Most strategy lists in this field present every item with equal confidence, which is misleading because the evidence varies enormously. These are ordered from well-supported to speculative, and labelled accordingly.
Well supported
1. Entity consistency. The clearest observable pattern: businesses that cannot be identified confidently are omitted. Fixing contradictions across listings reliably changes outcomes.
2. Third-party corroboration. Claims supported outside your own domain get used; claims appearing only on your site are treated as assertions about you.
3. Passage-level structure. Self-contained sections that answer completely get extracted; arguments that build do not.
Reasonably supported
4. Structured data. Clearly helps machines parse claims, though the direct effect on citation is harder to isolate from the other benefits it brings.
5. Recency signals. Visible preference for recent sources in time-sensitive categories, much weaker elsewhere.
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Plausible but thin
6. Author and expertise signals. Consistent authorship seems to help attribution, but isolating its effect from general authority is difficult.
7. llms.txt and similar declarations. Logical, cheap, and with limited evidence of adoption. Worth an hour once the first five are done, not before.
Why the ordering matters
Because effort is finite and the difference in return between the first three and the last two is large. A team that spends its quarter on authorship markup and llms.txt while its listings still contradict each other has optimised the wrong end of the list.
It also matters for judging advice. Anyone presenting item seven with the same confidence as item one is either not measuring or not telling you what they measured.
Frequently asked questions
What is the most effective AI search optimization strategy?
Entity consistency, by a clear margin. Businesses a model cannot identify confidently get omitted, and fixing contradictory information across listings reliably changes outcomes. It is also the cheapest work on the list.
Do author signals help AI search visibility?
Plausibly, but the evidence is thinner than for entity consistency or corroboration. Consistent authorship appears to help attribution, though isolating that effect from general authority is difficult.
How should I prioritise AI search work?
Entity consistency, third-party corroboration and passage-level structure first; structured data and recency next; author signals and declarations like llms.txt last, once the earlier items are done.