The WorkOps Podcast

SummaryCarmel 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.Chapters00:00 Cold open, the scary part of getting AI right00:45 Meet Carmel Smith and Tenstorrent03:45 From people operations to system architects06:45 Why she never mandates AI07:45 The painting lesson and psychological safety11:45 Leading a human revolution12:45 People data as an open source foundation20:45 Redefining productivity beyond AI ROI30:45 The monthly hackathon her leader defendsTakeaways-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 GuestLinkedIn: https://www.linkedin.com/in/carmelmoyal/Website: https://tenstorrent.comDisclaimer: The views and opinions expressed here are my own and do not reflect the official policy or position of Tenstorrent.SponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryOn 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.Chapters00:00 From the retail floor to people ops06:30 Staying curious without a second job07:45 The broken process behind every offer09:45 Building the offer validator in slack11:45 Two months in, the agent breaks12:45 Becoming the human agent13:45 Why a hallucinating agent is a liability14:55 Automation raises the bar15:45 From perfectionism to shipping V128:45 Build it, let it break, protect the timeTakeaways-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 builtConnect with the GuestLinkedIn: https://www.linkedin.com/in/kate-stewart00/ Website: https://www.horizon3.aiSponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

Summary 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. Chapters00:45 Welcome and Colleen's path into HR02:45 Leaving venture capital to operate again05:30 Keeping humans at the heart of AI07:15 The murky middle and betting on early career talent12:15 Why HR and internal systems belong together17:30 Swim teams versus soccer teams22:15 The bureaucratic misery index28:25 Why performance reviews are broken34:35 Tasty, not wastey, and hiring for taste37:15 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 SponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryWhat happens when the push to automate HR collides with the humans inside the process? In this episode of The WorkOps Podcast, host Jeet Mukherjee sits down with Nancy Luschkowski, Director of HR Infrastructure & Operations at PagerDuty, to unpack a live onboarding redesign happening amid restructuring, attrition, and the AI wave. Nancy explains why a perfect automation can still ruin the employee experience, why every cross-functional process needs an end-to-end owner, how PagerDuty created a new infrastructure and operations function to keep handoffs clean, and exactly where AI helps (accelerating the starting point) versus where it doesn't (the collaborative hard part). A practical episode for HR ops, people ops, and anyone responsible for employee experience.Chapters00:00 Cold open: the risk of over-indexing on automation01:45 Meet Nancy: why HR operations is the center of everything04:15 The story: onboarding amid reorgs, attrition, and the push to automate06:15 Notification black holes and "enabling the automation"08:45 PagerDuty's new HR Infrastructure & Operations function10:15 End-to-end process owners: one person, the whole experience12:15 Moving fast enough: rebuilding every time an owner leaves13:45 Manual work, tool stack decisions, and source of truth vs. agents18:15 AI as the starting point: process maps, drafts, and the legal persona trick22:45 The ideal onboarding experience: intuitive, customized, human27:45 Measuring onboarding success29:05 Final thoughts: come find me and talk to meTakeaways- 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 GuestLinkedIn: https://www.linkedin.com/in/nancy-luschkowski-pmp-shrm-cp-1a955665 Website: https://www.pagerduty.comSponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryIn 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.Chapters00:00 Ann Watson's path to CPO (starting at Starbucks)02:30 The three routes into people leadership and which one dominates right now08:00 AI as a workforce topic, not just a tooling decision13:30 The dysfunctional process Ann identified at every job she's ever had15:30 The breakdown of integrity inside every review cycle19:30 What the research actually says about pay-for-performance23:00 Pay transparency and the structural problem it exposed27:00 How Cover Genius inherited a comp quirk and leaned into it29:30 Building the anniversary raise system for 700 global employees32:00 The low-performer objection and loading the seat 35:00 AI in compensation: the accidental flight risk catchTakeawaysPay-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 GuestLinkedIn: https://www.linkedin.com/in/ann-watson-5404a48/Website: https://covergenius.com/SponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryOn 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."Chapters00:00 Why Jean never planned a career in HR03:50 The binder that optimized for firing, not hiring06:00 Hire slow, fire fast07:30 The bar raiser: never interview hungry11:00 The ideal employee profile: hiring for behavior13:20 Why AI adoption is a culture problem14:10 Citizen developers and the build board18:30 Putting AI enablement in People Ops, not IT23:00 Pepper and the rise of AI "employees"26:00 One piece of advice: just jump inTakeawaysOptimizing 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 GuestLinkedIn: https://www.linkedin.com/in/jean-parchewsky/Website: https://www.vendasta.com/SponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryOn this episode of The WorkOps Podcast, Jeet sits down with William West, VP People at Wrapbook, to dig into what happens when HR leaders stop trying to do everything in one place. William shares how a 20-plus-hour calibration process became six hours by separating ratings conversations from development conversations