The Definitive GEO Playbook for Local Search & Marketing (2026)

Key findings

Why is AI the biggest shift in local discovery in 20+ years?

Discovery is shifting from people comparing search results themselves to AI agents that compare for them. Over half of consumers now turn directly to AI-driven systems such as ChatGPT, Gemini, and Perplexity, which interpret intent and recommend businesses on the user’s behalf. If AI cannot understand or trust a location, that location’s visibility disappears.

Local commerce is undergoing its most significant transformation in over two decades. For years, discovery was dominated by Google: consumers searched, scanned, ranked results, compared options, and made decisions. That behavior still exists, but it no longer defines how discovery works at scale.

The consumer doesn’t search anymore. Increasingly, their personal AI does.
Our research shows a seismic shift in both frequency and flow. Consumers are searching more often and across more surfaces, with over half now turning directly to AI-driven systems such as ChatGPT, Gemini, and Perplexity. These platforms do not return ten blue links. They interpret intent, aggregate signals from across the web, and surface recommendations on the user’s behalf, before brands are actively evaluated.

This marks a fundamental shift in power and pace. Discovery is faster, more automated, and increasingly mediated by AI agents rather than humans manually comparing options. The risk for brands is clear: if AI systems cannot confidently understand, trust, or recommend a location or brand, visibility simply disappears. The opportunity is equally large.

An estimated $750bn in US consumer spend will flow through AI-driven search and discovery by 2028, concentrating value among brands that adapt now.

This playbook exists to help multi-location brands and enterprise marketing leaders respond to that shift. Drawing on insights from 2,000+ consumers across the US, UK, France, and Germany, alongside 200 senior-level US marketers and third-party research, it explains how discovery is evolving and what AI systems now rely on to make decisions and recommend your brand to consumers.

More importantly, it provides practical guidance. It outlines the new architecture of discovery and the new operating models for the AI era. For CMOs and marketing teams, the question is not whether this shift is coming, but how quickly they can adapt to the new landscape where AI-mediated discovery is the default.

How has search moved from keywords to AI agents?

Search started with exact keyword matching in the 1990s, then added link-based ranking, an understanding of intent and entities, and personalized results. Today generative systems answer directly, and personal AI agents interpret intent, synthesize information from several sources, and present ranked options or direct answers, so AI increasingly decides which businesses consumers consider.

The evolution of search: from keywords to context

Keyword search

1990s

Early search engines relied on basic keyword matching, ranking pages by exact word frequency rather than meaning.

Link-based ranking

Late 1990s-2000s

With Google’s PageRank, links and authority became key ranking signals, improving relevance and trust.

Semantic search

2010-2018

Search engines started understanding intent, entities, and meaning, powered by advances like Google Knowledge Graph and NLP.

Personalized & contextual search

2015-2023

Results became tailored to users, factoring in location, device, search history, and real-time context.

Generative & answer-based search

Today

Search is shifting from links to answers, with AI and LLMs generating direct, synthesized responses (ChatGPT, Perplexity, Google AI Overviews, etc.).

New vs old: a comparison

Lexical search → the past

• How it Works: Matches exact keywords in documents.
• User Experience: Requires precise query formulation.
• Result Type: Provides a list of links containing keywords.

AI-powered search → the present

• How it Works: Understands query context and user intent.
• User Experience: Allows for natural, conversational queries.
• Result Type: Delivers direct answers and highly relevant results.

The end of the human-led search era

For decades, local discovery followed a predictable pattern. Consumers searched online, compared multiple results, and made decisions themselves. Search engines returned links, and humans did the work of interpretation.

That pattern is changing. In 2026, local discovery will increasingly take place within personal AI agents, rather than conventional search engines. These context-aware AI agents, capable of knowing individual preferences and expectations, will deliver deeply personalized answers to consumer queries. The consumer's task of manually sifting through search results is being replaced. Personal AI agents now mediate discovery by interpreting intent, synthesizing information from multiple sources, and presenting ranked options, summaries, or direct and personalized answers. Even when consumers say they are “using Google,” their experience is often shaped by AI-powered overviews, map results, and summarized recommendations rather than traditional link-by-link evaluation.

Consumer survey data further highlights this shift. 51% of consumers indicated that their usage of AI tools has increased over the past 6 months, and 56.1% of consumers say they read AI-generated overviews when they appear. Only 24.2% say they actively avoid AI summaries, indicating that AI-driven answers are already part of mainstream discovery behavior.

