AI search engines are an evolution in search technology, shifting customers away from traditional search crawlers toward zero-click search experiences. Instead of indexing and displaying SERP content for customers to keep searching through (on sites like Google and Bing), AI search engines quickly research, analyze, and generate answers they share directly with customers (on platforms like ChatGPT and Perplexity).
AI search engine use and adoption are on the rise, accelerating customer engagement with voice assistants like Alexa and other types of agentic AI. This shift is transforming the search landscape from one of visibility into discoverability.
The differences between traditional search engine visibility and AI search engine discoverability
Think about the difference between Google Maps and Google Gemini:
In Google Maps, visibility is about being seen in a list of potential answers to a customer's query.
In Google Gemini, discoverability is about being surfaced as a trusted recommendation designed to meet a customer's expressed needs.
In traditional search engines, you gain visibility by using the right keywords. This approach helps you catch the algorithm's attention. Customers sort through the visible results, interpret what they see, and either engage or keep searching based on what's visible.
In AI search engines, discoverability occurs when artificial intelligence favors your brand and highlights your content as the answer to customer needs. The AI does all the sorting and interpreting, so your customer doesn't have to. Then, AI search engines generate clear, conversational, direct answers to customers. As a result, customers never need to click through for more information.
Think of traditional search engines like billboards. They point you toward information based on keyword matching. In contrast, AI search engines use artificial intelligence to operate like a trusted chauffeur. They quickly learn and adapt to your search needs and preferences so they can take you on a journey that aligns with your specific intentions.
That's why it's time to think about AI as a customer with its own preferences and needs.
How AI search engines and traditional search engines work differently
With AI search engines, search works like a conversation. Instead of searching for "adult orthodontia near me," customers using AI search engines like Microsoft CoPilot ask more personalized questions like, "Please recommend a local orthodontist who specializes in Invisalign for adults. Only surface orthodontists who are taking new patients. Must be open on Fridays and must take my Delta Dental."
Traditional SERP might bring up a Local Pack, some sponsored links, and a list of providers a customer has to evaluate. An AI search engine will curate a short list of one or two providers who meet the criteria communicated in the AI search prompt and summarize why those providers rise to the top. Trust is baked into the experience, so all customers have to do is call to set up an appointment, confirm insurance details, or ask their favorite AI agent to make the appointment for them.
AI search engines use structured data pulled and interpreted from a wide range of sources
AI search engines work hard to share trustworthy information with customers. They look beyond major directories and your brand's website when sourcing data and making recommendations. They also draw on content they find across smaller publishers, niche directories, and diverse sets of sources. Depending on what AI finds, AI search engines decide if your content is accurate, relevant, and appropriate to surface in response to customer intent.
AI search enginesrely on structured data sets to assess your content's accuracy, relevance, and value to customers. The more casual and contextual a search, the more a formally structured and interconnected data set will pay off.
How to make all AI search engines discover your brand
Whether your customers are searching for the best martinis and fries in NYC or looking for body-positive parenting advice, AI search engines pull their recommendations from billions of data points all at once. Then, they prioritize and share the data that's well-structured and interconnected (aka legible and understandable to AI). As both organic and direct traffic to websites decline and traffic migrates to AI search engines, marketers can (and, truly, must) adapt with a both/and strategy.
Yes, you should still optimize for Google search, including local search.
And yes, you should align your content strategy with your data strategy to beat competitors in AI search, too.
After all, winning in AI search isn't just about optimizing content. It's also about establishing trust with data integrity. That's why structuring data in a knowledge graph should be the priority for marketers looking for an edge in both traditional and AI search engine results.
Examples of the best AI search engines
Here are some examples of emerging AI search engines and the brands developing them:
Differences between OpenAI, ChatGPT, and ChatGPT Search
OpenAI is a research and development organization founded in 2015 with tech moguls Sam Altman and Elon Musk serving as co-chairs. Initially founded as a nonprofit, OpenAI shifted to a for-profit model in 2019. The tech firm conducts research in machine learning (ML), deep learning (DL), reinforcement learning, natural language processing (NLP), computer vision, robotics, and related fields.
ChatGPT and ChatGPT Search (formerly called SearchGPT) are both conversational AI tools from OpenAI, but they differ in how they access and provide information:
ChatGPT is a generative AI platform with fixed knowledge based on its latest training update. It's a helpful tool as a thought partner and content generation tool.
ChatGPT Search is an AI search engine that references and cites structured and unstructured data found on the web via third-party publishers. ChatGPT Search is an AI search engine that's helpful to customers who ask questions that need up-to-date, real-time answers.
OpenAI is also behind DALL-E, a text-to-image model that generates images from textual descriptions; Sora, a text-to-video model; and OpenAI Gym, an open-source toolkit for developing reinforcement learning algorithms.
