With the advent of artificial intelligence (AI) technologies enabling services such as Alexa, Google search, and self-driving cars, the next generation of search is about AI-powered search. In the past, text entered in a search box produced similar results for most users. Now, developments in AI have made it possible to produce more-relevant results that are adjusted in real time based on factors such as browsing history, the intent commonly associated with the words used, and high-performing content from similar searches. AI-powered search has the potential to give your site users exactly what they want, which in turn, can contribute to helping you meet your business goals for greater customer satisfaction, higher conversion rates, and increased revenue.

But what, exactly, does AI-powered site search mean? And why is it important for businesses to pay attention?

What is site search?

With 74% of people likely to switch brands if they find a company’s purchasing experience too difficult, and with those who conduct searches twice as likely to convert, site search has become a crucial element of the customer experience. Simply put, a site search solution makes content on your site easy to find, which leads to greater customer satisfaction and higher user engagement with your company. 

While excellent search has become a differentiator for companies, it’s a need that has grown in complexity. Thanks to sites like Netflix and Pinterest, consumers have high expectations for receiving instantaneous, personalized content. AI-powered search can help bridge the gap between users’ expectations and the search experiences that they find lacking.

What is AI-powered site search?

Artificial intelligence is a branch of computing that mimics human intelligence and can learn from and adapt to data input. With AI-powered search, the platform learns from data on users to automatically generate the most accurate and relevant search experiences. This learning, done in real time, leads to the tuning of results in the background as the person searches. The refinement is based on multiple factors programmed into the AI system, such as the person’s past purchases, common spelling errors, and the intent behind the language in the query.

While many of these factors can be manually accounted for or coded into a search platform, the AI part reflects the ability of these tasks to be accomplished at scale without human intervention. In addition, intelligent machines can make system adjustments for accuracy and relevance, and improve — or learn like a human — over time.

AI-powered search is behind the scenes on sites such as Pinterest, where AI learns from millions of image-based searches that people conduct each month. With more than 320 million active users and billions of “pins,” the company has a wealth of data, which it feeds through a deep learning model to understand the intents behind searches and surface personalized results for each person. For example, a search for “diy kitchen cabinets” returns results ranging from “How to build your own cabinets” to “Before and after kitchen transformations.” As the user clicks on more pins, the model better understands their intent and their search results narrow.

How does AI search work?

When applied to search, AI usually refers to the subsets of machine learning and natural-language processing (NLP) to determine the intent behind a search and return what the user wants. 

NLP and machine learning

Understanding and appropriately responding to the ways humans talk is a huge challenge for machines because of the unstructured nature and diversity of human speech. In conversation, we use synonyms such as “hot chili” and “spicy chili.” We use ambiguous terms, saying things like “Who crushed it at the Grammys?” 

With NLP, computers are able to detect language patterns and identify relationships between words to understand what people truly want. NLP is at the heart of voice assistants like Alexa and Siri, and it is why Google trained its AI to be more conversational by having the intelligence engine read 2,865 romance novels.

In order for computer programs to automatically act on their understanding of human language and provide responses that improve in time, machine learning is required. Machine learning is the science of getting machines to perform actions without explicitly being programmed using mathematical formulas. A machine analyzes data fed into the system and continuously finds patterns and connections in it using algorithms, performing tasks that would take a human team weeks or years. These tasks might include recommending books, based on similar users’ purchases, to someone who searches for “best political biographies,” and automatically detecting common spelling errors, such as the one in “easy zuchini bread recipe,” and then compensating for these errors in search results.

The need for AI-powered search is growing

The ways people search and their expectations around interactions with machines are changing rapidly, and AI-powered search is playing a central role.

Semantic search

A search for “best hand sanitizers” in 2021 gets you recommendations for products effective against COVID-19 without your needing to specify anything else, which is vastly different compared with what you would have received in 2019. Understanding a searcher’s intent through the query’s contextual meaning, rather than relying on the exact words a person enters, is the domain of semantic search. As search engines get better at understanding the meanings of queries and people discover the ease of using search engines, their expectations are changing. AI-powered semantic search with NLP and machine learning means that a search can function independently and return even more relevant results.

