How Generative Engine Optimization (GEO) is Reshaping Product Discovery in 2025

In 2025, product discovery and ecommerce SEO have shifted from Google search results and long-form blog reviews into conversational interfaces powered by AI shopping assistants. Shoppers now ask ChatGPT, Microsoft Copilot, or Perplexity for tailored recommendations and receive instant answers that summarize specs, reviews, and even pricing.

This change is structural. Instead of typing fragmented keywords into Google, users pose complete, conversational questions such as “What’s the best noise-cancelling headphone under $200?” The AI responds with curated recommendations — a role once filled by affiliate blogs and comparison sites (adQuadrant, 2025).

As one industry analyst summarized, “Consumers aren’t searching anymore, they’re asking. And AI is answering” (CMO Alliance, 2025). This shift means the buying journey begins inside AI chat windows, not in the search bar. According to Bain & Company, 80% of users are now satisfied with direct AI answers, often without ever visiting a brand’s website (CMO Alliance, 2025). If your brand isn’t present in that AI-generated answer, it may as well not exist in the decision set.

Why This Matters for Brands in the AI Shopping Era

• Zero-click behavior: Consumers complete their research directly inside AI chat. Bain & Company reports that users increasingly rely on consolidated AI answers without visiting brand sites (CMO Alliance, 2025).
• Traffic displacement: Adobe Analytics recorded a 1,200% year-over-year surge in retail site visits from AI sources between July 2024 and February 2025, peaking at +1,950% on Cyber Monday (Adobe Blog, 2025).
• Early adoption curve: Bloomreach found that over 60% of consumers have already used generative AI tools for shopping, and 27% now prefer chatbots outright over search engines (Bloomreach, 2025). In the B2B space, Forrester reports that 90% of buyers already use generative AI during purchasing research (MarTech, 2025).

The implication is clear: visibility in AI-generated answers is now a primary driver of brand awareness and conversions.

The New Buying Journey: From Google Search to AI Conversations

The path to purchase has been compressed into a single conversational thread. A typical sequence looks like this:

Stage	Old Journey	New Journey (AI-First)
Awareness	Google search → scan blog posts	Ask ChatGPT: “Best CRM tools for SMBs?”
Consideration	Click multiple comparison sites	Refine query in chat: “Only show budget options with free trials”
Decision	Visit vendor websites	Click product card surfaced directly in chat, proceed to checkout

This new flow is already being enabled by integrations. OpenAI’s partnership with Shopify is expected to let ChatGPT surface product cards with pricing, ratings, and direct checkout options inside the chat itself (Exposure Ninja, 2025).

What once required hours of browsing now takes minutes. For consumers, the benefit is efficiency and personalization. For brands, the challenge is ensuring they are included in the AI’s synthesized recommendation set.

Data-Backed Momentum: Explosive Growth in AI Shopping and Generative Engine Optimization in 2025

The rise of conversational AI in commerce is not a prediction — it’s measurable today. Across both consumer and B2B contexts, generative AI shopping assistants have already become mainstream tools for product research and decision-making.

Adobe Analytics reports that between July 2024 and February 2025, U.S. retail websites experienced a 1,200% increase in visits driven by AI chat sources, with traffic spiking as high as +1,950% on Cyber Monday. Importantly, Adobe also found these AI-referred shoppers were more engaged — browsing more pages per session and bouncing less — a sign they arrived informed and ready to purchase (Adobe Blog, 2025).

Adoption of AI Shopping Is Broad and Rapid

Multiple studies confirm the acceleration of AI-powered product discovery:

• Bloomreach: More than 60% of consumers have already used ChatGPT, Google Gemini, or Perplexity to shop online, and 53% plan to increase that usage in the next year (Bloomreach, 2025).
• Adobe: 87% of consumers say they are more likely to use AI for larger or more complex purchases (Adobe Blog, 2025).
• Forrester: Up to 90% of B2B buyers already use generative AI as part of their purchasing research (MarTech, 2025).

The buying journey is compressing into AI-driven Q&A sessions where research, comparison, and decision-making occur in a single flow.

Zero-Click Decisions Are Rising in AI Search

Generative engines are not just guiding discovery — they are becoming decision-making platforms. Bain & Company reports that 80% of consumers now rely on direct AI answers without visiting brand websites or affiliate links (CMO Alliance, 2025). Instead of opening multiple tabs, they trust the AI to consolidate specs, reviews, and recommendations into one definitive answer.

