How Does AI Affect Predictive Search?

You may not realize it, but when you search Google (or another search engine), and the rest of your search query pops up while you’re typing, you’re seeing predictive search at work. 

Today’s AI search engines are so sophisticated that they can anticipate what you’re going to search for as you’re typing it.

Although it happens lightning-fast, there are a lot of technical operations working behind the scenes to deliver those results, and AI plays a crucial role in the process. 

What is predictive search? How does AI affect predictive search? You may be surprised to learn just how integrated AI is in your everyday searches! Read on for all the details.

Understanding Google Autocomplete

When you search Google, it pulls information from your past searches and the past searches of millions of other users. According to Google’s guide on autocomplete, it looks at a wide range of factors, including: 

• The language of your search.
• Where your search is coming from geographically.
• How much interest there is in the topic based on trending searches.
• Your previous search history.

Google Autocomplete is finely tuned to filter predictions that don’t comply with the company’s terms of service, but it’s not perfect. That being said, users have the ability to turn off trending searches and personal results in their Google settings. 

Even beyond these basic personal search factors, there’s a lot happening under the hood when predictive search returns relevant results, and AI is tightly woven into the process. 

NLP and machine learning algorithms

Artificial intelligence (AI) uses Natural Language Processing (NLP) to better structure results based on how people actually speak and search. According to Search Engine Land, Google leverages NLP to establish context around search queries.

Example:

• If someone were to type “how to fix a leak,” they could be referring to everything from a leaky faucet to a roof leak. 
• Predictive search doesn’t know exactly, but by understanding the context, it aims to provide specific answers for the user.

Machine learning algorithms also come into play to recognize user search patterns and habits based on massive amounts of search data. These algorithms are always learning and improving in order to deliver more relevant results in less time.

Example:

• If you frequently search for recipes and Google “how to make,” machine learning algorithms spring into action.
• They’ll fill in the rest of the search with predictive text based on previous searches. 

This is why you’ll often see popular items like spaghetti or pancakes when starting such a search.

Personalized search results

AI learns from millions upon millions of searches conducted by users all around the world, but it also learns from your searches. 

Example:

• Let’s say you’re interested in new technology, particularly Apple products and releases. 
• AI can predict that if you Google “latest” (based on your search habits and interests), you could be looking for “Apple news” or “iPhone updates.”

Context awareness

When AI seeks to understand the context and specifics around your search, it can better tailor the results to your location, time of day, and other relevant searches. 

Example: 

• You’re out in the city at 7 pm, and you search for “restaurants.”
• Google could incorporate location data paired with predictive search to suggest restaurants near your location that are open at that time. 

Autocomplete and typos

You might not realize it, but search engines typically know when you include a typo in a search request. 

Example: 

• Typing “chicken receipt” instead of “recipe” will still get you chicken recipes. 
• AI-based autocomplete will even save time by completing the phrase.

Pulling from a variety of sources

AI isn’t just pulling from search results. It may have the capability to pull data from social media, current events, search trends, and popular searches (depending on the search engine you choose). Then, it analyzes all of that aggregate data in seconds to deliver results that are deeper and more detailed than precursory surface searches.

Example:

• Searching for Wimbledon might ordinarily give you the history of the event or past winners.
• However, as it gets closer to the championships, a predictive search may give you schedules and other relevant, timely details.

When it comes to the search results, Google also prioritizes high-quality content. Learn more about Google’s people-first content update.

Other Ways AI Is Transforming Predictive Search

So far, we’ve covered many of the basic ways that AI is affecting predictive search. But these are by no means the only areas where AI is playing a decisive role. It’s also tightly integrated into new and upcoming technologies.

Voice-activated assistants

With more voice-activated assistants coming onto the market, smart speakers, and other devices that understand common speech may be able to return helpful, accurate predictions. 

For example, in OpenAI’s partnership with Apple, they noted that one of the anticipated features was AI-powered virtual assistance through Siri, with enhanced capabilities to answer user queries.

Visual search

Imagine you spot a unique-looking plant in your neighbor’s yard, and you snap a photo of it. Visual search features (such as Google Lens) could use AI to help you identify what that plant is. It may show similar-looking plants and offer a few suggestions or even give you proper care instructions for the plant in case you’d like to try growing it yourself. 

Predictive shopping

One of the more innovative uses of AI in predictive search is evident in e-commerce. For instance, Google incorporates generative AI in shopping-related search results. Google offers a Google Shopping Graph, which includes 35 billion items with images of the products as well as reviews.

Sentiment analysis

Can AI and predictive search understand how you feel? It depends. Certain AI models incorporate sentiment analysis to evaluate the sentiment or mood of the searcher.

For example, Amazon Web Services notes that sentiment analysis may be incorporated to evaluate customer queries. If a customer is searching for answers to a question within a customer service search feature, AI could potentially identify more pressing inquiries. Then, it could forward them to customer service agents for a more detailed response.

AI and Predictive Search: Where Do We Go From Here?

It’s nothing short of fascinating to see just how much of a role AI has quickly carved out in the world of search engines. 

From shopping to voice assistance and visual searches to sentiment analysis, AI is rapidly becoming an integral part of search. 

As to what the future of predictive search looks like, that’s yet to be seen. However, it’s likely that AI will continue to become increasingly important to the advancing capabilities of search engines.

In addition to powering search engines, AI can also create content that appears on websites. Interested in learning if a website contains AI content? Use the Originaity.ai AI Checker or Site Scan to look for potential instances of AI content.