ProductCon San Francisco

Traditional SEO-driven moats are collapsing as AI-powered answer engines reshape discovery. Elena explained how product leaders can adapt by building AEO strategies that secure visibility in the new ecosystem. She shares frameworks for structuring data, creating defensible feedback loops, and partnering with model providers to build sustainable moats.

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Boards expect more than experimentation with AI—they want measurable business outcomes. Carlos presented a roadmap for evolving teams from AI-curious to AI-native, where agents, governance, and human talent are orchestrated to drive revenue and reduce cost. He told the story of Product School's own AI transformation and becoming our own customer in order to develop a bold, new approach to AI training for product teams.

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Since June, Vercel has shipped 100+ AI features. Aparna shared how small AI-native teams achieve this pace safely, using Slack-native decision-making, open-source feedback loops, and customer-0 dogfooding. In this hands-on talk, she demoed tools that make velocity a defensible moat for AI companies.

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As AI reshapes how teams collaborate, the real challenge for product leaders is knowing when and how to expand beyond their core product. In this conversation, Andrey will share lessons from scaling Miro beyond whiteboarding—what worked, what nearly broke, and how AI changes the calculus for timing expansion. What transpired was an unfiltered discussion about balancing core focus with platform bets, structuring teams for adjacent innovation, and avoiding the trap of chasing every AI trend.

In an environment where real-time changes are critically important, a command-and-control model of leadership won’t succeed. Lisa shared how product leads at Dow Jones build a culture of ownership where success is measured by the impact on end users, not just features shipped. She’ll dive into navigating stakeholder expectations and frameworks leaders can implement to maximize accountability across their organizations.

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Security is no longer just an engineering problem—it’s a product leadership problem. As enterprises adopt AI, questions about data boundaries, model safety, and compliance land directly on product leaders’ desks. Jeetu and Carlos unpacked how to design for trust as a first-class feature: from setting accountability frameworks to aligning engineering, product, and GTM around security-by-design.

Enterprise leaders shared what actually drives ROI in AI adoption—when to build vs. buy, how to govern without slowing down, and which metrics withstand executive and CFO scrutiny.

AI is collapsing boundaries between design, product, and engineering. Dylan and Carlos dug into what this means for product leaders: how to integrate AI agents into creative workflows without losing trust, how to set guardrails when AI is generating strategic IP, and how leaders can restructure orgs to manage both speed and quality.

Walmart operates across 18 countries, with scale, diversity, and legacy systems. Through real use cases, Tim showed how the company uses agent orchestration to turn these challenges into strengths, enabling faster delivery and impactful outcomes at enterprise scale.

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Digital interactions are becoming conversational, with Gartner forecasting 70% of customer interactions shifting to AI assistants by 2028. Rachel shared lessons from building agentic experiences at Contentsquare and Loris, including how to select the right problems, design for trust, and measure value.

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Traditional roadmaps can’t keep pace with AI-driven product cycles. Rob shared how Slack treats planning as a living process, embedding agentic workflows, aligning teams with adaptive governance, and balancing speed with sustained quality.

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