The world of search has always been in constant evolution, but the need for tools, strategies, and teams to adapt stays the same. That being said, the AI era goes far beyond an algorithm update. This is a fundamental shift in how people discover information and engage with brands.
ChatGPT has since been joined by Perplexity, Google Gemini (formerly Bard), Microsoft Copilot, and Claude from Anthropic in the AI chatbot space. AI search has arrived and is making massive waves across industries. SEOs and businesses alike need to pivot.
This rapidly-emerging channel presents new challenges (and opportunities!) across visibility, brand recognition, and brand perception. Here’s a primer on AI search platforms, the shifts in user behavior, and the potential business impacts for brands.
What are the Leading AI-Powered Search Platforms?
Let’s lay the foundation: these are the key platforms that users are flocking to and their unique approaches to integrating search. While ChatGPT remains the overall traffic leader, it faces competition.
ChatGPT
Undoubtedly the most recognized name in the industry, ChatGPT captured public attention with the release of its GPT-3.5 model in 2022. It was a sensation, reaching over 100 million users within 2 months.
The platform has undergone a rapid transformation, with custom GPTs, implementation of more advanced models, DALL-E AI image generator, and more.
All users are now able to access ChatGPT Search (also called SearchGPT), forming a hybrid model that calls upon external web sources to bridge the time limitation gap of its training models.
Noteworthy: ChatGPT Search uses Microsoft Bing’s index to inform its responses. If your brand isn’t appearing in Bing results, it’s unlikely to in ChatGPT Search.
Perplexity AI
Perplexity is a conversational search engine, also known as an answer engine, that processes results with additional LLMs to generate conversational responses to user questions. Founded in 2022, the company has similarly experienced rapid growth in popularity—now forming 37% of referral traffic together with ChatGPT.
Perplexity places emphasis on e-commerce and product discovery, launching AI-powered shopping assistant, which allows users to purchase products directly within the Perplexity platform. Alongside, there are also reports of affiliate links being tested within results, a development that needs to be monitored by businesses.
Google Gemini
Gemini is the latest chatbot release from Google, being announced in February 2024 as a union of their previous Bard chatbot and Duet AI, a programming-focused generative AI. Gemini follows a similar approach to ChatGPT search, where current search results are used to supplement the models it was trained with.
Gemini traffic represents about 12% of the total generated by AI platforms according to one study by Previsible.
Gemini has since replaced Google Assistant and been embedded across the Google ecosystem, including the Workspace apps and its Android OS, making the uptake of users difficult to judge. With this in mind though, recent data indicates Gemini receiving less than 20% of the app downloads that ChatGPT receives.
Microsoft Copilot & Bing
Copilot was born out of the ongoing partnership between Microsoft and OpenAI, using a reworked version of OpenAI’s GPT-4 model. Therefore, there are many similarities in their results. This model was implemented into Bing, Microsoft’s proprietary search engine, under the moniker of Bing Chat, before being relaunched. Since then, Copilot has grown to represent 14% of LLM referral traffic.
Similar to Google’s approach to Gemini, Copilot has since been embedded as an AI assistant across the Microsoft ecosystem, providing support across diverse tasks other than search.
Claude
Anthropic’s current family of Claude 3 models released in March 2024: Haiku, Sonnet, and Opus, with Haiku being focused on speed, Sonnet the allrounder, and Opus built for deep reasoning. All the Claude 3 models are built on a training-first approach, meaning that they face limitations in terms of current events.
The flipside of this is a greater focus on privacy and safety. Anthropic terms the process Constitutional AI, an approach to training that seeks to ensure harmless AI systems that largely self-regulate without extensive human feedback. While Anthropic is niche within the market, Claude 3 Opus, set new industry benchmarks in many capabilities.
What are the Different Types of AI Search?
As touched on above, there are fundamental differences in the approaches that the different platforms take to generating responses. Comparisons aren’t necessarily as simple as ChatGPT vs Perplexity. Each presents advantages and disadvantages—as well as visibility implications.
Training-First (E.g. Claude)
These AI systems rely on the training data that’s been supplied to them. This means that they tend to deliver more accurate responses as they have a defined knowledge set and capabilities. The flipside to this is less flexibility, especially in relation to advancements or current events.
What does this mean for businesses? Likely less opportunity for e-commerce, as anything released after the current training model will not be presented in answers. Secondly, optimization takes time. Efforts will need to be spent on developing strong brand-led content, establishing industry expertise, and PR activities that create impact, to inform the following update.
