AI Is Telling Customers the Wrong Info About Your Locations. Here’s How to Audit, Fix, and Monitor It (Before Revenue Leaks)
AI Overviews, AI Mode, ChatGPT, Gemini, and Perplexity are increasingly answering local business questions—often with confident errors. This editorial lays out a practical, repeatable audit and remediation system for single- and multi-location brands, plus how AYSA helps teams monitor and execute fixes with approval.
Local search used to be a fairly simple deal: show up in the Map pack, earn reviews, keep your address and hours consistent, and you’d win your share of foot traffic and phone calls.
That model is breaking—quietly, but quickly.
Today, customers increasingly ask AI systems a direct question and receive a Direct answer. Not a list of options. Not a set of links. A single synthesized response that describes your business before the customer ever sees your website or your Google Business Profile.
And here’s the uncomfortable part: those answers are often wrong.
Wrong postcodes. Incorrect hours. “They’re permanently closed.” Services you don’t offer. An address you moved away from two years ago. Confidently stated, with zero warning to you—and real consequences for customers.
This editorial is my practical playbook for how SMEs, multi-location brands, and agencies should handle AI’s new local reality: audit what AI says, fix the underlying sources, and monitor it as a business process—not as a one-time SEO project.
I’m using research originally reported by Search Engine Journal as a starting point (and you should read it), then expanding it into an operational system you can run internally or with an agency partner: AI Answers About Your Locations Are Often Wrong – Check Before Customers Do (Search Engine Journal).
Concise Summary
AI-generated answers are becoming a mainstream way people decide where to go and what to buy locally. But AI answers about business locations frequently contain errors, and traditional SEO reporting won’t alert you when those errors happen. The fix is not “do more AI.” The fix is to treat location data as an audited system across five surfaces (Google AI Overviews, Google AI Mode, Gemini, ChatGPT, Perplexity), backed by consistent first-party sources (website + GBP), reinforced by reputable third-party citations, and monitored continuously.
Key Takeaways
- Local search is shifting from Ranking to describing. A single AI narrative can decide a visit before a user Clicks anything.
- AI errors are common and often invisible to your dashboards. You may not see a traffic dip—just fewer calls and fewer walk-ins.
- You’re not managing one system. Google AI Overviews, Google AI Mode, Gemini, ChatGPT, and Perplexity can disagree.
- The best defense is “source hygiene.” Align your website, your Google Business Profile, and your key citations.
- Monitoring needs a process. A repeatable prompt list + logging + periodic rechecks beats ad-hoc panic.
- Execution matters. Insights without approved implementation are where most teams stall—this is where AYSA fits.
Table of Contents
- From Ranked To Described: The Big Shift Local Businesses Can’t Ignore
- What Changed in Local Search (And Why It Happened Fast)
- What Can Go Wrong When AI Answers for Your Locations
- The New Blind Spot: Why Your Existing SEO Reporting Won’t Catch AI Misstatements
- The Five-Platform Reality: You’re Not Managing “Google,” You’re Managing an Ecosystem
- A Practical Audit Playbook (That Doesn’t Require a Data Science Team)
- A Starter Prompt Library for Location Accuracy
- Triage: Which Errors to Fix First (Impact vs Effort)
- Fixing the Inputs: The Source Hierarchy That Shapes AI Answers
- Website Actions That Improve AI Location Accuracy (Without Guesswork)
- Multi-Location Reality: Governance, Delegation, and Change Control
- Agency Playbook: Productizing AI Location Accuracy
- Where AYSA Fits: Monitoring + Approved Execution for Local AI Search
- What to do next
- Sources and further reading
From Ranked To Described: The Big Shift Local Businesses Can’t Ignore
Traditional local search is comparative. A customer searches “dentist near me,” sees a map pack, reads reviews, checks a website, compares two or three options, then decides.
AI local discovery is different. The user asks:
- “Is Sunset Dental open on Saturdays?”
- “Does Sunset Dental do Invisalign?”
- “What’s the parking situation?”
- “Which clinic is best for emergency appointments today?”
The AI responds with a single narrative that collapses sources into one answer. That narrative can be helpful when accurate—but damaging when wrong.
Search Engine Journal summarized vendor testing indicating AI tools frequently return false facts about local businesses, including wrong postcodes and claims that a business is closed. The article also highlights a key operational problem: there’s no built-in “AI answer report” equivalent to Search Console impressions or GBP insights. You have to look for errors intentionally. (SEJ source)
So the new question is not just: “Do we rank?”
