Why Your Brand Needs to Rank Inside ChatGPT, Perplexity, Grok, etc. & Not Just Google

Introduction: The New Era of AI-Driven Discovery

Consumers today are increasingly bypassing traditional search engine results. Instead of sifting through pages of Google links, they expect AI assistants to deliver definitive answers, personalized and immediate. This marks a fundamental shift in user discovery behavior. In fact, one in ten U.S. internet users now turns to generative AI first for online search.

ChatGPT’s explosive growth – surging to 400 million weekly users by early 2025 – underscores how rapidly people are embracing AI-driven Q&A platforms over classic search engines. Google itself has introduced AI-generated answers (SGE), which now appear in 16% of all Google desktop searches in the U.S., fundamentally changing how information is found online.

For brands, this means discovery is no longer confined to the familiar Search Engine Results Page (SERP). Instead, Large Language Model (LLM) “answer engines” like ChatGPT, Perplexity, Claude, Bard, and even emerging tools like Grok (X’s new AI) are becoming the new front doors for customers.

To remain visible when AI platforms synthesize “the one answer” from the web’s knowledge, brands must evolve from SEO to AEO – Answer Engine Optimization. In this new era, ranking on Google is not enough; you need to ensure your brand is the one these AI assistants mention when users ask questions in your domain.

The Shift from Search Engines to AI Answers

User behavior is undergoing its biggest change since the advent of mobile search. Instead of typing keywords into a search bar and scanning results, many now directly ask AI chatbots complex questions and get a single, curated answer. This shift is backed by data and trends:

• Generative AI as a Search Starting Point:

As noted, 10% of U.S. internet users are already starting their searches on AI platforms first. Tools like ChatGPT have become research companions, especially for younger and tech-savvy audiences.

• Skyrocketing AI Usage:

ChatGPT reached over 400 million weekly active users by February 2025, doubling from just a few months prior. It handles an estimated 37.5 million daily searches as a conversational search alternative. Other LLMs are also on the rise – by mid-2025, 34% of U.S. adults reported having used ChatGPT (roughly double the share in 2023). Analysts forecast 105 million U.S. adults will use generative AI in 2025. This explosive adoption signals that AI-driven Q&A is moving mainstream.

• Integrated AI in Traditional Search:

Rather than cede ground, Google and Bing have woven AI answers into their ecosystems. Google’s Search Generative Experience (SGE) now provides AI summaries on top of results, and Google’s own CEO has stated AI-driven “Search Mode” is the future of search. When Google’s AI Overview appears, users often get their answer without clicking any result, leading to a steep decline in clicks.

Across industries, click-through rates drop by ~15% on average when an AI summary is present. Non-branded queries see the biggest drops (nearly -20% CTR) as users find what they need in the AI text. In fact, over 58% of all searches now end in zero-clicks (no user clicking through to any website) – a trend amplified by AI answers that satisfy intent immediately.

The implications are profound: customers are getting answers without ever visiting a website. The AI becomes the intermediary, synthesizing information from multiple sources into one authoritative response.

If your brand isn’t part of that synthesized answer, you simply don’t exist in that customer’s decision journey. Conversely, if your brand is featured by the AI, you gain an instant credibility boost as part of “the answer,” which can dramatically influence perception and choice.

AI Answer Platforms: A New Ecosystem for Brand Visibility

Just as Google dominated traditional search, a new ecosystem of AI answer engines is now vying for users’ questions. Brand leaders and marketers should be aware of the major players where their visibility matters:

• ChatGPT (OpenAI):

The pioneer of the AI Q&A wave, known for its detailed conversational answers. ChatGPT handles millions of queries daily and now even includes features like suggested web links. Users often begin research on ChatGPT before moving to other tools. It’s fast becoming a default “first stop” for questions ranging from trivial to business-critical.

• Perplexity AI:

An LLM-powered answer engine built explicitly for search-like interactions, Perplexity offers up-to-date information with cited sources. It’s popular among users who want current answers with references – for example, getting a concise answer and seeing which article or site it came from.

Notably, Perplexity drives 6–10× higher click-through rates than ChatGPT in some analyses, and brands report conversion rates of 20–30% from Perplexity traffic on high-intent pages. The volume may be lower, but the quality of traffic is often superior, as these users are highly engaged and trust the cited sources.

