Google’s Generative Search: From Snippets to SGE and Gemini

In this post, we explore Google’s journey from traditional search engine optimization (SEO) toward generative search. We trace how early direct-answer features like featured snippets and voice responses (Answer Engine Optimization, or AEO) set the stage for today’s AI-driven Search Generative Experience (SGE).

We also examine Google’s AI chatbot Bard and the upcoming Gemini model – key components in Google’s response to the large-language-model (LLM) revolution. Importantly, we’ll discuss how marketers can optimize for these AI-powered results using proven SEO best practices, and analyze the opportunities and threats generative search presents for various industries (including e-commerce, SaaS, and B2B).

Featured Snippets and the Roots of AEO (Answer Engine Optimization)

Featured snippets were Google’s first major step toward providing direct answers on the results page. Introduced around 2014, featured snippets are concise answer boxes that appear at the top of search results, pulling an excerpt from a relevant webpage. For example, a query like “What is a marketing funnel?” might show a boxed summary definition drawn from a marketing blog, above the usual list of blue-link results. Around the same time, Google’s Knowledge Graph and “People Also Ask” boxes also emerged, offering factual summaries and related Q&A pairs directly on the search page.

These features signaled a shift: Google was no longer just a gateway to information, but increasingly an answer engine delivering solutions immediately within the search interface. This shift gave rise to the concept of Answer Engine Optimization (AEO). First coined by forward-looking SEOs in the mid-2010s, AEO refers to tailoring your content to be selected as a direct answer in search results (featured snippets, answer boxes, voice assistant replies, etc.).

Unlike traditional SEO which prioritizes earning clicks to your site, AEO is about visibility even without clicks – making your content the authoritative answer that Google (or Siri, Alexa, etc.) provides.

Key AEO tactics included:

1. Structuring content around questions and answers: Content creators learned to incorporate common user questions as headings, immediately followed by succinct, factual answers. For instance, an FAQ page might ask “How does X product work?” with a 2-3 sentence answer directly below. This format increases the chance that Google will excerpt that answer in a snippet or voice response.
2. Using lists, tables, and steps: If a query is looking for a list (e.g. “top 5 CRM software features”) or a how-to procedure, formatting the answer as a bullet list or step-by-step numbered list improves snippet eligibility. Early research showed that Google often pulled numbered lists for “How to…” queries and tables for data-driven queries.
3. Schema markup and structured data: Webmasters began adding structured data (using Schema.org tags like FAQPage, HowTo, Recipe, etc.) to make the page’s Q&A content machine-readable. This helps search engines identify and trust the content format, increasing the likelihood of it being featured. For example, marking up an FAQ section with <FAQPage> schema can directly feed Google’s Q&A features.
4. Voice search optimization: As voice assistants became popular, AEO overlapped with voice SEO. The answers needed to be conversational and concise because devices like Google Home would read them aloud. This reinforced writing in a natural, easy-to-understand tone that still contained the key answer in the first sentence or two.
5. People-first, authoritative content: AEO still required quality. Google favored sources that demonstrated expertise and authority (what later would be codified as E-E-A-T: Experience, Expertise, Authoritativeness, Trustworthiness). So, while formatting was important, content creators also focused on accuracy, citing reputable facts, and providing genuinely helpful answers to earn Google’s confidence.

Together, these practices formed the foundation of AEO. Marketers recognized that search was evolving “from a gateway to a destination” – often answering users’ needs without a click. For example, on mobile or voice, a user might get the full answer read out, never visiting the site. The upside was that if your content was the one featured, your brand gained high visibility and implied endorsement by Google. The downside was fewer clicks and less control over how your content was presented. By the late 2010s, the question for SEO professionals had expanded from “How do I rank #1?” to “How do I become the source that Google’s AI or answer box cites?”.

In essence, AEO was an early playbook for what has now become Generative Engine Optimization (GEO) – optimizing content for AI-generated answers. It’s worth noting that many industries started leveraging AEO techniques. E-commerce sites began structuring product pages to answer common questions (return policies, materials, sizing) directly on-page, hoping to capture “People Also Ask” spots or voice answers about their products.

B2B and SaaS companies invested in rich knowledge bases and blog content targeting long-tail questions (e.g. “How to improve team productivity in agile software development”) so that their expertise could appear in featured snippets. In doing so, they not only aimed to drive traffic but also to build brand authority by being the answer users hear or see.

This was critical in fields where trust and thought leadership drive sales: if a prospective client keeps seeing a SaaS company’s name popping up in answer boxes about, say, data security best practices, it subtly positions that company as an authority before the user even visits their site.

Crucially, AEO set the stage for today’s AI-driven search. It trained a generation of content creators to think in terms of answers, not just keywords. The lessons learned – about concise phrasing, structured information, and anticipating user questions – are directly applicable to optimizing for AI summaries and chatbots.

As we’ll see, Google’s latest evolution, the Search Generative Experience (SGE), can be viewed as the next logical step in this “answer-first” evolution of search. SGE uses advanced AI to synthesize answers from across the web, but it still relies on well-structured, authoritative source content – exactly the kind of content that AEO practitioners excel at producing.

Google’s Search Generative Experience (SGE)

By late 2022 and early 2023, the search landscape was shaken by the rise of powerful LLM-based tools like OpenAI’s ChatGPT. Users flocked to these AI chatbots for quick answers and advice, raising an existential question for Google: Would people bypass traditional search for conversational AI? In response to this competitive threat, Google announced the Search Generative Experience (SGE) in May 2023.

SGE represents Google’s bold experiment to integrate generative AI directly into search results, providing AI-crafted answers above the list of links. 

What SGE Looks Like: In the SGE interface, queries that trigger the AI will display an “AI overview” at the very top of the results page, often with a subtle colored background to distinguish it. This overview is essentially an AI-generated summary of the user’s query, synthesizing information from multiple web sources into a few paragraphs of conversational text ( ^[1] ) ( ^[2] ).

For example, a user searches “best family SUV for safety and fuel efficiency.” In the SGE-enabled Google, an AI snapshot might appear first, saying something like: “For a family-focused SUV that excels in safety and fuel economy, consider models like the Toyota Highlander or Volvo XC90. The Highlander earns top safety ratings (5-star NCAP) and gets about 24 MPG, while the Volvo offers advanced driver-assistance features and ~27 MPG in its hybrid version…” – and so on, perhaps two or three key points per suggested model.

This AI answer is clearly labeled as experimental and is boxed off from the standard results ( ^[1] ). Importantly, SGE doesn’t just present AI text alone – it also cites sources and provides links for deeper exploration. Beneath or beside the AI-written sentences, you will see small citation numbers or clickable cards referencing the websites from which the information was drawn ( ^[3] ) ( ^[4] ). Google has emphasized that the AI overview is grounded in content from the open web: “Google will cite the websites it used to help generate the answer,” noted one report on SGE ( ^[3] ).