In practice, this means AI is increasingly acting as a gatekeeper. It shapes which businesses enter the consideration set, and this has profound implications for multi-location businesses.

How does AI collapse the consumer journey?

AI now handles the interpretation and comparison that consumers used to do step by step. The old journey ran from intent to search, results, comparison, selection, and action. In the new journey, a personal AI agent sits between intent and action, and the consumer never sees the alternatives.

As AI takes on more interpretation and comparison work, the traditional funnel has shortened dramatically.

Where discovery once unfolded across several stages, now it is compressed into a single, high-intent moment where intent, interpretation, and action happen in quick succession.

Old journey (2004-2024): Intent, Search, Results, Comparison, Selection, Action

New journey (2026): Intent, Personal AI Agent, Action

Search collapses. The funnel collapses. Comparison collapses.

One Intent. One Agent. One Selection.

The consumer never sees the alternatives. The entire middle section of the traditional funnel, where brands historically competed for visibility, has been eliminated.

Brands face a clear implication: visibility gaps directly cause lost revenue at the peak of consumer intent, where AI dictates which businesses are visible during early decision-making.

Consumers most often turn to AI for comparison and narrowing choices. Among consumers who use AI tools, 53.1% would use an AI chatbot to compare product features, while 42.9% would use it to find restaurants with specific qualities, such as dietary needs, atmosphere, or proximity. AI is also used for planning: 32.2% would use it to build itineraries or structured plans, especially when multiple variables are involved. Even for simple lookups, 25.8% would use AI to find basic information like phone numbers or addresses.

As AI becomes embedded at each of these moments, it increasingly shapes not just how consumers search, but which options are even considered. By the time a consumer validates a choice, AI has already filtered, summarized, and prioritized a small set of businesses based on the signals it can interpret. This shift elevates AI to a powerful gatekeeper of discovery. The question is not just to what extent AI influences the journey, but how accurately it represents the brands competing within it.

Why are brands missing or misrepresented in AI recommendations?

AI systems rebuild an understanding of each business from the signals they can access, such as listings, reviews, photos, and attributes, so brand order shifts between answers. When those inputs are fragmented or outdated, AI fills the gaps with approximation, and multi-location brands repeat small data issues across hundreds or thousands of locations.

68% of brands do not appear in AI-generated lists within their own categories (Pulp Strategy GEO Benchmark Index 2025, via Social Samosa).

AI is becoming the primary gatekeeper of discovery, and with it comes both a material opportunity and a growing threat for location-based brands. The very brand identity that companies have spent years and billions building is now being interpreted, reshaped, and in some cases distorted by AI systems that decide what is surfaced, summarized, and recommended.

The risk is already visible. A significant majority of brands now experience negative sentiment skew in AI-generated responses, inconsistent naming and recognition across AI models, or factual errors and misstatements. These are not edge cases. They are signals that AI systems are struggling to reconcile fragmented, outdated, or incomplete inputs at scale. When AI cannot establish confidence, it fills the gaps with approximation.

• 90% of consumer brands show a negative sentiment skew in AI responses
• 88% suffer inconsistent naming and recognition across AI models
• 52% face factual errors or misstatements

Source: Pulp Strategy GEO Benchmark Index 2025, via Social Samosa.

This is because AI does not surface results the way traditional search engines do. Brands do not “rank” in AI-generated answers in a relatively stable or predictable order. Instead, AI systems reconstruct an understanding of a business based on the signals they can access, interpret, and align. That reconstruction is only as strong as the underlying data.

Local signals play a decisive role. Listings accuracy, reviews, photos, attributes, and location-specific content all influence how AI summarizes and represents a brand. When those inputs are consistent and high quality, AI produces clear, confident recommendations. When they are fragmented, the output reflects that fragmentation, often in ways brands do not control or even see.

For multi-location brands, this creates a critical exposure point. With hundreds or thousands of locations, small data issues are multiplied at scale. An incorrect opening hour, a missing attribute, or an outdated photo does not remain isolated. It becomes part of the AI’s collective understanding of the brand. Over time, this erodes what can be described as “local truth at scale.” AI systems, designed to prioritize the most reliable and consistent information, will favor competitors whose local signals are cleaner and more unified, regardless of brand size or awareness.