Differences between Google AI, Google Search, Google Search AI, and Google Gemini AI
Google AI is the umbrella term for all of Google's AI initiatives, including Google Search, now called Google Search AI since it features AI Overviews, and Google Gemini AI.
Google Gemini, or Gemini for short, is the name Google has given to its LLM models (Nano, Pro, Ultra) powering AI features in Google Search, AI Overviews, and AI Mode. Google Gemini was initially launched as Bard, or Bard AI, in 2023. In 2024, Bard and Google's Duet AI relaunched as Gemini. Now, Gemini is also the name of the customer-facing AI assistant that operates as a standalone chatbot (Gemini app) and is integrated into Google Workspace (Gmail, Docs, Sheets, Slides, Meet, etc.).
Google Search AI, powered by Gemini, is the evolution of Google Search. Still called Google and Google Search, Google Search AI integrates AI Overviews and gives customers the opportunity to shift their search into AI Mode.
What's the difference between AI Overviews and AI Mode in Google Search?
Google AI Overviews are AI-generated summaries that appear at the top of search results.. They deliver quick summaries and static answers with links to the answers generated.
In contrast, Google AI Mode is an interactive search experience where customers can have a dynamic conversation with Gemini, where customers can shift from straightforward answers to deeper topical, conversational exploration to their questions.
Differences between Microsoft Bing AI and Copilot Search AI
Microsoft Bing is Microsoft's search engine, first launched in 2009. Since 2023, Bing has developed generative AI, powered by OpenAI's GPT-4 and Microsoft's proprietary Prometheus model. Bing now features Copilot as its conversational interface, providing advanced search, content generation, and real-time answers to customers who use it as an AI search engine.
Copilot is Microsoft's AI assistant. Available free to customers or with advanced features for enterprise brands, Copilot is accessible through Bing, Microsoft Edge, Windows, and across Microsoft 365 apps (Word, Excel, Teams, Outlook, etc.). Copilot offers customers a consistent AI-powered experience, whether they're using it as an AI search engine or producing content in their work ecosystem.
Differences between Perplexity AI and Claude AI
Perplexity AI is an AI search engine developed by Perplexity AI. Released in 2022, Perpexity is an alternative to Google, Microsoft, and ChatGPT's AI search engines. Perplexity is recognized for its high-quality, real-time, factual information; it always cites its sources for transparency. Customers turn to Perplexity for deep, generative research results and commercial-based queries where external, unbiased recommendations are important.
Claude AI is less of an AI search engine and more of a creative thought partner. Developed by Anthropic and released in 2023, Claude is lauded as an AI-driven conversationalist that prioritizes creativity and thinks through interactions with customers based on its ability to perform complex reasoning. Claude excels at summarizing complex content or technical topics. It's also strong at generating and explaining code. Customers also turn to Claude for support in creative writing projects and content development.
Structured data and knowledge graphs give you an edge in AI search engines
A brand's knowledge graph functions as a single source of truth inside and outside your organization. Knowledge graphs house complete, accurate, and context-rich data. Your brand can, and should, use it to publish content everywhere customers are searching (like Google Maps, Instagram, Alexa+). Likewise, knowledge graphs give you an advantage when you publish everywhere AI search engines (like ChatGPT, Apple Intelligence, and Claude) search, too.
As your offerings or brand information changes and as search evolves, your knowledge graph is ready to meet each moment. It has everything it needs to keep your brand flexible and relevant in the face of content changes, customer needs, and technological innovation.
Yext's solutions, like Knowledge Graph, Listings, and Scout, help brands prepare for our AI-driven future
Download this AI Search Engine Visibility Checklist to help make the shift from an SEO-only mindset to a search-everywhere strategy. You'll learn how to find out where your brand shows up — and where it doesn't; how to write content AI understands, how to use customer feedback to fuel your AI search strategy, and more.
Then, discover Yext Listings and Yext Knowledge Graph. With Yext Listings, your brand's information stays up-to-date and easily accessible everywhere customers and AI search engines look. Use Yext Knowledge Graph to structure your data and make it easier for both traditional and AI search engines to understand, rank, prioritize, and surface your brand no matter where your customers are searching.
Finally, try Yext Scout. Scout monitors your brand's presence and sentiment across AI search platforms like ChatGPT, Google Gemini, and Perplexity (along with traditional search engines like Google). At both the national and hyper-local levels, Scout benchmarks your search visibility against competitors, uncovers what's driving their success, and provides insights to help you take the lead in both AI and local search. You'll see how and where to make changes that boost your visibility and discoverability so customers and AI search engines find you, and find you first.