Voice and visual search

Search is increasingly going beyond text-based input, with voice and visual search rapidly becoming the preferred way to search on mobile and opening up more avenues for using AI-powered search. Gartner predicts that 70% of customer interactions will start with speech through smart speakers and assistants by 2023. With advances in computer vision, coupled with machine learning, computers can interpret what they “see” in an image to perform a visual search. 

The ability of computers to understand conversational language, which is largely what comprises voice search, is also quickly improving with AI. For example, you could ask your mobile device, “What is Justin Timberlake’s latest song?” and add “Play it” without needing to specify what you mean by “it.”

This trend is particularly evident among the Gen Z and Millennial populations, which are highly dependent on their mobile devices for ecommerce shopping. A ViSenze survey found that 62% of consumers in these groups want to be able to conduct visual searches (use images rather than text). And yet, according to another survey, 72% of marketers had no plans to optimize for visual search, and only 35% were planning to optimize for voice search in the next year, potentially presenting opportunities for companies that want to get ahead of their competitors.

Retailer Forever 21 took advantage of this trend, launching an AI-powered visual search tool that allows people to shop for clothing based on photos they’ve snapped. Among shoppers who used visual search, the company reported a 20% increase in conversions in the first month. 

Visual and voice searching on mobile is likely to only grow in popularity. Companies that experiment now with supporting these modes through AI will be in a better position to serve their customers and reap the attendant financial rewards. 

Benefits of using AI-powered search 

As more use cases and opportunities for AI in search become apparent, the benefits of AI-powered search for businesses in day-to-day interactions with users are becoming clear: 

• Optimizing long tail search results
• Greater customer satisfaction
• Growing customer conversions
• Freeing up of employees from repetitive search-related tasks such as correcting spelling errors and identifying synonyms

How to start bringing AI into search

At Algolia, we want to see our customers provide their users with the best possible search and discovery experiences. We introduced Algolia NeuralSearch, an AI-native SaaS-based solution that allows you to take advantage of AI-powered search within your existing stack while retaining the flexibility to control and tailor the search experience in order to reach your business goals. Algolia NeuralSearch can improve your understanding of your users and their intents, and then utilize the data to deliver the most relevant, personalized search experiences. 

NeuralSearch can help you:

• Optimize your entire catalog. Most retailers only have resources to optimize for the most popular products in their catalog. It makes perfect sense to spend time to optimize this part of your catalog; that’s where a large number of popular search queries for your catalog are coming from. However, if you only optimize for the “fat head,” you’re missing a huge opportunity. AI search improves understanding for all kinds of queries — from the “fat head” to the “long tail.”
• Works for exact matches too. AI search engines are typically powered by vectors embeddings which can be great for understanding concepts, but will struggle with exact matches. For example, a vector search engine will interpret a query like “Adidas” to find related results like “Nike” or “Rebok” shoes. NeuralSearch understands both exact matches and concept searches. This means it can interpret searches ranging from “support policies” to “Are online returns free?” to find the best results. 
• Scale effortlessly. Algolia NeuralSearch uses a proprietary neural hashing engine so it can deliver results incredibly fast, at scale. Each and every query is processed with AI and keyword algorithms to determine the best result — it’s the only search engine that can do both processes simultaneously.
• Constantly learning. NeuralSearch also makes of of reinforcement learning to dynamically re-rank results based on positive signals like clicks and conversions. The engine will improve both search relevance and search ranking with AI ranking. When configuring NeuralSearch, customers will also configure events they wish to use — clicks, conversions, signups, purchases, cart adds, etc., — and this is used to train NeuralSearch.

Learn more about AI search in this ebook.

Recommended content from Algolia

Embracing AI: Challenges and opportunities for the CTO

From keywords to conversions: Get ready for AI-powered search

AI-powered search: the next ecommerce growth drive

Editor’s note: This post was updated on May 30, 2023