Consumer behavior reinforces this dynamic. AI summaries are frequently read, but they are rarely treated as definitive. Only a small minority of consumers trusts an AI-generated summary without validation. Most move immediately to review platforms, maps, or source links to confirm what they see. This means AI does not replace trust signals. It amplifies them. Any weakness in local reputation or information quality is exposed faster and more visibly.

The implication for brands is clear. AI visibility is not a new channel to optimize in isolation. It is the outcome of how well local data, reputation, and content are governed and maintained across the entire digital ecosystem AI draws from. As AI becomes more deeply embedded in discovery, the cost of poor local signals rises sharply, and the margin for error narrows.
For brands that act, the opportunity is significant. AI rewards clarity, consistency, and credibility at scale. Brands that establish a reliable source of local truth, bolster their reputations, engage with consumers digitally, and maintain these activities continuously, are the ones that will control their representation in AI-driven discovery. For those who do not, AI will still tell their story. It just may not be the one they intended and that could be extremely costly.

Customer spotlight: Audika

“With AI-generated responses increasingly influencing the customer journey, we wanted to seize this opportunity and ensure we were among the first brands able to measure and optimize our presence.”

Dylan Paul, Digital, Audika

Read the story

What is Generative Engine Optimization (GEO)?

Generative Engine Optimization (GEO) is the strategy layer built for how AI systems read and summarize businesses. It works on the data AI depends on, such as listings, attributes, reviews, photos, and location signals, and keeps that data structured and consistent so AI can tell what a brand offers, where it operates, and why to recommend it.

With AI now acting as the primary interpreter and gatekeeper of discovery, brands must move beyond traditional optimization and adopt a new strategic layer designed specifically for AI-driven understanding: Generative Engine Optimization (GEO).

GEO addresses how AI systems interpret and summarize businesses. These systems rely on structured, consistent, and entity-rich data to form an understanding of what a brand offers, where it operates, and why it should be recommended. Listings, attributes, reviews, photos, and location signals all contribute to this interpretation.

Consumer behavior reinforces the importance of this layer. AI summaries are frequently read, but rarely treated as definitive. Most consumers continue by checking review platforms, maps, or source links, which place greater weight on the underlying data AI draws from. When that data is incomplete or inconsistent, the resulting summaries reflect those gaps.

Does GEO replace SEO?

GEO does not replace Search Engine Optimization (SEO). It extends it, ensuring that the information feeding AI-driven discovery is accurate, consistent, and aligned across locations. SEO remains essential as it provides the baseline visibility that allows brands to be discovered across digital surfaces.

What has changed is its role. Traditional search results increasingly sit beneath AI-generated answers, summaries, and recommendations that interpret information and make recommendations before consumers engage further. These experiences are already visible through AI Overviews and emerging AI-led search modes.

This shift is widely recognized within the local search community. As Darren Shaw, Founder of Whitespark, explains:

“Have you tried Google’s AI Mode? That’s a glimpse into what the future of local search will look like. The traditional search results will be replaced with AI results. We’re seeing them test the waters right now with AI Overviews.”

Darren Shaw, Founder at Whitespark

Crucially, this evolution does not invalidate strong SEO fundamentals:

“If you were doing good SEO before, then you should not need to change much.”

SEO continues to underpin discovery, but it does not govern the entire experience on its own as arguably it once did. As AI-driven discovery expands beyond any single channel or tactic, brands need a way to operationalize visibility, trust, and performance across every location.

What is Location Performance Optimization (LPO)?

LPO is the operating model that connects SEO and GEO enabling real-world outcomes across every location. It treats each location not as a static listing, but as a performance asset whose visibility, reputation, engagement, and conversion must be continuously optimized.

LPO works across four dimensions:

• Visibility
• Reputation
• Engagement
• Conversions

In an AI-mediated environment, these signals are inseparable. AI systems do not evaluate listings, content, or reviews in isolation; they synthesize them to determine confidence, relevance, and trust at scale. LPO provides the framework to manage that synthesis, ensuring that what AI interprets aligns with what consumers validate and how businesses actually perform.

As AI increasingly mediates discovery, LPO becomes essential to maintaining local truth, sustaining visibility, and turning AI-driven attention into measurable business impact across hundreds or thousands of locations. This operating model comes to life through three interdependent components that combine to form a robust AI search strategy.

Learn More About LPO

How do multi-location brands win in AI search?

