Jess (Cummings) Dallmar

TA leaders everywhere are grappling with the same question – how do we genuinely embed AI into our hiring process to drive real impact? For us at Klaviyo, the answer has become incredibly clear: BrightHire has been, hands down, one of the most transformative additions to our recruiting toolkit. It's been instrumental in helping us: ✅ Operationalize consistent and equitable interviews across the board. ✅ Arm hiring teams with actionable, real-time feedback, cutting through the noise. ✅ Elevate our overall hiring quality bar, all without ever slowing us down. In an environment as high-growth and high-expectation as ours, BrightHire isn't just a notetaker; it's become a critical, strategic layer in how we hire. It's helping us scale interview excellence. Excited to see more teams really lean into this kind of technology during this AI-first transformation. Benjamin Sesser Theodore Chestnut

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This acquisition announcement from Grammarly has me over the moon about the future of workplace productivity. 🚀 Grammarly agreeing to acquire Superhuman isn't just another tech acquisition—it's a masterclass in building toward a cohesive vision. While many companies are fragmenting the workspace with disconnected AI features, Grammarly is building AI that easy to use and works where you work. Proud to be part of this team! Learn more: https://lnkd.in/d_ZvbVZx

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High-performing HR teams are 1.5× more likely to be experimenting with tools than teams that are falling short. I keep thinking about this stat from Lattice's State of People Strategy Report. It’s easy to read this and assume experimentation causes high performance. But I think it runs the other way too: When teams are high-performing, they’ve earned the right (i.e., the trust, time, and budget) to experiment. And that capacity creates a success loop. You can see it across the other gaps too. When HR teams are high performing: 💗 Exec teams are ~2.5× more likely to make engagement a business priority and 5x more likely to invest in DEIB. That’s because they’ve linked it to outcomes and have proof points. 💵 Companies are ~3× more likely to have greater pay transparency, because they’ve aligned process with philosophy. 💪 They are ~4× more likely to feel engaged. Not because everything is easy, but because progress creates purpose. 🧠 They are ~1.5× more likely to be excited about AI and a whopping ~8× more likely to use agentic AI regularly — often because of mindset, not tooling. Curiosity is celebrated, and employees expect personal upside as well as wins for the business. And that last part is where things get exponential: with AI, the causal arrow starts running both ways — and the gap widens. Teams that experiment now will learn faster, scale better, and compound their advantage. Teams that don’t will feel further behind six months from now. So what can a resource-constrained team do right now? 1️⃣ Nail the basics to earn trust. 2️⃣ Tie every experiment to an existing business goal. 3️⃣ Run short tests and then adopt, iterate, or kill. 4️⃣ Reward curiosity. Spotlight wins, share failures, let the best idea win.

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🚨 Lattice's State of People Strategy is in! And the results for 2026 are clear: HR continues to be pulled in every direction, but the best teams are rewriting the rules (it was time for a change, wasn't it?!). A few stats from this year’s report that stood out to me: - For the first time in six years, performance (40%) has overtaken engagement as HR’s top priority. But what's interesting is the highest-performing HR teams are 5x more likely to prioritize DEIB. Equity and inclusion (when done well) fuel performance, they don't distract from it. - 42% of HR pros are already experimenting with agentic AI, and top-performing teams are 8x more likely than low-performing teams to use it daily. You might be tired of my AI posts by now but AI is here to stay, y'all. - And despite 79% of HR leaders feeling secure in their roles, 41% considered leaving HR in the past year. The emotional toll is real. And Gen Z is not loving RTO. 👉 The my take is the most effective HR teams aren’t choosing between performance, people, or tech. They’re building systems where all three reinforce each other. Check it out and let me know which stats resonate (or don't) with you. Full report in the comments! #HR #lattice #stateofpeoplestrategy2026

