AI search, also called AI-powered or AI-driven search, uses natural language processing (NLP), large language models (LLM), machine learning (ML), and other artificial intelligence to surface conversational answers to customer search queries. Compared to keyword search, AI search produces revolutionary results that better reflect search intent.
How? AI search processes complex data it reads in a knowledge graph. Then it turns all that structured data, unstructured data, and the relationships between them into new content. That content is communicated via generative AI as accurate, meaningful, and conversational search results that customers are learning to trust.
AI search for discoverability across the customer journey
AI search is fundamentally changing how customers discover brands. To rank in AI-driven search results, brands must adopt a flexible data strategy. That strategy must organize and combine structured data (like hours of operation and local listings) with enriched, unstructured data (like reviews and FAQs).
Why? When managed in a knowledge graph, unstructured data is connective content that drives ranking in unbranded search. It contextualizes brand information and elevates it for unbranded search results. The nuanced, conversational, relevant content unstructured data generates helps to attract new customers. For brands with physical locations, it helps grow foot traffic, too.
Unbranded searches lead the way in the attraction and discovery stage of the customer journey in AI search. In fact, unbranded queries dominate conversational AI platforms. Think of a nuanced conversational search like this: "best family-friendly brunch spot with outdoor seating, dairy-free and less than 20 min. from S Austin — must be open on Mondays."
At the same time, branded searches aren't going away. Branded searches are just as critical for engaging customers and retaining them as unbranded searches are for gaining visibility. It's branded searches that keep guiding customers back into brand experiences in the later stages of the customer journey.
The same customer who wants to find a dairy-free brunch spot might also want to plan their meal around a Target run and ask, "What time is Target curbside pickup open at S Austin?"
To surface in branded search, the structured data managed in a knowledge graph is key. From NAP data to hours to Google Business Profile attributes, structured data sets that are accurate, consistent, andwidely published are ideal. This type of data strategy makes it easy for artificial intelligence to tease out the facts customers want — no matter where they're searching for it.
Adapt now to stay ahead in AI search
AI-driven search is changing rapidly. Brands must optimize unstructured data and enhanced content for these searches to attract and engage with new customers. To scale data management and fuel AI search results, brands must centralize their structured and unstructured data in a knowledge graph.
Brands that don't optimize for AI search on all the sites, apps, and channels customers use risk falling behind competitors already investing in AI-ready strategies.