Your book could be perfect for someone, but if that person asks ChatGPT for a recommendation and your book does not show up in the answer, they will never know it exists. That is the shift happening right now in how people find books, and most authors are not prepared for it.

For years, the book discovery game was about Amazon rankings, retailer algorithms and Google search. You optimized for those channels because that is where readers looked. But reader behavior is changing faster than most authors realize. More people are starting their book searches inside AI tools now. They ask a bot for recommendations instead of browsing. They describe what they are looking for in conversation and take the suggestions that come back. They use AI to help them decide what to read next, not just to find what they already know they want.

When that shift happens, the old playbook breaks down. You cannot game an AI search engine the way you could game Amazon. You cannot buy your way to visibility the way paid ads work on retail sites. But you can absolutely position your book, your metadata, and your author presence so that when an AI system is synthesizing recommendations, your work shows up as a natural fit. The question is whether you understand how that actually works before the entire discovery landscape has already shifted underneath you.

Why AI search is different from everything that came before

Google search was about keywords. You optimized your website so that when someone typed specific words, you ranked well for those searches. That was a puzzle you could solve through technical SEO and content strategy.

AI search is different. An AI system like ChatGPT or Claude or Google's AI Overviews is not just looking for keyword matches. It is synthesizing information from multiple sources, understanding context and nuance, and generating answers that feel natural and conversational. When someone asks "I want a book about a woman rebuilding her life after loss with slow-burn romance and no spice," an AI is not searching for those exact words. It is understanding what kind of book experience that describes and pulling from its training data to find titles that fit.

That means your traditional SEO playbook only gets you halfway there. Yes, having clean metadata and strategic keywords still matters. But you also need your book, your story, and your author presence to be findable through conversational language, thematic understanding and the kinds of descriptions readers actually use when they are talking to AI.

The shift also changes who controls the narrative. With Amazon, the retailer controls what metadata shows up and how it is weighted. With Google, the algorithm controls the ranking. With AI search, the AI system is generating its own synthesis based on multiple inputs. You cannot control what it says, but you can absolutely influence what sources it pulls from and how your book gets positioned in those sources.

What AI search is actually looking at

When an AI system is answering a book recommendation question, it is pulling from multiple data sources and combining them. That includes book metadata from distributors, reviews from trusted sources, author information from websites and profiles, discussion from book communities and forums, and mentions in articles and lists across the internet.

The weight given to each source varies, but generally, professional or authoritative sources carry more weight than random social posts. A review in a respected publication counts more than a Goodreads review. An endorsement from a recognized voice in a subject carries more weight than a casual recommendation. This is where professional publicity and credibility start mattering in ways that pure retail marketing never quite captured.

Your author website and the information on it is one of those sources. When an AI is building context about who you are and what you write, it is looking at your site, your bio, your author description. If that information is vague, contradictory or missing, the AI has less to work with. If it is clear, consistent and strategic, the AI has better context to understand when your book is a good fit for specific reader needs.

Media mentions and interviews also factor in. When you are quoted as an expert in an article, when you appear on a podcast that gets transcribed and indexed, when your work is discussed in a professional or cultural context, that becomes part of the data the AI uses to understand who you are and what you bring. That is not just good for visibility with human readers. It is good for visibility with AI systems that are trying to understand your relevance and authority.

The difference between SEO and what we are calling GEO

Traditional SEO is about ranking in the list of blue links. You want to be number one, number two or at least on the first page when someone searches a specific term. That is the game most authors and marketers have been playing for years.

GEO—or what we think of as "getting into AI Overviews and conversational results"—is about being part of the answer the AI generates when someone asks a question. Instead of competing for a ranking position, you are competing to be one of the sources the AI pulls from when it synthesizes an answer.

The difference matters because the search behaviors are completely different. An SEO ranking matters when someone is searching for a specific thing. A GEO inclusion matters when someone is asking an open-ended question. "Best books about grief" is SEO territory. "I am grieving and want a book that helps me understand my emotions while keeping me engaged in a story" is GEO territory. The AI is not searching for keywords. It is synthesizing a recommendation based on conversational input.

For authors, GEO matters because it captures the moment when someone is actually ready to discover a new book but does not yet know specifically what they want. That is when they ask AI. That is when they need guidance. And if your book is part of the sources the AI draws from when generating that guidance, you have access to readers at exactly the moment they are most open to new recommendations.

How your metadata becomes part of the AI answer

Metadata sounds technical and boring, but it is absolutely foundational to how AI systems understand your book. When you upload your title to a distributor or retailer, the metadata includes your title, subtitle, description, categories, keywords, author bio, and other structured information. That information travels through the distribution system and ends up in multiple places.

An AI training on published book data is picking up that metadata. When it sees your book in the system with a specific description and set of keywords, it is learning what your book is about and who it serves. When that metadata is vague, your book becomes harder for the AI to categorize and recommend appropriately. When that metadata is specific, strategic and aligned with how readers actually talk about your book, the AI understands exactly when to surface your title.

This is why working with a publisher or distributor who understands metadata strategy matters so much. It is not just about getting your book into stores. It is about making sure the information traveling with your book through every system is clear, strategic and built for discoverability in an AI-driven world.

Your book description is one of the most important pieces here. It is not a marketing pitch. It is a clear, accurate description of what your book is about, who it serves, and what problems it solves or what experiences it offers. When an AI is trying to understand whether your book is a good match for a specific reader need, that description is often the first thing it looks at.

