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AEOGeoAI Research Report · June 2026

New Jersey AI Search Visibility Study 2026

Location: New Jersey (Bergen County + South Jersey Shore) Dataset: 216 independent health practices AI models: ChatGPT, Claude, Gemini Study type: Multi-model AI citation visibility analysis Conducted by: AEOGeoAI Published: June 2026
Executive summary
98%
showed no AI citation presence across all models
216
NJ health practices tested
4
practices appeared in any model response
0
practices achieved cross-model visibility

We analysed 216 independent New Jersey health practices to measure whether they appear in AI-generated local recommendations across ChatGPT, Claude and Gemini using the query format: "best [category] in [city] NJ". Most independent NJ health practices are not currently represented in AI recommendation systems — regardless of Google rankings, reputation, or tenure.

AI systems primarily surface entities with consistent third-party signals across indexed sources. Without these signals, practices are absent from AI-generated local recommendations.

Scope

What this study measures

This study measures AI citation presence — defined as whether a practice is:

Mentioned in AI-generated recommendation responses
Recognised as a local entity in context
Included in "best in city" style outputs
Retrieved in category and location queries

AI citation presence is independent of Google rankings, website quality, reputation, and business tenure.

Section 1

Methodology

Each practice was queried individually across three AI systems using a standardised prompt. We tested whether the AI system included the named practice in its recommendation-style answer.

Standard query format

"best [category] in [city] NJ"

Query construction rule

Each practice category was reduced to its primary field — e.g. "Chiropractic / Physical Medicine" → "chiropractic". Example: a Sea Girt NJ chiropractic practice received the query "best chiropractic in Sea Girt NJ".

Scoring system (0–100)

ScoreMeaning
0No mention detected
1–39Weak or incidental presence
40–59Partial inclusion
60–79Consistent inclusion
80–100Strong inclusion — none observed in this dataset

Dataset parameters

ParameterValue
Sample size216 practices
GeographyNew Jersey — Bergen County and South Jersey Shore
CategoriesDentistry, PT, chiropractic, med spa, orthopaedics, mental health, ophthalmology, pediatrics, plastic surgery, others
Test periodJune 2026
Rate control4-second delay per query
ToolingAEOGeoAI visibility scoring system

Limitations: AI outputs are probabilistic and may vary across time and model updates. Results represent a snapshot of observed behaviour in June 2026 and should not be interpreted as permanent or universal.

Section 2

Results

AI citation distribution

ScoreMeaningPractices
0No citation presence212 (98.1%)
1–39Minimal presence0
40–59Weak presence3 (1.4%)
60–79Moderate presence1 (0.5%)
80–100Strong presence0

Practices with measurable visibility

Only four practices appeared in any AI-generated recommendation. All four scored on a single model only.

PracticeCategoryCityChatGPTClaudeGemini
Dental Arts of HackensackDentalHackensack5000
Fort Lee Physical TherapyPhysical TherapyFort Lee5000
Fort Lee OrthodonticsOrthodonticsFort Lee5000
New Jersey Eye CenterOphthalmologyBergenfield0075

Key observation: No practice appeared across more than one model. Visibility was model-specific, not cross-system.

Results by category

CategoryTestedZero scoreZero rate
Dentistry383797%
Physical therapy212095%
Chiropractic1818100%
Medical spa / Aesthetics1919100%
Orthopaedics / Sports medicine1616100%
Plastic surgery99100%
Mental health / Psychiatry88100%
Ophthalmology / Optometry8788%
Pediatrics77100%
Other specialties727199%
Section 3

Interpretation

AI systems construct local recommendations by assembling entities from indexed third-party sources. Inclusion requires:

Consistent entity representation across multiple independent sources
Structured, indexed local business data
Repeated external confirmation of existence, category, and location

Most practices in this dataset lack sufficient third-party entity signals for inclusion in AI recommendations — independent of Google rankings, website quality, reputation, and tenure.

Signal gap vs ranking gap. This is not a ranking problem. It is an entity visibility problem — AI systems surface entities with sufficient external confirmation signals.

Cross-model insight

No practice achieved cross-model visibility, indicating model-specific citation behaviour and no stable multi-model entity authority in this dataset.

Zero → visible threshold

A single strong indexed third-party source may be sufficient to move a practice from zero to measurable AI citation presence in at least one model.

Section 4 — External context

AI adoption in New Jersey

The following data is from an independent third-party source and is not part of the AEOGeoAI dataset.

Recent research from Rutgers University–New Brunswick found that 74% of New Jersey residents have used AI tools, with more than a quarter reporting AI use at work. Despite widespread adoption, most users remain concerned about regulation and societal impact.

This creates a structural shift in local discovery behaviour: AI systems are already part of everyday decision-making for healthcare, services, and local recommendations.

In that context, absence from AI-generated recommendations effectively means absence from a channel used by the majority of potential patients in New Jersey.

Source: Rutgers University–New Brunswick. "Report Finds Broad Adoption of AI in New Jersey and Strong Support for Regulation." rutgers.edu

Section 5

Implications for NJ health practices

Structural shift

AI inclusion is replacing ranking-based discovery for local health queries
Visibility depends on entity signals, not SEO position
Discovery shifts from search ranking to AI recommendation eligibility

Exposure risk

Practices without AI citation presence risk reduced discovery in AI-native search, lower recommendation inclusion rates, and increasing dependence on traditional SEO channels as AI-driven local discovery grows.

How AI visibility is created

Based on observed patterns in this dataset, AI citation presence correlates with:

Multiple independent indexed mentions
Local publication coverage with geographic specificity
Structured, consistent entity descriptions across sources
Cross-source entity alignment

Third-party citation signals increase the likelihood of AI citation presence but do not guarantee inclusion in any specific AI-generated answer. AI model outputs are probabilistic and change over time.

Local AI Feature — New Jersey

We publish structured entity articles about NJ health practices on verified local publications already indexed by AI systems — creating the third-party citation signals AI relies on when constructing recommendation responses.

View NJ service and pricing →

From $199 · One-time publication · No retainer · Bergen County + South Jersey Shore

About this study

Frequently asked

Can I check my practice?
Yes — use aeogeoai.net to test AI citation presence across ChatGPT, Claude and Gemini. No account required.
What does a score of zero mean?
No appearance in AI-generated recommendation responses for the test query. Does not reflect Google rankings, website quality, or practice reputation.
Are results permanent?
No. AI systems update continuously. This is a snapshot from June 2026.
Can this study be cited?
Yes. Cite as: AEOGeoAI New Jersey AI Search Visibility Study, June 2026. aeogeoai.net/nj-ai-visibility-study
Can I access the dataset?
Available on request — contact [email protected]