heated executive conversations, rebuilt the philosophy for the next cycle, and what she'd do differently if she could go back. 

She and Jeet also get into the framework she uses to decide what AI can solve (process problems) versus what still needs a human (people problems), why she builds employee journeys the same way product teams build customer journeys, and her prediction that HR is about to consolidate back into generalists—powered by AI. If you've ever been the HR person caught between a founder who changed their mind and an executive team that needs answers, this one will hit close to home.

Timestamps

• 00:22 Kelsey's unusual path into HR: customer service, supply chain, and FP&A
• 01:43 The scenario: first professional HR hire walks into a compensation philosophy mess
• 05:31 Discovering the CEO was no longer aligned with what they'd approved
• 08:25 De-escalating an emotionally flooded executive mid-conversation
• 12:39 Redesigning the compensation philosophy for the next cycle
• 17:39 Protecting employees from executive-level dysfunction
• 24:10 The "people problem vs. process problem" framework for AI
• 28:38 Automate all you can so you can spend time on the moments that matter

Takeaways

• Reconfirm executive alignment right before executing—sign-off two months ago doesn't mean sign-off today
• Learn to name emotional flooding in the moment; asking "would you like to reschedule?" usually resets the conversation
• Script your CEO on what they need to say to the executive team instead of assuming they'll represent the decision correctly
• Separate people problems from process problems before deciding where AI fits—AI solves process problems well today, people problems not yet
• Build employee journeys the same way you'd build customer journeys, mapping output expectations around key lifecycle moments
• Automate everything you can within the employee lifecycle so your team has time for the moments that actually matter

Guest LinkedIn: https://www.linkedin.com/in/kelseybrowning/

Company website: https://invisibletech.ai/

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

Chesney Woodall, an HR Director initially unfamiliar with immigration, inherited this mess. She quickly found herself managing 25 simultaneous immigration cases while operating as a key contributor on a three-person US HR team for a 1,000-employee company. This "messy middle" between top-down goals and bottom-up work created an accountability gap that put talent retention and business continuity at risk. To systematize the process, Chesney leaned on cross-functional collaboration and existing tools, transforming a manual, fear-inducing workflow into a more transparent system. Now, Chesney uses AI as a personal learning assistant, democratizing complex knowledge and freeing up strategic time to focus on people-centric HR.

Timestamps

• 00:10 The undocumented immigration process creating a single point of failure
• 03:29 10% of the company's global workforce impacted by visa uncertainty
• 04:35 "If it touches the employee, it comes to HR": Navigating process ownership
• 06:07 The Excel spreadsheet chaos and undocumented immigration tracking
• 07:47 Employee fear: Losing work authorization and business continuity risk
• 09:22 How ChatGPT became Chesney's accidental immigration SME
• 10:46 Automating admin: Unlocking strategic time for people-centric HR
• 16:40 Building an internal operating system for immigration with Jira
• 18:46 The manual gaps: Jira’s limitations and the need for further automation
• 22:56 AI's role in improving employee experience and information discoverability

Takeaways

• Standardize critical processes to avoid a single point of failure, especially when they directly impact talent retention and business continuity.
• Utilize existing internal tools and cross-functional teams to build solutions, leveraging institutional knowledge before investing in new software.
• Systematize administrative tasks to free up HR for strategic initiatives like proactive talent management and improving employee experience.

Connect with the guest

LinkedIn: https://www.linkedin.com/in/chesney-woodall/

Learn more about Cedar Gate: https://www.cedargate.com/

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

Most companies have an offer process that looks clean on a whiteboard and turns into chaos in practice. The recruiter fights for more money. The comp team says no. Finance says it's too expensive. The business leader just wants it done. 

Everyone's working off different information, and by the time a decision is made, it's conflict resolution—not a hiring process. In this episode of the WorkOps Podcast, host Jeet sits down with Tony Castellanos, EVP of People at Nextdoor, for a sharp conversation about what happens when you replace serial decision-making with shared context. 

