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Original search sample

A dated snapshot of AI answers in white-label search.

In August 2026, Google showed an AI Overview on 32 of 36 US and Australian white-label searches we checked. See how to measure and prove the work.

Your agency keeps the client and the strategy. We do the work under your name.

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Run on the AI we built for ourselves.

The short answer

What did the 2026 white-label AI-search SERP sample find?

In a sample from 26 August 2026, Google showed AI Overviews on 21 of 23 US white-label SERPs and 11 of 13 Australian ones.

That makes citation tracking and source analysis necessary alongside rankings, but the sample is directional rather than a market-wide prevalence estimate.

Classic keyword volume misses part of this market. The sampled results showed AI Overviews frequently, while agency service pages, directories, communities and informational sources competed to become the answer.

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The answer layer was present more often than it was absent.

The sample covered 23 US and 13 Australian result pages selected from the working white-label territory map.

AI Overviews appeared on most of them at the time and location tested.

That observation changes measurement. A firm can influence discovery through a classic result, a cited source, a named mention or a recommendation. Those are separate outcomes and should be recorded separately.

Agency proof was uneven: an opening, not a ranking guarantee.

The sampled incumbent pages usually explained the service clearly. Public pricing, named staff, verifiable case context and account-protection detail were less consistent.

LOKAL’s response is to publish inspectable scope, protection, process and evidence. Whether search or answer systems reward that work remains an outcome to observe, not a promise hidden inside the methodology.

Reported volume is a cluster signal, not a buyer count.

The keyword figures behind the territory map used medians across three tools. Closely related phrases overlap heavily, so they cannot be added as unique audiences.

To estimate revenue, count overlapping phrases once, then apply a realistic click share, lead quality and close rate. Adding up raw volumes overstates demand.

Findings

What the search results actually showed.

Observed in the sample, with the method still attached.

21 / 23

US SERPs with an AI Overview

Observed once across the fixed US sample on 26 August 2026; not a universal prevalence claim.

11 / 13

Australian SERPs with an AI Overview

Observed in the companion Australian sample under the same collection date.

36

Result pages sampled

Twenty-three US and thirteen Australian white-label queries formed the working panel.

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Method

How this page was assembled.

A sourced sample, not a sales narrative dressed as research.

  1. We used a fixed list of 23 US and 13 Australian white-label queries from our territory map.
  2. We recorded the visible top results, AI Overview presence and cited or named sources on 26 August 2026.
  3. We kept the measurement separate from our interpretation, and did not treat a single observation as stable prevalence.
  4. We treated three-tool volume medians as directional inputs for each cluster and did not add close variants together as unique demand.
  5. Next, we will repeat the same panel over time and record named, cited and recommended outcomes separately from classic rank.

Directional, not a trend: one dated collection. We will repeat it before treating any pattern as stable.

Read the edges

Limitations.

  • Search features vary by user, location, device, query wording and time. A repeat run may not reproduce a single snapshot.
  • The sample was selected for the white-label territory analysis and is not a random census of all agency-service queries.
  • We publish the method and the totals here; the query-level list is available on request.
  • No causal claim is made between a page change and a later citation or ranking movement.

Sources

What we checked, and when.

Common questions

Questions worth settling first.

Pressure-test the decision on one real account.

Does an AI Overview appear on every white-label query?

No. It appeared on most pages in this selected snapshot, but features vary by context and time. Repeat measurement is required.

Can the sample predict how many leads AI answers will create?

No. It tells you what to measure, not how many leads to expect. A lead estimate still needs observed mentions, clicks where available, qualified enquiries and close rate.

Why not use keyword volume alone?

Related phrases overlap, and some answer-engine discovery has no clean search-volume metric. The fixed query panel captures visibility that a volume sum misses.

Will publishing proof guarantee a citation?

No. Proof makes the firm easier for buyers and systems to evaluate; selection remains outside LOKAL’s control.

Pressure-test it on real work

What would you hand over first?

Tell us what is already promised, who owns the client and where the work needs help. Thirty minutes with someone responsible for the work.

The brief

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