Search-First (E.g. Perplexity)
Platforms that follow a search-first methodology follow a similar process to traditional search engines. These use AI to pull answers from across the web before an LLM contextualizes them into a single conversational answer, removing the need to scroll and select a result. Because of this, they’re flexible with the information that can be gathered, but often lack the same reasoning capabilities of other platforms.
With this in mind, search-first platforms are arguably the most brand-friendly as they provide more immediate feedback and need an approach that’s informed by SEO.
First ensure that content is structured in a LLM-friendly way, then experiment with prompts based on your keywords and identify common sources used. Then, update any content that you can influence.
Hybrid (E.g. ChatGPT and Gemini)
Hybrid models pivot between leveraging their training data and what’s termed retrieval-augmented generation (RAG). This is when an LLM uses an external data source to develop its response.
First you’ll need to identify which prompts use training data vs RAG. For prompts that use RAG, follow the search-first approach, then training-first for prompts that use training data.
How Popular is AI search?
AI tools in general continue to grow rapidly. It’s predicted that the total number of users of AI tools will exceed 241 million by 2030, almost doubling the current total. And large numbers users are now shifting from traditional search engines to AI search.
Studies indicate that 1 in 10 U.S. internet users now turn to generative AI first for online search, bypassing conventional search methods entirely. This trend is even more pronounced among consumers, with nearly 60% preferring AI-powered recommendations for product research.
AI search has already become a vital channel for brands and will only rapidly grow in importance.
What are the Key Trends in AI Search Platforms?
Rapid Improvements in Natural Language Processing (NLP)
OpenAI’s release of GPT-3.5 featuring ChatGPT caused an incredible stir for its leap in conversational awareness, gaining more than a million users in just five days. Since then, leading LLM-based platforms like Gemini, and Claude have all proven themselves to be incredibly capable in understanding conversational questions, identifying the context and intent the user has.
While the rate of LLM progression is potentially slowing—ChatGPT-5 codenamed “Orion” is facing delays, the rate of adoption is only ramping up.
Further Personalization and Predictive Capabilities
While AI platforms have always learned from user preferences and their conversation history, this is now going a step further. ChatGPT users are now able to add precise details about themselves to customize responses. The platform also allows its users to assign personality traits to any responses.
The outcome of this is incredibly precise tailoring of responses. But to appear accurately, brands need to ensure their content is targeted to the relative demographics—as well as the sources likely to be used to inform them. Context is quickly becoming a new competitive edge.
Predictive capabilities are also expanding. LLMs anticipate follow-up questions and proactively show related content, giving brands an opportunity to engage users at multiple touch points and develop their relationship.
What Does This Mean for the Search Landscape?
Growing Demand for Conversational Interactions
In line with the adoption of generative AI, preferences are also shifting towards conversational answers. Users increasingly expect search platforms to follow natural dialogue and offer accurate resolutions to their queries.
Conversational AI platforms create interactive and dialogue-based search experiences, allowing users to ask complex, context-rich questions and receive nuanced answers.
Most importantly, these responses and recommendations are trusted. Research from Statista shows that nearly two-thirds of people are open to buying what’s recommended to them by AI.
This evolution is moving search interactions away from keyword-based queries toward natural, intent-driven conversations. This shift rewards brands that structure content in Q&A formats, leverage FAQs, and address latent user needs.
Search Engines Continue to Integrate AI Features
Traditional search engines are also responding to these preferences. Two key names that appear when discussing generative AI platforms? Google and Microsoft. Therefore it should come as no surprise that their search engines are embedding AI-driven features.
To cater to shifting user behavior and mitigate their newest competitors, Google’s AI Overviews and Microsoft’s Copilot integrate generative AI directly into results pages, offering summary-based answers and suggested follow-up questions.
Their usage is rapidly growing, with Google AI Overviews already identified in 74% of problem solving searches.
“Zero-Click” Journeys Continue to Rise
The flipside of this: zero-click journeys are rising. After being observed as a growing trend in 2022, the generative AI boom and introduction of similar features into search engines has increased this trend. For Google, about 58.5% of searches in the US and 59.7% in Europe are ending without a click.
While referral traffic from AI search platforms is is growing, they’re largely designed to remove the previously needed click. By generating a complete answer, users now only need to click to check a source or gain further information directly from them. In many cases the learning data is all that’s required for a response and no sources are given.
For search engines, early research has found that an 18-64% decrease in organic traffic for some websites could be caused by AI Overviews.
