Stop Global Search Leakage in Google AI Overviews

A weird thing is happening in local search: you Google something that clearly implies a U.S. buyer, and Google’s AI Overviews cite a page from the UK, Australia, or Canada. The information might be correct. The outcome for your business? Not so much.

If you’re a U.S. SMB that depends on organic search for calls, form fills, bookings, and online orders, this is more than an SEO curiosity. It’s a lead leakage problem—and it’s showing up more often as Google shifts from “ranking and serving the best local URL” to “synthesizing the most complete explanation.”

This post is part of our series, How AI Is Powering Technology and Digital Services in the United States. The headline story is progress: AI systems summarize faster and more accurately. The business reality is messier: the AI doesn’t care whether your customer can actually buy from the source it cites. You have to.

What “geographic leakage” means (and why SMBs feel it first)

Geographic leakage is when AI Overviews cite out-of-market sources for queries that have local or commercial intent.

If you sell in Texas and the AI Overview cites your company’s .co.uk page (or a competitor’s non-U.S. page), two bad things happen:

• Conversion drops: users hit the wrong currency, wrong shipping policy, unavailable SKUs, or “not offered in your region.”
• Attribution breaks: the AI Overview experience can keep users in Google longer, and the clicks you do get may route to the wrong country site—wrecking your reporting.

SMBs get hit hardest because you typically have:

• fewer pages competing for “fact chunks” (more on that below)
• thinner localization budgets
• less brand gravity than national players

This isn’t primarily caused by sloppy hreflang or “bad international SEO.” Even well-run sites can see leakage because the system’s incentives changed.

Why Google AI Overviews pull global sources (it’s not a bug to them)

AI Overviews are built to reduce hallucinations and increase factual coverage. That’s the engineering win. The tradeoff is that the system can treat geographic and commercial constraints as optional.

Query fan-out: your page isn’t competing as a page anymore

In classic SEO, your page competes against other pages. In AI Overviews, the competition is often the extractable snippet of information—a paragraph, a definition, a constraint, a policy sentence.

AI Overviews commonly use query fan-out, meaning one search becomes several behind-the-scenes sub-queries:

• “What is it?”
• “How does it work?”
• “What are the legal constraints?”
• “What are the edge cases?”
• “What varies by region?”

If a single out-of-market page answers one of those sub-questions more cleanly, it can win a citation—even if it’s useless for a U.S. buyer.

SMB implication: one well-written paragraph on a foreign page can outrank your entire local landing page inside the AI Overview.

Cross-language and cross-market retrieval is now normal

Modern retrieval systems don’t treat language as a hard boundary. Content is normalized into a shared semantic space (think: meaning, not keywords). That means:

• a Spanish-language page can be used to generate an English summary
• a Canadian page can be treated as “the same concept” as the U.S. page

If the non-U.S. URL is cleaner, fresher, or more explicit, it may become the “source of truth.”

The vector identity problem: different markets collapse into the same meaning

Here’s the uncomfortable truth: “Ships to U.S. only” is business metadata, not meaning.

In AI systems, pages become vectors representing semantic content. If your U.S. and UK pages say basically the same thing, the model may see them as interchangeable. Then tiny advantages—crawl recency, better formatting, one clearer sentence—tip retrieval toward the wrong market.

Why hreflang often doesn’t save you

hreflang was designed for a world where Google:

1. finds a relevant page
2. then serves the correct regional version

AI Overviews often decide what to cite upstream, during semantic retrieval. Once an out-of-market URL is pulled in as the best evidence for a “fact chunk,” geographic substitution comes too late.

If your hreflang strategy is your whole international plan, AI Overviews are going to expose that.

The business bug: AI Overviews don’t measure commercial harm

From an SMB owner’s perspective, search has one job: create action.

AI Overviews don’t evaluate:

• whether the user can buy the product in their state
• whether the service is offered in their metro area
• whether pricing is shown in USD
• whether compliance rules differ in the U.S.

The system optimizes for “defensible explanation,” not “successful transaction.”

A citation can be factually correct and commercially wrong at the same time.

And because AI Overviews contribute to zero-click behavior (users getting answers without visiting sites), the few citations that do show up matter more. When those citations point out of market, the opportunity loss is amplified.

A practical GEO plan for SMBs: how to reduce leakage and win citations

You can’t “turn off” AI Overviews. You can make your U.S. pages more likely to be retrieved when Google fans out queries.