The new question is: “How are we being described—and is it true?”
What Changed in Local Search (And Why It Happened Fast)
Three forces converged:
1) Customer behavior shifted to “ask-and-go”
When a user is mobile, time-constrained, and close to making a decision, they don’t want ten links. They want a yes/no and a direction.
2) AI answers moved into mainstream search surfaces
AI is no longer “a separate app.” Google is integrating AI directly into its search experience via features like AI Overviews and conversational experiences (often referred to as AI Mode in reporting and commentary). Even when customers end up on Google Maps or a business profile, they may begin their decision with an AI summary in search.
Google itself warns that generative AI responses may contain mistakes and encourages feedback on results. That warning is not a monitoring plan for businesses—but it is a signal: AI errors are an expected part of the current system, not an edge case.
If you need a primary reference point for Google’s position on AI-generated results and limitations, start with Google Search’s documentation and communications around AI experiences. (Note: the specific docs change frequently; if your legal/compliance team needs a stable citation, capture the version you rely on.) A general entry point is: Google Search.
3) AI systems synthesize across messy, conflicting local data
Local business information is fragmented. Even careful businesses have inconsistencies:
- Website says “Mon–Fri 9–6”
- GBP says “Mon–Fri 9–5”
- A directory lists “Mon–Fri 8–6” from years ago
- A social profile says “By appointment only”
Humans might detect the mismatch. AI might not. Or it might choose the wrong “most confident” source. Or it might blend two sources and produce a new wrong answer.
What Can Go Wrong When AI Answers for Your Locations
Let’s move beyond abstract fear and name the failure modes that actually cost money.
1) Hard factual errors (the immediate revenue killers)
- Wrong address or postcode → customers go to the wrong place
- Wrong hours → customers arrive when you’re closed (or think you’re closed)
- Wrong phone number → calls go elsewhere
- Claims you are permanently closed → demand collapses without a “traffic dip” to diagnose
2) Soft factual errors (the slow-burn trust killers)
- Incorrect “founded in” dates
- Wrong ownership or brand affiliation (common with franchises)
- Incorrect “size” statements (e.g., “a large chain” vs independent)
3) Invented or mismatched services
AI can incorrectly infer services based on similar businesses (“They do emergency dentistry,” “They offer same-day delivery,” “They repair iPhones,” etc.). This creates two problems:
- Customers show up expecting something you don’t do
- Your staff wastes time handling mismatched inquiries
4) Perception and recommendation issues (not “wrong,” but still damaging)
- AI frames you as “budget” when you’re premium
- AI highlights negatives without context
- AI recommends competitors first because it “understands” them better
These aren’t always fixable with a simple correction. They require strengthening your footprint and clarifying your positioning across sources—especially your own site.
The New Blind Spot: Why Your Existing SEO Reporting Won’t Catch AI Misstatements
Most SMEs and many agencies still run local performance management like this:
- Track rankings (or map pack presence)
- Monitor GBP performance
- Watch Search Console clicks
- Respond to reviews
Those are still valuable. But they don’t answer the question: “What is the AI telling users about our locations today?”
Search Engine Journal calls this a blind spot: there’s no default alert when an AI answer misstates your hours, phone number, or status. Even if Google provides a feedback link on an AI result, that’s reactive. It requires someone to see the problem first. (SEJ source)
And this is what makes AI location accuracy uniquely painful: the impact is often offline. Fewer calls. Fewer walk-ins. More “no-shows.” Staff hears “Google says you’re closed” at the front desk. Your analytics remain calm.
The Five-Platform Reality: You’re Not Managing “Google,” You’re Managing an Ecosystem
One of the most actionable points in the SEJ write-up is the operational framing: you’re dealing with multiple systems that don’t behave the same way.
In practice, most businesses should treat these as separate surfaces to audit:
- Google AI Overviews (within Google results)
- Google AI Mode (conversational within Google Search, where available)
- Gemini (Google’s assistant experience)
- ChatGPT
- Perplexity
Why separate them?
- They pull from different mixes of sources.
- They cite differently (or sometimes not at all).
- They may vary answer-to-answer even with the same prompt.
Testing only one surface can create a false sense of security. You may “look good” in one and be misrepresented in another.