Tony has spent his career watching offer processes break down because every stakeholder applies their own lens in isolation—and nobody has the full picture. He walks through how Nextdoor rebuilt the process to bring all parties into the room at the beginning instead of passing decisions down a chain, why the proudest moments in his career are when a recruiter says no to a hire they could have closed, and how the shift from "HR" to "people" isn't just branding—it changes the scope and responsibility of the entire function. 

Tony and Jeet also get into why information architecture is the unsexy foundation that makes AI-first people operations possible, how Nextdoor runs a dedicated build day once a month for AI experimentation, and why the biggest thing leaders can do right now is create space for their teams to be "courageously creative." If you lead a people function, run a recruiting team, or are trying to figure out where AI fits, Tony's framework of context over control will change how you think about the work.

Timestamps

• 00:18 Tony's path from recruiting agency to EVP of People
• 02:16 The evolution from HR to People—and why it's intentional, not just branding
• 07:35 The offer process: why the blueprint breaks in practice
• 10:16 Solving for context over control: bringing all stakeholders in upfront
• 13:11 The proudest moment: when a recruiter says no to a hire they could have closed
• 16:07 Who makes the final call when there's a deadlock
• 19:20 Building AI on top of solid information architecture
• 23:00 A day a month for AI experimentation and the "courageously creative" mindset

Takeaways

• Replace serial offer approvals with a shared context model where TA, comp, finance, and the hiring leader are all in the room from the start
• Celebrate when recruiters say no to a hire based on long-term team impact—that's the shift from closing at all costs to creating long-term value
• Invest in information architecture before building AI services on top; your underlying data has to be rock solid
• Create dedicated time for AI experimentation—a half hour between meetings isn't enough, context switching kills it
• Embrace "context over control" as a design principle across your people function, not just in comp decisions
• Trust your leaders to own the trade-offs; the people team that's always the blanket "no" disempowers the organization

Guest LinkedIn: https://www.linkedin.com/in/tcastellanos/

Company website: https://about.nextdoor.com

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com

Three and a half years later, she's rebuilt the entire system — career frameworks, competencies, performance reviews, and now an AI-powered interactive tool that took five minutes to prototype. What once required two quarters and a team of specialists now happens in a week. In this episode, Sarika walks through how she dismantled a one-sided performance process, designed for scale during contraction, and used Claude to compress months of work into days — all while solving for what employees and managers actually need, not what HR traditionally builds.

Timestamps
00:01 How Sarika fell into HR from management consulting at a federal contracting startup 
05:59 The broken nine-box system she inherited: no expectations, no framework, pure manager bias 
11:28 Why cross-functional partnerships with finance and business leaders matter more than HR credentials 
17:22 Building a career framework from scratch: IC paths, management tracks, and leveling for scale 
23:35 The SaaS downturn, AI disruption, and why the system went stagnant 
28:19 Using Claude to redesign competencies in days instead of quarters 
33:55 What replaces the time saved: ongoing enablement, adoption, and human-to-human coaching 
38:12 Where to start if you're overwhelmed: validate the real problem first, then ask AI where to begin

Takeaways
- Build your performance system from the business problem backward — not from what HR traditionally designs — or you'll solve for process instead of outcomes.
- Career frameworks need differentiation level-to-level so employees see the gap between "intermediate" and "senior" in concrete, actionable terms, not themes.
- AI compresses iteration cycles from quarters to days, but the time saved goes to enablement and adoption — the work HR has always neglected because building took too long.
- Start by validating what employees and managers actually need through exit data, engagement surveys, and one-on-ones — then use AI as a thought partner to fill the gaps you can't solve manually.
- Performance transparency isn't about revealing ratings — it's about documenting expectations, competencies, and decision-making frameworks so people understand how to grow.

Connect with the Guest
LinkedIn: https://www.linkedin.com/in/sarikal/
Learn more about Vidyard: https://www.vidyard.com/

Sponsor
This episode is brought to you by Kinfolk, the AI service desk built for HR.

See more at kinfolkhq.com