Executive Summary
The way people discover brands has changed more in the past two years than in the previous decade. For twenty years, SEO reports centered on a simple metric: how often are we on page one of Google? In 2025, that yardstick has lost its meaning. Users no longer stop at ten blue links - they’re increasingly turning to AI-generated answers from ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews. In these contexts, the critical question is not “What rank do we hold?” but rather “Are we even mentioned?”
That question is the foundation of the AI Visibility Index. In this Q4 2025 edition, we analyzed over 10,000 prompts across 12 industries to measure how frequently brands appear in AI-generated answers, how they are framed (leader, challenger, cautionary), what tone is applied (positive, neutral, negative), and whether answers provide usable citations. By distilling these results into a standardized AI Visibility Score, we can finally compare visibility across engines and sectors in a way that is meaningful and repeatable.
The topline findings are both encouraging and sobering:
ChatGPT provided the broadest coverage, citing brands in 42% of prompts, though citation quality was inconsistent.
Gemini rewarded incumbents with strong Knowledge Graph entries and schema, producing an average inclusion of 35%.
Claude was conservative, citing fewer brands (28%) but with greater stability over time.
Perplexity proved the most transparent - 33% inclusion, but 91% of those answers came with clickable citations, making it the best surface for measuring impact.
AI Overviews averaged just 22% inclusion and were highly volatile, but also the most responsive to structured data.
From an industry perspective, SaaS brands led with a 48% average inclusion rate, thanks to their digital-native presence and extensive review ecosystems. At the other end of the spectrum, Travel (19%) and Local Services (21%) lagged, as engines defaulted to directories and platforms over individual providers.
The single most important conclusion from this quarter is that AI Visibility is measurable and improvable. Brands that combined Answer Engine Optimization (AEO) with Generative Engine Optimization (GEO) saw significant gains in just 90 days. Those that ignored entity consistency, sentiment management, or structured data slipped further behind.
As 2026 approaches, the brands that treat AI Visibility as a KPI - measured monthly, reported quarterly, and tied to business outcomes - will define the competitive baseline for the next decade of digital marketing.
Methodology: How the Index Is Built
Any benchmark is only as credible as the framework behind it. AI search introduces unique challenges: models update frequently, answers vary by run, and some engines provide sparse or no citations. To overcome these issues, we designed the AI Visibility Index methodology to be transparent, repeatable, and comparable across time.
2.1 Prompt basket
The backbone of the Index is a 10,000-prompt basket designed to simulate real user behavior. We balanced prompts across four intent categories:
Definitional (“What is predictive analytics?”, “How does Invisalign work?”)
Evaluative (“Best SaaS tools for small businesses”, “Top activewear brands 2025”)
Comparative (“Salesforce vs HubSpot”, “YNAB vs Mint vs ClearBudget”)
Transactional (“Notion pricing”, “Is there a free trial for FlowBoard?”)
Prompts were tagged by industry vertical (12 total, from SaaS to Local Services) and by funnel stage (top, middle, bottom). This allowed us to see not just whether brands were included, but how their visibility shifted depending on query intent.
2.2 Engines tested
We focused on the five engines that currently shape discovery:
ChatGPT with browsing enabled
Google Gemini (Pro version)
Claude (latest Opus tier)
Perplexity (Pro)
Google AI Overviews as surfaced in SERPs
All prompts were tested in English, under consistent geographic conditions, and repeated multiple times to account for answer variability.
2.3 Scoring rubric
Each answer was parsed and scored along four axes:
Inclusion: 0 (not mentioned) to 3 (strong recommendation with reasoning).
Positioning: leader, challenger, alternative, niche, or cautionary mention.
Sentiment: +1 positive, 0 neutral, -1 negative.
Citation quality: first-party, authoritative third-party, or miscellaneous.
A typical answer might give a brand an inclusion score of 2 (shortlisted), framed as a challenger, with neutral sentiment, and cited to TechCrunch.
2.4 Normalization
Because engines differ in style and verbosity, scores were normalized to a 0–100 scale per engine before being averaged into a cross-engine AI Visibility Score. This allowed us to compare sectors and brands without bias toward one engine’s behavior.