• Claude (Anthropic):

An AI assistant known for advanced reasoning, often used by professionals for deeper analysis. Claude may not have the name recognition of ChatGPT, but it’s carving out a niche for complex problem-solving queries (e.g. in coding, research, or business strategy). Brands targeting B2B or technical audiences should keep an eye on Claude’s outputs.

• Google’s AI (Bard and Gemini):

Google has its Bard AI (and forthcoming Gemini model), which are being integrated across Search and the Google product suite. Google’s AI Mode can act as a conversational chatbot right on the search page, while AI Overviews summarize search results.

These platforms blend into the traditional search experience – meaning if you optimize for Google’s AI answers, you’re effectively optimizing for a significant portion of Google’s user base. Google’s reach is massive, so appearing in its AI answers can be as valuable as a page-one organic ranking.

• Bing Chat / Microsoft Copilot:

Microsoft’s OpenAI-powered assistant is integrated into Bing search, Windows, and Office apps. With billions of Windows users and Office users, Microsoft is weaving generative answers into everyday workflows.

For instance, someone might ask Bing Chat (via the Edge sidebar or Windows Copilot) for product recommendations or research while never opening a browser. Ensuring your brand is mentioned in these contexts is part of the new challenge.

• Emerging Players (Apple, Meta, and X’s Grok):

The AI search arena is expanding. Apple is reportedly developing AI search capabilities to integrate across iOS/macOS (leveraging its huge user base). Meta (Facebook) is building AI search tools for social and e-commerce discovery.

And Grok, from Elon Musk’s X (Twitter), aims to fuse real-time social content with AI answers. These might still be nascent, but they indicate where the trend is headed: nearly every major platform is infusing AI Q&A into their user experience. Brands will need to monitor a broad array of AI channels, not just one or two, to maintain comprehensive visibility.

Bottom line:

The search landscape is fragmenting into a multi-platform AI ecosystem. Users could ask any of these AIs for advice or answers. If your brand is absent on one of these platforms, it’s a blind spot in your customer acquisition funnel.

Just as companies had to ensure their SEO worked on Google, Bing, Yahoo, etc., now you must ensure your “Answer Engine Optimization” covers ChatGPT, Perplexity, Claude, Bard, Grok and beyond. Each may have distinct audiences and citation behaviors, but all share one common trait: they’ll mention only a select few brands in response to any given query.

How LLMs Choose Which Brands to Mention

The obvious question for marketers is: How do these AI engines decide what to say – and which brands to feature – when answering a question? The process is very different from traditional search rankings:

1. Pre-trained Knowledge (Historical Web Data):

Many LLMs, like GPT-4 (which powers ChatGPT), were trained on vast swathes of the internet. This includes sources such as Common Crawl, Wikipedia, Reddit, books, forums, news, etc.. For example, an estimated 62% of GPT-3’s training data came from Common Crawl (a web snapshot). These models have “read” millions of articles, posts, and documents.

So if your brand has been widely written about or discussed on public sites prior to the model’s cut-off date, the AI may already know of your brand and what it’s associated with. Established brands or those that have generated significant content (or chatter) have an advantage here. However, if your brand is newer or niche, it might not exist in the model’s memory unless it appeared in popular public datasets.

2. Retrieval-Augmented Generation (Real-Time Search):

Modern answer engines don’t rely solely on static training data. They often use live web search to fetch up-to-date information, which the AI then summarizes – a technique called RAG (Retrieval-Augmented Generation). For instance, when you ask ChatGPT (with browsing enabled) or Bing Chat a question like “What’s the best project management software for 2025?”, the system will perform a fresh search query in the background.

It might find recent articles or reviews (e.g. a “Top 10 Project Management Tools 2025” blog post) and then synthesize an answer listing the tools from those sources. In other words, the AI often picks brands straight from the top search results or from highly relevant content it finds at query time. This real-time capability means that even if a brand wasn’t in the training data, strong current content can get it mentioned.

3. Source Preferences of the AI:

Not all content on the web is treated equally. Each AI platform has its own biases or preferences in the sources it cites. Recent research shows user-generated content platforms dominate AI citations across the board. For example, ChatGPT’s answers tend to pull heavily from Wikipedia (nearly 48% of its citations) and forums like Reddit.