If you click on a citation or one of the suggested links, you’ll be taken to that source website. In many cases, the AI overview also displays a few “featured sources” as clickable panels – these may include an image (from the source page) with a headline or site name. This is akin to an expanded featured snippet, but with multiple sources featured at once ( ^[2] ) ( ^[4] ). Users can interact with SGE in a conversational way. 

Follow-up queries are supported: underneath the AI snapshot, there’s often a prompt like “Ask a follow-up” or suggested next questions related to your query. If you continue the conversation (for example, following the SUV query with “What about maintenance costs for those models?”), Google will remember the context (the models discussed) and generate a refined answer, possibly citing new sources. Essentially, SGE brings an element of chatbot-style dialogue into search. Google describes it as carrying context from question to question so users can “naturally continue your exploration”.

This is a notable shift from the one-and-done query style of classic search. Another notable aspect of SGE is how it handles different query types and verticals. In informational queries (like “how to improve remote team collaboration”), SGE might produce a multi-point summary answer, sometimes even splitting the answer with subheadings or bullet points if relevant.

For shopping and product searches, SGE taps into Google’s vast Shopping Graph to give AI-curated product recommendations. For example, a query like “best noise-cancelling headphones under $200” might trigger an AI snapshot listing 2-3 headphone models with key pros/cons and current price ranges, all compiled from product descriptions and reviews on the web. Google revealed that SGE can pull from over 35 billion product listings in its Shopping Graph, which is updated with 1.8 billion changes per hour to keep prices, reviews, and inventory data fresh ( ^[5] ).

This means the AI overview for shopping queries can include very up-to-date information – a critical factor for e-commerce. (In our headphones example, the AI answer might say ” Model X from Bose – currently around $180 – offers top-tier noise cancellation and 20-hour battery life, according to 1,200+ reviews,” with the text “according to 1,200+ reviews” linked to a source or Google’s own shopping data.) Google has so far limited SGE to certain query categories. It tends to appear for more complex informational searches, comparative questions, broad shopping queries, and advice-like questions – where a synthesized answer is helpful.

On the other hand, SGE is less likely to trigger for very simple factual queries (where a standard featured snippet suffices) or highly sensitive queries (medical, financial, or other YMYL topics), presumably because the risk of AI inaccuracies (hallucinations) in those areas is higher. For example, searching “symptoms of diabetes” might still show the traditional snippet or a panel from a trusted health site, rather than an AI-generated overview, due to Google’s caution around medical advice. Indeed, early testing of SGE showed that Google was imposing higher standards for AI answers on YMYL (Your Money or Your Life) topics, and sometimes SGE would simply not appear at all for such queries.

As SGE has evolved (still officially in “Labs”), Google has iterated on when to show the AI. By late 2023, reports noted that Google dialed back SGE’s prevalence: one analysis found the fraction of searches with an SGE result dropped from 75% down to about 35%, meaning Google was intentionally not showing AI on many queries unless it was confident in the value added. This reflects Google’s cautious approach – they are gradually adjusting the presence of AI to balance user experience, accuracy, and revenue considerations (more on that shortly). 

Rollout and Current Status: Initially, SGE was (and still is) an opt-in experiment. Users had to join the Search Labs program to test SGE. After Google I/O 2023 unveiled SGE, a waitlist was opened for US users; by mid-2023 many users gained access. In late 2023, Google expanded SGE Labs access to more regions – by early 2024 it was available in 120+ countries and 7 languages for testers, although notably some markets (like the EU and UK) had limited access due to regulatory considerations.

Google planned to run the experiment through 2023, but in a January 2024 update they announced SGE would remain in the Labs testing phase longer than expected. In fact, Google indicated SGE would continue as an opt-in experiment “for the foreseeable future,” rather than immediately rolling out to all users. This decision came after mixed feedback from users and the SEO community about SGE’s readiness.

Several factors likely influenced the extended test period. Quality and accuracy issues were a concern – early on, SGE sometimes made factual errors or drew from less-than-authoritative sources, which could undermine user trust. (A Washington Post tech columnist bluntly wrote in mid-2023, “I tried the new Google… its answers are worse,” noting instances where SGE misinterpreted questions and cited low-quality sources or non-sequiturs.)

Google, wary of damaging its search reputation, has been fine-tuning SGE’s AI models and citation mechanisms to improve answer quality. They even publicly stated that they hold SGE to a higher quality bar and have extra guardrails for sensitive topics. 

User experience and performance are another factor. Early testers observed that SGE could be slow to load – sometimes taking several seconds to generate the AI snapshot, which is far from the instantaneous response people expect from Google. Google’s own guidelines aim for <3 second page loads, and SGE in its initial form often overshot this, risking user frustration. The layout was also very dense, pushing traditional search results far down (especially on mobile screens). By late 2023, Google started tweaking the design: for some queries, instead of auto-generating the AI answer, they showed a “Generate AI Response” button that the user could click if they wanted the AI summary.

This opt-in trigger on a per-query basis can preserve screen space and loading time for users who don’t need the AI help. It suggests that Google is considering a more conservative integration – perhaps only surfacing the AI when it clearly adds value, rather than for every possible query.

Google also had to consider the operational costs and business impact of SGE. Generating AI answers on the fly is computationally expensive (LLMs require a lot of processing power per query compared to a normal keyword search). If SGE doesn’t significantly improve user satisfaction or defend market share, rolling it out broadly could be a costly gamble with little return. Moreover, SGE threatened to disrupt Google’s ad model: initial versions of SGE pushed organic results (and therefore ads) further down, potentially reducing ad visibility and clicks. As one marketing agency noted, the first iteration of SGE had minimal ad integration, sparking concern that Google’s revenue could dip if people got answers without scrolling.

Recognizing this, Google soon added ads into the SGE interface, ensuring sponsored links still appear in dedicated slots even with an AI snapshot present. During the 2023 I/O announcement, Google reassured advertisers that ads would remain a “distinct, identifiable part” of the search experience, even as AI is introduced ( ^[6] ). By early 2024, testers observed that some SGE results included ads at the top or bottom of the AI snapshot, labeled as such.

Google is clearly treading carefully to maintain its core business while innovating on the UX. So, as of 2024, SGE remains in an experimental phase, not yet the default Google experience for all. Industry experts are split on whether SGE will graduate from Labs into mainstream search soon or whether Google will keep it semi-optional until the kinks are fully ironed out. One thing is certain: Google’s competitors are not standing still. Microsoft’s Bing integrated GPT-4 into its search early in 2023, gaining a surge of interest.

Other platforms like Perplexity.ai launched with AI search that cites sources. Even social platforms (TikTok, Reddit) siphon search share for certain demographics, and Amazon remains the go-to for product searches. SGE is widely seen as a defensive move to protect Google’s dominance. In Google’s ideal scenario, SGE will keep users on Google by offering the convenience of an AI assistant within the familiar search page, rather than losing those users to external chatbots or apps.