The playbook’s strategy rests on three interdependent pillars, with SEO underneath. A verified source of truth for location data provides certainty, a dynamic content strategy proves relevance, and orchestration keeps data, content, and reviews consistent across every location, so AI can understand, trust, and recommend the brand.

1. Source of Truth: Location Data. Verified, structured facts about your business, synchronized across the entire ecosystem
2. Context & Relevance Engineering: Dynamic Content Strategy. Living proof of relevance: reviews, FAQs, hyper local content, social posts, offers, etc., dynamic, continuously gathered, structured, and amplified
3. Orchestration Capability: Outcome. AI agents that orchestrate across truth & context, decisioning, execution and distribution, in real-time

1. Source of truth: location data

AI systems reward certainty. The first pillar is establishing a verified, structured, and authoritative source of truth for every location.

This means ensuring that core business facts such as name, address, hours, services, attributes, and categories are accurate, consistent, and continuously maintained across the entire digital ecosystem. Listings, maps, directories, and owned properties must align because AI models synthesize information from multiple sources rather than trusting a single one.

When location data is fragmented or outdated, AI-generated summaries inherit those flaws. When it is consistent and structured, AI systems can confidently represent what a business does, where it operates, and whether it meets a user’s intent. Technical SEO underpins this pillar, ensuring data is crawlable, structured, and accessible, but the objective extends beyond search engines to any system interpreting local entities.

To establish location-specific sources of truth, meticulously manage your business profiles (like Google Business Profile and Apple Business Connect) and your website’s local pages. These profiles are the primary digital front door for high-intent local customers and are critical for AI search visibility, acting as the verified source of location representation and recommendations.

Websites provide high-quality, unique data for AI models to learn from, cite, and enable multimodal search. Notably, 41.6% of consumers visit a company’s website before deciding to visit a location (Uberall’s 2025 consumer report, The Lowdown on Local Search), highlighting its importance to human consumers as well as AI systems.

Ensuring accurate, complete, and actionable profiles and webpages is the most critical step in any AI search strategy, providing the structured data AI systems need for accurate recommendations.

Key actions:

• Standardize core location data.
  Ensure hours, addresses, services, and attributes are accurate and consistent across all platforms. AI systems aggregate signals from multiple sources; inconsistencies reduce confidence and suppress visibility.
• Validate geospatial accuracy.
  Correct map pins and coordinates are critical. AI weights proximity and navigation signals heavily when recommending local options for high-intent queries.
• Supply rich visual signals.
  Maintain current, high-quality photos across profiles and pages. Visual assets help AI validate legitimacy, category relevance, and real-world presence.
• Expose clear action paths.
  Enable structured actions such as call, book, and directions. These signals reinforce intent alignment and are frequently referenced in AI-generated recommendations.
• Maintain presence on authoritative directories.
  Trusted third-party platforms strengthen entity confidence and provide corroborating signals AI systems rely on for verification.
• Structure location content for AI interpretation.
  Reflect “near me” and conversational intent naturally in location pages, FAQs, and service descriptions to align with how AI parses user queries.
• Implement LocalBusiness schema at scale.
  Structured data allows AI and search systems to accurately interpret hours, services, pricing, and availability without ambiguity.
• Create dedicated, indexable location pages.
  Location-specific pages help AI connect brand entities to local context, services, and neighborhoods, strengthening relevance in AI-mediated discovery.

2. Context and relevance engineering: dynamic content strategy

AI evaluates relevance in context. The second pillar focuses on providing living proof of relevance through content that reflects real customer needs, intent, and scenarios.

This includes FAQs, service descriptions, location-specific pages, reviews, and hyperlocal content that explains what a business offers, and why it is the right choice for a given situation or set of requirements. Content must be refreshed, expanded, and structured so AI systems can extract meaning, connect attributes, and understand nuance.

As consumer behavior becomes more conversational and intent-driven, AI relies on this contextual layer to filter and prioritize options. Content that is generic, thin, or disconnected from location realities limits how effectively a brand can be surfaced in AI-mediated discovery.

