When discovery shifts from ranked lists to AI-generated summaries, viability depends less on position and more on extractability. Businesses must be easy to retrieve, easy to interpret, easy to verify, and easy to contextualize in a recommendation. This guide explains why ranking-era tactics fail in summary systems, the four extractability requirements that determine inclusion, and how to structure service, proof, and differentiation so AI can summarize you accurately.

Brands built for human attention often become ambiguous, conflated, or generic when AI systems paraphrase and aggregate them into recommendations. This guide explains the four failure modes that cause AI misrepresentation and offers a three-layer clarity model: literal naming signals, explicit positioning that survives paraphrasing, and redundant reinforcement across core platforms so AI can repeat your business accurately.

Traditional “be everywhere” marketing spreads attention across too many platforms, creating stale profiles, mismatched business info, and thin engagement. In AI-mediated discovery, that pattern can resemble manipulation. This guide explains the three red flags AI systems penalize and a focused platform strategy built around consistency, recency, and real customer interaction.