Early results are mixed: some users love the convenience of AI summaries on Google (“it saved me from clicking 5 different links to piece together an answer”), while others find it too authoritative, potentially limiting them from discovering diverse viewpoints. Google’s own surveys found that users appreciated citations and wanted even more transparency into how AI answers are composed – feedback Google has said it is incorporating ( ^[7] ).

SGE is also teaching Google a great deal about how AI can complement traditional search. Even if the exact current form of SGE doesn’t last, the core idea of AI-assisted search is here to stay. And for marketers and content creators, SGE offers a preview of a new kind of search results page – one where getting your content referenced in the AI overview could become as coveted as a Page 1 ranking. In the next sections, we’ll examine Google’s AI model strategy (Bard and Gemini) and then dive into how to optimize for these generative results.

Google’s Bard Chatbot and the Gemini AI Model

While SGE transforms the Google search interface, Google has a parallel AI initiative in its standalone chatbot Bard. Bard was Google’s answer to ChatGPT – literally. When ChatGPT’s popularity exploded after late 2022, Google fast-tracked Bard’s development and launched it to the public in March 2023 (initially to users in the US and UK). Bard started as a conversational AI service, separate from Google Search, accessible at bard.google.com. It was powered originally by LaMDA, a Google-developed conversational language model.

Early reviews of Bard in spring 2023 noted that it was less impressive than ChatGPT-4 in some areas, often providing shorter and sometimes less accurate answers. Google quickly iterated: by May 2023, they announced Bard would be powered by a more advanced model, PaLM 2, improving its capabilities (especially in coding and math).

But the real leap was yet to come – with Gemini. Gemini is Google’s next-generation foundation model, developed by the Google DeepMind team (a collaboration after Google merged DeepMind with its Brain team). In December 2023, Google officially introduced Gemini 1.0, heralding it as “our largest and most capable AI model” to date. Gemini is designed to be a direct competitor (or successor) to OpenAI’s GPT-4.

It’s notable for being multimodal from the ground up, meaning it can process and generate not just text, but also images, and even understand modalities like audio and video. This multimodality is an advantage – for instance, Gemini can analyze an image you upload and have a dialogue about it (describe it, answer questions about objects in it, etc.), a capability GPT-4 introduced with its Vision update but that Google aims to excel in natively. Google has built Gemini in different size tiers to serve various use cases: 

Gemini Ultra (the largest, most powerful model intended for highly complex tasks), Gemini Pro (a mid-tier model optimized for a wide range of tasks efficiently), and Gemini Nano (a smaller version that can even run on mobile devices for on-device AI). This tiered approach is similar to how OpenAI has GPT-4 for premium use and GPT-3.5 for lightweight tasks, but Google is explicitly optimizing Gemini variants for everything from cloud data centers down to smartphones.

For marketers, this hints at a future where advanced AI might be embedded directly in devices and apps (imagine AR glasses doing instant visual search with Gemini Nano, for example). Crucially, by early 2024 Google began integrating Gemini into Bard. In fact, Google started to brand Bard’s updates under the Gemini name – so much so that some tech observers refer to the latest version of Bard as effectively “Gemini Chat”.

One tech site noted: “Bard AI, Google’s chatbot, has since been renamed Gemini, unifying its generative AI technologies under the Gemini name”. In practical terms, Google launched “Gemini Pro” as the model behind the free Bard and a more powerful “Gemini Ultra” behind a premium version called Bard Advanced (which, similar to ChatGPT Plus, comes with a subscription fee). By March 2024, testers reported that the paid Gemini Ultra (Bard Advanced) could go toe-to-toe with GPT-4 in quality: “Gemini Ultra … provided marginally better responses than GPT-4… as well as better imagery” in one head-to-head test. The free Gemini (Pro) was also found much stronger than the free ChatGPT (GPT-3.5).

So what sets Bard/Gemini apart from ChatGPT in the search context? A few points: Real-time information access: Perhaps Bard’s biggest differentiator is its deep integration with Google’s search index. Bard has the built-in ability to “Google” things as needed. If you ask Bard about a very recent event or something that’s not in its training data, it can fetch up-to-date information from the web. (ChatGPT’s base model, by contrast, has a knowledge cutoff – as of 2023, GPT-4’s training data goes up to September 2021, and it requires plugins or a separate browsing mode to get newer info.)

For example, ask ChatGPT free version “Who won the 2024 World Cup?” and it cannot answer from its base knowledge (since that event is beyond its training); ask Bard, and it will search Google and tell you the result, citing a news source. This ability is thanks to Bard’s integration with Google Search – essentially a form of Retrieval Augmentation. 

Gemini was designed with real-time retrieval in mind, and indeed Google positions it as being able to access “the internet” as needed. The result for marketers is that Bard/Gemini may provide more current answers (stock prices, today’s weather, latest product releases, etc.) and thus content that is kept current has a better chance of being surfaced by Bard. 

Integration with Google’s ecosystem: Beyond just web search, Bard has been woven into other Google services. In late 2023, Google introduced Bard Extensions, allowing the chatbot to pull information from a user’s Google Workspace apps – like Gmail, Google Drive, Docs, Maps, etc. ( ^[8] ). For instance, Bard can summarize the content of your Google Docs, or draft an email using info from your Gmail, if you grant it permission.

ChatGPT offers plugins that can do some similar things (and Microsoft integrates GPT-4 into Office 365 in some ways), but Google leveraging its own ecosystem is powerful. For marketers, this means Bard could become a kind of personal assistant that straddles both personal data and web data. Imagine a scenario: a user planning a business trip asks Bard (Gemini) for “Recommend me some project management SaaS tools I can try during my flight to improve team workflow.”

Bard might not only list some tools (drawing from web content) but also note “I see in your Google Drive that your team uses Trello currently – one alternative to consider is Asana, which offers XYZ features…” etc. This level of personalization is on the horizon and could transform how product discovery happens via AI. 

Multimodal capabilities: As mentioned, Gemini is multimodal. Already, Bard can accept image inputs – for example, you can show Bard a photo of a chart or a math problem and ask for analysis. And Bard can generate images via integration with Google’s Imagen model or third-party generators (Google announced in 2024 that Bard can create images through a partnership with Adobe Firefly).

This out-of-the-box image generation is not something ChatGPT offered natively (OpenAI relies on DALL-E plugins). So, Bard is positioning itself as a more visual assistant. A practical use-case: an e-commerce marketer could ask Bard, “Generate an image of a modern office desk setup featuring our product” for a mockup – Bard could do it. Or a user could upload a screenshot of an error message and ask Bard for help, and Bard (via Gemini) could read the image text and provide guidance. These features make Bard more versatile and potentially more engaging for users, keeping them in Google’s orbit. 

Factuality and accuracy improvements: Google has touted Gemini’s performance on knowledge benchmarks. For example, Gemini Ultra scored 90% on the MMLU academic benchmark, making it the first model to exceed human expert performance on that test. This benchmark covers a wide range of subjects (math, history, law, etc.), indicating Gemini’s breadth of knowledge.

