Americans are already using artificial intelligence to find and evaluate local businesses, but that finding is not a straight replacement of search — it’s a fast-evolving complement that changes who wins attention and how merchants get discovered. A new consumer survey shows
Google Search (including Maps) remains the single most-used discovery surface, but
ChatGPT and other AI assistants are making rapid inroads, especially among frequent AI users; broader industry studies and platform-specific data underscore the same pattern: traditional search dominance alongside accelerating AI-driven discovery and clear operational risks for businesses that ignore the change.
Background
Why this matters now
Local discovery — the moment a consumer asks “Where do I get this near me?” — is the linchpin of store visits, bookings, and local commerce. The engines and assistants that answer that query determine which names a consumer considers, what facts they see (hours, price, reviews) and whether they eventually walk in, call, or convert online. The recent surge in conversational AI, combined with platforms that can now synthesize and act on real‑time local data, creates a new distribution layer for local signals. Businesses that appear in AI answers can capture demand without ever getting a direct click. Conversely, those absent from AI datasets risk being invisible during a crucial decision path.
The data sources reviewed
This piece synthesizes a new public consumer survey fielded by a digital strategist (reporting usage and preference shares), industry studies and vendor/agency analyses that measure public trust, tool penetration, and local discovery behaviors. Where possible, key numerical claims were cross‑checked across at least two independent research outputs to assess consistency and reliability.
Overview of the headline findings
- Google still leads discovery. In the consumer survey, 31.7% of respondents named Google Search (including Maps) as their top tool for finding local businesses. That single statistic underlines that web search and Maps are still foundational to local visibility.
- ChatGPT is non‑trivial and growing. The same survey showed 21.9% of consumers now prefer ChatGPT for discovering nearby businesses, a share that climbs among heavy AI users. This is an unusually fast adoption curve for an emergent discovery surface.
- Other assistants matter too. Google’s Gemini, Google AI Overviews, and Microsoft Copilot all appear meaningfully in consumers’ mental models for discovery, with Gemini and Overviews reporting double‑digit usage shares.
- AI reach is broad but usage intensity varies. Seventy‑five percent of Americans have used an AI system at least once in the past six months, but only about one‑third qualify as “Heavy AI Users” (daily or multiple times per day). That split means businesses must optimize for both legacy search and emergent AI surfaces.
- Trust remains tilted toward traditional search. Independent research finds consumers continue to trust Google much more than AI assistants for local business evaluation, frequently pointing to concerns about accuracy and timeliness of AI answers. In one industry study a large majority still stated a preference for Google when evaluating a local business.
The survey: what it measured and how reliable it is
Who was asked, and what they reported
The core dataset behind the headline numbers is a survey of 1,151 U.S. adults conducted through an online polling platform. Respondents were asked how often they use AI, which tools they use, and which they prefer when discovering local businesses. The survey reported a
2.83% margin of error at a 95% confidence interval and provided breakdowns by frequency cohorts — Heavy Users, Casual Users, and Users who rarely or never engage with AI.
Strengths of the data
- The survey is timely and granular about frequency of use, which matters for behavioral predictions.
- It includes named brands (ChatGPT, Gemini, Copilot) so readers get a sense of brand share rather than only tactical behavior.
Limitations and caution
- The sample is an online convenience sample rather than a probability‑based national panel. That means results can show directionally useful trends but are less robust for precise population estimates than random‑probability surveys.
- Self‑reported usage can overstate or misattribute platform names (consumers sometimes confuse platform brands), a problem the authors themselves acknowledge. This can inflate recognition metrics for lesser platforms or confuse related Google features.
- Independent industry reports show similar directional findings but differ on magnitude depending on methodology (panel analytics vs. recall surveys vs. vendor dashboards). Cross‑study comparison is necessary before concluding any single number is definitive.
Because of those caveats, specific percentages should be treated as
indicative rather than gospel; the strategic takeaway is the pattern (Google dominance, rapid ChatGPT growth, and a mixed trust environment), not any exact decimal point.
