What Is AI Search Optimization?

By Graham Hardt, Senior Director, Global Search & Digital

Marketers used to obsess over blue links and page one rankings. Now buyers ask a chatbot instead of typing a query into a search box, and a lot of teams are scrambling to keep up. According to the State of Marketing report , 85% of marketers say AI is reshaping their SEO strategy, and that shift is already underway. AI search optimization builds directly on traditional SEO, adapting proven fundamentals for a search experience that synthesizes answers instead of just linking to sources. Get it right, and your content shows up inside the answer itself.

Traditional SEO chases ranked link positions. AI search optimization chases something different: inclusion in a synthesized, conversational answer. A page can rank on page one and still never get cited if it doesn't give an AI system a clean, quotable fact to pull from.

The AI search landscape now includes Google AI Overviews, ChatGPT search, Perplexity, and Microsoft Copilot. Some marketers call the discipline answer engine optimization (AEO) or generative engine optimization (GEO). Those terms overlap heavily with AI search optimization, but the mechanics matter more than the label. The table below lays out what actually changes between the two disciplines.

Traditional search referrals to product discovery declined 15% between August 2025 and May 2026, according to the State of Commerce report. Over the same window, AI-referred traffic to commerce sites grew 5.3 times year-over-year, per the same report. Read together, those two numbers point in one direction. Search behavior has already moved, not approaching some future tipping point.

Enterprise buyers feel this shift too. B2B researchers evaluating software and vendors increasingly open a conversational AI tool before they open a search engine, asking a direct question rather than typing a string of keywords. That changes what "ranking well" even means for a marketing team. A page optimized only for keyword match can miss every one of those conversational queries entirely.

This is the gap most guidance on optimizing for AI search skips. It's easy to say the landscape changed. It's harder to say what a marketing team should actually do differently, which is where a clear framework helps.

Roughly 48% of marketers haven't yet figured out how to adapt their strategy to widespread AI use, per State of Marketing data. That gap is exactly why a framework matters here. For a B2B enterprise marketing team asking how to improve visibility in AI search, four pillars cover the ground that counts most.

AI systems favor non-commodity content: unique expert perspectives, original data, proprietary research, and first-hand experience. A blog post that restates a third-party report is commodity content. A post built on your own survey data with original analysis is not, and it's far more likely to get cited.

• Prioritize original research and owned data over aggregated summaries
• Include clear, quotable definitions for key concepts early in each page
• Organize with descriptive headings that match how people actually phrase questions
• Keep a review cadence so pages don't go stale

Large language models are entity-first. They understand a brand as an entity with attributes, not just as a domain with a stack of pages. An entity, in this context, is a uniquely identifiable subject, such as a company, a product, or a concept, that AI systems can recognize and describe with confidence. For most B2B teams, entity authority is the most underinvested of the four pillars, since building it takes coordination across PR, content, and web rather than a single page edit.

• Keep your brand name and description consistent across every web property
• Build third-party mentions through targeted digital PR outreach
• Apply schema markup that defines your entity's core attributes
• Audit how your brand's knowledge graph entry represents you, and correct inaccuracies

Google's AI Overviews and similar features rely on retrieval augmented generation: they pull pages from the standard search index before synthesizing an answer. If a page isn't crawlable and indexed, it can't be cited, no matter how well written it is. Don't prioritize llms.txt over the fundamentals of crawlability, indexing, content quality, and structured data. Google's own guidance on generative AI search says plainly that these tactics circulate widely online but aren't part of how Google Search actually works.

• Confirm your key pages meet Google's baseline indexing requirements
• Use semantic HTML so parsers can read page structure cleanly
• Implement schema markup relevant to the page's content type

Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) are the credibility signals AI systems use to judge whether a source is worth citing. E-E-A-T for AI isn't a new concept bolted onto search. It's the same standard traditional SEO already asked you to meet, applied to a system that now checks it automatically. For a B2B marketing audience, the most actionable gap sits in author and source credibility: plenty of enterprise content still ships without a byline or any visible expertise signal, and that's a real liability once an AI system is deciding who to cite.

• Add bylines with verifiable credentials to your key editorial pages
• Cite primary sources and your own research throughout, not just secondary summaries
• Keep content current with a scheduled review cycle
• Build external citations through media coverage and expert contributions

Most marketers will read this section first, so treat it as the part of this guide you can act on today.

1. Google Search Console's Generative AI performance report is the most direct, free option available, and it's Google's own recommended path. Track AI Overview impressions and clicks separately from standard organic performance so you can see the shift clearly.
2. Third-party AEO and LLM visibility tracking platforms track how often your brand gets cited across different answer engines. The category is growing fast enough that naming a specific vendor here would be outdated within months.
3. Manual prompt testing costs nothing but time. Periodically query your target keywords in ChatGPT, Perplexity, and Google AI Mode, and note whether your brand shows up, on which page, and in what surrounding context.
4. Citation page auditing identifies which specific pages get cited in AI-generated answers and how often. Use that data to decide which pages to optimize next, rather than guessing.

Start with an audit. Check whether your highest-traffic pages are indexed and actually generating AI Overview citations. Review E-E-A-T signals on your top content, and look at pages with strong backlink profiles to confirm they use clear headings and schema markup that make them easy for AI systems to retrieve.

Then build a plan that outlasts any single page update. Commerce leaders see this coming fast: 86% agree LLMs will be essential to product discovery within a year, per State of Commerce data. The brands that come out ahead won't stop at optimizing individual pages. They'll build a system for sustained entity authority, content freshness, and measurable AI visibility across the whole content portfolio.

Marketing Cloud gives teams the data foundation, content intelligence, and automation to treat AI search optimization as a repeatable program instead of a one-time project with Palmata . Paired with a clear view of enterprise AI, that combination turns a scattered set of page fixes into a durable practice your whole content team can run on.