ChatGPT vs AI Overview: Intent, Depth, and Strategy

The Fall of B2C Visibility and the Rise of Informational Intent

The rollout of Google’s AI Overview has caused a visible fracture in the organic traffic landscape, especially for B2C websites in the United States.

Since early 2024, when AI Overviews began expanding beyond their initial experimental phase, thousands of U.S.-based e-commerce and direct-to-consumer websites have seen significant declines in search visibility. According to recent data from Semrush and Amsive, organic traffic in high-intent commercial categories—such as skincare, nutrition, home appliances, and fitness gear—has dropped between 30% and 70%, depending on the query format and whether it now triggers an AI-generated summary.

Retail brands that had spent years climbing the ranks with product-optimized pages are now being outpaced by Google's AI interface, which delivers instant, consolidated answers from third-party sources—often without requiring the user to visit a single external site.

This isn't just a temporary shake-up. What we’re witnessing is a profound change in how users search and how they expect to receive information. The era of purely transactional search has weakened. Users today no longer type “buy protein powder” and scroll through ten blue links; instead, they ask: “What’s the best type of protein powder for muscle recovery?” or “How do plant-based proteins compare to whey?” These nuanced, informational queries now trigger AI Overviews—and in doing so, bypass product pages entirely unless they offer deep, relevant insight aligned with that curiosity. Google’s interface is adapting to this evolution by surfacing content that satisfies user intent at the top of the funnel, not the bottom.

This shift reflects a broader behavioral trend in online search: people want to understand before they commit. They seek educational value, transparency, and context. They want to read breakdowns, reviews, comparisons, and guides before even considering a click that leads to a purchase. In many ways, Google has realigned its search experience to serve this need—through AI Overviews that condense multiple angles, expert voices, and data points into one cohesive answer. But the unintended consequence is stark: B2C sites that once focused primarily on conversion-optimized product pages are being left behind.

What’s emerging in their place are knowledge-rich, topically authoritative sources—blogs, forums, news media, health sites, and expert profiles—that better match this new informational intent. These are the sources AI Overview pulls from, and these are the voices being elevated in Google’s increasingly conversational SERP environment. The implications for SEO and digital strategy are profound. Brands that fail to establish themselves as credible educators in their space are losing visibility to those that do—even if the latter don’t sell anything at all.

The challenge is clear: organic visibility is no longer awarded to whoever builds the best product grid or fastest landing page. It goes to those who offer answers before offers, substance before slogans. AI Overview is not just a feature—it’s a signal of where search is going, and B2C brands must either adapt to this informational-first model or risk fading from the screen entirely.

The Linguistic Foundation of AI Overview: Training at Scale

Understanding how AI Overview constructs its answers begins with understanding the linguistic and semantic data it was trained on. Google's generative models, including those that power AI Overview, are fine-tuned on trillions of tokens—fragments of text drawn from web pages, books, forums, and structured data sets. According to research published by DeepMind and Google Research, these models are not only trained on raw HTML and user-generated content but also on domain-specific corpora that reflect search behavior at scale. This includes data indexed through Google Search itself, encompassing billions of real queries typed by users every day.

High-Frequency Phrases and Search Patterns

AI Overview’s behavior reflects intense exposure to informational queries—those starting with "how to," "what is," "why does," "best way to," and question formats that reflect genuine curiosity. Google has confirmed that over 60% of all training data for AI Overview models includes natural language questions pulled from real search logs. For B2C and B2B industries alike, this means that the AI has learned to prioritize complete answers to nuanced user needs rather than keyword-stuffed prompts. For instance, instead of training on "protein powder buy online," the models are fed massive datasets featuring queries like "is plant-based protein better than whey for runners?" or "which protein helps with muscle soreness after 40?"

The Influence of Long-Tail Keywords

Long-tail search queries have had a disproportionate impact on the architecture of AI Overview. While short, high-volume commercial keywords like "best headphones" or "cheap flights" are still part of the model’s diet, much of its depth comes from understanding intent-rich, low-competition phrases. This helps explain why informational content now dominates the first screen of search results. AI Overview responds not with transactional answers, but with synthesized explanations drawing from long-tail queries that often contain demographic hints, tone indicators, or contextual qualifiers (e.g., "best flights from JFK to Lisbon with free baggage").

Multilingual Inputs and Training Bias

Although English dominates the training landscape, AI Overview has also been trained on multilingual corpora, especially in Spanish, Hindi, Japanese, Portuguese, and French. According to Alphabet’s internal benchmarks, over 70% of AI Overviews currently appear in English-speaking markets, but the multilingual capabilities of the models suggest broader expansion is underway. However, search intent differs significantly by culture and language—French users, for instance, tend to favor neutral phrasing and comparison formats, while American users often search in first-person or casual tones. This linguistic diversity further conditions how AI Overview generates and prioritizes summaries.