Key actions:

• Engineer intent-aligned FAQs and service content.
  Create structured, location-specific answers to common questions so AI systems can match nuanced intent with clear, authoritative responses.
• Continuously generate and refresh reviews.
  Maintain a steady flow of recent feedback to provide AI with up-to-date sentiment, experience signals, and proof of ongoing relevance.
• Respond to reviews at scale.
  Consistent, timely responses signal operational reliability and customer care, reinforcing trust signals AI uses to evaluate relevance and credibility.
• Localize offers and incentives in structured formats.
  Publish location-specific promotions and attributes that AI can associate with value, availability, and revisit intent.
• Publish hyperlocal content tied to real-world context.
  Reflect neighborhoods, services, and use cases to help AI understand where and why a location is relevant, not just what it offers.
• Align social content with location entities.
  Connect social profiles and posts to specific locations so engagement and proof points reinforce AI’s understanding of local relevance.
• Earn third-party validation.
  “Best of” lists, guides, and local features provide authoritative signals for AI systems to reference.
• Refresh content continuously, not periodically.
  Update FAQs, attributes, and descriptions as services, seasons, and demand change to keep AI interpretations current and accurate.

Customer spotlight: Cushman & Wakefield

“We’re rewriting key content to be more LLM-friendly, adding richer context, clearer intent, and structured detail. We’re improving over 400 location pages to make sure AI assistants can understand exactly what each location offers.”

Flo Guerrouani, Global SEO Lead, Cushman & Wakefield

3. Orchestration capability: outcome at scale

The third pillar is orchestration: the ability to execute, adapt, and distribute signals across locations in real time.

AI search rewards brands that operate as coordinated systems rather than collections of isolated locations. Orchestration capability brings together data, content, reviews, and engagement signals, supported by AI agents that assist with execution at scale. These agents can monitor changes, generate updates, respond to feedback, and help maintain consistency across hundreds or thousands of locations.

Without orchestration, even strong data and content decay quickly. With it, brands can maintain continuous readiness, respond to shifts in demand or AI interpretation, and ensure that improvements in one area are reflected everywhere they matter.

Key actions:

• Centralize control, enable local execution.
  Define global standards for data, content, and responses while allowing local teams to adapt within clear guardrails that AI systems can interpret consistently.
• Deploy AI agents as operators, not experiments.
  Use AI to monitor changes, generate updates, respond to reviews, and maintain accuracy continuously across locations.
• Continuously monitor AI visibility and representation.
  Track how locations appear in AI-generated answers, summaries, and recommendations to identify drift, gaps, or emerging opportunities.
• Close the loop between insight and execution.
  Ensure that changes in data, reviews, or content are propagated across all relevant platforms and discovery surfaces without delay.
• Coordinate signals across systems and teams.
  Align listings, websites, reviews, social content, and engagement tools so AI receives consistent, reinforcing signals from every source.
• Adapt in real time to demand and interpretation shifts.
  Respond quickly to seasonality, local events, operational changes, or AI model updates that affect relevance and visibility.
• Measure outcomes, not just activity.
  Evaluate orchestration effectiveness based on visibility, trust signals, engagement, and real-world performance across locations.

Together, these components define the strategy to win in AI search and a repeatable operating model for multi-location brands. A trusted source of location truth provides certainty. Contextual content establishes relevance. Orchestration turns both into sustained, measurable outcomes. SEO remains the foundation beneath them, but success in the AI era depends on how effectively these pillars work together to shape how brands are understood, trusted, and recommended at scale. This demands a shift in how marketing teams at multi-location brands operate.

What must marketing teams do differently in 2026?

Marketing leaders have to settle who owns local truth, run AI visibility as a continuous operation, and shift budgets and KPIs toward AI-native measures such as Share of Voice. In Uberall’s B2B survey of 200 senior-level US marketers, 61.5% find managing their local digital presence very or extremely complex, and 94.5% track share of voice in AI chatbot answers.

Read the full chapter: What marketing teams must do differently

What should multi-location brands do next?

Keep location data and content accurate at every location, update it continuously, and coordinate it across the platforms AI reads. SEO stays foundational, but AI search needs its own way of operating, because outdated listings, missing attributes, or weak trust signals are replicated across every location and surfaced by AI.

Local commerce has entered a decisive moment. The changes outlined in this playbook are not incremental, and they are not theoretical. They reflect a fundamental shift in how discovery works, how decisions are made, and how value is created at the local level.

Consumers are searching more frequently, acting faster, and relying on AI-generated answers, assistants, and summaries. The journey from intent to action has compressed into moments, mediated by systems that interpret, filter, and recommend before a human ever compares options directly. Trust signals such as reviews, accurate information, and real-world proof still matter, but the path to those signals is increasingly controlled by AI-driven intermediaries.

