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Lauren
Sandtorv
VP, Sales
monday.com
Lauren brings over 15 years of experience in the technology sector, building and leading high-performing sales organizations that drive revenue at scale. She currently serves as VP of Sales for North America at monday.com, where she leads go-to-market strategy and execution for the region. Lauren has built and scaled sales teams across both global technology companies and high-growth startups, with deep experience designing modern GTM motions that balance product-led growth with sales-led motions. Prior to monday.com, she spent more than eight years at Dropbox, most recently leading the Sales and Channel teams for the Americas. Previously, Lauren worked at Citrix and in scaling startups, where she gained firsthand experience working with enterprise customers and understanding their challenges from multiple perspectives across different stages of growth. Her leadership philosophy centers on developing talent, creating repeatable revenue engines, and fostering transparent, human-first cultures that deliver real customer impact through great products. Outside of her operating role, Lauren is deeply committed to mentorship and community engagement, regularly serving on boards and volunteering in support of local and professional communities.
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11 March 2026 11:45 - 12:30
Panel discussion: Measuring AIs real business impact
AI is everywhere in the revenue stack - but many CROs are still being asked to justify spend without clear proof of impact. Activity is easy to measure. Real business value is not. This session cuts through the hype to focus on how revenue leaders assess whether AI is actually improving outcomes that matter: productivity, consistency, deal quality, retention, and forecast confidence. We’ll explore how CROs separate signal from noise, decide what success really looks like, and communicate AI’s impact in a way that holds up with CEOs, CFOs, and boards. The goal isn’t more dashboards - it’s confidence that AI is moving the business forward. Attendees will leave with: - A clear framework for evaluating AI impact beyond usage and adoption metrics - Guidance on which revenue outcomes AI should be expected to influence — and which it shouldn’t