Whitepaper: The Impact of Google AI Overviews on Local Business Search Visibility

Introduction

Overview

Google AI Overviews (AIO / GAIO) is transforming the way search results are delivered - particularly for local business queries. By summarizing information directly in search results, GAIO changes how visibility is earned and how users discover local services. This whitepaper investigates a fundamental question for the future of local SEO:

Does the physical location of the searcher impact Google AI Overviews visibility today, and if so, how?

Our findings show that Google AI Overviews currently places less emphasis on proximity than traditional local packs. While businesses slightly closer to the searcher are more likely to appear, this proximity has little effect on ranking - the order in which they appear - once included.

Signs of location sensitivity are emerging, however, particularly in appearance rates when tested with a geogrid. These insights suggest a shifting paradigm: while local search visibility is becoming more dependent on content quality and authority, geographic relevance may yet play an evolving role as GAIO matures.

For local businesses, this represents both a challenge and an opportunity. Traditional SEO strategies anchored in proximity must now expand to prioritize structured, authoritative, and intent-aligned content - especially for informational queries. At the same time, understanding how AIO behaves across physical space offers a powerful new frontier for optimization. This paper presents data-driven evidence to guide that evolution.

Research Questions

The primary research question of this paper is, what role does searcher location play in AIO result appearance and business ranking?

To better understand that question and provide useful guidance to local businesses, we further explored elements of how GAIO functions, including:

1. How frequently do AI Overviews appear in local business-related queries in general?
2. What other factors impact the appearance of AI Overviews?
3. What are the SEO implications for local businesses today - and where is this trend headed?

Methodology

This section outlines the research design, data sources, sampling criteria, query generation process, and analytical procedures used to investigate the relationship between searcher location and GAIO (Google AI Overviews) visibility in local search results.

Experimental Design

To evaluate the impact of searcher location on GAIO visibility and ranking behavior, we conducted a large-scale empirical study using programmatic search queries across a diverse and representative set of local businesses. The experimental design incorporated both cross-sectional and grid-based geographic sampling to simulate realistic local search scenarios at scale.

Business Selection and Sampling

We drew our sample from Local Falcon's proprietary database, selecting businesses across the 250 most common Google Business Profile categories. Key parameters included:

• Total businesses analyzed: 4,423 unique businesses across 20 countries where Google AI Overviews are currently supported.
• Sampling distribution: Up to 20 businesses per category, stratified to ensure geographic and industry diversity.
• Focused U.S. subset for hyperlocal analysis: 430 U.S.-based businesses were selected for grid-level geographic testing to enable statistically robust proximity analysis.

Businesses were only included if located in a country with GAIO availability and if they operated in a language supported by GAIO for query interpretation.

Query Generation

For each business, we generated seven structured query intents designed to reflect distinct user intents commonly observed in local search. These were categorized as:

Type	Example
Informational	What services do HVAC contractors in Paris offer?
Commercial	Best-rated plumbers near me
Transactional	Schedule a dental appointment near me
Boolean	Does this bakery offer gluten-free options?
Instructional	How to find a reliable car repair shop in Tokyo
Reasoning	Why choose a certified electrician?
Navigational	Acme Plumbing contact page" or "Joe's Pizza menu

To assess the impact of explicit geographic references, half of all queries included the business's location, and half did not. All queries were generated in the local language of the business, using OpenAI's GPT-4o-mini model to simulate natural user phrasing and behavior. English translations were stored for interpretive consistency across multilingual datasets.

Geographic Simulation and Execution

We conducted all queries using precise geotargeting, simulating search activity from the business's physical location. For local testing, we implemented a 7x7 latitudinal-longitudinal grid (49 distinct lat/long), spanning a radius of ~4 square miles around each business location:

• Grid spacing: Each grid point was spaced ~0.67 miles apart.
• Total simulated locations: ~21,000 unique search points.