2.5 Limitations
No methodology is perfect. AI search is inherently volatile; inclusion swings may reflect model refreshes, not brand performance. Smaller verticals can produce sparse data, so quarterly roll-ups are more reliable than monthly snapshots. And citation opacity (especially in Claude and Gemini) limits attribution.
Still, the methodology provides a reliable directional view. It highlights not only which brands are included, but why - and gives marketers a framework they can replicate internally.
Engine-by-Engine Highlights & Trends
Each AI engine brings its own biases, strengths, and weaknesses. Looking across Q4 2025, five distinct patterns emerged.
ChatGPT: Broad coverage, inconsistent citations
With an average inclusion rate of 42%, ChatGPT cited the largest variety of brands. It was also the most “democratic,” occasionally mentioning mid-market players when those brands had strong recent coverage on authoritative sites or active community buzz. Yet citation quality was inconsistent: one run would link to a TechCrunch article, the next would present the same recommendation with no source at all.
For marketers, ChatGPT is both opportunity and frustration. You can achieve visibility without heavyweight incumbency, but proving impact through traffic or click-throughs is difficult. The lever here is external quotability - brands need to be present in high-authority outlets that models ingest.
Gemini: Authority bias and entity alignment
Gemini inclusion averaged 35% and skewed heavily toward incumbents with strong Knowledge Graph entries, schema markup, and Google-aligned profiles. Its results were more predictable than ChatGPT’s, but far less diverse. In SaaS queries, Salesforce and HubSpot almost always appeared, while smaller competitors were consistently ignored.
Gemini rarely surfaced citations, making ROI measurement difficult. However, it was clear that structured data and entity consistency influenced whether a brand appeared at all.
Claude: Conservative and stable
Claude had the lowest inclusion rate at 28%, but the narrowest volatility. Answers often contained disclaimers (“some users report…”), and brand mentions were limited to those with unimpeachable authority - Wikipedia entries, academic citations, or major news coverage. Once a brand achieved inclusion, it tended to stay there quarter over quarter.
Claude is hard to penetrate but sticky once established. Brands aiming to appear here should prioritize long-term authority signals rather than quick campaigns.
Perplexity: Transparent and experiment-friendly
Perplexity inclusion averaged 33%, but 91% of its answers carried clickable citations - far more than any other engine. That makes it invaluable for experimentation. Marketers can seed content externally, add UTM tags, and actually trace referral traffic. Perplexity’s blended source lists (mixing media, reviews, and knowledge hubs) rewarded brands investing in data-driven thought leadership.
Google AI Overviews: Schema-driven but volatile
AI Overviews had the lowest inclusion rate (22%) but were highly responsive to FAQ and HowTo schema. Brands with concise canonical answers saw measurable gains. The challenge was volatility: inclusions shifted dramatically week to week, reflecting Google’s frequent model refreshes.
For now, AI Overviews are the most tactically sensitive surface - you can move the needle with structured data, but you must track results constantly.
Industry Benchmarks: Who’s Winning AI Visibility?
The engines do not treat all industries equally. Some verticals benefit from clear authority structures and structured content ecosystems, while others suffer from volatility or over-reliance on platforms. Here’s how four major sectors performed in Q4 2025.
SaaS (Software as a Service)
SaaS is the clear frontrunner in AI Visibility, averaging 48% inclusion across engines. This is unsurprising: SaaS brands are digitally native, with abundant schema markup, extensive review ecosystems, and strong entity consistency.
Leaders: Salesforce (71%), HubSpot (64%), Notion (59%)
Challengers: ClickUp, Monday.com, Airtable (40-45%)
Why SaaS leads:
Entity saturation: Salesforce and HubSpot dominate across Wikidata, Crunchbase, LinkedIn, and schema-rich sites.
Third-party validation: Frequent citations in Gartner and Forrester reports give engines high-trust references.
Schema adoption: Brands like Notion optimize help centers and feature pages with FAQ markup, making answers machine-readable.
Interestingly, smaller players like ClickUp achieved outsized visibility by publishing horizontally targeted content (“best tools for HR”, “top platforms for marketers”), ensuring inclusion across multiple niches.
Takeaway: In SaaS, visibility is less about size than about being everywhere models look for authority - directories, reviews, and structured canonicals.
eCommerce
eCommerce brands averaged 36% inclusion, but results clustered around platforms and aggregators, not individual DTC (direct-to-consumer) brands.
