How Should Search Leaders Navigate the AI Shift?
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing PodcastShow Notes
Organic traffic down 30% while revenue holds flat points to an attribution problem, not a visibility problem. Jordan Koene, CEO and Co-Founder of Previsible, former CEO of Searchmetrics Inc. and former Head of SEO and Content Development at eBay, advises nearly 100 enterprise clients through the shift to AI discovery. He outlines why synthetic prompt-based visibility scores should give way to lagging KPIs such as revenue, conversion, and product-category attribution, with first-party revenue, sales, and signup tables as the source of truth for what drives brand selection. He also covers a brand-asset hygiene framework (help centers, pricing, and about pages, plus review volume and freshness, citation coverage, and backlink freshness) and a "follow the money" budget approach that keeps Google as the core channel while funding ChatGPT citation work. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
More from Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
Paywalled education filters out talent before hiring begins. Venkata Pagadala, AI Product Manager for Search at AT&T, argues that financial barriers to learning hide capable candidates from employers. He defines work as a profit-sharing exchange of human time and skills, and education as a separate good that money should not restrict. He makes the case for open-access education as a talent-sourcing strategy, because a public knowledge base widens the candidate pool and lets business owners hire the right person. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Enterprise SEO teams can turn first-party data into better search results.
Teams solve classification problems manually, then repeat the same work. Venkata Pagadala, AI Product Manager for Search at AT&T, builds agentic AI systems for enterprise SEO. He covers documenting each solved problem in structured systems such as Obsidian or Notion, so AI agents can run the next classification. He also covers turning a manual solution into a repeatable system as soon as the problem is solved, and using voice input like Whisper Flow, since people speak about twice as fast as they type. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
SEO, AEO, and GEO now compete as labels for the same enterprise discipline. Venkata Pagadala, AI Product Manager for Search at AT&T, leads agentic AI work for enterprise search. The conversation covers turning first-party data into deeper consumer understanding, applying agentic AI to enterprise SEO workflows, and treating search as the core discipline with AEO and GEO as facets of it.
Agentic AI workflows fail without classified first-party data. Venkata Pagadala, AI Product Manager for Search at AT&T, classified 5.8 million queries, removed 3 million noise queries, and uses the resulting persona-based insights to build mockups that win leadership buy-in. He outlines a competitor-navigation and Wikipedia-driven L1/L2/L3 taxonomy that surfaces zero-search-volume, high-intent demand from Reddit, Quora, and YouTube, plus a UGC-based research framework that treats reviews, ratings, and brand response rates as primary inputs. He also covers persona-based query fan-outs as a replacement for single-prompt tracking, and documentation systems that let AI agents repeat classification at scale. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.