Search context: Each query was executed on both desktop and mobile platforms to capture device-level differences in AI Overviews behavior.

At each location, we recorded Google AI Overviews presence, AI-generated content snippets, source URLs cited in AI Overviews, and the rank position (if any) of the target business.

Data Management and Analysis

All search output was parsed and stored in a PostgreSQL database to support structured analysis. The final dataset included:

• Total queries executed: 60,000 (30,000 desktop and 30,000 mobile).
• Intent distribution: 10,000 queries per intent category.
• Character length distribution: Majority of queries (85%) were between 40-80 characters.

For the local grid analysis, we conducted spatial correlation tests to assess the relationship between searcher distance and AIO ranking. Key metrics included:

• AI Overviews appearance rates by query type.
• AI Overviews ranking variance across grid points.
• Distance-to-rank correlation coefficients.
• Ranking volatility by business category.
• Impact of query length and phrasing on Google AI Overviews trigger rates.

Limitations and Considerations

While our approach offers high geographic and category coverage, results should be interpreted within the current scope of the Google AI Overviews rollout and algorithmic behavior. Future updates by Google may affect these dynamics, and real-world consumer engagement metrics (e.g., clicks or conversions) were not measured in this phase of the study.

This section provides an overview of the methodology used in this study. That includes how the experiment was conducted, what data was used as inputs, how we segmented queries, and what output data was collected.

Experimental Setup

To analyze the imact of Google AI Overviews on local search, we designed a large-scale experiment that runs structured search queries across multiple business categories, locations, and languages.

Local Businesses Database

• We selected businesses from the 250 largest business categories in Local Falcon's database of local businesses.
• Total unique businesses analyzed: 4,423 businesses across 20 countries currently supporting AI Overviews.
• Up to 20 businesses per category were chosen.
• Experiment results were stored and analyzed using a PostgreSQL database.
• For location specific results, we focused on deep analysis of 430 US businesses across the top 250 categories to achieve a statistically significant result.

Query Generation

For each business, we generated seven structured search queries designed to reflect different search intents:

Type	Example
Informational	What services do HVAC contractors in Paris offer?
Commercial	Best-rated plumbers near me
Transactional	Schedule a dental appointment near me
Boolean	Does this bakery offer gluten-free options?
Instructional	How to find a reliable car repair shop in Tokyo
Reasoning	Why choose a certified electrician?
Navigational	Acme Plumbing contact page" or "Joe's Pizza menu

To ensure our experiments accurately reflected real local search behavior:

• Queries were carefully designed to mimic how real customers search for local businesses.
• Half of all queries included the specific location name of the business, while half did not, allowing us to analyze the impact of location-specific searches.
• Queries were generated in the local language of the target business, with an English translation included where applicable.
• We only selected businesses in countries where AI Overviews are currently supported and where one of the primary languages is also AI Overviews-supported.

In addition, to understand the impact of location on the appearance of a specific business in Google AI Overviews results:

• We generated queries that triggered Google AI Overviews and included the target business within the results.
• Queries represented various intent types (informational, commercial, transactional, etc.
• Both location-specific and non-location-specific queries were tested.

The queries were generated using OpenAI's GPT-4o-mini model, ensuring they reflected natural user search behavior.

Search Execution & Data Collection

To ensure our experiment accurately simulated real local search scenarios:

• We used the exact latitude and longitude of each business to precisely geotarget queries, simulating searches conducted from the business's location.
• We used a 7x7 grid (49 distinct lat/long combinations) with a 4 mile radius, simulating searches conducted from varying locations near the business.
• This approach mirrors how potential customers would search for local businesses while physically in the area.

We analyzed search results to detect whether Google AI Overviews appeared for each query, and collected the following data points were collected for each search:

• AI Overviews presence (whether an AI Overviews result appeared).
• AI-generated text snippets.
• URL references within AI Overviews.
• AIO ranking positions of local business.

