Search is no longer driven purely by keywords — it’s driven by clarity. Large language models now read, interpret, and rephrase information, choosing which brands to reference in their responses. An AI search strategy reshapes content for this reality, prioritizing being understood and cited over simply being ranked and clicked. Structured data identifies entities and their relationships; clear, compact statements make those entities easy to extract; CRM integrations convert invisible exposure into trackable impact. Click volume may fall, but authority doesn’t have to. In AI search, every sentence becomes a fresh entry point for discovery. This article explains what an AI search strategy is and how content marketers and SEOs can put one into practice. You’ll also see how to track performance and which tools can support your efforts. Assess your AI visibility with HubSpot’s AEO Grader to understand how AI systems currently portray your brand. Table of Contents What is an AI search strategy? Where Inbound Marketing Fits AI Search Strategy for Content Marketers and SEOs How Loop Marketing Integrates With Your AI Search Strategy How to Measure AI Search Strategy Success How HubSpot’s AEO Grader Can Help Frequently Asked Questions About AI Search Strategy What is an AI search strategy? An AI search strategy is a framework for optimizing content for AI-driven search engines and answer engines. It aligns your content with the way large language models (LLMs) and answer engines interpret, condense, and attribute information. While traditional SEO aims at rankings and clicks, AI search optimization emphasizes eligibility and precision so that when AI systems generate an answer,…