Scaling AI Search Optimization with Proactive AI-Powered Agents

Scaling AI Search Optimization With Proactive AI-Powered Agents Category 4: Operations, Systems & Content Management We approached AI search optimization the same way we approached SEO — with time-consuming research and content planning. Then we learned that AI search optimization at scale requires AI. This case study details how we built proactive, AI-powered workflows for AI search, and how we use AI to identify trending micro-topics, automate content refreshes, and monitor campaign performance in real time. Rather than relying on manual analysis and reactive content updates, this approach uses autonomous agents to shorten the time between spotting a signal and taking action — keeping content optimized for AI search engines as the landscape shifts. You'll walk away with: • How to build proactive AI agents for AI search that reduce time from signal to action • Ways to reduce manual analysis with autonomous content AI agents • How to create a trigger system for proactive content updates • Strategies to deploy AI workflows that scale AI search execution • Methods to test content with AI before publishing About the Speaker Dale Bertrand has been an SEO specialist to Fortune 500 companies and venture-backed startups around the world for two decades. His clients include global brands such as Citizen Watch, Nestle, Raymond Weil, Exxon Mobil, and Bulova. He applies his graduate school work in artificial intelligence to search engine marketing, and has trained marketing professionals from TripAdvisor, Microsoft, HubSpot, Digitas, Exxon Mobil, and Procter & Gamble. Dale speaks at industry conferences, leads corporate training events, and serves as Entrepreneur in Residence at the Harvard Alumni Entrepreneurs Organization. He holds BS and MS degrees in Computer Engineering from Brown University.