At AYSA, we treat this as an answer ecosystem problem, not a “Google-only” local SEO task. That’s why our framework on AI search visibility starts with monitoring and repeatability, not one-off optimization.
A Practical Audit Playbook (That Doesn’t Require a Data Science Team)
The goal is not perfection. The goal is to reduce costly errors and build a repeatable process you can run quarterly, monthly, or weekly depending on your business complexity.
Step 1: Build a standardized question set
Use one list across all locations so results are comparable. Keep it short enough to run regularly.
Include:
- Hours questions (weekday, weekend, holiday, “open now”)
- Address/parking/access questions
- Phone/email/booking questions
- Service eligibility questions (insurance, delivery radius, specialties)
- Reputation questions (“best for X,” “good for kids,” “wheelchair accessible”)
Step 2: Test across platforms and log everything
Run the same questions across each platform. Record:
- Prompt
- Answer
- Date/time
- Platform
- Whether sources were cited
Important: Where possible, test in a clean context (incognito, signed-out, no prior chat history). AI outputs can vary with personalization and conversation memory.
Step 3: Repeat prompts to detect variance
Ask the same question multiple times. If answers flip between “open Saturdays” and “closed weekends,” that variance itself is a risk signal: AI doesn’t have stable grounding for your business facts.
Step 4: Categorize findings
Don’t throw everything into one “AI is wrong” bucket. Use categories:
- Blocking errors (hours, address, closed, phone)
- Service errors (invented services, wrong service list)
- Missing info (can’t find phone, can’t confirm hours)
- Perception/recommendation issues (sentiment, positioning)
Step 5: Map each error to its likely source
This is where most teams fail: they see the wrong answer and immediately blame “the AI.” That’s not actionable.
Instead, ask: Where could the AI have gotten this?
- Your website location page
- Your Google Business Profile
- An old directory listing
- A press mention from years ago
- A similarly named business
- User-generated content (reviews, forums)
When sources are cited, click them and verify what they actually say. SEJ explicitly recommends verifying cited sources and correcting what you control first. (SEJ source)
A Starter Prompt Library for Location Accuracy
Below is a practical starter set you can adapt. Replace bracketed items with your business and location.
Core facts
- “What is the address for [Business Name] in [City]?”
- “What is the phone number for [Business Name] [Neighborhood/City]?”
- “What are the opening hours for [Business Name] at [Street/Area]?”
- “Is [Business Name] permanently closed?”
Time-sensitive intent
- “Is [Business Name] open right now?”
- “Is [Business Name] open on Saturdays?”
- “What time does [Business Name] close today?”
Services and eligibility
- “Does [Business Name] offer [Service]?”
- “Does [Business Name] accept [Insurance/Payment Type]?”
- “Does [Business Name] have same-day appointments?”
- “Does [Business Name] offer delivery? If yes, what areas?”
Trust and recommendation framing
- “Is [Business Name] a good option for [Use Case]?”
- “Which is better for [Use Case], [Your Brand] or [Competitor]?”
- “What are common complaints about [Business Name]?”
Not every prompt is about “correcting facts.” The trust prompts help you detect whether AI is shaping perception in ways you need to address with clearer positioning, better FAQs, and consistent third-party validation.
Triage: Which Errors to Fix First (Impact vs Effort)
SMEs don’t have infinite time. Agencies don’t have infinite scope. Use triage.
Priority 1: Anything that prevents a visit or call
- Closed/open status
- Hours
- Address
- Phone number
- Booking link / appointment instructions
Priority 2: Service mismatch that causes wasted labor
- Invented services
- Wrong service area
- Wrong eligibility (insurance, age groups, etc.)
Priority 3: Brand framing and competitive recommendation
This is where AEO/GEO work begins to blend with positioning and PR. It can be higher effort, but it’s also where the upside lives.
My rule: fix “can’t find us / can’t visit us” first, then fix “shouldn’t visit us,” then work on “choose us over them.”
Fixing the Inputs: The Source Hierarchy That Shapes AI Answers
When AI is wrong, the fix is rarely “tell the AI it’s wrong.” You improve the likelihood of correct answers by strengthening and aligning the sources AI learns from.
Here’s a practical hierarchy to work through.
1) First-party sources (you control these)
- Your website (especially location pages)
- Your Google Business Profile (GBP)
If your own site contradicts GBP, you’re basically asking the ecosystem to guess. SEJ emphasizes that conflicting details across GBP and your website are a clear reason AI answers can be wrong, and that classic NAP consistency remains foundational. (SEJ source)
Primary reference for GBP setup and guidelines: Google Business Profile.