Additionally, Google claims Gemini has advanced reasoning capabilities – it can “think through” problems better, rather than just blurting out the first answer. In practical terms, one early comparison found “Gemini’s answers often provided more nuance and context than ChatGPT’s”, albeit sometimes at the cost of being a bit wordier. Google is clearly aiming for an AI that is both knowledgeable and context-aware. If Gemini-powered Bard can reduce hallucinations and cite sources more reliably, it might alleviate one of the big concerns users and publishers have with AI answers. 

Competitive positioning: By 2024, ChatGPT had over 1.6 billion monthly visitors (as of Feb 2024) and was a household name ( ^[9] ). Google, despite being a leader in AI research, was seen as playing catch-up in the public eye. The launch of Gemini and its integration into Bard is Google’s attempt to leapfrog. Early community tests (some of which leaked on forums) suggest that Gemini is extremely capable – for instance, one comparison of “Gemini vs GPT-4 Turbo” in late 2023 found that while GPT-4 might still be a bit better in strict accuracy, Gemini was faster and produced more human-like, creative outputs.

Google also has the advantage of scale and distribution – the moment they decide to push Bard/Gemini to all Android phones or Chrome browsers, the user base could explode. We may soon see Bard’s capabilities (like a “search with Bard” option) integrated more into core Google products. In the search context, Google could eventually unify the Bard experience with SGE – for example, a user might switch from the AI snapshot to a full “Bard chat” within search to further discuss the query.

In fact, some experiments in late 2023 showed a conversational mode directly in the Google app, which is essentially Bard in Search. From a marketing perspective, the rise of Bard and Gemini means that optimizing for Google’s ecosystem isn’t just about classic SEO, but also understanding how your content might be accessed and presented by an AI.

For instance, if Bard is summarizing information about your company (say, pulling from your About Us page or recent news articles), you’d want to ensure those sources are accurate and highlight the points you’d want a summary to include. We might soon consider “ LLMO ” (Large Language Model Optimization) as parallel to SEO – making sure our content is digestible and favorable to AI models like Gemini (we’ll discuss optimization in the next section).

To give a concrete example, consider a SaaS B2B company offering project management software. In the old world, they’d focus on SEO for keywords like “best project management tool” to rank their blog or comparison page. In the new world with Bard, a user might ask the AI, “Which project management software is best for small teams? Explain why.” Bard will draw on its training data (which includes tons of web content, maybe including tech review sites, user forums, the SaaS company’s own content if it’s prominent, etc.) plus any real-time info (maybe Gartner’s latest report or Reddit discussions).

If our SaaS company has done a great job at publishing high-quality, fact-rich content (like a detailed comparison of tools or noteworthy case studies), Bard might incorporate points from it: e.g., “According to a case study by AcmeCorp (a project management SaaS), small teams often struggle with tools that lack integrations. AcmeCorp’s software emphasizes integration with Google Drive and Slack ( ^[10] ) ( ^[11] ).” 

This would be a huge win – the AI essentially quoting the company’s own content as authoritative. On the flip side, if a competitor’s content or a third-party blog is more visible to the AI, our company could be left out of the narrative, even if they have a great product. This scenario is exactly what some companies have faced: one case study showed a B2B firm was frustrated that competitors with “inferior” products were being summarized and cited by AI for key queries, while they were absent ( ^[10] ). The firm had to proactively adjust their content strategy to get included (we’ll revisit this case study later) ( ^[11] ).

In summary, Google’s strategy with Bard and Gemini is to ensure it has the best underlying AI model and a compelling chatbot interface to keep pace with or surpass OpenAI’s offerings. For users, this means more powerful AI capabilities at their fingertips (especially if you’re in Google’s ecosystem). For online businesses and marketers, it means preparing for a world where both search results and chatbot answers might drive discovery of your brand. Content needs to be optimized not just for the ten blue links, but for the AI dialogues and overviews that millions of people will increasingly rely on.

Optimizing for Google’s AI-Powered Results (SGE and Beyond)

Google has stated that, from a webmaster’s perspective, nothing fundamentally new needs to be done for SGE – in other words, the same SEO best practices that help your content rank well will also help it be included in AI overviews. In late 2023, Google’s Search Liaison team reassured site owners that they weren’t introducing any special meta tags or SGE-specific ranking algorithms; the AI overview draws from Google’s regular index and ranking signals. “If you’re producing helpful, people-first content, you’re already doing the right thing,” is the general message from Google. That said, optimizing for an AI-driven results page does put a new lens on some familiar tactics. Essentially, we need to ensure our content is accessible, understandable, and authoritative to both search engine algorithms and the AI systems generating summaries. Let’s break down the key optimization considerations.

Ensure Crawlability and Access for AI

First and foremost, your content must be indexable by Google. The AI can’t summarize or cite what it can’t read. This might sound obvious, but it’s worth double-checking: are any critical sections of your site blocked by robots.txt or meta noindex tags? Ensure that your robots.txt isn’t disallowing important directories (like /blog/ or /knowledge-base/).

Likewise, avoid using heavy client-side rendering for content that could be an answer – if the content only loads via JavaScript after page load, Google’s crawler might not always see it. A pure HTML text version (or server-side rendering) of key content is safer for ensuring the AI has access to it. It’s also important to note that currently there is no way to opt out of being included in AI overviews without opting out of search altogether. Some publishers, concerned about content being used by AI without clicks, asked Google for a “SGE opt-out” meta tag. Google’s answer was that SGE is just a new way of presenting search results, so the usual rules apply – if your page is indexed in search, it may be used in an AI answer.

The only surefire way to exclude content from AI summaries would be to add a noindex (which would remove it from Google search entirely) – obviously not a desirable solution for most. There has been discussion of a future Google-Extended tag to control use of content in AI training, but for search outputs like SGE, nothing granular exists yet. Therefore, for now, assume all your indexed content is fair game for AI summaries. This is actually an opportunity: it means even pages that might not rank #1 on their own could still get visibility if they contain a relevant snippet that the AI finds useful.

One more technical note: make sure your content delivery is fast. SGE currently has some performance issues, and Google will likely favor content that can be retrieved and parsed quickly (especially if it’s assembling answers on the fly). So, core web vitals (good LCP, TTFB, etc.) and overall site speed indirectly help. If your page is very slow or unresponsive to Googlebot, the AI might skip it in favor of a faster source when constructing an answer, especially for time-sensitive queries.

Use Structured Data and Semantic HTML

Structuring your content clearly – both for human readers and machine parsing – is vital for AI optimization. Structured data markup (Schema.org) can give Google explicit clues about the content on a page.

