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Published by Giac Soliman
The podcast of the Range Community. Real AI adoption stories, for the people who run compensation and total rewards.
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Four year equal vesting with refreshes on top looks generous until year five, when it drops off a cliff. Colette Leung is Head of Total Rewards at Chainlink Labs, and she built a simulator to show what a new joiner actually experiences rather than what the grant letter says. She models the alternatives live, including the front loaded schedules she thinks Robinhood and others have explored, against a market competitive line. What we get into The first thing she ever built with AI, which was a working manual of her manager. Why she thinks the development work has shrunk from about 70% of a build to maybe 10%. Moving long term incentive off Google Sheets into a system carrying 1,200 users. What she would fix with a magic wand, which is quantifying the difficult to quantify. About Colette Head of Total Rewards at Chainlink Labs, based in the Netherlands. She transformed the long term incentive programme and built an in house equity platform supporting 1,200 plus users. 15 plus years across high growth technology and consulting, including Uber and Deloitte. Show notes at range.community .
Martin Smit spends his weekends building whatever has caught his attention. a gardening site, an art site with 200,000 pieces on it, etc. The same habit has now read the annual compensation reports of the world's 10,000 largest companies, for 500 dollars a month.
Greg Laney has spent over twenty years between compensation and HR technology, most of it running Workday and HRIS strategy before pivoting back into comp roles. Job matching is the part of the work almost nobody says they enjoy. A manager sends over a paragraph, and somebody has to turn it into a survey match, an internal level, a salary grade and an explanation that manager will accept. He built the thing that does it. It started as a ChatGPT agent at home that wasn't good enough, became a Copilot version at work that cut four hours a day down to about ten minutes, and is now an n8n workflow he put together in roughly four hours. Submit a job description and it checks the market survey, the internal job catalogue and the salary structure, returns a primary and a secondary match with a confidence flag, drafts the email to the manager, and writes the whole run back to a spreadsheet so anyone can see six months later why the job was graded the way it was. 01:38 Recruiting, Hay points, and the mentor who taught him Access 03:10 Where to start when work only gives you Copilot 10:20 Job matching, the job nobody says they love 13:49 Screen share, the workflow walkthrough 24:15 How n8n reaches your other systems 29:01 How does it know your pay philosophy 32:37 The judgement AI hasn't caught up to
Theresa Cortese runs total rewards at Nirvana Insurance, with twenty plus years across retail, semiconductors, ed tech and HR tech. Her business operations team owns the metrics and can't see pay data, so every quarter two spreadsheets got checked against each other by five senior people. Too expensive to keep doing by hand, not big enough for an enterprise platform to make sense. Her CEO had told the company that experimenting with AI wasn't optional and that everyone should build something that week. So she did. The system she shipped runs Google sign in at the database query level, compares any two calculation runs, generates payout letters explaining how each number was reached, and passed Q1 actuals for every employee. She had never opened Terminal on her Mac. The frame she uses is the useful part. She treats it as an entry level headcount she finally got approved. She teaches it, checks it, and coaches it forward when it gets something wrong. She is clear that it cost her time before it saved any, and that her own knowledge of all eleven variable comp plans is what makes any of it safe. 00:00 Who Theresa is 00:55 How far she trusts the output 01:19 The first thing she built 02:39 Fifteen years of merit spreadsheets 03:10 Where the ideas come from 04:25 No mentor, a team that experiments together 05:10 Shipping V1 to IT 06:35 Version thinking and imposter syndrome 09:00 Whether Claude Code is actually hard 11:03 They process, they do not think 13:04 Does AI save time or create more work 15:34 Why startups underinvest in total rewards 21:14 The story behind the tool 24:24 Screen share walkthrough 30:03 Advice to her earlier self 31:40 What comp teams look like in a year
Arif Ender on why compensation teams need a product mindset, cleaner data, and stronger human oversight to make AI actually work in the real world.
Ryan Buhrke and Giac explore how to shift from dependency to empowerment with AI, especially if you’re ready to stop waiting for IT approval and start building.
Ivan Nosov leads HR technology and AI adoption at Campari Group, and he's been running pay equity analysis internally using Claude Code. We get into how he built a protected pay equity repository from scratch, what it's doing to the operating model, and where he thinks team productivity with AI actually goes next.
Josh Lemon leads Total Rewards at Resideo. He's been embedding AI into his HR function without writing a line of code. This episode covers how. We get into how he's automating job descriptions, a chatbot he built for employee engagement, and the review process his team runs with legal and cybersecurity before anything goes near production data. He's operating across multiple jurisdictions, so the governance piece isn't optional. He starts small on purpose. Email drafting, job matching, holiday calendar population. Experiments that are easy to check and quick to walk back. He builds confidence at the edges before touching anything that matters. We also cover how he measures success. He's looking at whether managers are making better decisions, whether employees are more engaged, and whether his team can revisit calls they used to make once and move on from. Speed is a byproduct. If you work in Total Rewards and you're trying to figure out where to start, this one's a good reference point.
Evert and Giac delve into the transformative impact of AI tools in the compensation and benefits domain. They explore the learning journey, risk taking, and the impact of AI tools on productivity. Evert then shares a tool his team built to assist managers in communicating pay outcomes at Bolt.
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Apple Podcasts rankings via the Mato Topic Intelligence Platform.
Observed September 12, 2026. Cached outside the daily freshness window; the positions keep the date they were taken on.
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