entirely. He also makes the case that HR's distinct job in an AI transformation isn't governing tools — it's owning the human argument for why the change matters. And in his closing remarks, he offers a frame on vulnerability that redefines what effective People leadership looks like right now.Chapters00:00 William West's path from nonprofit HR to VP People at Wrapbook04:00 How HR changes across sectors: pace, complexity, and scale07:30 The calibration problem: 20-plus hours and still unclear12:30 The fix: reading instead of explaining16:30 Separating calibration from development, permanently21:00 AI and human connection: what the technology is actually for25:00 Who leads AI adoption, and why HR owns the why29:00 Vulnerability as a change management strategyTakeawaysCalibration and development are two different conversations with different goals — combining them makes both worse.Switching from verbal summaries to a read-and-discuss format cut Wrapbook's calibration from 20-plus hours to around six.HR's distinct role in an AI transformation is owning the why, not governing the tooling; technical teams are better positioned for the what.Late adopters don't move without context-specific reasons; the leader closest to people in each function is best positioned to provide them.Naming openly that you feel behind on AI creates space for others to start learning instead of waiting for certainty.Connect with the GuestLinkedIn: https://www.linkedin.com/in/williamcwest/Website: https://www.wrapbook.com/SponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryA top performer walks out the door at Qualtrics holding an outside offer for double their salary. Michael MacArthur, then Head of People, has a choice: pretend the market is wrong, or admit the comp process was. His read, years later from the COO seat at Recharge: the 2x counter-offer isn't a market signal. It's an audit finding.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.Timestamps01:00 Michael's path from sales comp to head of people at Qualtrics to COO at Recharge03:00 The Qualtrics dysfunction: forced curves and 18-month time-in-seat gates04:00 The "double their salary" diagnostic signal for a broken comp process06:30 Recharge's fix: 6-month cash cycle, no forced curve, multi-level calibration10:00 Process transparency versus salary transparency13:00 Nectar's anonymous follow-up and the context thesis for AI in HR14:30 Why Anthropic's engagement score doesn't matter to Recharge16:00 Build versus buy on the people side: when trust outweighs context21:30 The CEO move that put Recharge's exec team on terminals23:00 Audit the workflow before you prompt the modelTakeaways- 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 GuestLinkedIn: https://www.linkedin.com/in/mimcarthur/Recharge: https://getrecharge.com/about/SponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryIn 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.Timestamps00:00 Welcome and Wes's career path (TFA kindergarten to 1Password)02:45 Inheriting a broken hiring process at a 10,000-person company07:00 "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 expected11:00 Sideways socialization: leading without authority across HR peers13:30 1Password rolls out org-wide agent-building, and modeling AI as a leader17:30 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 role26:00 The reframe Wes would apply today: people systems as infrastructure30:00 Where People leaders should start their own AI journeyTakeaways- 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 GuestWeston Fillman on LinkedIn: https://www.linkedin.com/in/westonfillman/1Password: https://1password.com/SponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryWhat 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.Timestamps00:23 Amie's path from psychology to global payroll04:46 Inheriting a high-touch onboarding process powered by "human APIs"06:12 Google Forms, missed steps, and a candidate experience built on anxiety08:23 Hours spent hunting down headshots for town hall slides11:00 The three-year fight to get buy-in to automate13:00 Getting the project approved, then resigning right after15:13 Rebuilding similar processes today with full stakeholder buy-in19:47 The "critical thought vs. machine work" test for what to automateTakeawaysAudit the hidden labor inside "high-touch" processes before you call them cultureQuantify manual work in overtime and bottom-line impact, not just employee experienceBuild stakeholder buy-in by making the decision feel like theirs, not yoursUse "critical thought vs. machine work" as your test for what AI and automation should touchProtect the human moments—leave processes, serious diagnoses, tough conversations—no matter how advanced your tooling getsRemember: if the process doesn't scale, it's not your culture, it's tech debtGuest LinkedIn: https://www.linkedin.com/in/amie-taylor-5b99b810/SponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryWhat 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.Timestamps00:33 From accidental psychology major to SVP of People Success02:13 Why SolarWinds calls it "People Success" instead of HR03:02 The exit survey problem at Zendesk and why David shut them down06:16 How bias from both employees and managers corrupts exit data12:34 Why employees are more comfortable asking AI sensitive questions16:16 The contractor agent pilot SolarWinds shelved and what it taught them21:05 Freeing up business partners for strategic HR through automation23:33 Flipping exit surveys on their head with predictive signalsTakeawaysInterrogate the data you're treating as gospel; exit surveys carry more bias than most HR teams admitStart with one high-impact area when adopting AI instead of trying to flip everything at onceUnderstand where a human needs to stay in the loop before building an agent—narrow scope without judgment