Google’s AI summaries lean on community Q&A and videos – e.g. Reddit (21%), YouTube (19%), Quora (14%), LinkedIn (13%) for Google’s SGE. Perplexity — which is designed to always cite sources — has an even stronger bias toward Reddit, with almost half of its citations coming from Reddit threads (see Table 1).

Table 1. Top Sources Frequently Cited by AI Answer Engines

As the table above shows, AI bots love human-like content. Community discussions, Q&A sites, and knowledge bases (like Wikipedia) often outrank corporate or marketing content in the AI’s eyes. The reasoning is simple: these AIs are trained to sound conversational and comprehensive, so they gravitate toward sources that already have a conversational tone or aggregate broad perspectives.

For brands, this means your content strategy for AEO must extend beyond your own website. Are people talking about your product on Reddit or Quora? Is your company listed on Wikipedia or cited in popular articles? If the consensus of the internet (as captured in these sources) points to your brand for a certain topic, the AI is more likely to mention you. Conversely, even the best product might be omitted if it’s absent from the AI’s favorite sources.

4. Content Format and Structure:

Beyond where the AI gets information, how information is presented affects whether it gets included in an answer. LLMs don’t actually “think” – they assemble answers by extracting and recombining text. Thus, they prefer content that’s easy to digest and quote. A recent analysis of 177 million AI citations found that “listicles” (list-style articles) make up 32% of all cited content – far more than any other format (the next most-cited format is generic blog articles at ~10%).

The takeaway: well-structured, scannable content wins. An AI would rather grab a neatly formatted “Top 5 Reasons” list or a comparison table from one page than have to pull bits and pieces from multiple disorganized pages. For brands, creating authoritative list posts, comparison guides, or Q&A-style content can boost the likelihood of being the chosen source.

Similarly, adding the current year in your content titles (e.g. “Best CRM Software in 2025”) can increase citation chances, because many AI tools favor recent, up-to-date info. Technical details matter too – using clear HTML structure, proper headings, and even new meta tags like llms.txt to guide AI crawlers can improve your content’s accessibility to AI models.

5. Brand Authority and Sentiment:

Finally, if an AI is going to recommend a brand as part of an answer (say, “the best accounting software for small businesses”), it will favor brands that feel authoritative and well-regarded in the data it has. This isn’t a precise science, but signals like positive mentions, expert reviews, high ratings, or being frequently recommended by users all feed into the AI’s training.

If your brand has strong reviews on G2 or Gartner, or is often praised in forums, those signals increase the chance an AI deems it worth mentioning. On the flip side, if your brand is controversial or low-profile, the AI might exclude it to err on the side of caution or relevance.

In essence, AIs mirror the wisdom of crowds – they pick up on which brands people collectively consider top-tier for a given query, and those are the ones that surface in answers.

Understanding these mechanics helps inform your strategy. To get mentioned by an AI, you must feed the ecosystems that feed the AI: contribute knowledge to Wikipedia, encourage user reviews and discussions, publish content that is easily “chunkable” (like lists and FAQs), and keep that content fresh.

In traditional SEO you fought for a spot on page 1; in AEO you’re fighting to be included in the one answer that the AI delivers. The criteria overlap with SEO (quality, relevance, authority) but the context of delivery is very different.

SEO vs. AEO: From Blue Links to Being the Answer

It’s important to clarify that Answer Engine Optimization (AEO) isn’t about abandoning SEO best practices – it’s about extending them to a new arena. Think of AEO as the next evolution of SEO in an AI-driven world. Let’s break down the key differences and overlaps between traditional SEO and this new “answer optimization”:

• Objective:

SEO aims to improve a website’s ranking on search engine results pages (SERPs) for certain keywords, ultimately to drive clicks to your site. Success is measured in impressions, click-through rates, and traffic from those rankings.

AEO, in contrast, aims to secure your brand a mention or citation in AI-generated answers. The goal is brand visibility within the answer itself, even if the user never clicks through to any site. Success is measured in share-of-voice: how often does your brand appear when an AI answers questions in your category?

• User Experience:

In traditional search, a user sees a list of 10 blue links (plus ads, snippets, etc.) and has to choose one or more to click. The burden is on the user to compare and gather info from multiple sites.