This represents a sharp break from traditional digital marketing economics. For decades, visibility was a function of ranking in Google SERPs. Today, it is about being cited in AI-generated answers.

From Discovery to Purchase in a Single AI Shopping Channel

Integration is accelerating. OpenAI has piloted product carousels in ChatGPT, displaying images, specs, reviews, and even pricing, while a partnership with Shopify is expected to enable direct checkout within the chat itself (adQuadrant, 2025; Exposure Ninja, 2025). Microsoft Copilot and Amazon Rufus are also embedding AI shopping recommendations directly into their platforms (Microsoft, 2025; About Amazon, 2025).

This means the consumer journey is evolving from:

1. Discovery → 2. Consideration → 3. Purchase
   → Into a single conversational channel, reducing opportunities for brands to capture attention outside of AI ecosystems.

The data is unambiguous: generative AI is no longer experimental — it is a fast-scaling acquisition channel that already influences billions of dollars in purchase decisions. For brands, the question is not whether this shift will matter — it’s whether their products are present in the AI’s answer set.

Why Brands Risk Invisibility in the Age of AI Search and GEO

The rise of conversational AI shopping assistants has created a new visibility challenge. When shoppers ask ChatGPT or Microsoft Copilot for recommendations, they often receive a single consolidated answer that includes just a handful of brand mentions. Bain & Company found that 80% of users rely on these zero-click answers without navigating to brand websites or affiliate links (CMO Alliance, 2025). In this environment, if your brand is not cited by the AI, it is effectively invisible during the buying decision.

The Zero-Click Risk for Brands

In traditional search, Google displayed at least ten blue links per query, giving multiple brands visibility opportunities. By contrast, LLMs tend to cite only 2–7 domains per response (Andreessen Horowitz, 2025). This drastic reduction in “slots” means competition for inclusion is sharper, and being left out carries higher stakes.

• Missed demand capture: Consumers may complete their purchase journey entirely inside the AI interface without ever seeing your site.
• Lost narrative control: If AI engines rely on third-party sources, they may summarize your brand inaccurately — or hallucinate details.
• Reduced traffic: Forbes reports that referral traffic from AI Overviews and similar modules can be an order of magnitude lower than from classic SERPs (Forbes, 2025).

The B2B Evidence: AI Answers Driving Enterprise Conversions

The impact is not limited to consumer shopping. Forrester research shows that 90% of B2B buyers already use generative AI tools as part of their research (MarTech, 2025). One analysis found that sales conversions driven by ChatGPT recommendations grew 436% year-over-year, demonstrating how directly AI answers influence enterprise purchasing (MarTech, 2025).

In sectors where buying cycles are long and complex, this behavior change is especially significant. Vendors that secure presence in AI-generated answers gain an early advantage in the evaluation process, while those absent may never enter the conversation.

The Competitive Gap in AI Discoverability

Brands that act early to adapt their content and product data for AI discoverability are already capturing disproportionate attention. Bloomreach found that brands appearing in AI-powered searches see measurable gains in visibility and conversions (Bloomreach, 2025). Conversely, companies that continue to optimize only for traditional SEO risk declining relevance as AI engines capture a larger share of product discovery.

The CEO of Bloomreach summarized the stakes: “Consumers have new expectations for online shopping… it’s up to brands to meet them where they are or risk falling behind” (Bloomreach, 2025).

Bottom Line: SEO Alone Is No Longer Enough

Brands can no longer assume that strong SEO guarantees visibility. The competitive frontier has shifted into AI-generated answers, where visibility is scarcer, consumer trust is higher, and the rewards for inclusion are exponentially greater.

From SEO to GEO: The Strategic Shift in AI Search Optimization

For two decades, brands have relied on Search Engine Optimization (SEO) to capture consumer demand by ranking high in Google results. But as product discovery shifts into AI-driven conversations, the SEO playbook is no longer sufficient. Generative Engine Optimization (GEO) is emerging as the strategic successor.

From Keywords to Conversations in AI Search

Traditional SEO revolved around short, keyword-driven queries — “best CRM software” or “cheap headphones.” In contrast, AI search queries are longer and conversational, averaging 23 words compared to just 4 words in typical search (Andreessen Horowitz, 2025).