Data Storage & Analysis

The collected data was stored in a PostgreSQL database for structured analysis. Our final dataset included:

• Total queries analyzed: 60,000 searches (30,000 desktop and 30,000 mobile).
• Query distribution: 10,000 queries for each of the 6 query intent categories.
• Business coverage: 240 queries for each business category analyzed.
• Query length distribution: Varied across length buckets, with most queries (51,004) falling in the 40-80 character range.

Locality specific analysis details:

• For each successful query, we created a 7x7 grid of search locations centered on the business, yielding more than 21,000 distinct geographic data points.
• Each grid point was approximately 0.67 miles apart, creating a total coverage area of ~16 square miles.
• The same query was executed from each grid point to precisely simulate searches from different locations.
• At each grid point, we recorded whether Google AI Overviews appeared, and the target business's rank position within the results, if present.
• We analyzed the patterns of business rankings across grid positions.
• We measured the correlation between distance from the business and ranking position.
• We compared ranking consistency across the grid for different business categories.

Key metrics analyzed include:

• Google AI Overviews trigger rate per query intent type.
• Impact of query length and phrasing.
• Business categories most affected by Google AI Overviews.

Results

This section provides the outcome of our investigation. It first looks at the core question of locality in AI Overviews results (Does searcher location matter?), and then provides additional useful findings related to general AI Overviews behavior.

Key Findings

Our experiment revealed several important insights about how Google AI Overviews handle geographic proximity in local business queries as of today (May 7, 2025):

No Distance-Based Ranking Correlation for Rank Position

Unlike traditional local pack results, we found effectively no correlation between distance from business and ranking position within Google AI Overviews results (correlation coefficient: 0.001) when the business appears in results at all. This indicates that Google's AI Overviews do not apply the same distance-based ranking algorithm used in local packs.

Center vs. Edge Appearance Rates

Businesses were slightly more likely to appear in AI Overviews results when searches were conducted closer to their physical location:

• Center grid positions (within 1 mile of business): 72.0% appearance rate.
• Edge grid positions (1-2 miles from business): 68.5% appearance rate.

Center vs. Edge Ranking

Rankings within AIO showed virtually no improvement with proximity:

• Average ranking in center grid positions: 3.39.
• Average ranking in edge grid positions: 3.49.
• This small difference (0.10 ranking positions) confirms that once a business appears in AIO results, its ranking position (the order in which it is presented) is determined by factors other than physical proximity.

Ranking Consistency

Our data shows substantial variations in how consistently businesses maintained their ranking across different search locations:

• Some businesses showed high consistency with minimal ranking fluctuations.
• Others exhibited significant ranking volatility across the grid.

Business Category Differences

The consistency of rankings varied notably by business category:

• Insurance brokers and cell phone stores showed relatively stable rankings across the grid.
• Personal injury attorneys and some service businesses exhibited higher ranking volatility.

Implications for Local Business SEO

These findings have several important implications for local businesses adapting to AI Overviews:

1. Beyond Proximity: While proximity has a small influence on whether a business appears in AIO, it has virtually no impact on ranking position once included. This differs significantly from traditional local pack optimization, where proximity is a dominant ranking factor.
2. Content Authority Trumps Location: The near-zero correlation between proximity and ranking confirms that content quality, relevance, and authority are likely much more important for AIO rankings than physical distance.
3. Business Category Considerations: Different business categories experience varying levels of ranking stability in AIO results:

• Businesses in categories with stable rankings across locations can focus on optimizing for initial inclusion in AIO.
• Businesses in categories with volatile rankings may need broader optimization strategies to maintain consistent visibility.

4. Consistent Appearance: The relatively high appearance rates across the entire grid (both center and edge positions) suggest that Google AI Overviews offer good coverage across geographic areas, with only a slight preference for businesses closer to the search location.

