Search behaviour is splitting into two distinct paths: traditional keyword search for quick, transactional lookups, and conversational AI discovery for research-heavy, comparison-driven decisions. Businesses optimized only for the first path are losing visibility in the second.
Users increasingly turn to AI assistants for anything that requires synthesis — comparing vendors, understanding a category, or getting a recommendation — reserving traditional search engines for navigation and simple fact lookups. That means the moment of consideration, where buying decisions actually form, is shifting away from the search results page.
LLM discovery rewards clear, unambiguous factual content that models can retrieve and summarize with confidence, and structured data and schema markup that make your offerings machine-parseable, not just human-readable. Consistency of information about your business across every source a model might reference matters just as much as genuine third-party citations and mentions, which carry more weight than self-published claims.
Discoverability audits should now include a simple test: ask the major AI assistants questions a prospective customer might ask about your category, and see whether — and how accurately — your business shows up. The gaps you find there are the priorities for the next content and structured-data investment.