Your author website and online presence in an AI world

Your author website is no longer just a place for fans to find you. It is also a primary source of information that AI systems use to understand who you are and what you write. When an AI is synthesizing information about an author, it often starts with the author's website.

That means your site needs to be clear about several things: who you are, what you write, who you write for, and what your books are about. It sounds obvious, but most author websites bury this information under vague bios and tangential details about creative process or personal life. An AI does not care about your journey or your inspiration. It cares about what your books do for readers and what subjects or themes you are known for.

Your website also needs to be well-structured so AI systems can actually parse it. That means using clear headers, logical organization, and semantic HTML that signals what is important and how information relates. When your site is well-structured, AI systems have an easier time extracting accurate information about you and your work.

The content on your site matters too. Blog posts, articles, resources and other content that demonstrate your expertise in your subject area all contribute to how AI systems understand your authority and relevance. If you write about grief, write about parenting, or teach about leadership, that content on your site is part of the signal that tells AI systems you are a credible voice in that space.

How media, reviews and expert positioning feed AI discovery

When you appear in media, write articles, give interviews or become known as an expert voice on a topic, you are building more than visibility with human readers. You are building sources that AI systems draw from when they are synthesizing recommendations and context.

Professional reviews matter differently in an AI world than they did in a pure retail world. A starred review in a trade publication carries weight because it is a professional source. A feature article about your expertise carries weight because it is published content from an established outlet. These become the sources that give AI systems confidence that you are authoritative and worth including in recommendations.

This is where a coordinated PR strategy becomes even more valuable. Every media placement, every expert mention, every review you secure is not just reaching current readers. It is building the source material that AI systems will draw from when they are answering future readers' questions. You are not just doing publicity. You are building the information ecosystem that makes AI recommend your work.

Author platforms and positioning matter too. If you are known as a voice on a specific subject, if your expertise is established and visible across multiple sources, AI systems understand that context. They are more likely to recommend you when someone asks for an author who understands a specific topic or brings a specific perspective.

What this means for your metadata and keyword strategy

The old keyword strategy was about finding low-competition, high-volume terms and optimizing your content around them. In an AI-driven discovery world, the strategy is different but still strategic.

You want keywords and descriptions that are both accurate and conversational. Instead of stuffing metadata with obscure genre tags, you want language that mirrors how readers actually talk about books like yours. If your book is about rebuilding after loss, you probably should not just tag it with genre. You should describe it in ways that capture emotional themes and reader needs.

You also want your metadata to be consistent across platforms. When your book is described one way on Amazon, another way on your website, and a third way in a press release, AI systems notice that inconsistency. They have a harder time building a coherent picture of what your book is. When your metadata is consistent across sources, AI systems have a clearer understanding and are more confident recommending you.

This is also where secondary and tertiary keywords become valuable. The main keyword might be "romance novel." But the secondary keywords that capture what specific kind of romance it is and what emotional or thematic elements it includes are what help AI systems understand whether it is right for a specific reader asking a specific question.

Building an AI-ready author presence

An AI-ready author presence is not fundamentally different from a good author presence. It is just intentional about the sources AI systems actually use.

That starts with your website. Make sure it is clear who you are, what you write, who you write for, and how readers can find your work. Make sure your bio is accurate and emphasizes the aspects of your identity and expertise that matter most. Make sure your books are described clearly with emphasis on themes, audiences and problems solved rather than vague plot summaries.

Second, build visibility and authority in your subject. Write articles, appear on podcasts, do interviews, contribute to conversations in your space. Every time you show up as a credible voice, you are building source material that AI systems draw from.

Third, make sure your metadata is clean, consistent and strategic across all platforms. Work with your distributor or publisher to ensure your book information is accurate and aligned with how readers talk about books like yours.

Fourth, pursue professional reviews and endorsements from recognizable voices in your space. These carry weight with AI systems because they are third-party validation from established sources.

Finally, maintain an active, authentic online presence as an author. Blog, speak, contribute to communities, show up genuinely in your space. When AI systems are building context about who you are, they are looking for evidence of your ongoing work and engagement, not just a static author profile.

The timeline matters

AI discovery is not the future. It is happening now. Readers are using ChatGPT, Claude, Perplexity and other AI tools to discover books today. Google is integrating AI Overviews into search results. Library apps are experimenting with AI-powered recommendations.

That means if you are building your author platform around the assumption that Amazon search and Google will always be the primary discovery channels, you are already behind. You do not need to abandon those channels. You need to expand your thinking about how readers find you and make sure you are visible in those emerging channels too.

The advantage for authors who move on this early is real. Right now, the AI index is still building and stabilizing. Books that are well-positioned with clear metadata, strong sources, and consistent information across platforms are more likely to be accurately represented as the AI systems continue to evolve. Wait too long and you might find yourself trying to fix metadata and positioning for a system that has already indexed your information in ways that are harder to correct.

Your next step

Start by auditing your author presence through an AI lens. Is your website clear about who you are and what you write? Is your metadata consistent across Amazon, your distributor, your website and other platforms? Are you visible as a voice in your subject through media, articles, interviews or community engagement? Where are the gaps?

Then prioritize. If your website is unclear, that is a quick win to fix. If your metadata is inconsistent, coordinate with your distributor or publisher to align it. If you are not visible as a credible voice in your space, start building that visibility through whatever channels make sense for your work and your audience.

You do not need to become an AI expert or obsess over technical optimization. But you do need to understand that how readers discover books is changing and position yourself to be findable in that new landscape. The authors who do that early will have the advantage when AI discovery becomes as normal and expected as Google search is today.