Search didn’t disappear in 2025, but it fundamentally changed how decisions are made. Instead of comparing ten blue links, people increasingly ask full questions, get synthesized answers, and move on. As a result, discovery is no longer a set of isolated touchpoints, but a connected system where visibility, trust, and action happen together.
This article recaps what actually changed in AI search during 2025, why reviews and local data suddenly mattered more than ever, and what brands should take into 2026 if they want to stay discoverable in an AI-driven world.
2025 Was the Year AI Became Part of the SERP
AI-powered features stopped being “tests” and became default experiences. Google expanded AI Overviews and introduced AI Mode across Search and Maps, while conversational interfaces increasingly shaped how users asked questions and made decisions.
Instead of typing fragmented keywords, users asked things like “Is this café worth going into?” or “Which gym nearby has parking and a sauna?” often without clicking anything at all.
Independent research confirmed this behavioral shift. According to McKinsey & Company, around 50% of consumers already use AI-powered search, and AI-generated summaries appear in a growing share of Google queries, with projections exceeding 75% by 2028.
The implication is clear: decision-making is moving into AI interfaces, not just happening after a click.
At the same time, we observed that Google steadily blurred the line between traditional results, AI answers, Maps, and local packs. AI features were embedded directly into Google Business Profiles, reviews, and Maps discovery, especially for local intent.
Fewer Clicks, Faster Decisions, Higher Stakes
One of the most debated topics in 2025 was whether AI summaries “kill clicks.” The answer turned out to be more nuanced.
Some studies showed declining click-through rates for informational queries, while others suggested users still clicked, just later and with higher intent. What did change consistently was where decisions happened.
Research from Pew Research Center and McKinsey indicates that many users now complete their evaluation before ever visiting a website. By the time they click, they are often already confident in their choice.
For brands, this means visibility alone is no longer enough. If AI systems don’t clearly understand, trust, and recommend your location or service, you may never enter the consideration set at all.
Shift in Success Metrics
The shift isn’t only about fewer clicks. It’s also forcing teams to rethink how they measure “local performance” in the first place. For years, local SEO reporting leaned on rankings and traffic as the main proof points.
But when discovery and evaluation happen inside AI summaries, local packs, and Maps experiences, the stronger indicators are actions and outcomes: calls, direction requests, bookings, message volume, and the quality of leads those actions generate. In other words, the question isn’t just “Did we get the click?,” it’s “Did we get chosen, and did it turn into real demand?.”
Reviews Became One of the Strongest AI Signals
If 2025 taught us one thing about local discovery, it’s this: reviews are no longer just social proof, they are training data.
AI systems increasingly rely on reviews to understand what a location is known for, not just how it’s rated. Language patterns, sentiment, frequency, and recency all feed into how AI summarizes and compares options.
This also explains why 2025 was such a turbulent year for reviews. Many businesses experienced disappearing reviews, stricter moderation, delayed reinstatements, and sudden profile suspensions, often after small changes.
These shifts likely reflect Google tightening data quality as AI systems depend more heavily on trustworthy signals. From an AI perspective, inconsistent or suspicious reviews look risky and reduce confidence.
Gemini and GPT Pushed Local Search in Different Directions, But Toward the Same Goal
While Google deepened AI inside Search and Maps, ChatGPT made quieter but meaningful progress in local discovery.
By late 2025, more than 60% of ChatGPT users were performing local searches. ChatGPT began surfacing local knowledge panels, leaned more heavily on Google Business Profile data, and improved its ability to summarize nearby options.
Despite their different ecosystems, Gemini and GPT converged on the same behavior:
they evaluate, filter, and recommend, rather than list options.
AI doesn’t “rank” businesses the way search engines used to. It selects them based on confidence. That confidence comes from clarity, consistency, and corroboration across multiple trusted sources.
How AI Search Exposed Weak Local Foundations
One of the clearest lessons from 2025 is that AI didn’t introduce new problems, instead it amplified existing ones. AI systems struggled with:
- Inconsistent opening hours
- Duplicate or outdated locations
- Missing attributes and amenities
- Generic or unstructured local pages
- Sparse or stale reviews
When faced with ambiguity, AI systems don’t ask follow-up questions. They simply choose another option.
This aligns with McKinsey’s finding that brand-owned sources often account for only 5–10% of the information AI systems reference, meaning AI validates claims across a wide ecosystem. If your data isn’t aligned across that ecosystem, visibility becomes fragile.
Is This Still SEO Or Something Else Entirely?
Throughout 2025, the industry debated whether AI optimization is just “good SEO” or something fundamentally different.
The truth sits somewhere in the middle.
Traditional SEO focuses on pages, rankings, and clicks. AI-driven discovery focuses on extractable information, context, and trustworthiness. Success isn’t measured by position, but by inclusion and recommendation.
That doesn’t replace SEO, but builds on it. Clean technical foundations, structured content, and credible signals are now prerequisites for being understood by AI at all.