2) Second-party sources (partners and platforms)
- Major directories relevant to your category and country
- Booking platforms
- Delivery platforms
- Industry associations
If you change hours seasonally or move locations, these sources often become stale. AI systems may pick them up anyway.
3) Third-party sources (you don’t control these)
- Press coverage
- Blog posts
- Forums
- Review content
You can influence these, but you can’t edit them at will. Your best lever is to ensure first-party sources are unambiguous and to correct major platforms where inaccuracies are common.
Website Actions That Improve AI Location Accuracy (Without Guesswork)
This is where local SEO fundamentals turn into AI resilience. And you don’t need to “optimize for AI” in a mystical way—you need to make your facts easy to extract, consistent, and anchored to a specific location entity.
1) Build (or fix) true location pages
A real location page is not a thin template with a city name swapped in. It should include:
- Exact name used in GBP
- Full address in one consistent format
- Primary phone number for that location
- Hours with clear exceptions (holidays, seasonal)
- Directions, parking, landmarks
- Service list that is specific to that location
- “Last updated” date (useful for humans; may help reduce ambiguity)
2) Use structured data for LocalBusiness
Structured data helps machines parse business facts. For a primary reference on schema vocabulary, see: Schema.org LocalBusiness.
For implementation guidance and eligibility notes, Google’s structured data documentation is the primary source: Google Search Central: Structured Data.
Important: structured data doesn’t guarantee AI will be correct. But it reduces ambiguity and makes it easier for systems to extract the same truth every time.
3) Eliminate contradictions across your own pages
Common contradiction patterns:
- Header/footer phone differs from location phone
- Contact page lists old address
- PDF menus or brochures show outdated hours
- Blog posts mention “new location” with old details still live
4) Make service claims explicit and bounded
If you do not offer a service, say so plainly in an FAQ. This sounds counterintuitive, but it prevents AI from “helpfully” inferring services based on category similarity.
Example for a clinic:
- “Do you offer emergency appointments?” → Yes/no + what qualifies
- “Do you accept walk-ins?” → Yes/no + hours + booking link
5) Clean up entity confusion (same name, multiple branches)
If you have multiple locations, ensure each has:
- A unique location page URL
- Clear location identifiers (neighborhood, cross-streets)
- Unique phone numbers where possible
This is old-school multi-location SEO—but now the payoff is not only rankings. It’s accurate AI descriptions.
Multi-Location Reality: Governance, Delegation, and Change Control
Multi-location businesses get hurt more because there are more moving parts:
- Store managers update hours ad-hoc
- Franchisees change phone systems
- Seasonal staff changes update holiday hours inconsistently
- Local landing pages drift out of sync with GBP
Here’s the governance model I recommend:
1) Establish a single source of truth document
This is not glamorous, but it’s essential. For each location, store:
- Canonical name
- Canonical address format
- Canonical phone
- Canonical hours (with effective dates)
- Service list
- Primary URLs (location page, booking link)
2) Require approval for public changes
The failure mode isn’t “wrong information”—it’s conflicting information. That’s why an approval layer matters. If a location changes hours, someone has to update:
- GBP
- Website location page
- Major directories
- Any booking platform hours
If only one gets updated, AI will eventually pick the wrong one.
3) Run audits on a predictable cadence
Don’t overcomplicate this. Choose:
- Quarterly for stable locations with rare changes
- Monthly for most multi-location operations
- Weekly for highly seasonal businesses (hospitality, events) or during relocations
The SEJ article notes that fixes don’t automatically propagate into AI answers quickly, so re-checking is part of the process—not a sign you failed. (SEJ source)
Agency Playbook: Productizing AI Location Accuracy
If you’re an agency, this is a new retainer line item that clients will understand immediately—because it maps to real-world pain.
What to sell (deliverables)
- AI Answers Baseline Audit (5 surfaces × standardized prompt set × top locations)
- Location Source Alignment (GBP + website + top citations consistency pass)
- Monthly Monitoring Report (delta changes, new errors, fixed status)
- Approved Implementation (site changes queued, approved, executed)
What to clarify (boundaries and expectations)
- You cannot force an AI model to update on your timeline.
- You can improve accuracy by improving sources and consistency.