For instance:

1. Adding FAQPage schema to a list of Q&As on your site could increase the chance that one of those Q&As is pulled into an AI answer (or at least appears in the “People Also Ask” which the AI might consider). Google’s documentation notes that using schema for FAQs, how-tos, products, etc. helps make your information eligible for rich results, and by extension, these structured pieces are easier for an LLM to digest and trust.
2. HowTo schema can highlight step-by-step instructions. An AI overview may not list all your steps, but it might say “There are 5 key steps to do X” and cite the source – which could be you, if your how-to is marked up and indexed.
3. Product and Review schema are crucial for e-commerce content. As mentioned, SGE’s shopping answers pull in product specs, prices, and ratings. That data often comes from Google’s Shopping Graph, but Google also scrapes schema on product pages for things like aggregate ratings and price ranges. Ensuring your product pages have up-to-date Product, Offer, and Review structured data means the AI is more likely to use your page as a source for product recommendations or comparisons.
4. Article or BlogPosting schema can help with news or informational content. While not directly guaranteeing inclusion, it provides clear metadata (like author, publish date) that could lend credibility. Google’s AI might be programmed to prefer up-to-date info, so seeing a recent date on an article (via structured data) could help, for example, an AI answer about “2024 economic outlook” to choose your article if it knows it’s fresh.

Beyond formal structured data, pay attention to your HTML structure. Use descriptive headings (<h1>…<h2>…) for sections, especially for any question-answer pairs. If you have content that answers a specific question, make that question an <h2> and the answer a paragraph below it. Not only does this align with AEO tactics (featured snippet optimization), it also makes it easy for an LLM to extract the Q&A pair.

Consider a page structure optimized for AI: an initial concise answer or summary (that could be read on its own), followed by details. In fact, some SEO experts suggest using an “inverted pyramid” style of writing for GEO – put the direct answer or conclusion at the top (which might be what the AI grabs), and then elaboration afterwards. This way, if the AI overview quotes your content, it’s quoting the punchline you want, not an incomplete mid-explanation sentence.

Lists and tables: If applicable, use HTML lists (<ul>, <ol>) for lists of recommendations, features, pros/cons, etc. SGE has been observed to present a lot of information in bullet form (the AI itself often formats key points as bullets or numbered steps). If your content already has a well-structured list, the AI might incorporate it or at least more easily parse it. For example, a site listing “10 benefits of using CRM software” in a neat <ol> could be directly distilled by the AI into a summary, citing that site. Similarly, tables (for structured data comparisons) can be read by AI – if you have a comparison table of specs, the AI might use it to call out a specific number (“the Nikon camera has a 24.2 MP sensor vs 20 MP on the Canon” and cite the source).

To give a real-world example: a travel blog might have a well-formatted comparison of Bryce Canyon vs Arches National Park for families (recall the SGE example query from Google’s demo). If that blog uses clear subheadings like “## Bryce Canyon for Families” and “## Arches National Park for Families,” and maybe a bullet list of pros for each, then SGE’s AI might pull points from each section to form its answer.

In Google’s own demo, the AI said, “Bryce Canyon has more toddler-friendly short hikes, while Arches requires more scrambling; Bryce’s elevations mean cooler temps, etc.” – these details had to come from someone’s content. If your blog had a section stating “Bryce Canyon: Several short, easy trails like Mossy Cave are suitable for toddlers.” and another stating “Arches: Some trails involve rock scrambling not ideal for kids under 5.”, you greatly increase your chance of being one of the cited sources in the AI snapshot.

And notably, those points need to be explicit and crisp – an AI might not infer or combine scattered info as reliably as we think. It’s often doing pattern matching and summarizing in a local way. So make important facts stand out (bold them, list them, or at least not bury them in fluff).

Focus on Helpful, Authoritative Content (E-E-A-T)

Google’s core advice with SGE is that “helpful, people-first content” will be rewarded. This aligns with the Helpful Content system and the E-E-A-T principles (Experience, Expertise, Authoritativeness, Trustworthiness) from its search quality guidelines.

Why does this matter for AI results? Because Google’s generative AI is likely using signals of authority and accuracy when choosing what to include in an overview. In fact, Google has hinted that their SGE algorithms consider page quality and ranking just as a normal result would. One agency noted: “Currently, the best way to appear as one of these relevant [AI overview] pages is to be one of the top-ranked results”. In other words, if your page already ranks on page 1 for the query (or a closely related query), it’s a prime candidate to be cited.

The AI isn’t intentionally seeking obscure sources; it’s drawing from the cream of search index for that topic (with perhaps a few exceptions as noted earlier). Thus, the traditional pillars of SEO remain crucial: good content that satisfies user intent, backed by backlinks/authority, and demonstrating expertise. Ensure your content is accurate and well-researched. AI has a habit of confidently spouting wrong info if its sources are poor.

Google will try to avoid citing sources that have inaccuracies or thin content because that reflects back on the AI’s quality. If your site has reviewed by experts or author bios with credentials, that might indirectly help (for example, a medical article written by a doctor might be deemed more trustworthy by Google’s algorithms and chosen for an AI summary on a health question). Additionally, consider adding references and data in your content. If you provide a statistic or important claim, cite a source (and link to it). This might sound counter-intuitive (why send people elsewhere?), but it can boost your credibility.

There’s a scenario where the AI might even mention that data point and your site together. For instance, “According to YourSite, 65% of small businesses saw ROI from CRM within 6 months.” If that statistic is real and you cited a study, the AI might choose to use it and cite you (and possibly the original study if it knows it). We are essentially treating the AI as another consumer of our content – one that has read millions of pages.

If your page consolidates useful facts or insights in one place, the AI may prefer it to having to pull from multiple different pages. Original research and unique insights are also key. In a world where AI can generate generic content easily, having something truly original (a proprietary survey, a case study, a unique viewpoint) sets your content apart.

Google’s algorithms (and by extension SGE) aim to highlight “information that AI cannot easily produce itself”. Long-term, content that just rehashes common knowledge may be deemphasized, while content with genuine expertise or firsthand experience will be valued. So, for example, an e-commerce site could publish internal data about how customer satisfaction improved after using their product, or a SaaS company might release survey results from their user base – these kinds of things, if cited by others, increase your authority.

They also give the AI something novel to latch onto (e.g., “Brand X’s report says 3 in 4 remote teams work across time zones, highlighting need for scheduling tools…” – that could feed an AI answer on remote work challenges). In practice, this means content quality and depth should not be sacrificed. Just because an AI summary will be brief doesn’t mean you only write brief content.

It means the most important parts of your content should be easily extractable, but you still want to have depth for the users who do click through (and for overall topical authority).

Topical authority is another concept: cover your subject comprehensively across your site. If you run a fintech blog, having a cluster of well-written articles on related subtopics (and interlinking them) helps Google view you as authoritative in that domain. We suspect that SGE’s selection of sources leans towards sites that have authority on the topic at hand (e.g., it might pull from a specialist site on a medical query rather than a generic site).

In one observation, early AI overviews about programming questions often cited sites like Stack Overflow or official docs (authoritative sources) rather than random blogs. So, building your site’s topical authority and reputation (through content and links) is key to being one of the chosen sources in AI results.

