The most common question we hear from prospective customers is: "Does this actually work?"
Fair question. AI visibility is a new concept. The tools and tactics are still being proven. And there are plenty of vendors in the digital marketing space who overpromise and underdeliver.
This post shares real results from Scope customers who've been monitoring and improving their AI visibility. We've anonymized or aggregated specific identifying details but the underlying data is real.
The Starting Point: Most Businesses Are Invisible
When businesses first connect to Scope, we run a baseline AI visibility audit — testing their appearance rate across a standard set of queries on ChatGPT, Gemini, Claude, and Perplexity.
Typical baseline results by business type:
- Solo service professional (therapist, attorney, accountant): Appears in 8-15% of tested queries
- Small local service business (plumber, HVAC, dentist): Appears in 12-22% of tested queries
- Regional multi-location business: Appears in 18-35% of tested queries
- Well-established business with strong digital presence: Appears in 30-50% of tested queries
Most businesses are surprised by how low these numbers are. They've been building their business for years, they have hundreds of Google reviews, their website looks professional — and AI is still recommending them in fewer than 1 in 4 queries for their own category and location.
The gap reveals how AI visibility is different from Google visibility. Your GBP might be perfect, but if your schema is missing, your service pages are thin, and your third-party citations are sparse — AI has low confidence in recommending you.
Result Type 1: Quick Wins From GBP and Schema Fixes
The pattern: Business has strong Google presence but hasn't optimized for AI specifically.
Example: A dental practice in a mid-sized Midwestern city. 210 Google reviews (4.8 stars), active website, well-staffed office.
Baseline Scope audit: Appeared in 18% of tested queries.
Issues identified:
- No service schema on their website
- GBP services section had only 4 entries (they offer 15+ services)
- No FAQ content on their website
- Missing from 3 major dental directories
Changes made (6 weeks):
- Implemented DentalBusiness schema on homepage
- Filled out all 15+ services in GBP with descriptions
- Wrote FAQ page with 15 Q&A pairs about their services
- Created complete profiles on Zocdoc, Healthgrades, and US News Health
Result at 90 days: Appeared in 47% of tested queries — a 161% improvement in AI recommendation frequency.
The notable aspect: their Google search rankings didn't change meaningfully. AI visibility improvement was independent of traditional SEO gains.
Result Type 2: Content Strategy Driving Long-Term Gains
The pattern: Business underinvested in website content, relying primarily on GBP.
Example: A family-owned HVAC company operating for 22 years in the Southwest. 340 reviews, excellent local reputation, almost no website beyond a basic homepage and contact page.
Baseline Scope audit: Appeared in 24% of tested queries. For emergency HVAC queries specifically, appeared in 41% — above average because of their strong review profile.
Issues identified:
- Website had no service-specific pages
- No content at all beyond homepage and contact
- No schema markup
- Strong GBP but no supporting web infrastructure
Strategy: A 6-month content buildout:
- Created 8 dedicated service pages
- Added HVACBusiness schema to homepage
- Wrote 12 FAQ entries about HVAC questions
- Published 4 seasonal content guides
Result at 6 months: Appeared in 61% of tested queries overall. For their target "emergency AC repair" queries during summer, appeared in 78%.
The owner reported a measurable increase in "how did you find us?" responses attributing ChatGPT and Gemini as the discovery channel.
Result Type 3: Recovering from AI Reputation Issues
The pattern: AI is describing the business inaccurately, creating customer friction.
Example: A law firm that had moved offices 18 months prior. The old address was still appearing in AI descriptions because several directory profiles and some third-party articles still listed the old address.
Impact: Clients were showing up at the old office (now a different business). The law firm was getting calls asking "are you still on [old street]?"
Scope diagnosis: AI confidence metrics showed that ChatGPT and Gemini were describing the business with a "conflicting information" flag — they'd detected inconsistent address data across sources.
Fix: Systematic address update across 34 directory profiles and reaching out to 2 local news publications to update old articles with the correct address.
Result: Within 60 days, AI address descriptions were consistent and correct across all four platforms. Client confusion calls stopped. AI recommendation frequency increased 22% (conflicting data had depressed recommendation rates).
Result Type 4: New Business Building AI Visibility From Scratch
The pattern: Business launched within the last 2 years, competing against established incumbents with years of web presence.
Example: A physical therapy practice that launched 14 months ago, competing against 3 established practices in their market.
Challenge: The established practices had 5-10 years of review history, well-developed websites, and strong directory presence. A new practice can't accelerate time.
Strategy: Focus on differentiation signals and accelerated review acquisition.
- Specialized in two underserved areas: pediatric physical therapy and sports performance (not just general PT)
- Created comprehensive content around those two specializations
- Implemented schema with specialty service documentation
- Active review request protocol post-appointment
- Secured listing on pediatric health directories their competitors weren't on
Baseline: 9% appearance rate (new businesses always start low)
Result at 12 months: 38% appearance rate for general PT queries. For "pediatric physical therapy" queries specifically: 67% — higher than the established competitors.
By owning a specialty niche in AI recommendations, they built a referral channel despite being the newest practice in the market.
Result Type 5: Multi-Location Business Standardizing AI Visibility
The pattern: Multi-location businesses often have significant inconsistency in how different locations appear to AI.
Example: A regional urgent care network with 14 locations across a metro area.
Problem: AI was recommending different locations inconsistently, sometimes recommending locations that weren't nearest to the query origin, and sometimes not recommending the network at all.
Scope diagnosis: Each location had different GBP completeness, different schema, and different review profiles. AI was treating them as unrelated businesses rather than a coherent, high-quality network.
Standardization project:
- Updated all 14 GBP profiles to use consistent naming (all with location identifiers: "[Brand Name] - [City/Neighborhood]")
- Implemented standardized LocalBusiness schema across all 14 location pages
- Filled GBP services for all locations (some had been left empty)
- Standardized review response templates and cadence
Result at 90 days: Overall network AI mention rate improved 43%. More importantly, the right location was recommended for location-specific queries 81% of the time (vs. 52% before standardization).
What These Results Have in Common
Looking across all result types, the businesses that improve most share these characteristics:
They act on the diagnosis: Scope tells you what's missing. The businesses that act — who implement schema, fill out services, build FAQ pages — see results. The businesses that monitor without acting see flat lines.
They're patient but consistent: Most improvements take 60-90 days to appear in AI recommendation rates. The businesses that implement changes and check results weekly for improvement tend to be disappointed. The businesses that implement changes and check monthly see clear improvement curves.
They focus on the highest-leverage items first: GBP services section, LocalBusiness schema, and FAQ content consistently deliver the highest improvement per hour of effort. Advanced tactics (link building, PR coverage) matter but come after the fundamentals.
They monitor competitors: The businesses that track competitor AI visibility can identify gaps in their own strategy. If a competitor is appearing for queries you're not, understanding what they have that you don't accelerates your optimization.
Getting Started
The businesses in these examples all started with a Scope audit. Running a baseline tells you:
- Where you currently stand across ChatGPT, Gemini, Claude, and Perplexity
- What AI says about you (accurate or inaccurate)
- Where your biggest gaps are relative to appearing more often
From there, the path is consistent execution on the fundamentals — and systematic monitoring to see what's working.
Start with a free Scope scan to see your current AI visibility score.