In an AI answer, the user asks one question and gets a single synthesized response (perhaps with a couple of source links or citations). There’s often no need to click at all – the answer is the end-point. As a result, being the top organic result on Google matters less if the AI answer above it already satisfies the query. If your brand isn’t in that answer, many users won’t scroll further.

• Content and Optimization Focus:

SEO has taught us to focus on keywords, meta tags, backlinks, mobile-friendly pages, and satisfying the E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) principles for content. Those remain foundational for AEO, but the focus shifts from just ranking a page to having digestible, authoritative content that the AI can easily ingest and reuse.

Lengthy articles behind paywalls or heavy scripts might rank on Google, but an AI crawler might ignore them. AEO puts emphasis on things like: concise, fact-rich paragraphs that directly answer questions; structured data and schema (so AI can identify key facts or lists on your page); and multi-platform presence (because an AI might “know” about you from a news site or forum, not just your site).

• Metrics of Success:

SEO metrics include your Google rank for target keywords, organic traffic volume, and conversion rate from that traffic. You try to improve, say, your rank from #5 to #1 to get more clicks.

AEO metrics are more about brand mention share and citation frequency. For example, if out of 100 AI responses about “best project management tools,” your brand appears in 20 of them, that’s a 20% share of voice. There is no first page or second page – it’s binary (mentioned or not) within a given answer. Another key metric is referral traffic from AI sources (when AIs do provide clickable citations).

As AI platforms begin to drive visits, tracking those is crucial. Interestingly, the traffic you do get from AI answers can be highly qualified. Users who click a link in an AI-generated answer have essentially been pre-sold on your brand by the AI’s endorsement. This likely contributes to why LLM-sourced traffic converts at higher rates than traditional search traffic (as seen in the earlier examples).

• Stability vs. Volatility:

SEO rankings, while competitive, tend to be relatively stable on a day-to-day basis once established (barring algorithm updates). If you’re the #1 result today, your content will likely hold that spot until a competitor or Google’s algorithm displaces it, which can take weeks or months.

AI answer visibility is far more volatile. Because AI outputs can change based on slight prompt differences or new content being indexed, your brand might be cited one day and gone the next. In fact, month-to-month changes in AI citations are very high – one study found over 50% of the sources cited by ChatGPT and Google’s AI changed from one month to the next.

In Google’s AI overviews, for instance, only ~40% of citations remained the same month-over-month. This volatility means continuous optimization and monitoring are needed; you can’t just get to “position 1” and stay there. It’s more like a flowing conversation where your brand needs to keep being part of the dialogue.

In short, SEO and AEO overlap in their foundation (great content, technical soundness, authority building), but they diverge in execution. AEO is an expansion, not a replacement – the next chapter in search marketing. You still need to do keyword research, on-page SEO, link earning, etc., because those practices help your content be discoverable by both search engines and answer engines.

But AEO adds new dimensions: optimizing for zero-click visibility, ensuring your brand and content are present in the feedstock that AI systems consume, and measuring success in terms of AI-driven exposure rather than just website visits.

One practical example:

an SEO-focused content strategy might prioritize a long-form blog that targets a high-volume keyword, aiming to rank and get clicks. An AEO-focused strategy might take that same topic and also produce a succinct, FAQ-style summary or a list of key points, which is then cross-posted on your site, LinkedIn, Medium, etc., to maximize its reach and chances of being picked up by an AI.

You’d also ensure the content is frequently updated (since AI citations skew very fresh – most occur within 2–3 days of content publishing). In essence, you’re structuring your knowledge to be AI-ready.

The Case for AEO and GEO: Adapting to an Answer-First World

We’ve established the why and how of AI brand visibility – now let’s underline the urgency and opportunity. Many experts are dubbing this shift as the rise of Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) – different names for the same challenge of optimizing for AI-driven answer platforms.

Here’s why investing in AEO/GEO should be a top priority for brands and marketing teams going forward:

• User Behavior Has Tipped:

The data points we’ve discussed paint a clear picture – AI-driven search is not a niche behavior; it’s accelerating into the mainstream. Over 100 million people will use generative AI for search this year, and millions now use it daily for everything from shopping advice to tech support.

This is “the most significant change in search behavior since mobile”. Just as businesses a decade ago had to adjust to mobile search (ensuring websites were mobile-friendly, for example), today they must adjust to AI search or risk irrelevance.