Search engines list URLs. AI shopping assistants synthesize answers. Instead of scanning ten links, a user now receives a concise, authoritative response that cites only a few sources. This means visibility no longer depends on ranking position, but on whether the AI trusts and cites your data.

The End of PageRank as the Anchor

Google’s PageRank and backlinks once served as the cornerstone of online visibility. In AI-driven search, citation authority and structured data quality matter more than backlinks. Andreessen Horowitz notes that we’ve entered “Act II of search — driven not by page rank, but by language models” (a16z, 2025).

Metric	Traditional SEO	GEO
Primary ranking factor	Organic position	Visibility in AI answers
Trust signals	Backlinks, domain rating	Citation authority, structured data
User input	Keywords	Conversational prompts
Success benchmark	Traffic volume	AI citations, share of voice

The Market Reality: SEO vs GEO

The shift is already visible at scale. Gartner projects a 25% decline in traditional search volume by 2026 as users turn to AI chatbots and agents (Search Engine Land, 2025). Publishers are already reporting referral traffic drops from Google AI Overviews, which summarize answers directly in the SERP with fewer click-throughs (Digiday, 2025).

Meanwhile, Apple has announced that AI-driven search engines like Perplexity and Claude will be integrated into Safari, raising questions about Google’s distribution dominance (a16z, 2025). The $80+ billion SEO industry faces structural disruption.

Why GEO Matters Now for AI Discoverability

GEO is not about replacing SEO — it builds on the same foundation of high-quality content, structured markup, and authority. But it shifts the goalposts:

• SEO Goal: Drive clicks from Google to your website.
• GEO Goal: Ensure AI assistants cite your brand and recommend your products directly in AI-generated answers.

As AI assistants increasingly handle product discovery, comparison, and purchase, brands must optimize not just for visibility in links, but for presence in conversations.

The Strategic Conclusion

The conclusion is unavoidable: the frontier of digital visibility is moving from search results to AI-generated answers. Companies that realign their strategies toward GEO will secure their position in the next era of product discovery. Those that remain SEO-only risk vanishing from the consumer journey altogether.

What GEO Involves: Core Pillars of AI Search Optimization

Generative Engine Optimization (GEO) is more than a buzzword. It’s a practical framework to ensure your brand’s information is discoverable, citable, and actionable by AI engines such as ChatGPT, Google AI Overviews, Microsoft Copilot, and Perplexity.

While SEO focused on ranking for keywords, GEO focuses on preparing your data, content, and systems so AI can:

1. Find it
2. Trust it
3. Use it to recommend, book, or purchase

According to Andreessen Horowitz, visibility now depends on whether AI engines can parse, trust, and act on your information (a16z, 2025). The following five pillars define a strong GEO strategy.

1. Entity & Knowledge Graph Optimization

At the foundation of GEO is clarity about who you are and what you offer. AI systems increasingly rely on structured knowledge graphs to reduce ambiguity. Brands must ensure their entities — organizations, products, services, and even people — are:

• Defined with structured schema.org markup
• Linked to authoritative IDs (e.g., Wikidata, Crunchbase, LinkedIn)
• Consistent across all properties and locales

When AI engines see inconsistent or missing entity data, they are more likely to cite third-party summaries, creating the risk of narrative drift (Search Engine Land, 2025).

2. Structured Data & Commerce Feeds

Product and offer data must be machine-readable to be included in AI shopping recommendations. Google Help and Google for Developers highlight that feeds through Merchant Center, schema.org Offer markup, and product availability APIs are critical signals.

Best practices include:

• Keeping price, availability, and returns policies up to date in structured data
• Avoiding reliance on JavaScript-only content for critical information
• Ensuring real-time updates via feeds and content APIs

If your product information is incomplete or hidden, AI shopping assistants will recommend competitors with cleaner, more accessible data (Google Developers, 2025).

3. LLM-Friendly Documentation & Datasets

AI engines need short, canonical, high-signal documents to cite. Unlike humans, they prefer clarity over flourish. Brands can support this by publishing:

• Concise FAQs that directly answer user questions
• Product and policy hubs with structured specs
• /llms.txt indexes pointing crawlers to authoritative sources (Search Engine Land, 2025)

This approach reduces hallucinations and improves the odds that your brand is cited as the source of truth.