- Some “perception” issues require content strategy and reputation work, not a quick fix.
What to measure (KPIs that make sense)
Don’t promise “AI mentions will go up” as your only outcome. Measure:
- Error rate in audited prompts (by category)
- Time-to-correct on Priority 1 errors
- Consistency score across website vs GBP vs key citations
- Operational compliance (how often locations submit changes through the process)
These are operational metrics tied to customer experience. They’re more defensible than chasing mention volume alone.
Where AYSA Fits: Monitoring + Approved Execution for Local AI Search
Most teams don’t fail because they don’t know what to do. They fail because the workflow breaks between:
- finding issues,
- agreeing on changes, and
- actually implementing them safely.
AYSA was built to close that gap with a model I believe is the only sustainable one for AI-era SEO: monitor → propose → approve → execute.
1) Monitoring: track what matters
AI answer monitoring is still early across the industry, but the principle is clear: you need a place to log and revisit AI representations as they evolve. AYSA’s monitoring approach is designed to support that operational cadence and keep teams from relying on memory and screenshots scattered across Slack.
Learn more: AYSA Monitoring
2) Visibility: connect AI answers to business outcomes
AI visibility isn’t only “did we get cited.” It’s “did the AI describe us accurately and help customers take the next step.” Our framework on AI search visibility focuses on both representation and discoverability.
Explore: AI Search Visibility
3) Tools: treat AEO/GEO as execution, not theory
Location accuracy depends on disciplined on-site execution (templates, structured data, consistency checks, content updates). That’s where automation helps—but only with approvals in place.
See: AI SEO Tools
4) Approved execution: the “last mile” most teams ignore
Even when a marketer identifies the fix, implementation often stalls:
- Developer backlog
- CMS access limitations
- Fear of breaking templates
- Internal politics (“who owns location pages?”)
AYSA’s model is simple: we prepare changes, you approve them, and then we execute what you accepted. That keeps velocity high without sacrificing control—especially critical for multi-location brands where one wrong deploy can affect hundreds of pages.
5) Practical adoption: start small, scale up
If you’re an SME, you don’t need an enterprise rollout. Start with top locations, highest-value services, and Priority 1 prompts.
Pricing details: AYSA Pricing
And for ongoing education and implementation ideas, our editorial hub is here: AYSA Blog
Concrete SME Scenario: The “Closed Location” That Wasn’t Closed
Let’s make this real with a scenario that mirrors what I see frequently in the market.
Business: a 3-location physical therapy clinic.
Change: One location moved suites in the same building and changed its phone routing.
What went wrong:
- The website location page was updated.
- GBP address was updated, but the suite number format differed.
- An old directory listing still showed the old phone number.
- A blog post from years ago still mentioned “our old office.”
What the customer experienced: They asked an AI assistant whether the clinic was open and how to call. The AI gave an outdated phone number and implied the location had “moved/closed.” The customer booked elsewhere.
Why analytics didn’t show it: There was no click to measure. The customer never visited the site.
How to prevent it: A monthly AI answers audit would flag the wrong phone/hours narrative, and the fix path would focus on aligning first-party sources plus cleaning key citations. That’s not “AI magic.” That’s operational data hygiene with accountability.
What to do next
- Pick 5 locations (or your only location) and run a baseline audit using the prompt library above across Google AI surfaces, ChatGPT, Gemini, and Perplexity.
- Log every answer with date/time and platform. Don’t rely on memory.
- Triage: fix “closed/open, hours, address, phone” first.
- Align first-party sources: make your website location page and GBP agree exactly.
- Fix the biggest external contradictions on major directories/partners where feasible.
- Re-test weekly for a month after changes to see if answers stabilize.
- Turn it into a cadence: monthly for most multi-location businesses, quarterly for stable single-location SMEs.
- If execution is your bottleneck, use a system that prepares changes and deploys only what you approve—this is precisely the workflow AYSA is built for.
Sources and further reading
- Search Engine Journal: AI Answers About Your Locations Are Often Wrong – Check Before Customers Do
- Google Business Profile
- Schema.org: LocalBusiness
- Google Search Central: Structured Data documentation
- Search Engine Journal: Local Search category (context and ongoing coverage)
Related AYSA resources:
Note: AI search features and documentation evolve quickly, and each platform’s behavior can differ by geography, personalization, and time. Treat audits as snapshots and focus on improving the stability and clarity of your source data over time.
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