Local SEO in the AI Era: Why Proximity Still Wins

‘Near me’ searches have not declined. They’ve quietly become the most competitive real estate in search — because now they’re being contested on two fronts simultaneously.

For years, local SEO was a relatively self-contained discipline: claim your Google Business Profile, earn reviews, build local citations, win the local pack. Straightforward. But in 2026, the same query that surfaces your three-pack result is also being fed into AI-generated summaries on ChatGPT, Gemini, and Perplexity — systems that don’t pull directly from Maps, but do synthesise structured data, reviews, and web content to construct their own version of “best near you.” If your local presence isn’t built for both, you’re likely winning one battle and losing the other.

The ‘Near Me’ Signal Hasn’t Weakened — It’s Migrated

SEO.com’s analysis of current local search behaviour confirms what practitioners are seeing in the data: “near me” as an explicit modifier has actually plateaued in raw volume, but implicit proximity intent has expanded dramatically. Users increasingly skip the phrase entirely because search engines have become good enough at inferring location from device signals, search history, and session context. The implication is counterintuitive — optimising purely for the keyword “near me” is now a weaker strategy than optimising for the proximity signal itself.

For Southeast Asian markets, this shift is sharper. With mobile internet penetration above 70% across Indonesia, Thailand, and the Philippines, and the majority of search happening on mobile within apps or via voice, location context is almost always present in the query environment. A Jakarta consumer searching “kopi susu terdekat” on their phone is implicitly triggering proximity logic before they finish typing. Your Google Business Profile’s completeness, review velocity, and category accuracy are doing more work than any keyword in your website copy.

GEO Is Now a Local SEO Problem Too

Generative Engine Optimisation — structuring your content so AI systems cite and surface it — started as a concern for informational and e-commerce queries. It’s now directly relevant to local search. Search Engine Journal’s upcoming webinar with OpenAI on selling through ChatGPT signals something the industry has been slow to internalise: AI interfaces are beginning to handle transactional and location-sensitive queries, not just research-phase ones.

When a user in Singapore asks ChatGPT “which gym near Tanjong Pagar has good classes for beginners,” the system draws on indexed web content, structured data, and review sentiment — not the Maps API. Businesses that have invested in structured schema (LocalBusiness, Review, FAQPage), detailed service-area pages, and consistent NAP data across the web are disproportionately represented in these AI responses. Those that relied solely on their GBP are invisible.

The practical implication: local SEO teams need to start thinking like GEO practitioners. That means writing location-specific content that answers specific, intent-heavy questions — not just maintaining a landing page with your address and phone number.

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What Actually Needs to Change in Your Local SEO Stack

SEO.com’s reporting highlights three operational shifts that distinguish local programmes performing well in the current environment from those coasting on 2023-era tactics.

First, review management has become a content signal, not just a trust signal. The text of your reviews — the specific services mentioned, the neighbourhood references, the problem-solution language — is being parsed by AI systems looking for relevance signals. Responding to reviews with keyword-rich, contextually relevant replies is no longer optional housekeeping; it’s content strategy.

Second, local landing pages need to earn their keep with specificity. A page for “Digital Marketing Agency — Kuala Lumpur” that contains one paragraph of generic copy and a Google Map embed is not a local page; it’s a placeholder. Pages that address neighbourhood-level context, reference local landmarks or business districts, answer hyper-specific questions (“Do you work with SMEs in Bangsar South?”), and include genuine local social proof will earn both rankings and AI citations.

Third, citation consistency across Southeast Asian platforms matters more than most teams acknowledge. Foursquare and Yelp citations are largely irrelevant in this region — but consistency across Grab, Klook, Agoda (for hospitality), and local business directories in each market is directly relevant to how AI systems cross-reference and validate business information.

The GBP-AI Gap Is a Tactical Opportunity Right Now

Here’s the thing about competitive windows: they close. Right now, the gap between what Google Business Profile signals and what AI-generated answers surface is wide enough that brands with strong GBP hygiene but weak web presence are winning traditional local packs while losing AI citations — and vice versa. The brands that bridge both will disproportionately own local intent queries within 18 months.

The practical playbook isn’t complicated. Audit your structured data for LocalBusiness schema completeness. Build or refresh service-area pages with genuine depth — 600 words minimum, question-answer format, neighbourhood-specific. Establish a review response cadence that treats every response as a piece of indexable content. And monitor which local queries are now returning AI Overviews or ChatGPT-style responses in your markets, because those are the queries where your GBP alone is no longer enough.

For multilingual SEA markets, add one more layer: ensure your schema, GBP categories, and landing page content are consistent across language versions. A Thai-language GBP listing that doesn’t match the structured data on your Thai-language landing page is a trust signal mismatch that both Google and AI systems penalise implicitly.

Proximity was never just about being close. It’s about being findable, credible, and relevant — at the exact moment someone decides they want something near them. That calculus now runs across two parallel systems. The brands that understand this are already building the infrastructure. The question is whether the gap they’re opening is still closeable for those who haven’t started.

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At grzzly, we work with mid-to-large brands across Southeast Asia on exactly this kind of integrated local and AI search strategy — from GBP audits and structured data implementation to GEO content frameworks that actually get cited. If your local search programme was built for 2023 and the results are starting to show it, we’d enjoy the conversation. Let’s talk