Optimize for Conversational Queries and Prompts

In the age of generative AI search, users are likely to phrase queries more conversationally or specifically. Instead of the old terse keyword searches, people might ask the AI, “What should I do if my e-commerce site’s traffic drops after SGE rolls out?” – something they might not type in the regular Google. Marketers should anticipate these natural language queries and embed answers to them in their content. This is akin to “Prompt SEO” – optimizing content to align with the kinds of questions users will pose to AI assistants ( ^[12] ) ( ^[13] ).

A practical tip: maintain an FAQ section or Q&A content that covers the “who, what, why, how, when” around your niche. If you have a comprehensive FAQ, each question there might match a conversational query a user asks the AI. For example, if you sell eco-friendly home products, an FAQ question like “How can I reduce plastic waste in my kitchen?” with a well-crafted answer could be exactly what someone asks Bard or SGE. Your content might then be used in the AI’s answer (with a citation).

It can also be useful to mirror likely user phrasing. If people often type questions like they’re talking (e.g., “What’s the best way to learn SEO in 2025?”), consider having a blog post titled “The Best Way to Learn SEO in 2025 – Answered” or literally phrasing a header as a question: “What Is the Best Way to Learn SEO in 2025?”. This gives the AI a clear signal that your section answers that exact query. It’s similar to old featured snippet targeting, but even more conversational.

Tools that analyze People Also Ask questions and community forums (like Reddit, Quora) can give insight into how real users ask things – use those insights in your content creation.

Also, consider that AI chat follow-ups might combine concepts. For instance, a user might start general (“give me tips to improve my website’s UX”), then follow up specifically (“how about for mobile users?”). If your content covers both general and specific subtopics (desktop vs mobile UX), the AI could draw from different parts of your site across turns. Ensure your site’s internal linking and structure connect these related topics so Google knows you cover them all.

A pillar page with links to specialized subpages can work well. In some cases, embedding likely prompts verbatim in your content can be useful. This is a bit experimental, but some have tried adding, say, a hidden HTML comment or very small footer text like “ Prompt: What are the benefits of using [Your Product]? ” followed by a brief answer. The idea is to literally feed the LLM an exact Q&A. Whether this works or is sustainable is unclear (and doing it at scale could be seen as manipulative if overdone). A safer approach is just making sure your content naturally includes the question phrasing in visible text.

Technical SEO and Site Signals in the Generative Era

Beyond content and schema, technical SEO factors still matter in an AI context. Google has indicated that core ranking signals apply to AI selections, which likely includes things like PageRank (backlinks), content relevance, freshness, and even user experience signals.

So continue to invest in:

High-quality backlinks from reputable sites: If many sources link to your content as a reference, Google’s algorithms are more likely to deem it trustworthy and therefore safe to include in an AI answer. Off-page authority can indirectly boost your presence in AI overviews.

Freshness: For topics where information changes over time (tech, finance, trends), updating your content regularly can be crucial. Google’s AI will incorporate real-time info for sure via search, but if your page is outdated, it may favor citing a fresher source. We’ve seen AI overviews include phrasing like “As of October 2024, …” which implies it looks for timestamped info. So keep dates updated and content current. If you have evergreen content, consider adding a “Last updated” date in the text if you refresh it – the AI might pick that up.

Robust website structure: Ensure your site is well-organized so that Google can understand context. Use clear URL structures, breadcrumbs, and category pages that demonstrate how topics are related. A well-structured site helps Google’s index and also means if the AI is looking for related info (to answer a follow-up question for example), it might find it on your site (since your related content is interlinked and easy to crawl).

Preventing errors: Fix or redirect broken links, resolve 404s, etc. If an AI tries to cite your page and it’s not reachable, that’s a missed opportunity. Google’s index might drop it if it’s consistently erroring.

Also, if you move content, use redirects so that any accumulated signals (including being a known source for an answer) carry over to the new URL. Finally, monitor what queries and answers your site is appearing for. Google Search Console in 2023 added some reporting for “AI Features” impressions (for sites in SGE) – it would show if your site was cited in an AI overview and for what query (though in limited fashion). These tools will likely improve. By keeping an eye on this, you can learn which content of yours is resonating with the AI and expand on those areas or refine them.

Conversely, if you notice competitors appearing in AI answers where you think your content is better, analyze why – do they have better structured data? More concise answers? Perhaps their page is just slightly more on-target for the query. This insight can inform your content optimization.

In summary, optimizing for Google’s AI results is an evolution, not a revolution, of SEO practices. It demands a renewed focus on clarity, structure, and truly useful content. The upside is that many of these practices (like AEO) were already being adopted by savvy SEO professionals. The key difference now is thinking about how an AI bot reads and synthesizes your content, in addition to how a human or a traditional crawler would. By ensuring your site is technically sound, your content is well-structured and authoritative, and by anticipating the kinds of questions users (or the AI on their behalf) will ask, you position yourself to be a prominent player in the generative search landscape.

Opportunities and Threats of Generative Search for Marketers

The emergence of AI-generated search results brings a mix of exciting opportunities and significant challenges for online marketers. It’s a classic double-edged sword scenario: on one hand, generative search can dramatically expand a brand’s visibility by pulling in information from all corners of the web (potentially giving smaller or niche sites a chance to shine); on the other hand, it threatens the traditional traffic model by often satisfying users’ queries without a click, which could erode website visits and the ability to convert or monetize those users. Let’s break down the key opportunities and threats, with real-world context.

The Zero-Click Paradigm – Threat to Traffic, Challenge to Adapt

One of the most obvious impacts of AI answers is the reduction in click-through rates (CTR) for organic results. If an SGE overview or Bard gives the user exactly what they need, they may have little incentive to click any of the source links. This trend was already underway with featured snippets and Google’s direct answers (zero-click searches have been rising for years), but AI takes it to another level by handling more complex queries end-to-end. Early studies have tried to quantify this potential traffic loss.

A notable analysis by Search Engine Land in mid-2023 modeled the impact of SGE across 23 different websites. The findings projected an organic traffic drop between 18% and 64% on average if SGE-like AI results rolled out widely. Some websites in the study could lose as much as 95% of their organic traffic in the worst-case scenario, essentially decimated if their content is fully cannibalized by AI answers.

That said, a few sites were projected to gain traffic (up to +219%) because the AI might highlight them more than traditional search did. This wide range indicates that the impact is not uniform – it heavily depends on the type of content and queries a site targets. Informational, how-to, and FAQ-style content is most at risk for zero-click consumption (since AI can often satisfy those queries). For example, a simple recipe site could see fewer visits if SGE lists the key ingredients and steps right on the results page.

Transactional or interactive content (like tools, calculators, product purchases) may be less impacted since users still have to click to complete an action. Marketers should brace for potentially lower raw traffic from SEO, especially for top-of-funnel informational queries. However, traffic quality might shift more than quantity.