4. Agent Integrations for AI Shopping Assistants

Generative AI assistants are shifting from explainers to doers. To capture commercial intent, brands must expose agent-ready actions that allow AI to transact:

• OpenAPI-described endpoints for bookings, quotes, or availability
• Function-calling contracts with platforms like OpenAI
• Model Context Protocol (MCP) servers to safely expose workflows (OpenAI, 2025)

If your brand doesn’t provide callable actions, assistants will favor competitors who do (OpenAI Platform; Anthropic MCP, 2025).

5. Crawler & Licensing Strategy

The AI data supply chain is professionalizing. Platforms like OpenAI GPTBot and Google-Extended allow webmasters to control AI crawler access. At the same time, licensing programs (e.g., Perplexity’s revenue-share for publishers) are emerging (Wall Street Journal, 2025).

Brands must make a deliberate choice:

• Block crawlers and risk invisibility
• Enable crawlers while providing structured, inference-ready content
• Negotiate licensing for compensation where feasible

A restrictive stance without alternative feeds trades short-term control for long-term irrelevance (Search Engine Journal, 2025).

The Bottom Line: GEO Strategy for AI Visibility

GEO is not a single tactic. It is the integration of entities, structured data, high-signal documentation, callable actions, and licensing strategy. Together, these pillars ensure that when consumers ask AI assistants for recommendations, your brand is consistently included in the answers.

Most important GEO metrics in 2025

In the era of Generative Engine Optimization (GEO), brands can no longer rely on legacy SEO metrics like impressions or SERP ranking to measure success. Since AI assistants consolidate answers rather than list multiple blue links, performance must be tracked using metrics that reflect presence, influence, and conversion inside AI ecosystems.

The following four metrics define success in AI-driven product discovery.

1. Visibility

• Definition: The extent to which your brand appears in AI-generated answers across platforms like ChatGPT, Copilot, Perplexity, and Google AI Overviews.
• Why it matters: If your brand is absent from the AI’s response, you’re invisible to the consumer during the critical decision moment. In GEO, visibility is the foundation for awareness and demand capture.
• How to measure: Track AI citations across a defined set of priority queries using emerging tools and answer engine monitoring platforms. Benchmark visibility trends over time to understand share gains or losses.

2. Share of Voice

• Definition: Your brand’s proportion of mentions and citations compared to competitors within AI-generated recommendations.
• Why it matters: AI assistants typically surface only a handful of options — far fewer than the ten blue links of traditional search. Securing a larger share of those mentions ensures your brand dominates the conversation.
• How to measure: Run comparative audits across high-intent queries to evaluate the percentage of AI answers in which your brand appears, and how often it is positioned favorably against competitors.

3. Sentiment

• Definition: The tone and quality of how your brand is represented inside AI-generated content and recommendations.
• Why it matters: Visibility alone is not enough — if AI engines summarize your brand with outdated, inaccurate, or negative information, it can erode trust and credibility. Positive, accurate representation directly influences conversion likelihood.
• How to measure: Monitor brand mentions in AI outputs for accuracy, recency, and tone. Identify misrepresentations or hallucinations and address them by strengthening your structured data, FAQs, and authoritative documentation.

4. AI-Attributed Conversions

• Definition: Purchases, bookings, or signups that can be traced directly to AI surfaces, such as ChatGPT product cards, Microsoft Copilot shopping integrations, or Amazon Rufus recommendations.
• Why it matters: Adobe reports that AI-driven shoppers convert at higher rates, since they arrive more informed and with intent already established (Adobe Blog, 2025). AI-attributed conversions are the ultimate measure of demand capture in the GEO era.
• How to measure: Use UTM tagging, platform attribution reporting, and conversion analytics as ecosystems like Shopify + ChatGPT mature. Track AI-driven conversions alongside traditional channels to quantify incremental impact.

The Bottom Line

Traditional SEO metrics no longer capture the reality of discovery in an AI-first world. GEO requires a shift toward visibility, share of voice, sentiment, and AI-attributed conversions — the metrics that reflect whether your brand is not only present but trusted, preferred, and chosen inside AI-generated answers.