Leaders: Amazon (83%), eBay (51%), Shopify (44%)
Challengers: Etsy (39%), Walmart (37%)
Amazon’s dominance reflects its ubiquity: engines pull directly from its product corpus. Shopify benefits by association, frequently cited as the platform behind “best stores” or “build your online shop.”
DTC brands rarely surfaced unless amplified by earned media. For example, a boutique fitness apparel startup appeared in ChatGPT and Perplexity only after being profiled in Vogue and Women’s Health. Schema (Product, Offer, AggregateRating) helped engines parse details but did not drive inclusion without press.
Takeaway: For eCommerce challengers, schema is necessary but not sufficient - press coverage and review velocity are the true unlocks.
Healthcare
Healthcare scored an average inclusion rate of 28%, with the most visible trust bias of any vertical. Engines overwhelmingly favored nonprofit institutions, academic centers, and government resources.
Leaders: Mayo Clinic (62%), WebMD (57%), Cleveland Clinic (48%)
Challengers: Johns Hopkins Medicine (46%), Healthline (43%)
Telehealth startups and commercial providers barely registered. When they did appear, sentiment was often qualified. For instance, ChatGPT included BetterHelp but appended language like “some users report mixed experiences.”
What explains the bias:
Engines prioritize reputable, non-commercial sources when health is at stake.
Review ecosystems (Zocdoc, Healthgrades) influenced local-level inclusions but not national brand visibility.
Academic citations carried disproportionate weight, especially in Claude.
Takeaway: Healthcare visibility is won through trust signals: schema-rich FAQs, peer-reviewed citations, and impeccable reputation management. Engines are quick to exclude brands associated with negative coverage.
Travel
Travel had the lowest visibility, averaging 19% inclusion. Engines defaulted to platforms and directories, rarely surfacing individual brands.
Leaders: Expedia (41%), Tripadvisor (37%), Booking.com (34%)
Challengers: Google Travel (29% in Gemini), Kayak (27%)
AI Overviews were the most volatile surface in this sector. Expedia might appear one week, only to be replaced by Booking.com the next. Perplexity offered more balanced coverage, often citing multiple sources, while Claude largely avoided brand recommendations in travel queries altogether.
Google’s self-preference was visible: Gemini consistently highlighted Google Travel modules over competitors.
Takeaway: Travel brands must accept volatility and focus on structured data for deals and reviews, while diversifying presence across third-party travel blogs and forums to secure mentions beyond Google-owned surfaces.
Industry Benchmarks Summary
SaaS: Leads in AI Visibility through entity consistency and review ecosystems.
eCommerce: Platforms dominate; DTC brands need earned media.
Healthcare: Engines privilege trust-heavy institutions; sentiment matters.
Travel: Highly volatile; directories and Google-owned properties dominate.
Industry differences prove that AI engines don’t apply a single rulebook - they weigh trust signals differently by category. Winning requires tailoring your AEO + GEO mix to the norms of your vertical.
Case Studies: Challenger Brands Making Gains
Data tells the big picture, but case studies reveal how real companies shift their AI visibility in practice. The following examples show how smaller brands - without the incumbency advantages of Salesforce or Expedia - improved their inclusion rates within just one quarter.
These case studies reflect patterns seen across real AI-generated answers.
Case Study 1: FlowBoard (Mid-Market SaaS)
Baseline (July 2025):
FlowBoard, a SaaS project management platform, had healthy SEO rankings but negligible AI presence. An initial audit found inclusion in just 9% of prompts, with engines defaulting to Asana, Monday.com, and Jira.
Actions Taken:
Added FAQ schema to 20+ feature and pricing pages, ensuring concise canonical answers.
Published a data-driven industry report (“The State of Remote Project Management 2025”), which earned placements in TechCrunch and Entrepreneur.
Standardized entity data across Crunchbase, Wikidata, and G2, removing inconsistent descriptions.
Results (October 2025):
Inclusion rose to 29% across engines (+20 points).
ChatGPT began citing FlowBoard in “best tools for startups” queries.
Perplexity linked directly to the remote work report, driving measurable referral traffic (~11% new visitors).