Consider that users who do click through after seeing an AI summary might be deeper in the funnel – because either the AI spurred their curiosity for details or the query was such that the AI encouraged them to “dig deeper” on a specific link. In other words, if the AI overview didn’t fully answer their need, the click they make could be more qualified (they’re specifically interested in what your site can offer beyond the summary).

Some optimists suggest that while overall impressions and clicks might drop, the conversion rate of the remaining clicks could rise (since casual info-seekers might have been satisfied by the AI, leaving the more motivated users to click). It will be crucial to monitor metrics like engagement, conversion, and user behavior for AI-referred traffic versus traditional traffic. Nonetheless, the threat is real for many content businesses.

Publishers that rely on page views for ad revenue are particularly worried. If a site’s painstakingly written article on “10 ways to reduce mortgage payments” is distilled by Google’s AI into a neat bullet list that requires no click, the site loses the ad impressions and possibly the affiliate link clicks it might have gotten. News and how-to publishers are already strategizing about this – some are exploring alternate revenue models or focusing more on content that AI can’t easily display (like interactive tools, videos, or very in-depth pieces).

Brand Visibility and Authority – Opportunity in the AI Spotlight

On the flip side, being featured in an AI overview can significantly boost brand visibility and credibility, even absent a click. It’s akin to being quoted as an expert in a news article – even if readers don’t immediately run to your website, your name being present lends you authority.

In fact, one marketing commentary likened AI Overviews to word-of-mouth recommendations : “Google AI Overviews are like friends who save you time by giving quick info… For brands, AI Overviews can increase your brand visibility in the same way positive reviews boost your reputation. Brand visibility builds recognition and trust – pillars that turn strangers into customers.” ( ^[14] ) ( ^[15] ).

In other words, if your brand is consistently popping up as a cited source for answers in your domain, users begin to trust and recognize you, even if they haven’t visited your site yet. Imagine someone sees an AI answer about fitness that ends with “– info provided by MyFitnessSite.com.” The user might not click, but subconsciously they register MyFitnessSite.com as a knowledgeable source. After a few times, they might directly seek out that site or feel more comfortable converting when they do land there, because the AI (an ostensibly neutral party) has effectively endorsed it by citation.

In marketing, this is often called “brand imprinting” – repeated exposure through trusted channels builds familiarity. There’s also a long-term SEO angle: if AI overviews drive what some are calling “ implicit traffic ” (exposure without click), it could still lead to branded searches later. A business might see an increase in people searching their brand name or typing their URL directly after frequently being mentioned in AI results. Those are highly valuable because they often convert better.

Another opportunity is that AI can surface a wider range of sources than traditional top-10 results. We touched on this: SGE might pull relevant bits from sites that weren’t ranking #1. Maybe they were ranking #5 or #10 or beyond, but had a perfect sentence the AI needed. For niche sites or new entrants, this is a chance to get in front of users without beating giants on every keyword. A 2024 report noted that “marketers have noticed not all recommendations [in AI overviews] are from the top search results”, indicating Google sometimes includes diverse sources beyond the usual suspects ( ^[7] ) ( ^[16] ).

For example, an AI answer about a programming question might cite a specific developer’s blog that had exactly the solution code, even if that blog isn’t StackOverflow or doesn’t have massive PageRank. In normal search, that blog might be buried, but the AI “fanned out” to find the precise info needed. This leveling of the playing field is an exciting prospect for high-quality content creators who might not have had the SEO clout to appear on page one.

It encourages focusing on depth and specificity – if you answer a very specific question really well (even as part of a larger article), you might get picked up by AI for that piece of info. To harness this opportunity, brands should ensure their name and website are clearly associated with their content. For instance, use a consistent brand name in bylines or within content if appropriate (“According to a study by [Brand], …”). The AI often will cite the source with the site name or sometimes the publication name. You want to be sure it’s attributing it to your brand and not just a generic description. Schema markup can help here too (e.g., publisher name in Article schema).

New Content Strategies – From Click Acquisition to Information Shaping

Generative AI in search will push marketers to adjust their content strategy and KPIs. The goal is no longer just to get the click, but to shape the conversation even if the click doesn’t happen. This means success might be measured in part by being referenced in AI outputs, not just by traditional traffic metrics. We’re moving toward what some call “post-click SEO” or “zero-click marketing” ( ^[17] ) ( ^[18] ). In zero-click marketing, the focus is on ensuring your brand/message reaches the user within the search interface itself.

One strategy is to provide content that complements AI rather than competes with it. For instance, AI can summarize general knowledge quickly – but it might encourage users to click through for details, examples, visuals, or interactive elements. If you anticipate that the basic answer to a question will be handled by SGE, consider what extra value a user would get by coming to your site. Then make sure to highlight that on the site so that the AI overview might even hint at it.

For example, a cooking site might know that an AI can list ingredients and basic steps for a recipe. So on the recipe page, they include a tip like “watch our 2-minute video for a clever chopping technique” or mention “use our interactive ingredient scaler for different serving sizes”. The AI might say, “For detailed technique, see Video – [Site Name]” if it was trained to note that, or at least the user sees there’s something on that site beyond the text.

While currently AI overviews don’t generate new media, in the future they might. But at least for now, unique content like videos, infographics, downloadable templates, quizzes, etc., cannot be fully conveyed in a text summary. Those are click-worthy enticements.

Additionally, consider creating more “AI-friendly” content pieces such as: 

• One-stop guides that cover a topic comprehensively. AI prefers not to have to merge too many sources, so if your single page covers multiple facets of a question, it might just use your page alone for an answer, listing you as the sole citation (a big win). We saw AI answers that sometimes only cited one source for a chunk of text. 
• Concise summaries within pages (maybe as a highlighted box or intro paragraph) that the AI could grab. Essentially giving the AI what it needs on a silver platter, and then providing elaboration for the human readers. 
• Conversational tone where appropriate. If the AI is looking for a phrasing to use, content written in a straightforward, conversational manner might be more directly usable. Extremely academic or complex language might get reformulated by the AI (losing your wording) or ignored if it’s not easily digestible. 
• Up-to-date content : We mentioned freshness – but also jumping quickly on emerging questions. When a new trend or problem arises (say a new software update causing issues that people will ask AI about), being among the first to publish a clear answer increases the odds you become the referenced source before others catch up. Essentially, there’s a first-mover advantage in feeding the AI certain answers (until it retrains or shifts to new sources).
• Let’s not forget local and transactional opportunities too. Generative search will likely extend to local search (“What’s a good pizza place near me that’s open now?” might yield an AI answer listing a couple of restaurants with details). Ensuring your Google Business Profile data is accurate is critical so that if AI pulls operating hours or reviews, it’s correct. If you’re a local business, the AI overview might list you – even if previously you struggled to rank in the pack – based on specific attributes like “kid-friendly” or “pet-friendly” gleaned from reviews or content.

This is an opportunity to influence what the AI says by managing your online reputation (encourage happy customers to mention specific positives in reviews, etc., because the AI could summarize common sentiments). E-commerce stands to face threats in terms of affiliate site traffic (as mentioned, aggregator sites might see less clicks if AI lists products). But retailers could gain if the AI funnels users directly to product pages. Google’s SGE, for example, often provides buying options within the overview.