design usually failsBe explicit with your team about why you're automating; relinquishing work requires trust in what comes nextLook for retention signals in the conversations already happening, not in surveys you have to force people to fill outThink of yourself a little more as a technologist and a little less as a psychologist in this era of HRGuest LinkedIn: https://www.linkedin.com/in/davidhanrahan/Company website: https://www.solarwinds.comSponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryA 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.Timestamps00:22 Kelsey's unusual path into HR: customer service, supply chain, and FP&A01:43 The scenario: first professional HR hire walks into a compensation philosophy mess05:31 Discovering the CEO was no longer aligned with what they'd approved08:25 De-escalating an emotionally flooded executive mid-conversation12:39 Redesigning the compensation philosophy for the next cycle17:39 Protecting employees from executive-level dysfunction24:10 The "people problem vs. process problem" framework for AI28:38 Automate all you can so you can spend time on the moments that matterTakeawaysReconfirm executive alignment right before executing—sign-off two months ago doesn't mean sign-off todayLearn to name emotional flooding in the moment; asking "would you like to reschedule?" usually resets the conversationScript your CEO on what they need to say to the executive team instead of assuming they'll represent the decision correctlySeparate people problems from process problems before deciding where AI fits—AI solves process problems well today, people problems not yetBuild employee journeys the same way you'd build customer journeys, mapping output expectations around key lifecycle momentsAutomate everything you can within the employee lifecycle so your team has time for the moments that actually matterGuest LinkedIn: https://www.linkedin.com/in/kelseybrowning/Company website: https://invisibletech.ai/SponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryWhen the sole operator of a global company’s immigration process left, a critical workflow supporting 10% of the workforce imploded. What remained was an undocumented, email-based system, causing widespread employee anxiety about work authorization and threatening the company’s ability to retain essential US-based talent. This created a strategic nightmare: a global healthcare data analytics firm, where much of its data could not be sent offshore, faced losing critical personnel due to a broken internal process.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.Timestamps00:10 The undocumented immigration process creating a single point of failure03:29 10% of the company's global workforce impacted by visa uncertainty04:35 "If it touches the employee, it comes to HR": Navigating process ownership06:07 The Excel spreadsheet chaos and undocumented immigration tracking07:47 Employee fear: Losing work authorization and business continuity risk09:22 How ChatGPT became Chesney's accidental immigration SME10:46 Automating admin: Unlocking strategic time for people-centric HR16:40 Building an internal operating system for immigration with Jira18:46 The manual gaps: Jira’s limitations and the need for further automation22:56 AI's role in improving employee experience and information discoverabilityTakeawaysStandardize 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 guestLinkedIn: https://www.linkedin.com/in/chesney-woodall/Learn more about Cedar Gate: https://www.cedargate.com/ SponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryMost 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.Timestamps00:18 Tony's path from recruiting agency to EVP of People02:16 The evolution from HR to People—and why it's intentional, not just branding07:35 The offer process: why the blueprint breaks in practice10:16 Solving for context over control: bringing all stakeholders in upfront13:11 The proudest moment: when a recruiter says no to a hire they could have closed16:07 Who makes the final call when there's a deadlock19:20 Building AI on top of solid information architecture23:00 A day a month for AI experimentation and the "courageously creative" mindsetTakeawaysReplace serial offer approvals with a shared context model where TA, comp, finance, and the hiring leader are all in the room from the startCelebrate 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 valueInvest in information architecture before building AI services on top; your underlying data has to be rock solidCreate dedicated time for AI experimentation—a half hour between meetings isn't enough, context switching kills itEmbrace "context over control" as a design principle across your people function, not just in comp decisionsTrust your leaders to own the trade-offs; the people team that's always the blanket "no" disempowers the organizationGuest LinkedIn: https://www.linkedin.com/in/tcastellanos/Company website: https://about.nextdoor.comSponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com

SummaryVidyard's performance system was broken. Managers placed employees in nine-box grids with no clear expectations, no career framework, and no two-way conversation. Decisions on compensation and promotions happened in silos, riddled with bias. Employees felt the process was opaque and unfair — yet Sarika Lamont, Vidyard's Chief People Officer, had no technology, no team bandwidth, and a business contracting through a SaaS downturn.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.Timestamps00:01 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 beginTakeaways- 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 GuestLinkedIn: https://www.linkedin.com/in/sarikal/Learn more about Vidyard: https://www.vidyard.com/SponsorThis episode is brought to you by Kinfolk, the AI service desk built for HR.See more at kinfolkhq.com