Risks of Ignoring GEO in the Age of AI Search

Brands that delay adapting to Generative Engine Optimization (GEO) face material risks in visibility, demand capture, and brand control. The shift to AI-driven product discovery is not incremental — it is structural. Gartner projects that traditional search volume will decline by 25% by 2026 as users shift to AI assistants (Search Engine Land, 2025).

Those who continue to optimize only for Google SERPs risk becoming invisible in the very channels where consumers are increasingly making decisions.

1. Visibility Decay Despite Strong SEO

Even brands with robust SEO strategies are seeing diminishing returns. Digiday reports that publishers already experience traffic declines under Google AI Overviews, which satisfy user intent within the results page and reduce click-throughs (Digiday, 2025).

Strong rankings no longer guarantee traffic if AI engines surface answers directly.

2. Narrative Drift and Inaccuracy in AI Answers

When AI engines cannot parse authoritative brand data, they rely on third-party summaries. This can lead to:

• Distorted messaging: Engines may emphasize outdated or irrelevant details.
• Hallucinations: In high-stakes categories, errors can erode trust and credibility.

As Search Engine Land notes, publishing canonical, structured entity data across all properties reduces this risk (Search Engine Land, 2025).

3. Lost Commercial Intent in AI Shopping

Assistants like ChatGPT, Microsoft Copilot, and Amazon Rufus increasingly surface shoppable or booking links inline (About Amazon, 2025; Microsoft, 2025).

If your catalog, prices, or inventory are not machine-readable, assistants will recommend competitors whose data they can trust. This means lost revenue opportunities, even if your product is otherwise competitive.

4. Weak Negotiating Position in the AI Data Economy

The AI data supply chain is being formalized through licensing and revenue-sharing programs. Perplexity, for example, has launched a compensation model for publishers (Wall Street Journal, 2025).

Brands that delay developing a crawler and licensing strategy risk negotiating from weakness later — having already ceded ground to competitors who shaped early partnerships.

5. Strategic Irrelevance for Late Adopters

Ultimately, ignoring GEO creates strategic irrelevance. As Andreessen Horowitz observes, “If you’re not ranking in the LLM’s answer, you’re invisible” (a16z, 2025).

Consumers increasingly complete their product discovery, evaluation, and decision-making inside AI platforms. A brand absent from that context forfeits its seat at the table.

The Compounding Cost of Inaction

The cost of inaction compounds over time. Every quarter that competitors optimize for GEO while you do not widens the visibility gap.

By the time adoption reaches mass scale, late entrants will struggle to displace entrenched competitors in AI-generated answers.

Conclusion: The Urgency of GEO in AI Search Optimization

The evidence is overwhelming. Generative AI has already transformed product discovery:

• 60%+ of consumers have shopped using AI tools like ChatGPT, Google Gemini, or Perplexity (Bloomreach, 2025).
• AI-driven retail traffic surged more than 1,200% year-over-year during the 2024 holiday season (Adobe Blog, 2025).
• 90% of B2B buyers now incorporate generative AI into their purchasing research (MarTech, 2025).
• 80% of users are satisfied with direct AI answers, often without visiting brand websites (CMO Alliance, 2025).

These data points signal a paradigm shift: consumers are not searching, they are asking — and AI is answering.

The Strategic Imperative: From SEO to GEO

For two decades, SEO has been the cornerstone of digital visibility. But as Andreessen Horowitz emphasizes, we’ve entered “Act II of search — driven not by page rank, but by language models” (a16z, 2025).

The foundation of the $80+ billion SEO industry has cracked, replaced by the logic of Generative Engine Optimization (GEO).

• SEO Goal: Win page-one rankings.
• GEO Goal: Be cited in AI-generated answers and recommended by AI shopping assistants.

The brands that act now to optimize their entities, structured data, documentation, and callable actions will secure a disproportionate share of voice in AI-driven commerce. Those that delay risk declining visibility, lost revenue, and misrepresentation by third-party summaries.

The Competitive Advantage of Early Movers

As Bloomreach found, brands that appear in AI-powered search results are already gaining a competitive edge in both visibility and conversions (Bloomreach, 2025). Meanwhile, Adobe reports that AI-referred shoppers arrive more informed and are more likely to convert (Adobe Blog, 2025).