This hyperlocality analysis confirms that Google AI Overviews represent a significant shift from Google's traditional location-based ranking system, with much greater emphasis on content relevance and query intent than geographic proximity. For local businesses, this means that while optimizing for local relevance remains important, the quality and authority of content has a much greater impact on Google AI Overviews visibility than physical proximity to the searcher.

Searcher Location Matters - Sometimes

While our average results show that rankings for Google AI Overviews are not strongly correlated with physical proximity, real-world examples from our data reveal that in certain scenarios, searcher location does appear to affect both appearance and ranking. These examples highlight edge cases where hyperlocal behavior emerges, suggesting that location can be a factor - depending on query phrasing, business category, and the density of competitors.

Below, we showcase two sets of examples: when searcher location does influence GAIO results and when it does not.

Example 1: Fitness Classes (Lockport, IL)

"Can anyone recommend the best fitness classes for beginners in Lockport, IL that offer flexible schedules?"

• AIO Grid Visibility Pattern: Strong clustering of high-rank AIO presence near the business.
• Interpretation: Inclusion of both a place name and service-level detail may have activated location-sensitive AIO logic.
• Implication: Businesses targeting lifestyle and community-based services may see more local AIO behavior when searcher intent is highly contextual.

Example 2: Nail Salons with Gel Manicures Query

"Can anyone recommend a top-rated nail salon that offers gel manicures and pedicures?"

• GAIO Grid Visibility Pattern: Slight gradient effect; closer grid points more likely to show the business.
• Interpretation: Not a perfect correlation, but enough variation to suggest proximity could influence inclusion.
• Implication: Searchers looking for discretionary or preference-based services may trigger light geo bias in GAIO.

Examples 3: Frozen Dessert Spots (Philadelphia)

"Can someone suggest popular dessert spots in Philadelphia that are known for their refreshing frozen treats?"

• GAIO Grid Visibility Pattern: Uniform visibility across all grid points regardless of distance.
• Interpretation: Dense, highly reviewed businesses in a metro area may override location signals.
• Implication: In saturated markets or "best of" style queries, GAIO seems to default to authority over proximity.

Example 4: Computer Repair in Sparkman, AR

"Can anyone recommend the best computer repair services in Sparkman, AR that are known for quick turnarounds?"

• GAIO Grid Visibility Pattern: Consistent results regardless of searcher location.
• Interpretation: In lower-competition or rural regions, fewer results may lead to uniform inclusion.
• Implication: GAIO may apply proximity only when it has enough local options to choose from.

Takeaways

AI Overviews' use of location is not binary - it's conditional. While the algorithm does not rank businesses strictly by distance, it appears to weigh proximity in certain scenarios, particularly when:

• The query includes highly specific or service-oriented language.
• The market has multiple nearby competitors.
• The intent suggests urgency or local context.

As AI Overviews evolve, these edge cases may become more common or more consistent. That makes it increasingly important for local businesses to monitor visibility of AI Overviews across neighborhoods - not just nationally or by keyword intent.

Supplementary Findings

As part of our research, we investigated AIO more broadly to understand how it functions and what the impact would be for local businesses. We focused on some core elements of Google AI Overviews optimization, including the impact of including a specific location in a query, the query intent type, the business category, the query length, and overall appearance prevalence.

Location Inclusion Effect

One of our most significant findings for local search is the impact of including a specific location name in the query:

Location Inclusion	GAIO Appearance Rate	Sample Size
Location not included	46.1%	32,699
Location included	35.0%	37,301

Queries that do not include a specific location name in the text are 11.1 percentage points more likely to trigger Google AI Overviews than queries that do include location names. This finding has major implications for local SEO strategies, as it suggests that location-specific searches may preserve more traditional search results.