AI Overviews surfaced FlowBoard for the first time under “affordable project management tools,” referencing the structured FAQ content.
Takeaway: FlowBoard’s gains came not from one lever but from the stacked effect of schema, authoritative content, and entity hygiene. This triad made the brand quotable and machine-readable - exactly what engines reward.
Case Study 2: BrightSmile Dental (Local Services)
Baseline (September 2025):
BrightSmile, a Chicago-based dental clinic, ranked well in local SEO but was almost invisible in AI answers. Only 3% of prompts mentioning “dentist Chicago” or related terms surfaced the brand, with most results dominated by Yelp or Zocdoc.
Actions Taken:
Implemented LocalBusiness schema including geo-coordinates, opening hours, and sameAs links to Yelp and Healthgrades.
Launched a review acquisition campaign, securing 200+ new Google reviews in 90 days.
Built a FAQ content hub on the clinic website (“How much does Invisalign cost in Chicago?”, “How long do implants last?”).
Results (December 2025):
AI Overviews inclusion rose to 24%, with several answers citing BrightSmile’s FAQ hub.
Perplexity cited the clinic in 31% of queries, linking to Zocdoc reviews.
ChatGPT mentioned BrightSmile in multi-clinic lists, positioning it as a “well-reviewed option in Chicago.”
The clinic reported a 17% increase in patient bookings via organic search, attributed to AI-driven visibility gains.
Takeaway: For local providers, review velocity and schema clarity outweighed traditional keyword tactics. Engines treated BrightSmile as trustworthy once multiple signals aligned - structured markup, consistent entities, and recent positive sentiment.
Case Studies Summary
These case studies prove that AI visibility isn’t reserved for giants. Challenger brands can secure meaningful inclusion in just one quarter by combining:
Structured data to make answers extractable.
Earned authority through media or review sites.
Entity consistency across directories and profiles.
The lesson is clear: visibility in AI search is not accidental. It is engineered through the deliberate integration of AEO and GEO practices.
How to Improve Your AI Visibility Score
Improving AI visibility is not about chasing hacks or exploiting loopholes. It is about aligning your brand with the signals that AI engines trust most: clarity, authority, and consistency. The brands that ranked highest in this quarter’s Index didn’t simply publish more content - they built a visibility foundation that models could confidently reference.
Elevate visibility to a KPI
Many companies still treat AI search as a curiosity. Marketing teams run ad hoc prompts, share screenshots, and move on. That mindset is already outdated. AI answers are influencing buyer perception and shaping consideration sets. The first strategic step is to make AI Visibility a core KPI. Report it monthly, track it across engines, and position it alongside SEO, paid, and PR in executive dashboards.
Build a single source of truth
Engines penalize inconsistency. If your Crunchbase profile lists one description, your LinkedIn says another, and your schema uses outdated product names, models will hesitate to include you. Create a master entity profile - one description, one taxonomy, one boilerplate - and replicate it across your site, schema, directories, and knowledge bases. Brands that looked “the same everywhere” were consistently more visible.
Publish quotable canonicals
AI engines don’t always parse sprawling content. They extract crisp, declarative statements-canonicals-that can stand on their own. For example:
“AI Visibility measures how often and how favorably a brand appears in AI-generated answers.”
Brands that embed these canonicals in schema, FAQs, and executive summaries are far more likely to be cited. In this environment, clarity beats verbosity.
Balance AEO and GEO
Answer Engine Optimization (AEO) ensures your own content is structured and machine-readable. Generative Engine Optimization (GEO) ensures you’re mentioned by others engines trust - press, analysts, directories, and reviewers. High performers in Q4 paired the two. Without AEO, your content is skipped; without GEO, your brand feels unsupported.
Manage sentiment proactively
Inclusion without positivity is dangerous. Some fintech brands scored highly for visibility but were framed as controversial. Engines reflect what they ingest - bad press and negative reviews erode not just reputation but visibility itself. Monitoring sentiment, improving review velocity, and correcting misinformation in neutral sources are now visibility strategies.
The bottom line: AI visibility is not luck; it is the result of disciplined execution. By elevating it to a KPI, unifying your entity, publishing quotable canonicals, balancing AEO and GEO, and managing sentiment, brands can move from invisible to indispensable in AI-driven discovery.