• Competitive Advantage for Early Adopters:

Whenever there’s a big platform shift, those who adapt early reap outsized rewards. We’re seeing this already in AI search. Early adopters of AEO are dominating AI answers across major industries. For example, Profound’s June 2025 visibility data shows Bank of America commands 32.2% of all banking-related mentions across AI platforms.

In healthcare, Mayo Clinic was cited in 14% of health AI answers, and in retail Amazon appears in well over half of retail-related AI answers. These companies have effectively become the “default answers” for their domains.

On the flip side, brands that have been slow to engage with AEO are watching competitors claim those coveted AI mentions, potentially siphoning away future customers. As Amsive’s SEO leaders observed, “brands focused only on traditional search are watching market share evaporate as competitors gain AI visibility – becoming the trusted source that’s difficult to displace”.

• Opportunities for Challenger Brands:

Interestingly, AI answers can level the playing field in certain cases. Since LLMs strive to provide comprehensive answers, they might include an up-and-coming brand if it has some notable buzz or unique offering, even if that brand lacks top Google rankings.

We’ve seen how smaller financial brands like Navy Federal Credit Union or Upstart have punched above their weight in AI-generated answers – gaining a share of voice that would have been hard to achieve via traditional SEO alone. This means AEO isn’t just a defensive play for incumbents; it’s also an offensive opportunity for disruptors.

If you can establish your brand as an authority in the content AI models consume (through thought leadership, data insights, community engagement, etc.), you could skip years of climbing the SEO ladder and directly enter the conversation via AI answers. As one analysis put it, “Smaller brands have new opportunities to gain share of voice. They can be disproportionately represented in LLM answers, achieving consideration where they previously struggled in traditional marketing.”

• Higher Quality Traffic & Conversions:

Not only can AEO boost visibility, it can drive extremely valuable traffic. As mentioned, when an AI does refer a user to your site, that user comes primed with information – they’ve essentially been pre-qualified. It’s like having a knowledgeable assistant whispering in the customer’s ear, “This brand is a good choice.”

The results are striking: an insurance website saw its conversion rate jump to 3.76% for AI-sourced visitors vs. 1.19% from organic search, and an e-commerce site saw 5.53% vs 3.7% from organic. In both cases, AI traffic converted about 3× better than traditional SEO traffic.

The volume of AI-driven visits may be smaller right now, but their impact per visitor is often greater. As AI adoption grows, this could translate to higher ROI on content that is optimized for AI visibility.

• Zero-Click Branding and Trust:

In many AI interactions, the user might never see a link or source – they just hear your brand name as part of an answer or see it mentioned in passing. This is a new kind of “zero-click branding.” Even without a click, an AI’s mention can plant a seed in the user’s mind. For example, if someone asks an AI, “What project management tools should I consider?” and the answer says, “Tool A, Tool B, and YourBrand are top options for small teams…”, that user now has awareness of YourBrand even if they didn’t visit your site (and perhaps they’ll ask the AI more about it, or go search it later).

Being woven into the answer makes your brand part of the narrative early, rather than hoping the user finds you in a list of links. It’s akin to being featured in a respected publication or recommended by an expert – the AI is the expert voice and it just recommended you. This confers trust and authority by association.

• Strategic Imperative – Don’t Get Left Out:

There is a sense of a land grab happening in the AI answer space. Brands that act now can become the de facto examples or recommendations that AIs keep repeating, because they’ll be entrenched in the training data and in the feedback loop of what works. Those that delay will face “increasingly expensive catch-up requirements,” as competitors will have by then solidified their positions as the go-to answers.

Future AI systems might heavily rely on established AI training data – meaning if, for the next year, every AI is citing your competitor as the top solution, that narrative could get baked into models and be very hard to dislodge.

As one industry commentator warned, “Brands face a narrow window to establish the authoritative positions that AI engines will consistently cite… early adopters capture dominant market share in AI responses, while late adopters will find themselves competing for increasingly scarce citation opportunities at significantly higher costs.”. In other words, the AI train is leaving the station – it’s time to get on board or risk missing it.

Given all of the above, making a strong case for AEO/GEO is not difficult. It’s about safeguarding your brand’s discoverability in a future where asking “AlexaGPT” or “ChatGPT” or “Grok” becomes as common as Googling. It’s about not ceding the narrative about your brand to whatever the AI happens to scrape up. Instead, you actively shape and feed that narrative with intentional content and optimization strategies.