At the same time, marketing leaders recognize that AI search and generative discovery are reshaping the competitive landscape. Industry benchmarks show rising investment, growing confidence in GEO initiatives, and increasing reliance on integrated platforms. Yet readiness remains uneven. Many organizations struggle with fragmented data, siloed teams, limited measurement, and operating models designed for a slower, campaign-led world. This gap between consumer behavior and organizational capability is widening.

For multi-location brands, the implications are amplified. Every inconsistency scales with outdated listings, missing attributes, or weak trust signals being replicated across hundreds or thousands of locations and interpreted by machines at speed. In an AI-mediated environment, these gaps are profound. They are surfaced, summarized, and reinforced across discovery surfaces that increasingly shape which brands are considered and how they are perceived.

This is why AI search cannot be treated as another channel to optimize or another experiment to run. It demands a different way of operating. SEO remains foundational, but it is now only part of what is imperative for local visibility.

The direction of travel is clear. AI will increasingly mediate local search. The brands that succeed will be the ones that build local truth and contextual relevance at scale, maintain it relentlessly, and orchestrate it across the systems that now shape discovery.

Multi-location brands must act now to shape how they are discovered, trusted, and chosen as AI becomes the default layer between consumers and the physical world.

Those that wait will become irrelevant.

To put the three pillars into practice, the 90-Day Local GEO Checklist sets out a four-phase plan for the first 90 days.

How was the GEO Playbook researched?

Uberall Proprietary Research: Two commissioned surveys, both fielded in December 2025: one of 2,000 consumers across the US, UK, France, and Germany examining local search behavior, AI usage, trust signals, and decision drivers; and a second survey of 200 senior-level US marketers assessing AI search adoption, operational readiness, measurement practices, and investment priorities.

• 2025 Consumer Report | The Lowdown on Local Search (2025)
  Used as a longitudinal reference point to track how discovery behaviors and trust signals are evolving.
• Whitespark Local Search Ranking Factors
  Third-party research validating the continued importance of reviews, proximity, data consistency, and engagement in local discovery.
• Expert Commentary and Industry Perspectives
  Insights from search and AI practitioners, including leaders in local search, SEO, and generative AI, used to contextualize survey findings and market direction.
• Other Third-Party Research, Statistics & Benchmarks:
  AthenaHQ: State of AI Search & Generative Engine Optimization (AI visibility dynamics, GEO KPIs, operating models)
  90% of consumer brands face negative AI sentiment: Report

About Uberall

Uberall is the only multi-location marketing platform that connects your digital presence to local revenue impact. We help you grow by optimizing your location performance across visibility, engagement, reputation, and conversion.

Our AI-powered platform simplifies multi-location marketing with one easy-to-use solution. From powerful analytics, location data management, listings, reviews, store locators, and local pages to social media and messaging: everything you need to boost your local impact in one platform.

Founded in 2013 in Berlin, Uberall powers over 1.4 million business locations globally. Trusted by leading brands in retail, hospitality, and automotive, Uberall is shaping the future of location marketing.

Frequently asked questions

Do consumers trust AI-generated summaries of local businesses?

Consumers read AI summaries, but few rely on them alone. In the playbook’s consumer survey, asked about an AI overview at the top of a search page, 56.1% would read the AI overview and may still visit the website, 19.8% would read it and not visit, and 24.2% would skip it.

Which business profiles matter most for AI search visibility?

The playbook names business profiles such as Google Business Profile and Apple Business Connect, together with your website’s local pages. It calls these profiles the primary digital front door for high-intent local customers and the verified source AI uses to represent and recommend a location.

How do reviews affect visibility in AI search?

Reviews feed the context AI uses to judge relevance and credibility. A steady flow of recent reviews gives AI up-to-date sentiment and experience signals, and consistent, timely responses signal operational reliability and customer care. Consumers also check review platforms to confirm AI answers, so any weakness in local reputation shows up faster.

Do location pages still matter for AI search?

Yes. Dedicated, indexable location pages help AI connect a brand to local context, services, and neighborhoods, and LocalBusiness schema lets AI read hours, services, pricing, and availability without ambiguity. Websites also give AI models high-quality, unique data to learn from and cite. Cushman & Wakefield, for example, says it is improving over 400 location pages so AI assistants can understand each location.