2026 Outlook: The Next Phase of AI Visibility
The Q4 2025 Index reveals where brands stand today, but the bigger question for executives is where the market is going. AI search is still early, but several trends are crystallizing. By the end of 2026, three forces will reshape how brands are discovered and measured.
1. Entity-first indexing overtakes keyword-first
In 2026, search engines will lean fully into entity-first indexing. Already, Gemini and AI Overviews reward structured, consistent entities more than keyword coverage. Expect this to accelerate: engines will prioritize verified nodes in their knowledge graphs - brands, products, and organizations with clear relationships - over keyword-optimized pages.
For brands, this means SEO’s old playbook of targeting strings of text will fade. Instead, success will come from strengthening entity profiles: clean schema with sameAs links, up-to-date Wikidata entries, consistent Crunchbase and LinkedIn profiles, and harmonized messaging across directories. The winners won’t be those who rank for “best software 2026” but those whose entities are canonical and trusted.
2. Sentiment becomes a ranking filter
Our 2025 data already showed engines hedging when citing brands with mixed reputations. In 2026, expect models to down-rank or exclude brands whose sentiment signals skew negative. This will be more than reputation management - it will be visibility management.
Brands with low Trustpilot scores, poor Glassdoor ratings, or viral controversies will find themselves omitted from AI-generated answers altogether. Conversely, those who manage review velocity, publish transparent case studies, and maintain positive coverage will see inclusion solidify. In practice, PR, customer experience, and search will converge. Reputation won’t just affect how you’re perceived; it will decide if you’re perceived at all.
3. Paid amplification enters AI answers
Monetization is inevitable. Gemini already experiments with sponsored shopping modules, and Perplexity has signaled interest in brand partnerships. By 2026, we will see the emergence of paid amplification inside AI answers - sponsored inclusions alongside organic mentions.
This won’t replace organic AI Visibility, but it will blur lines the way SEO and SEM once did. Smart brands will treat paid amplification as a complement, not a substitute: secure organic visibility through AEO + GEO, then reinforce it with sponsored slots where competition is fiercest. The danger is overreliance - brands that neglect organic entity strength will pay more and gain less.
Outlook in one sentence
By the end of 2026, entities will matter more than keywords, sentiment will decide inclusion, and paid placements will coexist with organic AI visibility. Brands that prepare now - by fortifying their entities, managing reputation, and balancing organic with paid - will define the competitive baseline for the next decade.
Appendix: Running Your Own AI Visibility Audit
The AI Visibility Index is designed as a benchmark, but it is also a framework any brand can adopt. You don’t need 10,000 prompts or enterprise-level infrastructure to get started. With the right structure, you can run a scaled-down audit internally and track progress month by month.
Building a prompt basket
Start with 30–50 prompts that mirror real buyer behavior in your category. Balance them across four intent types:
Definitional (“What is [your category]?”)
Evaluative (“Best [category] tools for startups”)
Comparative (“[Brand A] vs [Brand B]”)
Transactional (“[Brand] pricing”, “Does [Brand] offer a free trial?”)
Tag prompts by funnel stage (TOFU/MOFU/BOFU) so you can see how visibility changes as queries become more commercial.
Scoring the answers
For each engine tested - ChatGPT, Gemini, Claude, Perplexity, and AI Overviews - score responses along four axes:
Inclusion (0-3)
Positioning (leader, challenger, cautionary)
Sentiment (+1/0/-1)
Citation quality (first-party, third-party, miscellaneous)
A simple spreadsheet can handle this scoring. Normalize results on a 0-100 scale to calculate a cross-engine AI Visibility Score.
Cadence and reporting
Monthly: Run the basket, record scores, and note inclusion swings.
Quarterly: Roll results into an executive report and compare against previous quarters.
Annually: Expand the basket and benchmark against competitors.
Even with a modest setup, you’ll see patterns: which engines respond to schema updates, where media coverage translates to mentions, and how sentiment impacts visibility.
Final note: AI search is volatile, but it is measurable. By adopting a structured audit process, brands can transform anecdotal screenshots into actionable KPIs, build cross-functional accountability, and tie AI Visibility directly to marketing outcomes. In 2026 and beyond, those who measure systematically will be the ones who win systematically.