A brand exploring AI discovery

The starting challenge

AI answers can vary by wording, time, product, location, account context, and model behaviour. A brand mention does not establish endorsement, accuracy, or customer impact. For Google's AI search features, established search fundamentals remain relevant; there is no guaranteed inclusion formula.

A sensible first work cycle

1. Define a small prompt set from real customer decisions. Group informational, comparison, and selection questions separately. Record language, market, product, date, and observable settings so later checks can be compared meaningfully.
2. Capture the answer, visible sources, and the distinction between an unlinked mention and a cited page. Check whether statements about the business are correct. Repeat representative observations before describing a pattern as a trend.
3. Improve the underlying resource: direct explanations, original evidence, accurate business details, clear ownership, and accessible pages. Add useful structure for readers, and maintain consistency between visible content and any structured data.
4. Report observations with coverage and limitations. Explain what was tested and what was not, compare like-for-like prompts, and track qualified referral behaviour where it can be measured. Treat prompt-level visibility as one signal in a broader discovery strategy.

Evidence to review before scaling

• Mention and citation frequency in a defined sample
• Accuracy of business information in observed answers
• Measurable qualified referral actions, with attribution limits

What would make the plan change?

A discovery issue, a mismatch between search intent and the page, unreliable measurement or an implementation dependency can change the order of work. Keep a record of assumptions, verify each change and agree on a review point before extending the scope.

Read the detailed methodology ↗