The impact of location inclusion varies dramatically by query category:

Query Category	Without Location	With Location	Difference
Reason	77.7%	41.7%	36.0 pts
Informational	67.9%	57.9%	10.0 pts
Instruction	57.1%	51.7%	5.4 pts
Transactional	47.8%	47.0%	0.8 pts
Boolean	35.3%	28.2%	7.1 pts
Commercial	19.9%	16.5%	3.4 pts
Navigational	13.9%	7.1%	6.8 pts

"Why" queries (Reason category) show the most dramatic difference - including a location name drops the GAIO appearance rate by 36 percentage points. This suggests local businesses should particularly focus on optimizing for location-specific "why" queries to maintain traditional search visibility.

GAIO Appearance by Query Type

Interpretation

Google appears to favor AI Overviews for exploratory, top-funnel queries and preserves traditional listings for purchase-driven and branded searches. This offers local businesses an opportunity to defend high-intent traffic while adapting top-of-funnel content for AIO inclusion.

Business Category Analysis

Our research identified substantial variation in AIO appearance rates across different business categories. Service-based businesses consistently show the highest AIO trigger rates:

Business Category	GAIO Appearance Rate
Window cleaning service	65.0%
Carpet cleaning service	64.6%
Cleaning service	62.5%
Personal injury attorney	62.1%
Wedding photographer	61.8%
Pest control service	60.7%
Day care center	60.7%
Psychotherapist	60.0%
Mover	60.0%
Water damage restoration service	59.6%

This pattern suggests that local businesses providing services that consumers typically research before purchasing - particularly cleaning, legal, and creative services - are most affected by Google AI Overviews. These are precisely the types of local businesses where consumers often ask informational questions about processes, pricing, and service details.

For these high-AIO-exposure categories, optimizing content to be cited within AI Overviews will become increasingly important for local businesses, as traditional organic visibility may be diminished.

Query Length Impact

Our analysis reveals a strong correlation between query length and AI Overviews appearance rate:

Query Length AIO Appearance Rate SampleSize:

• 0-20 chars.: 19.7% (1,288)
• 20-40 chars.: 16.4% (9,448)
• 40-60 chars.: 38.2% (29,010)
• 60-80 chars.: 50.1% (26,704)
• 80-100 chars.: 52.1% (3,474)
• 100+ chars.: 60.5% (76)

This data demonstrates that longer, more specific queries are significantly more likely to trigger AIO than shorter queries. Specifically:

• Very short queries (under 40 characters) have AIO appearance rates of only ~16-20%.
• Medium and longer queries (40+ characters) have AIO rates of 38-61%.

This pattern aligns with our findings on query intent, as longer queries tend to be more informational or instructional in nature, while shorter queries often have navigational or commercial intent.

The implication for local businesses is clear: users asking detailed, specific questions about business services or processes are much more likely to encounter AIO results, potentially reducing click-through rates to business websites for these informational queries.

Overall AI Overviews Appearance Rate

This section provides important supplement information about the current implementation of AI Overviews and the likelihood it will trigger in general.

Based on 60,000 total queries for businesses in the largest categories, we observed Google AI Overviews appearing in 40.2% of queries, as of April 7, 2025.

Interpretation

This baseline metric indicates that more than one-third of all searches related to local businesses potentially deliver AI-generated content to users, representing a significant shift in how search results are presented for local businesses.

The prevalence of AI Overviews in local business search has considerable implications for local visibility. With AI-generated content appearing at the top of search results for 40% of all queries, local business owners and SEO professionals must understand which factors influence AIO appearances to adapt their strategies accordingly.

Discussion

This section examines the impact on local business SEO and the broader search context given the results we found during our investigation.

Implications for Local Business SEO

The findings from our AI Overviews analysis have profound implications for local business SEO strategies. With over 40% of local business queries now triggering AI-generated content at the top of search results, local business owners and SEO professionals must adapt their approach to maintain visibility.