Structured and Semi-Structured Query Embeddings

The final layer of training involves embedding user intent through structured snippets like FAQs, People Also Ask sections, and Schema.org tags. These markup elements give weight to specific types of knowledge, helping the model disambiguate between similar topics and decide what kind of answer a user wants. For example, a query like "can I eat eggs after a workout" may be tied to structured data from fitness blogs, health forums, and authoritative medical databases—all of which help shape the language and sources that appear in the AI Overview box.

AI Overview, therefore, doesn’t just guess—it’s been conditioned to answer based on the patterns, phrasings, and value-laden content most often typed, clicked, and trusted by real users across Google Search.

Understanding the Unmapped Territory: Where AI Overview Stops, Strategy Begins

AI Overview has rapidly changed how users experience search results. Yet despite its reach, roughly 40% of queries remain outside the scope of this generative interface. Understanding where the model focuses its efforts—and more crucially, where it does not—opens a new space for SEO professionals and content strategists.

AI Overview's Linguistic and Intentional Boundaries

The language distribution and intent mapping within AI Overview are not random. Most responses are still served in English, which reflects both the training bias and the user base. However, international versions in French, Spanish, and Japanese are gradually gaining traction. Intent-wise, nearly 88% of the queries triggering AI Overview are informational. These often take the form of question-based keywords, especially long-tail variations beginning with "how to," "what is," "difference between," or "best way to."

These queries are typically low in keyword difficulty and commercial value, often showing CPC values under $2. The model seems to prioritize educational clarity over transactional incentive. As a result, product-focused or navigational queries rarely trigger AI Overview. Search Engine Journal and Amsive confirm that only 8% of commercial and 1–2% of branded or navigational keywords receive AI Overview support.

The Shape of the Remaining 40 Percent

The untouched segment—comprising around 40% of search queries—reveals patterns that are anything but random. These include commercial queries, high-CPC keywords, and transactional phrases with a clear intent to buy, book, or subscribe. They are not only harder to summarize in a single AI-generated answer but also more competitive, involving product comparisons, pricing nuances, user preferences, and sometimes legal or medical disclaimers.

This space is fertile for traditional SEO practices like content clustering, internal linking, and intent-specific landing pages. These 40% of keywords also include newer formats of hybrid intent, where users combine informational and transactional aspects in one query, such as "how to start a small business in Texas with under $10,000" or "best insurance plans for freelancers 2025." This type of request is more often handled by ChatGPT, as it performs semantic projection based on what it has crawled from the web and its evolving understanding of your needs.

Building Around the Gaps: Strategic Opportunity Zones

Rather than seeing AI Overview as a threat, marketers can reframe it as a filter that reveals unmet content needs. These include markets where Google’s language models are less developed, niches where commercial data is complex or sensitive, and micro-intents that escape general summaries. Furthermore, AI Overview avoids polarizing, subjective, or fast-changing queries that require context-aware judgment, such as product reviews or community-specific comparisons.

By identifying these strategic gaps, content creators can tailor their materials not to chase what the AI can replicate, but to serve what it cannot. This includes crafting high-authority content with complex internal linking, developing regionally nuanced content in non-English languages, and integrating video or audio elements AI cannot summarize effectively.

Data-Driven Insight for Future Planning

Multiple studies from Semrush, SurferSEO, and Advanced Web Ranking suggest that the 40% not covered by AI Overview actually carry higher intent and more conversion potential. While AI excels in giving generalized responses to broad questions, it falls short in guiding users through full purchasing journeys or high-stakes decisions. Thus, B2C and B2B marketers should focus on detailed funnel pages, case studies, comparison tools, and regional personalization.

Ultimately, the boundary of AI Overview is not a dead end, but a strategic border. Those willing to work in this nuanced space will discover less competition, more control over brand messaging, and the chance to shape the post-AI search experience before it fully crystallizes.

Beyond the Query: What AI Overview Can't (Yet) Understand

The Great Divide: Query vs. Intent

When someone types a query into Google, it might seem like a straightforward question. Yet in marketing, we know that every query is only the tip of the iceberg. What matters most is the intent behind it—the invisible driver of action. A query like “email marketing software” may come from a student doing a school project, a small business owner looking to automate campaigns, or a marketing director benchmarking tools before a procurement decision. The same keywords, entirely different business contexts.

Search engine optimization has long distinguished between informational, navigational, and transactional intents, but AI Overview responds primarily to informational ones. According to a recent Amsive study, over 88% of AI Overview triggers come from queries that reflect general curiosity or a desire to learn something, not to act. That’s a staggering figure that reveals how surface-level this system remains. It works best with "how to", "what is", or "difference between" style queries. But intention is not always explicit. And in B2B especially, understanding intent can dramatically change the outcome of a campaign or positioning strategy.

In professional settings, decision-making is rarely binary. Intent is layered: there’s the declared need, the organizational context, and the psychological trigger behind a search. If an executive types "should we rebrand after a merger," they are not looking for a dictionary definition of rebranding. They're likely in a moment of organizational transition, internal alignment, or even personal doubt about brand equity. AI Overview isn't designed to engage with that. It treats the query as final and delivers a flattened answer. But in business, queries are often just the beginning of a thinking journey. Ignoring the layers of intent behind them is missing the very place where strategy begins.