The implication is simple: GEO is not just a defensive move to prevent invisibility. It is an offensive strategy that can unlock higher-intent demand and stronger conversion rates.

A Call to Action for CMOs and Digital Leaders

The next frontier of digital marketing is not about ranking on Google — it’s about being included in AI conversations.

As consumers increasingly rely on chat interfaces for discovery, comparison, and purchase, the critical question for every brand becomes:

When consumers ask, will the AI answer with you — or with your competitor?

The urgency is clear. The brands that embrace GEO today will shape how they are represented tomorrow. Those that wait may find themselves excluded from the very conversations where decisions are now being made.

Sources:

CMO Alliance — “How LLMs are reshaping product discovery” https://www.cmoalliance.com/how-llms-are-reshaping-product-discovery/

adQuadrant — “How Consumer Brands Can Optimize for Product Discovery in ChatGPT” https://www.adquadrant.com/how-consumer-brands-can-optimize-for-product-discovery-in-chatgpt/

Bloomreach — “More Than 60% of Consumers Have Used Conversational AI for Shopping” (Press release) https://www.bloomreach.com/en/news/2025/bloomreach-announces-findings-from-conversational-ai-shopping-study/

Adobe Blog — “Traffic to U.S. retail websites from Generative AI sources jumps 1,200 percent” https://blog.adobe.com/en/publish/2025/03/17/adobe-analytics-traffic-to-us-retail-websites-from-generative-ai-sources-jumps-1200-percent

MarTech — “How ChatGPT search reshapes the B2B buyer’s journey”
https://martech.org/how-chatgpt-search-reshapes-the-b2b-buyers-journey/

Exposure Ninja — “Everything Marketers Should Know About ChatGPT’s Shopping Update”
https://exposureninja.com/podcast/dojo-49/

Microsoft — “Find online deals with Copilot Shopping” https://www.microsoft.com/en-us/microsoft-copilot/for-individuals/do-more-with-ai/general-ai/find-online-deals-with-copilot-shopping

Microsoft — “Introducing the Copilot Merchant Program”https://www.microsoft.com/en-us/microsoft-copilot/blog/2025/04/18/introducing-the-copilot-merchant-program/

About Amazon — “Amazon announces Rufus, a new generative AI-powered shopping assistant”
https://www.aboutamazon.com/news/retail/amazon-rufus

About Amazon — “How to use Amazon Rufus”
https://www.aboutamazon.com/news/retail/how-to-use-amazon-rufus

Andreessen Horowitz (a16z) — “How Generative Engine Optimization (GEO) Rewrites the Rules of Search”
https://a16z.com/geo-over-seo/

Search Engine Land — “Will traffic from search engines fall 25% by 2026?” (Gartner projection coverage)
https://searchengineland.com/search-engine-traffic-2026-prediction-437650

Digiday — “Google AI Overviews linked to 25% drop in publisher referral traffic, new data shows” https://digiday.com/media/google-ai-overviews-linked-to-25-drop-in-publisher-referral-traffic-new-data-shows/

Forbes — “New Data Shows Just How Badly OpenAI And Perplexity AI Search Hurt Referral Traffic” (TollBit analysis) https://www.forbes.com/sites/rashishrivastava/2025/03/03/openai-perplexity-ai-search-traffic-report/

Google for Developers — “Intro to How Structured Data Markup Works”

https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data

Google Help — “Product data specification — Google Merchant Center”
https://support.google.com/merchants/answer/7052112

Google Search Central — “AI features and your website”
https://developers.google.com/search/docs/appearance/ai-features

OpenAI — “GPTBot” (crawler documentation)
https://platform.openai.com/docs/gptbot

OpenAI — “Function Calling in the OpenAI API”
https://help.openai.com/en/articles/8555517-function-calling-in-the-openai-api

Anthropic — “Introducing the Model Context Protocol (MCP)”
https://www.anthropic.com/news/model-context-protocol

The Wall Street Journal — “Perplexity Is Launching a New Revenue-Share Model for Publishers”
https://www.wsj.com/business/media/perplexity-ai-search-publisher-revenue-507987e5

Search Engine Journal — “AI Crawlers Are Reportedly Draining Site Resources & Skewing Analytics”
https://www.searchenginejournal.com/ai-crawlers-draining-site-resources/543011/