AI, short for artificial intelligence, is a technology that gives computers and machines the ability to learn, communicate, and problem-solve in ways that resemble humans.
In 1950, English mathematician Alan Turing first imagined AI as "thinking machines" in a paper, "Computing Machinery and Intelligence." In 1956, the term "artificial intelligence" emerged at a Dartmouth College event led by computer scientist John McCarthy. It marked the start of AI as a research field.
The practical applications of AI for search
Since the 50s, AI research and innovation have been happening without a lot of fanfare. Before generative AI took the world by storm in 2023, artificial intelligence has been changing search for longer than most people realize.
In 2014, Google introduced Smart Bidding for pay-per-click (PPC) advertising.
In 2015, Google launched RankBrain — its first AI into search.
In 2016, Yelp started using artificial intelligence to identify details about restaurants by analyzing photos shared in the local business listings.
While many marketers have adopted AI's use cases in their own workflows, most teams aren't meeting the demands of AI-driven search. Brands are losing ground in unbranded search, AI Overviews, and artificial intelligence platforms like ChatGPT.
After all, the customer journey no longer begins and ends in one predictable place. Only 64%* of customers search for products and services on traditional search engines first. Even when they do, proximity isn't enough to put your brick-and-mortar in the Local Pack. Traditional SEO strategies may still matter, but they aren't the sole discoverability driver anymore either.
AI-driven search experiences don't care if you're the biggest brand name with the biggest ad spend. Artificial intelligence prioritizes the best answers it can find. So, what makes for the best answers? Two big things:
Answers supported by brands with credible data
A broad digital presence where your brand shows up virtually everywhere Brands must adapt their data strategy to reflect this shift or risk becoming invisible in AI-driven search systems.
Your data strategy is your AI strategy
In the age of AI, brands must prioritize data accuracy and consistency across channels. It's vital for an authoritative, visible digital presence. A robust data strategy — one that integrates both structured and unstructured data — is the foundation for success in this environment.
AI can find and interpret structured data. If AI finds your structured data everywhere it looks, and that data is consistent, AI trusts it. In turn, AI tools will be more likely to surface your data so customers can find you.
Unstructured data is often published as enhanced content (like blog posts or video). It gives AI the context it needs to surface your data in conversational queries and respond using large language models (LLMs) and natural language processing (NLP). Unstructured data fuels AI engagement with your brand data, providing the rich, contextual answers customers expect from search today.
To scale data management and reach all the sites, apps, and channels that AI uses, brands must centralize their data in a knowledge graph. A knowledge graph makes it easy to maintain data accuracy, monitor data for inconsistencies, and keep brand information up-to-date everywhere.
Yext's Knowledge Graph works as the cornerstone of an AI-ready data strategy. With Yext, brands can surface in branded and unbranded searches, delivering brand information with the context and specificity that AI demands — and customers trust. This gives brands the ability to compete effectively in search, regardless of size or marketing budget.
*Survey details: The results are from an online survey of 2,312 adults who purchased something online within the past year. The survey was conducted from June 14 to 25, 2024, by Researchscape International on behalf of Yext. Results were weighted by country population, age, and gender. Respondents were from five countries: France, Italy, Germany, the United Kingdom, and the United States.