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There's a secret to what makes pay programs thrive. I've seen it again and again at Google, Cruise, Instacart, and now as CEO of Pequity. The secret? Your pay programs don’t need to be complex. The best programs are clear, consistent, and grounded in data—building trust and fueling scalable growth. They can easily be articulated. The best pay programs can be spotted in one critical piece: pay ranges. They’re the foundation of any comp program that lasts. Here’s how to build ranges that scale, drawn from battle-tested best practices: 1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱 𝗹𝗲𝘃𝗲𝗹𝘀, 𝗱𝗼𝗻'𝘁 𝗿𝗲𝗶𝗻𝘃𝗲𝗻𝘁 𝘁𝗵𝗲 𝘄𝗵𝗲𝗲𝗹. Across major surveys (Mercer, Radford, etc.), 9 core levels emerge. Levels range from Entry (L1: HR Coordinator) to Executive (L9: CXO). Tie IC and manager tracks: L3 Career/Lead, L5 Expert/Sr Manager, L7 Architect/Sr Director. This creates a leveling system that grows with you. You can add more levels later, but this will give you the closest reflection of the market to start with. 2. 𝗚𝗿𝗼𝘂𝗽 𝗶𝗻𝘁𝗼 𝗷𝗼𝗯 𝗳𝗮𝗺𝗶𝗹𝘆 𝗰𝗹𝘂𝘀𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆. Examples of job families are Tech Premium (SW Eng, Data Science), Business (Recruiting, IT), and Sales. Benchmark broadly unless precision adds real value—e.g., don't split Full-Stack from Back-End if the delta's just $5K in a 100K–$140K range. It keeps maintenance sane and prevents overcomplication. 3. 𝗦𝗲𝘁 𝗺𝗶𝗱𝗽𝗼𝗶𝗻𝘁𝘀 𝗮𝘁 𝗽𝗲𝗿𝗰𝗲𝗻𝘁𝗶𝗹𝗲𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝗰𝗵 𝘆𝗼𝘂𝗿 𝗲𝗱𝗴𝗲. Most tech companies aim for tech roles at P75 for base/equity/total comp and Business/Support/Sales P50. Tweak from here for what fits your strategy. This positions you competitively -- blend data from your stage and the next to stay ahead. 4. 𝗕𝘂𝗶𝗹𝗱 𝘄𝗶𝗱𝗲, 𝗼𝘃𝗲𝗿𝗹𝗮𝗽𝗽𝗶𝗻𝗴 𝗿𝗮𝗻𝗴𝗲𝘀 𝗳𝗼𝗿 𝗳𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆. Salary: 40% spread (min 20% below midpoint, max 20% above). Equity: 60% (min 30% below, max 30% above). Assume new hires/promos start at min, midpoint for fully performing, max for exceeding. Allow some negotiations, but anchor on pay parity. 5. 𝗛𝗮𝗻𝗱𝗹𝗲 𝗲𝗾𝘂𝗶𝘁𝘆: 𝗩𝗲𝘀𝘁 𝘀𝗺𝗮𝗿𝘁, 𝗿𝗲𝗳𝗿𝗲𝘀𝗵 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰𝗮𝗹𝗹𝘆. The standard has been 4-year grants with 1-year cliff (Bay Area standard). If offering % equity, understand what surveys' "total held" means— you have to adjust for assumed vesting/promos/refreshes to keep ranges conservative yet competitive. For multiple pay regions: Anchor ranges on HQ cost of labor (not living—helps avoid subjectivity), then add a % to adjust for the other regions you pay in. I like ERIERI and Numbeo to find cost of living differences. Also, if you've read this far, I have a surprise for you 👇 I put together a guide on exactly how to build salary ranges + a range creation training deck (based on the exact systems used by top-performing comp and HR teams). 💬 Just 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 "𝗥𝗮𝗻𝗴𝗲𝘀" below, and I’ll message you the link to the Google Drive.

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HR is R&D. At Zapier, at least. And we’re not alone. I keep coming back to this article by Ethan Mollick. Ethan argues that instead of seeing AI as a downsizing tool, HR should treat it as a partner to enrich roles and go after business results that were traditionally unreachable. AI-enabled teams accomplish more, while routine tasks run in the background. In many cases, it’s up to HR to lead the way on boosting AI fluency across the org. This work has been incredibly energizing for our People team here at Zapier. Ethan also makes the case that everyday practitioners (including HR folks) are best-positioned to redesign work with AI. That makes sense. Practitioners are closest to the work, deeply understand the end-user, and have a solid handle on what excellence looks like. All-in, the AI transformation playbook is simple: 👉 Start small, focused on business impact vs. shiny new tools 👉 Pilot in a focused area (in this case, HR) 👉 Enable functional practitioners to lead the way 👉 Share and scale what works Each win builds confidence. Momentum and results follow. The side effect I love most? Engagement jumps when people become confident AI builders, not just recipients of change. Full article from Ethan here: https://lnkd.in/g6WZFka2

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⏺️ Recorded 🎙️Live in San Francisco🌉 🎤 That too in an historic studio!!! Was an amazing experience recording this one with Kathryne Friend !!! This was a day before the fabulous #HigherAltitudeSummit. In this episode of Hiring On All Cylinders we discuss what it really means to 💪Build a Tech Enabled Talent Function 💪. Synthesizing my Talent & HR leadership experience from Accenture, Zalando, OLX and now Miro. Shared insights on rethinking your TA Operating Model & integrating tech strategically, to enabling recruiter skill growth in an #AI powered future. 🗝️ Most importantly keeping razor focussed on #HumanCentricAI principles. Link for the podcast in comments. Happy Listening. HIGHER Community Talentful #futureoftalent #AIinHR

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Talent acquisition leaders, if your recruiting process was a building, the entryway has stairs. And Disabled candidates need ramps (metaphorically). Many employers tell us they want to hire Disabled talent but aren’t sure how to build a pipeline or worry about saying the wrong thing when reaching out. Here’s what we’ve seen attract Disabled jobseekers in our community: - Recruiting events: virtual and or accessible networking opportunities - Job descriptions: mention of work flexibility and benefits, inclusive language (i.e no weight lifting or driving requirements for a remote job) - Interviews: consideration of transferable skills built from lived experience If this sounds good in theory but complicated to implement and get buy in for, we’ve got you. Making Space and Indeed are partnering over the next year to share resources and host events for TA, HR, and leaders on recruiting and retaining Disabled professionals. To get our event invites, sign up for our newsletter, (scroll down to the "stay ahead" section). https://lnkd.in/ergeNHNs Repost from Making Space

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