What teams get wrong about AI Overviews

AI Overviews have triggered a predictable reaction: panic about losing clicks, and a rush to “optimise for AI” without a clear definition of what that means. Most of the noise misses the underlying shift. The mechanics of the interface will keep changing. What stays constant is that systems now care more about entities, relationships, and clarity than about strings of keywords.

The first mistake is treating AI Overviews as a separate channel to optimise for, rather than as a visible symptom of how retrieval and summarisation already work across the stack. Teams go hunting for specific prompts that produce their brand in the overview, then make tactical page tweaks in response. This can produce satisfying screenshots, but it rarely translates into durable visibility.

The second mistake is over-focusing on surface features: FAQ blocks, “people also ask” style content, or mechanically structured sections designed to look “answer friendly”. Those patterns can help, but only when they sit on top of a site that has coherent structure, clear entities, and strong signals of authority. Without that foundation, they just add more noise to an already cluttered estate.

A more grounded way to think about AI Overviews is to ask: if a model tried to explain this topic to a non-expert using only our public content, would it have an easy time? That question highlights gaps that traditional SEO sometimes ignores:

• Weak or ambiguous definitions of key concepts.
• Inconsistent naming for the same ideas across product, docs, and marketing.
• Sparse connective tissue between related topics, making it hard to understand relationships.

The work is less about chasing specific overview placements and more about becoming the cleanest, most coherent source of truth on the topics that genuinely matter to your product and customers. That means:

• Tightening information architecture so related subjects live in sensible clusters.
• Clarifying entities — products, features, problems, segments — and using their names consistently.
• Writing in a way that makes summarisation easy: clear headings, direct answers, and unambiguous language.

AI Overviews will keep changing shape. Instead of trying to predict each move, treat them as a forcing function to improve the underlying system. A site that is easy for a model to summarise accurately is usually easier for humans to evaluate and navigate too.