Below is a Generative Engine Optimization (GEO) approach I’ve found works best for resource-constrained teams.

1) Build semantic parity across regions (yes, it’s annoying—and yes, it matters)

Answer first: AI Overviews reward the most complete, extractable explanation. If your U.S. page is missing one clarifying line that your UK page has, the UK page may become the citation.

Do this:

• Audit your top 20 revenue-driving pages (products, services, pricing, location pages).
• Compare across country folders/domains.
• Standardize critical “fact chunks” so they’re present everywhere.

What to standardize:

• concise definitions
• eligibility/availability statements
• shipping/coverage areas
• warranty/returns differences
• regulatory notes (especially for healthcare, finance, supplements, and trades)

Rule: don’t let the “best explanation” live on the wrong regional URL.

2) Write in “fan-out blocks” (so your best info is easy to extract)

Answer first: AI Overviews cite content that’s structured into clear, atomic units.

On key pages, add short sections that map to common sub-queries:

• What it is (2–3 sentences)
• Who it’s for (bullets)
• How pricing works (simple ranges or factors)
• Availability in the U.S. (explicit, machine-readable language)
• Common mistakes (3 bullets)

This isn’t about making pages longer. It’s about making the important parts unmistakable.

Snippet-friendly lines you want on U.S. pages:

• “Available for purchase in the United States (USD pricing).”
• “Service area: Austin, Round Rock, Cedar Park, and nearby suburbs.”
• “Not offered outside the U.S. due to regulatory requirements.”

3) Reinforce utility signals the AI won’t infer reliably

Answer first: If the model collapses markets into meaning, you need to make market validity explicit.

Tactics that help:

• Put U.S. availability and service area near the top of the page, not buried in the footer.
• Use consistent formatting for addresses, phone numbers, and service regions.
• Add clear shipping/coverage and currency cues (USD, “ships within the U.S.”).

If you have multiple locations, create a location hub that:

• lists cities served
• links to each location page
• states the relationship (franchise vs corporate vs independent)

4) Manage freshness strategically (because recency can override “local correctness”)

Answer first: If one regional page is updated more often, it can unintentionally become the dominant citation.

I recommend a simple cadence:

• Update U.S. money pages quarterly (even small clarifications count).
• When you update one region’s explanation, copy the improvement to the others immediately.
• Track content change logs so parity doesn’t drift.

This is boring work. It prevents expensive leakage.

5) Run a “generative search audit” monthly (30 minutes, high payoff)

Answer first: You can’t fix what you don’t monitor, and AI Overviews change fast.

Create a short list of queries that drive leads:

• “[service] near me”
• “[service] cost”
• “[product] warranty”
• “[brand] pricing”
• “[product] vs [product]”

Then:

1. Search from a U.S. location (or use a location simulator).
2. Screenshot AI Overviews.
3. Record cited URLs and markets.
4. Flag any out-of-market citations.
5. Update the U.S. page to include the exact missing “fact chunk.”

If you do this consistently, you’ll start seeing patterns—especially which sub-questions trigger leakage.

Example scenario: how leakage kills leads (and how to patch it)

Say you run a U.S. e-commerce store selling ergonomic office chairs. A user searches “chair lumbar support warranty.”

Your U.S. page:

• has the warranty info, but it’s a PDF link buried at the bottom

Your UK page:

• has a clean FAQ section with “Warranty length: 5 years” in one sentence

AI Overviews cites the UK page because it’s the best extractable warranty “fact chunk.” The user clicks, sees GBP pricing and different coverage terms, and bounces.

The fix isn’t mystical:

• Put the one-sentence warranty fact chunk (plus U.S. applicability) directly on the U.S. product page.
• Add a short warranty FAQ section formatted in plain language.
• Make sure the UK and U.S. versions stay in parity.

That’s GEO in practice: make the most citable version of the truth the most usable one for your market.

Where this is heading for U.S. SMB marketing

AI-powered search is reshaping technology and digital services in the United States by changing what “visibility” even means. Rankings still matter, but retrieval and citation are the new battleground.

If your business relies on local SEO, you’re now optimizing for two systems at once:

• classic search results (ranking + serving)
• generative search results (retrieval + synthesis)

The reality? It’s simpler than it sounds: write clearer, keep regional content aligned, and make U.S. actionability impossible to miss.

If you want more leads from AI Overviews, start with one question: When Google summarizes our topic, is the U.S. version the most complete—and the easiest to cite?