There Is No Quick Fix
One practical lesson from 2025 is that some brands can’t “optimize their way” into immediate visibility if they’re starting with a trust gap, for example, thin local pages, weak review activity, or inconsistent listings.
In those cases, the smartest move is to treat AI local discovery like a two-track system. You still clean up the foundations (data accuracy, reviews, pages), but you also build trust outside the SERP: stronger brand presence, community and PR activity, social proof in places customers already spend time, and campaigns that generate recognition before a customer ever searches.
That’s how you stop relying on a single ranking moment and start creating demand that AI systems can understand across multiple signals.
Where PinMeTo Fits in the New AI Search Reality
What AI-driven discovery primarily rewards is clarity. Brands become invisible when their local data, reviews, and signals don’t tell a consistent story. PinMeTo acts as the connective layer that removes this friction, aligning data, signals, and actions across the local ecosystem so AI systems can confidently understand and recommend each location.
Reducing ambiguity across platforms
When opening hours, services, or location details differ across search engines, maps, and social platforms, AI systems lose confidence. PinMeTo helps brands maintain one reliable, aligned version of their presence wherever customers and AI systems look.
Making local signals easier to interpret
AI systems don’t just read data, they interpret context. Structured location pages, consistent listings, and meaningful review signals help AI understand what each location offers, who it’s relevant for, and when it should be recommended.
Keeping trust signals current and credible
AI favors brands that appear active, accurate, and trusted. Fresh reviews, up-to-date attributes, and timely responses signal reliability, and directly influence whether a location is included or excluded in AI-driven recommendations.
What Brands Should Take into 2026
AI search isn’t replacing traditional discovery overnight, but it is reshaping expectations.
Brands that perform well in AI-driven environments tend to do a few foundational things exceptionally well:
- Keep local data accurate and aligned across platforms
- Maintain steady, authentic review activity
- Clearly communicate what each location is known for
- Use structured, location-specific pages
- Monitor visibility beyond just Google
Dive deeper into the most effective AI search optimization tactics.
To Sum Up
AI local discovery has fundamentally changed how customers find and choose nearby businesses. Instead of browsing lists, users increasingly rely on AI systems to interpret intent, evaluate options, and recommend locations with confidence. In this environment, discovery happens before the click, right inside AI interfaces that synthesize local data, reviews, attributes, and real-world signals into a single answer. For brands, this means visibility is no longer about ranking everywhere, but about being consistently understandable, verifiable, and trustworthy wherever AI looks.
FAQ: AI Local Discovery
What is AI local discovery?
AI local discovery refers to how artificial intelligence systems help users find, evaluate, and choose nearby businesses. Instead of showing long lists of results, AI systems interpret user intent, compare locations, and recommend businesses based on data such as location accuracy, reviews, attributes, and real-world signals.
How is AI local discovery different from traditional local search?
Traditional local search focuses on rankings, listings, and clicks. AI local discovery focuses on recommendations and confidence. AI systems synthesize information across multiple sources and often complete the evaluation step before a user ever clicks, meaning brands must be clear, consistent, and trustworthy to be included at all.
Which platforms influence AI local discovery the most?
AI local discovery is shaped by multiple ecosystems, including Google Search and Maps (powered by Gemini), Apple Maps and Siri, and conversational AI platforms like ChatGPT and Gemini. Each system evaluates local data slightly differently, but all rely on accurate business information, structured content, and customer signals.
Why do reviews matter so much for AI local discovery?
Reviews act as one of the strongest AI signals for understanding what a location is known for. AI systems analyze sentiment, recency, language patterns, and consistency across reviews to assess trust and relevance. A steady flow of authentic, recent reviews helps AI systems recommend a business with confidence.
What causes businesses to be excluded from AI local discovery?
AI systems often skip businesses when they detect uncertainty or conflicting information. Common issues include inconsistent opening hours, duplicate locations, missing attributes, outdated photos, weak review activity, or unstructured local pages. When AI lacks confidence, it simply chooses a clearer alternative.
Is AI local discovery just SEO under a new name?
AI local discovery builds on SEO but goes beyond it. While SEO focuses on pages, rankings, and traffic, AI local discovery focuses on extractable information, trust signals, and inclusion in AI-generated answers. Clean SEO foundations are still required, but clarity and consistency across platforms are what determine AI visibility.
How can brands improve their performance in AI local discovery?
Brands improve AI local discovery by keeping business data accurate across platforms, maintaining fresh reviews and photos, completing attributes and amenities, using structured location pages, and ensuring each location has a clear and distinct identity. Consistency across the ecosystem matters more than any single optimization tactic.
How does PinMeTo support AI local discovery?
PinMeTo helps brands reduce ambiguity at scale by centralizing local data, maintaining consistency across platforms, supporting structured location pages, and keeping trust signals like reviews and updates current. This makes it easier for AI systems to understand, verify, and confidently recommend each location.
Lily Adamyan, January 15
