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Laura
Sardegna
Senior Director, Strategy & Operations
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Laura Sardegna is an accomplished executive leader with extensive experience in sales, go-to-market strategy, and marketing, consistently driving growth and innovation through transformational leadership. Her expertise lies in building C-level relationships and advancing digital media strategies, particularly within the retail, retail media, restaurant, and CPG industries. Previously, as Senior Director of Global Sales Strategy and Operations and Sales Enablement GTM at Reddit, Laura lead a global team in developing and executing sales strategies and scaling comprehensive sales enablement programs. During her tenure at Google, she served as Director of Global Sales Enablement and Activation GTM, where she pioneered AI-leveraged program platforms that significantly grew the global sales pipeline of AI-based solutions by 700% and increased sales productivity by 14%. Her work at Google also included leading retail, restaurant, and CPG industry sales teams, and leading strategy for Google’s retail media network approach, demonstrating her deep understanding of technology adoption and market dynamics. Laura's background also includes leadership roles in retail, brand management and corporate turnaround management consulting, where she advised on strategic and financial restructurings for revenue growth and operational efficiency. She holds an MBA from Kellogg School of Management and a BBA from the University of Michigan Ross School of Business.
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29 January 2026 10:30 - 11:15
Panel discussion: Build vs buy? Designing an AI stack that actually works
As AI floods the RevTech landscape, RevOps teams are stuck between two paths: invest in building tailored, in-house AI capabilities or rely on off‑the‑shelf platforms that promise “instant intelligence.” This panel brings together RevOps leaders to unpack the trade-offs - cost, speed, control, and risk - and share real stories of what worked, what didn’t, and what they’d do differently. You’ll learn how to: - Evaluate when it makes sense to build custom AI (e.g., routing, scoring, forecasting) versus standardizing on vendor capabilities - Weigh total cost of ownership: data infrastructure, engineering support, model maintenance, and vendor lock-in - Create a practical decision framework that RevOps can use with product, data, and finance to choose the right AI approach for your revenue engine