AI Overview real estate also comes at the expense of the top results—and the source content may not even use them at all. Only 57% of sources used by Google’s Search Generative Experience (the precursor to AI Overviews) came from the first page of organic results.
To mitigate the visibility risk, brands must optimize content for “snippet-worthy” answers, emphasizing clarity, conciseness, and data-backed claims.
To maintain traffic, it’s recommended to optimize for intent as commercial searches are less likely to trigger an AI Overview.
Search Tactics Need to Evolve
Gartner’s prediction of a 25% decline in traditional search traffic may suggest that the era of search engines is fading, but the reality for SEO is more of an evolution. AI search optimization (also known as generative AI optimization or answer engine optimization) needs to be prioritized.
Pages optimized for keyword density or backlink volume often struggle to rank in AI search. LLMs prioritize content that directly answers user intent with clarity and depth.
To maintain visibility, businesses should investigate semantic SEO strategies, focusing on structured data, entity-based optimization, and comprehensive topic coverage.
Structured data is also emerging as a critical factor for AI-driven search. Implementing this is showing visibility boosts for both AI platforms and AI search features. Markup types like Product, FAQ, and HowTo help LLMs scan content for inclusion in summaries, while improper implementation can lead to misrepresentation or exclusion.
What are the Implications for Brands?
The Need for a Multi-Channel Approach
While SEO has always been a key part of multi-channel marketing but now search itself needs this same approach.
Search behavior is fragmenting between AI-powered search, search engines, visual or voice search, and social media—especially for Gen Z. Amongst younger searchers, Google sits in third place behind Instagram and TikTok.
Reddit is also emerging as a key player here, as its content structure is easy to process. Publishers like the Washington Post have been experimenting with AMA formats for their writers and Reddit already has licensing deals in place with both Google and ChatGPT.
Content optimization and brand marketing will be more important than ever. Brands need to be looking ahead and creating content for diverse platforms while maintaining a cohesive narrative across them.
For AI search in particular, Brand and PR efforts play a critical role. Only the strongest sources get featured or cited, so proving your brand’s expertise through robust, trustworthy content like news articles, whitepapers, and verified social media profiles, is a must.
Brand Narratives Face Challenges
AI search platforms construct answers from model data and external sources, which may not be brand controlled. Therefore for brands–especially global enterprises with multi-brand portfolios–this new search format presents significant challenges.
For example, when requesting recommendations for running shoes, none of the sources describing their products come from the brands themselves:
This makes it critical to know your key sources and the sentiment they’re covering your brand and products with.
For CMOs inaccurate representation is huge challenge. Recent surveys from Frontify listed 90% of CMOs declaring that protecting their brand is more important with AI, and 74% mentioning that a “fake brand partnership” would be their worst nightmare.
A problem brands face is lack of visibility into how they’re represented across AI platforms. While we know how the different models function, the platforms are intentionally opaque about the factors their models base ranking and sourcing on.
Without data or insights, it’s extremely difficult for brands to manage their brands in AI search and make meaningful, strategic decisions.
AI Search Opportunities Abound
As AI search evolves and grows in popularity, brands must monitor changes in their website rankings and traffic to adapt to shifts. Alongside this, understanding and tracking AI search sources and key queries is also critical.
But, as with any emerging technology, there’s a definite first-mover advantage here for brands. Fewer businesses are optimizing for AI platforms, which gives early adopters an enormous competitive edge in claiming the available visibility.
As LLMs are heavily trained on decoding user intent, when your brand appears, it’s almost certainly doing so in front of a potential customer.
Conclusion
AI search isn’t just another digital trend. It’s a massive shift in how users search for information, interact with brands, and ultimately make decisions. Platforms like ChatGPT, Perplexity, Gemini, and Claude are changing the game, shaping user behaviors, and demanding fresh approaches to visibility and brand protection.
They’re also opening up new channels for hyper-targeted discovery and meaningful engagement.
For businesses, the fundamental work of providing clear, trustworthy content and forging a strong brand story hasn’t changed. But the ways to deliver and optimize that content—across a growing landscape of AI-driven chat tools, social media, and evolving SERPs—have expanded drastically.
Adopting a multi-channel mindset, evolving your SEO with semantic and structured data, and building brand credibility through robust PR and authoritative content will be the keys to standing out.
AI-powered search opens doors to bigger audiences and deeper connection—if brands are ready to adapt, innovate, and lead the conversation.