What the numbers say about discovery behavior
Who uses AI for local discovery?
- Heavy AI Users (about one‑third of respondents) are most likely to ask assistants for local business recommendations and are almost as likely to use ChatGPT as they are to use Google. Among that cohort, ChatGPT usage approaches parity with Google. This is critical because heavy users represent a disproportionately engaged audience and early adopters whose behavior often spreads.
- Casual Users (the largest cohort) still rely primarily on traditional search and review platforms, using AI irregularly and often for non‑local tasks (writing, brainstorming, learning).
What assistants look for when making recommendations
AI assistants and search‑integrated copilots pull signals from:
- Structured business listings (name, address, hours)
- Reviews and ratings
- Website content and schema markup (FAQ, services)
- Map data and booking availability when integrated with reservation systems
Different assistants weight these signals differently: some rely more on live web queries (Bing Copilot, Google Overviews), others on curated knowledge graphs or proprietary indexes (some versions of ChatGPT or vendor-specific models when linked to plugins). That means a business visible on Google Maps but absent from other structured feeds may still be omitted from some AI recommendations.
The trust and accuracy problem: hallucinations, stale data, and user expectations
AI can summarize and recommend quickly — but those same compressions bring two hazards:
- Hallucinations and attribution errors. Generative assistants can confidently assert incorrect facts or omit crucial context that a consumer relies on when choosing a business (e.g., closed hours, temporary closures).
- Data freshness. AI systems that do not query live sources (or that rely on stale cache layers) risk surfacing outdated contact details or booking windows. Consumers care deeply about timeliness for local intent (hours, reservations), and surveys repeatedly flag outdated information as a top complaint about AI recommendations.
Because of these problems, independent research shows consumers still prefer Google for the
reliability of local facts, even if they experiment with AI for idea generation or quick recommendations. That trust gap is AI’s single biggest short‑term obstacle to fully replacing search for local discovery.
Business implications: what merchants and local brands need to know
Visibility is now multi‑surface
Being visible in Google is necessary but no longer sufficient for future‑proofing local discovery. Businesses must ensure structured, machine‑readable presence across:
- Google Business Profile (GBP)
- Key aggregator feeds (Yext, Data Axle, etc.
- Website structured data (schema.org for LocalBusiness, FAQ, opening hours)
- Reservation/booking APIs if applicable (OpenTable, Resy, Mindbody, etc.
- Emerging AI indexes and answer‑engine feeds where available
If an AI assistant cannot ground its answer in a reliable, authoritative source that includes your business, it will not recommend you. In the AI era, the cost of being omitted is not just a lost click — it can be total exclusion from a consumer’s shortlist.
Practical checklist for local businesses
- Claim and fully populate your Google Business Profile; verify categories, hours, services, photos and booking links.
- Implement structured data (LocalBusiness, openingHours, service details, FAQ schema) across your site so answer engines can read facts easily.
- Maintain NAP consistency across directories and citation sources; inconsistent addresses and numbers degrade machine trust.
- Collect and respond to reviews — AI systems use ratings and sentiment as credibility signals.
- Provide real‑time availability and booking links (where relevant) so agentic assistants can surface actionable options.
- Monitor referral traffic and new sources of intent signals; instrument pages to detect emerging AI referrers.
Short‑term tactical wins
- Add a succinct, machine‑readable “what we offer” block on service pages to help summarizers synthesize intent quickly.
- Publish clear cancellation and booking policies to reduce AI‑sourced friction at the booking step.
- Use a canonical business facts page (single source of truth) that aggregators and publishers can reference.
Strategic concerns: competition, gatekeepers and economics
Who controls the shortlist?
When AI surfaces one or two recommendations rather than a list of dozens, the platform that selects those names gains outsized influence over who gets demand. That raises near‑term strategic questions:
- Are AI platforms likely to monetize or privilege partner booking integrations?
- Will businesses become dependent on a small set of AI indexes and face new referral economics?