Intent-Based Strategy: Informational vs. Commercial/Navigational

The stark contrast between AIO appearance rates for informational queries (~58%) versus commercial (17.2%) and navigational queries (10.5%) reveals a deliberate strategy by Google to preserve the traditional search experience for high-commercial-intent and directly navigational searches. This creates both challenges and opportunities for local businesses:

1. Challenge: Local businesses may see reduced organic traffic from informational queries as users find answers directly in AIO content without clicking through to websites.
2. Opportunity: The relatively low AIO appearance for commercial and navigational queries means that traditional local SEO tactics remain effective for capturing high-intent traffic.

This divide suggests that local businesses should pursue a multi-pronged SEO strategy:

• Optimize commercial-intent landing pages for traditional organic and local pack rankings.
• Develop strong branded navigational content to capture direct searches.
• Create comprehensive, authoritative content for informational queries that Google might cite within GAIO.

Location Specificity Advantage

One of our most actionable findings for local businesses is that including location names in search queries substantially reduces the appearance rates (46.1% vs. 35.0%) of AI Overviews. This provides a clear strategic advantage for location-specific content:

1. Local businesses should double down on location-specific keyword targeting, as these searches are more likely to preserve traditional organic and local pack visibility.
2. Content strategies should emphasize geo-specific information that ties directly to the business's service areas.
3. Local SEO campaigns should consider promoting location-specific search behaviors among customers through call-to-actions like "Search for [service] in [location]" rather than generic service searches.

The fact that "reason" queries show the largest difference (36 percentage points) when location is included suggests that local businesses should particularly focus on creating location-specific content that answers "why" questions about their services.

Service Business Vulnerability

The high Google AI Overviews appearance rates for service businesses (cleaning services, legal services, photographers, etc.) indicate these sectors face the greatest potential disruption from AI-generated content. For these local businesses:

1. AIO may fundamentally change how consumers research services before making contact.
2. Traditional informational content marketing strategies may become less effective at generating website traffic.
3. Being cited as a source within AI Overviews becomes increasingly important for brand visibility.

Service businesses should prioritize creating content that addresses specific customer questions comprehensively and with local relevance, as this information is likely to be incorporated into the responses Google AI Overviews provide.

Query Length Strategy

The strong correlation between query length and AI Overviews appearance suggests that user search behavior itself influences the likelihood of triggering AI-generated content. This insight has implications for local keyword targeting:

1. Short, commercial keywords (e.g., "plumbers near me") remain valuable as they rarely trigger AI Overviews.
2. Long-tail informational queries now frequently deliver AI Overviews, potentially reducing organic click-through rates.
3. Featured snippet optimization tactics may need to evolve toward AI Overviews optimization for longer, locally-relevant queries.

Broader Context: The Evolution of Local Search

The introduction and rapid expansion of Google AI Overviews represents a fundamental shift in Google's approach to local search. Rather than simply directing users to relevant local business websites, Google is increasingly becoming the destination for information itself. This evolution has parallels to previous search features like featured snippets and local packs, but with significantly broader application through AI-generated content.

For local businesses, this shift accelerates the trend toward Google as an intermediary between businesses and potential customers. While this creates challenges for traditional traffic-focused local SEO, it also offers opportunities for businesses that can adapt by:

1. Optimizing for presence within the new AI Overviews ecosystem.
2. Focusing on differentiating factors that drive customer decisions beyond basic informational content.
3. Developing multi-channel marketing strategies that reduce dependence on organic search traffic.
4. Leveraging location-specific advantages that preserve traditional search visibility.

