Generative AI in Search: What is it About?

June 11, 2024

The expansion of artificial intelligence worldwide has been rising in popularity lately, merging into different features and places. ChatGPT has potentially boosted this fame, reaching even more people who use it daily for their basic tasks or work.

Among these potential uses, artificial intelligence can be used in search engines, influencing and changing how traditional searches work. Google’s search bar has become more familiar with these changes, synthesizing data and delivering proactive feedback.

Exploring the impact of generative AI in search is vital to understanding how these modifications will shape searches. This will also affect real-world applications and various industries, adapting to new technologies.

What is Generative AI?

Generative AI can be defined as the process of artificial intelligence that refers to deep-learning models that can generate high-quality text, images, or videos (as well as other content) only based on data provided or trained on.

The applications for this technology seem to grow every day. Some people use it to write high-quality software code faster, learn about new topics, train new trustworthy conversational chatbots for business, and more.

It refers to a deep-learning model that takes raw data (from Wikipedia, for example) and learns from it to generate probable outputs when prompted. At a high level, this allows for creating new work similar to the trained data but not identical.

What’s the Difference Between Generative AI and Traditional Search?

One huge difference that separates generative AI from traditional search is how it relies on search queries. 

The traditional method requires the user to type keyword-based queries, especially since sites are designed to emphasize certain words when searching.

While this alternative has worked for a long time, it’s not 100% accurate and has its own weaknesses. Keyword-based searches tend to flood users with irrelevant information at some point, which is what generative AI searches are trying to fix.

Gen AI can understand natural language and its context, delivering a more accurate and relevant search result than its traditional counterpart. It can also analyze previous searches and narrow results based on them, providing even more context and accuracy.

These proactive features make Gen AI way more superior than traditional search due to a couple of things. Such a proactive method delivers better results to user experience, helping people save time since they’d be quickly getting the most relevant information needed.

How Generative AI Will Change Google Searches

Search engines, especially Google, haven’t ignored the current trend of employing AI for various uses. The new updates address this technology, bringing changes to the SERP and its interface.

Google search will expand bigger than ever, building a knowledge base of billions of facts about people, things, and places that can provide users with information about what they need in seconds.

From researching to brainstorming, generative AI in search will be able to reduce the work users will have to do, greatly increasing efficiency and high-quality results.

Answering Complex Questions

Google’s SERPs will be greatly improved and will be able to show responses for more complex questions more efficiently with generative AI. Gemini’s new model has multi-reasoning capabilities, increasing its help with long-tail search queries.

This functionality is requested and appreciated by users because it saves them precious time, additionally guaranteeing they get correct and specified answers based on Google’s vast database.

For example, a question like “What are the economic impacts of weather in coastal towns, and what mitigation techniques are only?” can now be answered with an in-depth, synthesized reaction that considers numerous statistical factors and views in real time.

Better Search Planning

Generative AI enhances search planning by predicting Internet users’ needs. This allows it to proactively suggest associated subjects and questions.

Once AI can experience the purpose behind a user’s initial search, it can offer a structured direction for exploration, offering a logical path towards the required information.

This guided technique allows customers to delve deeper into topics, uncovering layers of information that they wouldn’t have considered otherwise.

For instance, a user searching for “pleasant sustainable farming practices” may also obtain related topics regarding the “impact of sustainable farming on biodiversity” or “economic advantages of sustainable agriculture,” broadening their expertise.

Exploring AI-Organized Pages

AI-organized pages constitute a massive advancement in information retrieval. This year, Google will leverage generative AI to curate and prepare search effects into cohesive, smooth-to-navigate pages.

These pages will contain institution-related content, spotlight key points, and present facts in a logical order, similar to studying a nicely prepared article or document.

This feature is so special because it removes the need to go through numerous search results, offering a streamlined experience. This applies to historical activities, scientific phenomena, or product comparisons.

These features are currently being implemented for users in the United States. Users will start to see AI-organized search result pages when looking for inspiration, such as dining recipes, movies, TV series, hotels, shopping, and more.

Getting AI Video Help

Integrating AI isn’t only useful for typing into a search query. It has many other benefits, such as being used as video content material. This is one of the most important transformative improvements for Google searches in 2024.

Users can now access personalized video responses to their queries, with AI generating and compiling video content that immediately addresses their questions.

This should contain videos from current photos, combine relevant clips, and even produce new content with virtual presenters.

If a user asks, “How do I repair a leaky tap?” They’ll be highly likely to be provided with a tailored video manual that combines the best strategies from diverse assets, providing a step-by-step visible tutorial that will help them improve their academic knowledge.

How is it Going to Be Implemented?

Currently, generative AI in search is still in a very experimental phase, which means it’s unavailable to anyone. Its ongoing testing is focused on improving accuracy using Google’s Gemini software, which is not yet fully implemented.

These features are currently being tested in the United States. Meanwhile, Google has also continued its development in the United Kingdom. Google’s AI overviews are slowly appearing in search queries for logged-in users from Britain.

Studies and research say that the demand for these features is very high, with a growing interest from users to try them out. The upcoming implementation of these tools means many changes will be added, especially in how SEO works. Implementations will affect:

  • SERP layouts: the introduction of generative AI in search queries means that organic search results may be pushed down. This can greatly impact SEO, potentially leading sites to get fewer clicks.
  • More immersive experiences: One of the main goals of AI implementation in searches is the possibility of making them more engaging. Users can anticipate interactive search experiences with follow-up questions and more personalized results.
  • Increase of Voice Search use: As the search generative experience keeps improving, Voice Search will be given even more priority.

All of these changes directly affect SEO strategies, which means that marketing companies like Kala and businesses must collaborate to find a solution that can mitigate the impact of upcoming modifications.

Common Challenges that Generative AI Faces

These new technologies are going to make the lives of many people much easier and improve the accuracy of many search queries. However, many people still have some relevant concerns about artificial intelligence.

First, one of the main issues that generative AI in search faces is misinformation. Anyone can publish and access data online, which can be true or false. Generative AI can take information from such false information.

Search engines will need to polish these new features to ensure that the sources of information are trustworthy and to prevent their tools from spreading inaccurate information.

As for the business side, there are many challenges, too. Having an AI that provides a fast and effective answer to what you’re looking for means that people will be less likely to surf the web to find the answers they’re trying to get, leading to fewer clicks and less visibility.


Generative AI in search represents a huge leap in how data is accessed and utilized. The development of many advanced algorithms and vast datasets allows AI to provide more personalized, relevant, and sophisticated search results than traditional methods.

It’s important to note that these changes go deeper than keyword matching. It enables search engines to understand and respond to nuanced queries more accurately.

As generative AI continues to evolve, its integration into search technologies promises to enhance user experience, streamline information retrieval, and drive innovation across various industries.

Even if all of these changes are promising, issues also arise. Experts face many challenges, such as algorithmic biases and the potential for misinformation, which require human supervision and constant fixing.

Always remember that getting the services of an agency like Kala can help you manage these new technologies more efficiently. This way, you’ll ensure successful strategies that can provide you with good generative AI for business in the long term.

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