How Brands Build Trust & Visibility in AI Search
- Part 1 How Brands Build Trust & Visibility in AI Search
- Part 2The most common misinformation in AI Search
- Part 3The biggest mistake brands make when trying to correct inaccurate information in AI search
- Part 4The source of information that will become more important to AI search
- Part 5Will structured product feeds become more or less important to AI search?
- Part 6Will search marketers spend more time optimizing for humans or AI agents?
- Part 1 How Brands Build Trust & Visibility in AI Search
- Part 2The most common misinformation in AI Search
- Part 3The biggest mistake brands make when trying to correct inaccurate information in AI search
- Part 4The source of information that will become more important to AI search
- Part 5Will structured product feeds become more or less important to AI search?
- Part 6Will search marketers spend more time optimizing for humans or AI agents?
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Part 1How Brands Build Trust & Visibility in AI Search
Roughly 20% of AI search responses contain factual inaccuracies. Alex Sherman, co-founder and CEO of Bluefish, an AI accuracy and visibility platform serving Fortune 500 brands, previously co-founded PromoteIQ, a retail media platform acquired by Microsoft. He outlines a first-party content strategy for closing model knowledge gaps, a systematic diagnostic-and-measurement framework for tracking post-optimization accuracy improvement, and a matrix-organization model for aligning search, content, commerce, and compliance teams around a single agentic marketing mandate. Sherman also details the shift toward structured brand data feeds as the next scalable pathway for influencing how models represent products and services.
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Part 2The most common misinformation in AI Search
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Part 3The biggest mistake brands make when trying to correct inaccurate information in AI search
Most brands fight AI inaccuracies in the wrong place. Alex Sherman, co-founder and CEO of Bluefish AI and former co-founder of PromoteIQ (acquired by Microsoft), now helps enterprise brands close AI accuracy gaps across search and discovery platforms. He outlines why first-party content and direct data feeds to models outperform third-party correction efforts, how large language models increasingly weight brand-verified information over unverified sources like forum commentary, and the diagnostic framework brands need to identify where their AI accuracy gaps actually originate.
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Part 4The source of information that will become more important to AI search
Brand data will define AI search visibility next year. Alex Sherman, co-founder and CEO of Bluefish AI, previously built and sold PromoteIQ to Microsoft before turning to AI-driven discovery infrastructure. He breaks down what structured, syndicated brand data built specifically for large language models looks like in practice, and why the agentic customer journey demands a new data toolkit heading into the holiday season. The discussion centers on why brand data is poised to become a more powerful lever for AI visibility than it was through 2025.
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Part 5Will structured product feeds become more or less important to AI search?
Structured data specialization will separate winners from also-rans in AI search. Alex Sherman, co-founder and CEO of Bluefish AI, brings that perspective from building PromoteIQ, the retail media platform Microsoft acquired, and now serving Fortune 500 brands exclusively on AI discovery accuracy. He argues AEO and agentic marketing are still in their earliest innings, with today's do-everything platforms giving way to segment-specific players—data, analytics, optimization, measurement, and paid media specialists forming a new "lumascape." Sherman predicts a wave of new entrants within 12 months as the category matures past its generalist phase into genuine specialization.
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Part 6Will search marketers spend more time optimizing for humans or AI agents?
The SEO versus AEO debate misses the point entirely. Alex Sherman, co-founder and CEO of Bluefish AI, previously built and sold PromoteIQ to Microsoft, giving him rare perspective on how marketing channels transform at scale. He argues that treating search and agent optimization as competing disciplines is a losing strategy, that historical marketing channel shifts consistently open doors for new talent and new market entrants, and that brands need to prepare now for customer journeys increasingly mediated by AI agents rather than waiting for the split between human and agentic traffic to fully materialize.
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