Monitoring and Mastering Your Brand’s AI Visibility (Introducing Thirdeye)

If the rise of AI answer engines is the challenge, then data and insights are the solution. In SEO, teams have long relied on tools to track their search rankings, keywords, and traffic. Similarly, in the world of AEO, tracking your brand’s presence across various AI platforms is critical.

You need to know: Are we being mentioned? In response to what questions? By which AI? How often? And how do we stack up against competitors? Given the dynamic, fast-changing nature of AI answers, monitoring this manually is impractical. This is where tools like Thirdeye come into play – purpose-built to provide “AI-search insights for performance-oriented marketing teams.”

Thirdeye is an AI visibility platform that acts as a “third eye” on all the places your brand might appear in AI-generated content. It is designed to find out how your brand performs in AI results and citations across ChatGPT, Google’s Gemini/Bard, Claude, Perplexity, and other AI tools. In essence, it’s like a rank tracker, but for AI answers rather than search engine pages. Here’s how a solution like Thirdeye helps brand and SEO teams stay ahead:

• Unified Dashboard for Multiple AI Models:

Instead of juggling each AI platform separately, Thirdeye aggregates data from all the key AI engines in one place. Whether it’s ChatGPT mentions, citations in Perplexity, or your appearance in Google’s AI overview, you can see it all side by side. This comprehensive view is crucial because your performance can vary dramatically by platform.

You might be a top mention on Bing Chat but absent on Claude – information you need in order to act. Thirdeye continuously monitors platforms like ChatGPT, Claude, Gemini (Google), Perplexity, Bing Copilot, and more, giving you a broad “AI share of voice” report.

• Mentions, Sentiment, and Context Analysis:

It’s not just about if you’re mentioned, but how. Thirdeye tracks every mention of your brand and even analyzes the sentiment or context around it. Was your product mentioned positively as a top recommendation, or negatively as an example of a problem? What related keywords or questions tend to bring up your brand? This qualitative insight helps you understand your brand’s positioning in the AI narrative and can inform PR or content efforts to improve sentiment.

• Dynamic Prompt and Q&A Tracking:

A unique challenge of AEO is figuring out which user questions you want to be the answer to. Thirdeye allows users to find and create dynamic prompts to test where their brand ranks or appears. For example, you could simulate asking “What’s the best VPN for privacy?” across various AI models and see if/where your VPN product is mentioned.

If you’re absent, that’s a gap to address; if you’re present but low in a list, that’s something to improve. Essentially, this feature turns the opaque AI answers into something you can probe and measure. It answers the question: for what kinds of questions does the AI think of my brand, and where do I stand among competitors in those answers?

• Competitive Benchmarking:

Thirdeye doesn’t just track your brand – it also lets you monitor your competitors’ mentions and compare share of voice. This is vital for understanding the landscape. If a competitor is consistently being recommended by AI assistants and you are not, you have a strategy issue.

Thirdeye’s competitor analysis can highlight such gaps and opportunities, showing where a rival is getting traction in AI responses that you’re missing. It effectively brings the concept of “SEO competitive analysis” into the AI answer realm, identifying which competitors are leading in AI citations and why.

• Real-Time Alerts and Trend Monitoring:

Given the volatility of AI answers, real-time awareness is key. Thirdeye provides instant alerts (via email, Slack, etc.) whenever your brand appears in AI citations. For instance, if a new answer or a viral prompt suddenly starts mentioning your brand (or perhaps misrepresenting it), you get notified. You can also set up custom triggers – say, alert me if my brand falls out of the top 3 suggestions for “best project management tool” in any AI.

This immediacy allows you to react quickly, either by amplifying content, addressing misinformation, or doubling down on a trending opportunity. The platform uses smart throttling to ensure alerts are meaningful and can maintain an audit trail of changes, so you can track patterns over time.

• Insights to Drive Strategy:

The data Thirdeye (and similar tools) provides can inform a proactive AEO strategy. For example, if you notice that AI X never cites your website but often cites a specific Reddit thread about your product, you might decide to officially participate in that thread or create a similar Q&A on your own forum that the AI can pick up. If AI Y is frequently citing a competitor’s whitepaper, you might prioritize creating an even better, more up-to-date resource on that topic.