Early agentic booking experiments demonstrate how discovery can move closer to transaction — and with that shift comes negotiation about commissions, integration fees, and new commercial terms. Businesses should watch how booking integrations and subscription tiers evolve in AI search products.
Regulatory and fairness considerations
Concentrating local referrals in a small number of curated answers invites regulatory attention. Issues include transparency (how were choices ranked?, competition (do platform defaults disadvantage rivals?, and consumer protection (who is liable for an AI recommendation that proves incorrect?. Businesses and local regulators will need clearer rules on accountability and disclosure as agentic features scale.
Cross‑checking and verification of key claims
- The headline preference shares (Google 31.7% vs. ChatGPT 21.9%) come from the digital strategist’s 1,151‑respondent survey and are replicated in that report’s public write‑up. The survey’s methodology is described in the report, and the author lists the online sampling sources and margin of error. Given a convenience sampling approach, these figures are best interpreted as directional but credible for showing relative brand momentum.
- Independent industry research and agency reports corroborate the general pattern: traditional search still dominates for trust and frequency, while AI assistant use is growing and shows higher adoption among frequent digital tool users. Multiple industry studies emphasize that AI must prove accuracy and currency before it displaces Google for local decisions. These independent studies reinforce the core conclusion even when their absolute numbers differ by methodology.
- Analysts and trade reports identify the same operational signals that AI systems consume (structured data, reviews, booking availability), giving businesses a consistent set of optimization priorities across platforms.
Where any claim depends solely on a vendor case study or a non‑probability survey, it is flagged in the analysis above as having caveats.
Risks and unanswered questions for local businesses
- Scope and pace of change. Will heavy AI users proportionally increase, or will AI remain a niche discovery path? If heavy usage expands rapidly, the discovery landscape could shift faster than many businesses can adapt.
- Attribution and measurement. Many AI-driven interactions leave little or no referral trace for site analytics, making it hard to measure impact. That will complicate ROI analysis for marketers.
- Data governance and privacy. Feeding business data into multiple indexes and third‑party AI services raises consent and privacy questions, especially for businesses that handle consumer data in regulated sectors.
- Content theft and accuracy. AI summarizers that quote or paraphrase publisher content without linking — or that misattribute claims — can create reputational or legal headaches for content owners.
Each of these risks should be considered as part of a fuller AI readiness and governance plan.
Tactical playbook for the next 12 months
- Short term (0–3 months)
- Audit and fix all public business facts (GBP, NAP consistency).
- Add or update schema markup on key pages.
- Ensure reservation and booking links are machine‑readable and live.
- Medium term (3–9 months)
- Implement event‑level analytics to try to capture AI‑driven intent (instrument CTAs and micro‑conversions heavily).
- Test small experiments with AI‑native publishers, aggregator feeds, or third‑party indexing programs where available.
- Strengthen review acquisition and response cadence.
- Long term (9–18 months)
- Build a structured data pipeline or feed for partners and major publishers.
- Explore partnerships with trusted aggregator services that supply AI indexes.
- Design governance rules for what business data can be shared with AI partners.
Conclusion
The consumer picture is unequivocal in one sense:
search is not dead, but the
shape of search is shifting. Google and Maps remain central and trusted for local discovery today, but
AI assistants like ChatGPT and Google’s own generative features are rapidly carving out space — especially among heavier digital users. That duality creates a transitional era where businesses must be simultaneously excellent at classic local SEO and rigorous about being machine‑readable for AI answer engines.
For local merchants, the immediate priority is simple and pragmatic:
control your facts, publish them in machine‑readable formats, and instrument conversion paths so you can spot and benefit from new referral channels. Ignoring AI as a distribution surface risks being left out of the shortlist that increasingly determines footfall and bookings; over‑investing exclusively in AI without addressing reliability and trust risks reputational mistakes. The balanced strategy—strong presence on Google, clean structured data, and selective engagement with AI indexes—will keep a business visible today and resilient tomorrow.
Source: Drug Store News
How are Americans using AI to find local businesses?