If you’re a merchant and your product is one recommended by the AI, you might actually see higher conversion because it’s like a trusted suggestion followed by a direct link to purchase. This raises the stakes for feed optimization and product SEO – you want your products to be well-represented in Google’s Merchant Center, with great reviews, so they get picked by the AI as part of the answer.

Mitigating the Risks – Strategies to Encourage Clicks and Engagement

To address the traffic threat, marketers can deploy several tactics to make their snippet in the AI overview as enticing as possible – essentially to “earn” the click even when the answer is partly given: 

1. Tease additional value: If appropriate, ensure the text that might be pulled (often the first 1-2 sentences of your answer) hints at more to be found. For example, “The three main strategies are A, B, and C. Each comes with unique challenges – for instance, Strategy A [something intriguing]…”. The AI might include “Each comes with unique challenges – for instance, Strategy A involves hiring new staff (according to SiteName)…”. A curious user will realize the site likely explains all the challenges, not just Strategy A, and may click to learn them. This is delicate – you must still answer the question, but you can also create a curiosity gap. 
2. Provide rich media on click-through: Users who click after an AI answer may be looking for depth or confirmation. Greet them with something that validates their click. If they come from an AI summary about, say, “tips for reducing mortgage payments,” and your site has an interactive calculator or a detailed case study that obviously couldn’t be in the summary, they’ll feel rewarded for clicking. This reduces pogo-sticking and shows Google (and users) that your site delivers beyond the snippet. 
3. Optimize titles and meta descriptions (for the links that do show): In SGE, when sources are shown as cards or link bubbles, often the page title or a truncation of it is visible. A compelling title may convince a user to click “Learn more on [Site Name].” If all sources have generic titles and yours is catchy or promises something extra, you could win the click. For example, if the query is answered by AI and sources show up, a title like “Ultimate Guide to X (Free Template Inside)” might draw a click over a title like “Guide to X”. 
4. Leverage branding: If you have a strong brand and logo, that could help. In SGE, some source cards show a thumbnail (like a favicon or image from the page). Ensure your favicon is recognizable. Also, if your brand is known for quality, users might click your source over others. This is more about overall brand building – one reason to invest in content marketing, PR, etc., outside of just SEO, is so that when your name does appear as a source, people trust it. 
5. Monitor and shape the narrative: If the AI summaries consistently misrepresent something or cite a competitor with possibly outdated info, that’s a signal to produce content clarifying that issue (and perhaps even overtly comparing or debunking if tactful). While you cannot directly control the AI, you can put out content that sets the record straight on topics related to your brand. Over time, as the AI training data updates or the retrieval algorithms improve, your perspective might get picked up. For instance, if an AI answer about your product’s pricing is wrong, ensure your site clearly states the pricing in a prominent way. Google’s AI might then pick the correct info next time (and cite you).

There is also a defensive strategy to consider: diversification of traffic sources. If organic search traffic becomes more volatile due to AI, smart marketers will hedge by building up other channels: email lists (so you can reach your audience directly), social media communities, referral partnerships, etc. The js-interactive article noted that “the go-to marketing platform may no longer be exclusively Google. Diversify to other channels… TikTok, LinkedIn, etc., depending on where your audience is” ( ^[19] ) ( ^[20] ).

In other words, don’t put all your eggs in the Google basket. While SEO remains crucial, ensuring you have a strong brand presence outside of search will help if AI search changes the rules unexpectedly.

Case Study Reflections: Winners and Losers in Early Generative Search

To ground this in reality, let’s briefly look at a couple of examples from the field: A tech blogging site (we’ll call it TechSite) found in late 2023 that for many “how to fix X error” queries, SGE was providing an answer with a step-by-step from their content – but users hardly clicked through because the overview was sufficient. Their initial traffic from those queries dropped ~20%. However, they noticed an interesting trend: their brand searches increased slightly, and on forums people referenced TechSite’s advice (likely seeing it via SGE).

So TechSite pivoted by putting a “video demonstration” on those how-to pages. When SGE began occasionally mentioning “see video at TechSite for demonstration,” their click rate improved. They also doubled down on more complex content (like troubleshooting flowcharts) that the AI could not easily display, ensuring at least some portion of the answer requires a visit.

A B2B SaaS company offering data analytics tools saw an initial drop in blog traffic after Bing and Bard started answering questions like “How to build a sales dashboard”. Much of the basic “how to” was given by AI. However, they participated in the Bing Chat and ChatGPT plugin ecosystem to create a ChatGPT plugin for their tool and a Bard Extension. Now, when users ask how to do something, the AI can actually hand off to their product or give a tailored answer using their plugin, often mentioning the brand. This drove qualified leads who directly tried the tool via AI referrals. It’s a reminder that integration with AI platforms (via APIs, plugins) is another opportunity – though outside Google SGE’s domain, it’s part of the wider generative AI landscape.

An affiliate content site (monetized by Amazon affiliate links) in the home improvement niche experienced a stark warning: one of their top articles “Best cordless drills 2024” saw traffic sink as SGE’s snapshot literally listed 3 drills with brief bullet points (sourced partly from their content, among others) and users had little reason to click their roundup. In SGE’s cited sources, bigger sites (like Wirecutter) were listed. This affiliate site realized they needed to differentiate. They updated their content to include more in-depth reviews, personal testing insights, and unique photos.

They also pivoted to more long-tail content like “Which cordless drill is best for DIY furniture building?” – something AI might not directly answer with a generic snippet. Their strategy is to target queries that require more nuance, and to make their roundup so comprehensive (with pros and cons, user comments, etc.) that a user would want to click to get the full picture. It’s a tough game – some affiliate sites may consolidate or shift focus to either become the authority (so AI picks them) or find topics AI answers can’t fully cover.

In conclusion, generative search is reshaping SEO and content marketing, but it’s not an apocalypse for those who adapt. The fundamental need – users seeking trustworthy information and solutions – remains. What’s changing is how the information is delivered and credited. Marketers who focus on earning that credit (being the cited, trusted source) and who think beyond the click (leveraging brand impact and multi-channel strategies) can still thrive. Indeed, those who move quickly to optimize for AI search may capture a competitive advantage, at least in the interim.

Generative AI will likely keep evolving – Google Gemini’s improvements might reduce hallucinations, increase the breadth of sources used, or even give more explicit citations (maybe even logos or snippet previews). There’s even talk of AI answers eventually containing embedded content like videos or interactive elements – imagine a future SGE that could play a 5-second clip from a source’s video within the answer.

Marketers should stay agile, keep user experience at the center, and treat the AI not as an enemy but as the new intermediary to please. In many ways, we are optimizing for an AI audience on behalf of the human audience. It’s a challenging new frontier, but as we’ve seen from the early cases, those who experiment and learn will find ways to turn these shifts into new forms of success.