Conclusion & Recommendations

Key Takeaways

Our comprehensive analysis of the Google AI Overviews feature reveals several critical insights for local businesses and SEO professionals:

1. Prevalence: AI Overviews appear in 40.2% of local business searches, representing a significant shift in local search result presentation.
2. Query Intent Patterns: Reason queries (59.9%) and informational queries (58.3%) are much more likely to trigger AI Overviews than commercial queries (17.2%) or navigational queries (10.5%), creating a bifurcated search landscape for local businesses.
3. Location Effect: Queries that include specific location names have significantly lower Google AI Overviews appearance rates (35.0%) compared to non-location-specific queries (46.1%), offering a strategic advantage for local SEO.
4. Business Category Impact: Service-based businesses, particularly cleaning services (65.0%), legal services (62.1%), and creative professionals (61.8%), face the highest AI Overviews exposure rates.
5. Query Length Effect: Longer, more detailed queries are 3-4 times more likely to trigger AI Overviews than short queries, correlating with the informational nature of longer searches.

These findings demonstrate that AIO is not uniformly applied across all search contexts, but rather appears to be strategically implemented based on query intent, device type, location inclusion, and business category - providing multiple avenues for local businesses to adapt their strategies.

Strategic Recommendations

Based on our findings, we recommend the following strategies for local businesses adapting to the AI Overviews landscape:

For All Local Businesses

1. Location-Focused Content Strategy:
   • Emphasize location names in your content, if you want to avoid appearing in AI Overviews (and instead trigger a local pack).
   • Create location-specific pages for each service area you target.

2. Intent-Based Optimization:
   • Focus primary SEO efforts on commercial and navigational queries with lower AI Overviews appearance rates.
   • Create locally-relevant content for common questions customers ask before purchase.
   • Optimize your Google Business Profile to capture the local pack for high-intent searches.

3. Google AI Overviews Source Optimization:
   • Structure your content to be easily cited within AI Overviews.
   • Include clear, concise answers to common local customer questions.
   • Do not rely on proximity alone if you want to appear in AI Overviews.
4. Monitor Brand Mentions:
   • Track which of your target local keywords trigger AI Overviews.
   • Analyze competitors being cited in AI Overviews results for local queries.
   • Adjust local content strategy based on the AI Overviews appearance patterns in your market.

For High-AIO-Exposure Local Businesses (Service Categories)

1. Local Authority Building:
   • Create comprehensive local service guides that answer specific customer questions.
   • Develop location-specific expertise content that Google may cite as authoritative sources.
   • Consider developing multimedia content like videos showcasing your local services.

2. Local Differentiation Strategy:
   • Emphasize unique local selling points that differentiate your business from generic AI Overviews answers.
   • Highlight local expertise, community connections, and specific service area knowledge.
   • Create content that showcases your local reputation and trustworthiness.
3. Traffic Diversification:
   • Expand local marketing channels beyond organic search.
   • Develop direct customer relationship strategies (email, local social media).
   • Consider strategic local paid search campaigns for high-value keywords where AI Overviews appear frequently.

Future Outlook for Local Search

The Google AI Overviews product is likely still in its early stages of development and deployment; just yesterday (May 20, 2025), Google announced the expansion of AI Overviews and the inclusion of AI Mode as next steps, for example. We anticipate several potential evolutions in the local search landscape:

1. Expanded Coverage: AI Overviews will likely appear for an increasing percentage of local searches over time, potentially including more commercial queries.
2. Enhanced Local Context: AI Overviews responses may become more tailored to local relevance and specific geographic contexts.
3. Local Business Adaptation: Citation within AI Overviews for local business information may become a primary SEO objective for informational content.

For local businesses, staying adaptable to these changes while maintaining focus on delivering value beyond what AI can summarize will be essential for long-term local search visibility.

Conclusion

Google AI Overviews is not fully local - yet. But the signals we're seeing suggest that proximity will increasingly play a role, just not in the traditional way. Businesses that want to stay ahead will need to understand when and where AI Overviews visibility shifts. Tracking those patterns is the next frontier of local SEO.

By understanding the patterns in AI Overviews appearances and tailoring strategies based on business category, query intent, and location-specific advantages, local businesses, chains and enterprise brands can maintain their search visibility and continue to attract customers in the local search landscape which is changing regularly.

Acknowledgments: Thanks to several peers for early feedback on this analysis.