AI Overview: A Statistical, Surface-Level Engine

AI Overview is not designed to understand your business. It’s designed to respond to the most statistically likely interpretation of a given query. Built to scale across billions of searches, its logic is rooted in distribution, not depth. It identifies patterns in search behavior and delivers answers that reflect the most common denominator. In doing so, it sacrifices specificity for accessibility. This is not a flaw—it’s a design choice optimized for user volume and speed. But it also limits its value for professionals looking for insight rather than just information.

Most responses from AI Overview are generated based on long-tail informational keywords, and most of them still appear in English. The system’s prioritization aligns with global search trends. As of 2024, more than 60% of all web content is still in English, and over 90% of AI-generated snippets are served in that language, according to Semrush’s Q1 analysis. That means the feature inherently skews toward Anglo-American patterns of inquiry, both linguistically and culturally. Its pragmatism is useful—but only for clear-cut questions that can be answered in two or three factual paragraphs.

Where this becomes a limitation is when users input complex queries with vague or evolving needs. For example, a marketer looking for “ways to improve B2B customer retention” might receive a general overview of loyalty programs and CRM tools. What’s missing is context: the industry, customer lifecycle stages, internal KPIs, or sales process complexity. In B2B marketing, those nuances matter more than the generic answer itself.

Studies show that over 70% of high-intent B2B queries involve multi-session research, often across different stakeholders. AI Overview does not track or refine its output based on that journey. It treats each query in isolation. So while it may give you a digestible answer, it won’t connect it to your larger ecosystem. That’s why treating it as a strategic assistant is premature. It’s not a flawed tool—it’s just not the right tool for complex, decision-driven business questions.

ChatGPT: Navigating the Why Behind the What

Unlike AI Overview, ChatGPT does not rely on traffic volume or keyword frequency to determine relevance. Instead, it works with language in real time, allowing the user to refine and reframe their question based on evolving understanding or changing needs. This flexibility makes it uniquely capable of navigating professional ambiguity. It’s not bound to pre-programmed FAQs or high-volume queries—it adapts to your context, your tone, and your level of expertise. That makes it radically more useful for marketers, strategists, and business owners trying to turn ideas into action.

When a user types a specific yet layered question like “Which CRM should I choose if my wine export business is scaling toward B2B sales in Scandinavia?” ChatGPT can parse the geographical dimension, the industry specificity, and the operational goal behind the query. Rather than defaulting to generic lists, it might bring in considerations like language preferences, compliance norms in the Nordic market, and the typical sales cycles for international trade. That’s intent recognition at work—not just a static answer, but an evolving dialogue.

A 2024 Forrester report on AI-assisted marketing tools highlighted that professionals using conversational AI for strategic exploration reported a 47% higher perceived insight value than those using one-shot AI summary features like AI Overview. The difference lies in interactivity. ChatGPT makes it possible to go deeper, challenge assumptions, and recalibrate strategy midstream. For example, when asked about rebranding after a merger, it might not only define what rebranding means but also ask questions about stakeholder alignment, customer perception, and operational risks, helping the user articulate their own priorities more clearly.

In client-facing roles, that’s indispensable. The real value of language in business isn’t in the answer—it’s in the shared understanding that leads to decisions. ChatGPT, when used well, becomes a thinking partner. It helps professionals not just clarify what they want to know, but why they’re asking in the first place. That shift—from answering to co-thinking—is the core of its power.

Cultural Logic: American Efficiency vs. European Depth

AI Overview is the digital offspring of a culture rooted in speed, clarity, and action. It reflects a mindset that values output over nuance, where success is measured by how fast a question is resolved. This is not inherently negative. For basic queries, this pragmatism is powerful. A user types “how to update DNS settings” and receives a direct, actionable answer. In that context, AI Overview performs exceptionally well. But this same logic becomes reductive when applied to professional contexts where uncertainty, ambiguity, and complexity are the norm.

In contrast, European professional cultures, particularly in fields like strategy, branding, or policy, often operate with a different expectation: that the process of inquiry is itself part of the answer. Professionals may not seek immediate conclusions but rather a framework to think within, a conversation that helps them identify the right problem before proposing a solution. French or German executives, for instance, are statistically more likely to value context-setting and critical debate in B2B interactions, according to data from the 2023 Edelman Trust Barometer.

ChatGPT is more compatible with this model. It doesn’t assume the user is always ready for an answer. Instead, it engages in the process of shaping meaning. It accommodates reflection, contradiction, even hesitation. That makes it more aligned with high-stakes decision-making processes, where what’s at stake is not just information accuracy but strategic alignment and internal buy-in.

While AI Overview reflects a culture of transactional search, ChatGPT opens the door to relational thinking. It doesn’t just solve—it explores, rephrases, and reframes. In an age where strategy requires listening as much as informing, that difference is not philosophical. It’s operational. And for professionals navigating cross-cultural markets, this nuance could very well shape the success—or failure—of their next move