The Evolution of SEO in the Age of AI: A Deep Dive into Google's Gemini and Bing's Copilot

John Walker

June 17, 2024

•

5 min read

For over twenty years, Google has been the go-to answer engine for most of us. Want to know something? “Google it.” But now Artificial Intelligence (AI) offers an answer engine that’s more efficient and more precise. Ask it a question and you get more than a list of links- you get a real answer, one that does not require any more clicking or searching. At least that’s the promise of AI that’s coming into being.

And as this emerges, it will have a profound effect on how marketers think about search marketing because use of traditional search engines will decline and there will be more “zero click” searches- one that don’t lead to website visits. Also, we don’t exactly know if or how we can optimize website content to increase visibility within the large language models (LLMs) that feed AI. So let’s look at this in detail.

‍

First, What does “Generative AI” Mean?

‍

Of course, ChatGPT is where I went for the answer to this question. Here’s what it says:

“Generative AI, often abbreviated as "GenAI," refers to a class of artificial intelligence systems designed to generate new content, often in the form of images, text, audio, or even videos. These systems operate by learning patterns and structures from large datasets and then generating new content based on that learned information.

The term "generative" emphasizes the capability of these AI systems to create novel content rather than simply recognizing or classifying existing data. Unlike traditional AI systems that are often used for tasks like classification, regression, or decision-making, generative AI focuses on creativity and the production of new content.

People may use the term "GenAI" rather than just "AI" to specifically highlight the generative aspect of the technology. This distinction helps to differentiate generative AI from other types of AI applications, such as those focused on natural language processing, computer vision, or reinforcement learning, which may not necessarily involve content generation.”

‍

Google's AI Tool is Called Gemini

‍

Google is rolling out its AI tool called Gemini. Gemini will be integrated into the Google search engine experience. Gemini uses advanced machine learning algorithms to understand user intent and context. Then it generates dynamic, AI-powered snippets that directly answer user queries, bypassing the traditional list of search results.

For example, when a user searches for "best restaurants in New York City," Gemini can generate a concise snippet that highlights top-rated eateries based on user reviews, ratings, and other relevant factors. This direct answer streamlines the user experience, providing instant access to actionable information without the need to sift through multiple search results.

Furthermore, Gemini's AI algorithms continuously learn and adapt to user behavior, refining search results over time to better meet user needs. By analyzing user interactions and feedback, Gemini can tailor search results to individual preferences, delivering personalized experiences that enhance user satisfaction and engagement.

‍

Bing's AI Tool is Called Copilot

‍

Bing's AI tool is called Copilot and it’s positioned as a collaborative search assistant. Copilot uses AI technologies to provide users with real-time guidance and assistance as they navigate the search process.

One of Copilot's key functionalities is its ability to generate contextually relevant search suggestions and recommendations based on user inputs and browsing history. For example, if a user is researching vacation destinations, Copilot can suggest relevant keywords, websites, and resources to help streamline the search process and facilitate informed decision-making.

Furthermore, Copilot incorporates advanced natural language understanding capabilities, allowing users to interact with the search engine in a conversational manner. Users can ask complex, multi-part questions, and Copilot will intelligently parse the query, extract relevant information, and provide accurate responses in a conversational format.

Copilot is also integrated with other Microsoft products and services, such as Office 365 and Microsoft Teams. Users can seamlessly access Copilot's search capabilities within these platforms.

‍

Gemini and Copilot’s Impact on SEO

‍

Traditional SEO is designed for search engines that are at their core, just lists of links. And traditional SEO is focused on optimizing for, primarily, the Google search algorithm. AI derives answers from large language models, not the search algorithm. This is a fundamental change in how search engine visibility will work.

And we are at an early stage in this change but it’s coming quickly and the changes it brings will be profound. For example, Gartner predicts that traditional search engine volume will fall by 25% in the next two years with AI chatbots taking the place of search engines for many searches.

So does this mean that marketers need to learn to optimize content for the large language models that power AI? Maybe. But at present we don’t know how exactly to do that or even if it will be an efficient use of marketing resources.

‍

How Marketers Should Adapt to the Rise of AI-Powered Search

‍

First marketers need to track and learn about the changes as they’re happening. I’ve started to test and use Gemini to see how I like it, and to try to understand how Google is balancing traditional search results (and their associated links) with answers provided by AI. I’m also looking at traditional SEO metrics to see if there are changes that might be attributed to changing search engines. Specifically I’m looking at organic traffic and keyword rankings.

Next, marketers should think about what would happen if organic traffic to their websites from search engines declines considerably. If that happened, how would the marketing plan need to be adjusted?

Lastly, marketers need to study and learn about how it may be possible to optimize content for large language models and can it be done efficiently. For example, can content be optimized for direct answers?

In light of Gartner's prediction, my LinkedIn feed is blowing up with marketers discussing these changes. And at this point in my blog post I’m probably supposed to say something about “embracing change” and “staying ahead of the curve,” right?

But what comes to mind instead is the wisdom of Clayton Christensen in his book “The Innovators Dilemma.” He explains how disruptive technology creates new markets- ones that may be very different than the markets they displace. He also talks about the need to study and understand the impact of disruptive technology before presuming to make any decisions about how to react to it. So that’s what I’m going to try to do.

Share this post