Essentially, you gain visibility into the “black box” of AI recommendations, allowing you to take targeted actions. Thirdeye even highlights the sources of your domain mentions and the types of content being cited – e.g., are AIs referencing your blog, your docs, third-party reviews, etc. – which helps you focus your optimization on the most influential content.

All these capabilities make a platform like Thirdeye an indispensable ally for Generative Engine Optimization efforts. It’s about bringing the same rigor and performance tracking to AI that we’ve long had for traditional SEO. As the Thirdeye team puts it, “As ChatGPT, Claude, and Gemini become the new homepage, brands must optimize for visibility across LLMs.

That’s where Thirdeye comes in – powering the next wave of AI Engine Optimization.” With the AI search market projected to explode from $43.6 billion in 2025 to $108.9 billion by 2032, investing in such tools and strategies is not just forward-thinking – it’s necessary for staying competitive.

Importantly, tracking and optimizing for AI answers does not mean abandoning SEO or other channels. It means adding a new layer to your digital strategy. Thirdeye’s approach of integrating AI visibility with performance marketing analytics acknowledges that modern marketing teams need a holistic view: how SEO, AEO, and other channels interplay.

For example, you might find a situation where your SEO traffic is dipping for a keyword because Google’s AI overview is stealing clicks – but Thirdeye data shows that your site is cited in that overview. You could then work on making that citation more compelling, or ensure your branding is strong so that even a zero-click search still leaves an impression on the user (since they saw your name in the AI blurb).

In summary, Answer Engine Optimization is here, and it’s here to stay. Brands that take it seriously will not only safeguard their current search equity but also unlock new growth from emerging AI channels. It’s a rare chance to get ahead of a paradigm shift.

By combining smart AEO content tactics (structured answers, multi-platform presence, schema optimization, etc.) with intelligent monitoring via tools like Thirdeye, you can continuously refine your approach. You’ll know exactly where you stand in the AI answersphere and what to do to become the trusted brand that these engines cite.

Conclusion: Becoming the Answer (Not the Afterthought)

The writing is on the wall: AI-driven answers are rapidly becoming a default way people find information. Whether it’s an executive asking ChatGPT for a software recommendation, a student consulting an AI tutor, or a consumer using a voice assistant to decide on a purchase, the pattern is the same – one question, one answer (with a handful of sources behind it).

In this environment, brands face a stark choice: adapt and become part of the answer, or remain invisible. As one industry report put it, “The strategic question is no longer whether AI search will impact your business, it’s whether your organization will be positioned as the trusted authority when consumers turn to AI engines for answers – or whether you’ll be absent from the conversation entirely.”

To ensure your brand isn’t absent, start treating these AI platforms with the same importance you’ve given Google over the past two decades. Leverage Answer Engine Optimization (AEO) techniques to make your content easy for AI to find, understand, and trust.

Cultivate your brand’s presence on the sites that feed the AI brain (community forums, reputable third-party sources, knowledge bases). Embrace Generative Engine Optimization (GEO) as a natural extension of SEO – an evolution that accounts for how algorithms now synthesize information, not just index it.

Equally important, equip your team with the tools and metrics for this new landscape. If you can’t measure it, you can’t improve it. That’s why platforms like Thirdeye are becoming the secret weapon for forward-thinking marketing teams: they shine light on where you stand in AI-driven results and how to boost that presence. Tracking share of voice in AI answers, monitoring sentiment, and getting alerted to changes give you the agility to stay on top of the fast-moving AI tide.

We are witnessing a transformative moment reminiscent of the early SEO era or the mobile revolution – except this time, the change is happening at AI speed. Brands that recognize the opportunity can leapfrog competitors by securing their place in the “answer graph” that will define tomorrow’s customer journeys. Those that ignore it may find their hard-won Google rankings losing relevance as more queries never even make it to a traditional SERP.

Your brand has spent years earning trust, building content, and climbing search rankings. Now is the time to reuse and reimagine those strengths for AI. Ensure that when someone asks an AI about your industry or product category, your name is on the tip of its tongue. With a robust AEO strategy and the right analytics, you can make your brand ubiquitous in the AI-generated answers that millions will rely on. The future of discovery is unfolding now – and it’s demanding a new kind of optimization. Embrace it, and make your brand not just searchable, but answerable.