Loudmink tested ChatGPT, Perplexity, and Gemini across 26 industries, from plumbers to CRMs to running shoes. As of June 2026, the results share one consistent finding: the businesses AI search engines recommend have almost nothing in common with the businesses dominating Google. Across all 26 experiments, Google's top-ranked results overlapped with AI recommendations less than 10% of the time. The businesses that showed up in AI responses were smaller, more specialized, and present across third-party sources that most businesses ignore.
This article organizes the findings by pattern, not by industry. If you want the full experiment for a specific industry, each section links to the detailed writeup. The goal here is to show what works across categories, what fails everywhere, and where the differences between local services, professional services, B2B SaaS, and ecommerce actually matter.
The Bottom Line
- Most businesses are invisible to AI search engines. In 23 of 26 industries tested, the top Google result did not appear in any AI response. Paid search, directory listings, and review volume on a single platform do not translate to AI visibility.
- Third-party mentions are the universal requirement. Businesses recommended by AI appeared on at least 3-4 independent platforms (directories, editorial content, community discussions). Businesses missing from AI had concentrated their entire presence on one or two channels.
- Specialization beats scale in every category. National chains, franchise operations, and "full-service" providers were consistently absent from AI recommendations. Boutique firms, niche specialists, and businesses with clear positioning for a specific buyer appeared instead.
What Every Recommended Business Had in Common
Across 26 industries, the businesses AI search engines recommended shared four traits regardless of whether they were plumbers, therapists, hotels, or software products.
They existed on multiple third-party platforms. Every business appearing in AI recommendations had mentions across at least three independent sources: directories, review sites, editorial content, or community discussions. A plumber in Austin recommended by ChatGPT appeared on Yelp, Angi, Thumbtack, and a local home services blog. A dentist in Denver appeared in a parenting blog, a local "best of" roundup, and Reddit. AI search engines build recommendations from what multiple independent sources say about you, not from what you say about yourself. Loudmink's research confirms that 85% of AI citations come from third-party sites, and this held true across every industry we tested.
They had specific, extractable content. AI search engines scan web pages looking for passages that directly answer user queries. The businesses that got recommended had detailed, specific content on their websites: service descriptions, pricing transparency, specialization pages, and use-case-specific landing pages. Generic "we do everything" messaging produced zero recommendations across 26 industries.
They were discussed in online communities. Reddit threads, Facebook groups, niche forums, and industry communities surfaced consistently as recommendation drivers. In the mechanic experiment, independent shops discussed in car forums dominated over franchise chains. In the CRM experiment, products discussed in r/startups and Indie Hackers outperformed category leaders on G2.
They had editorial or journalistic mentions. Blog posts, "best of" roundups, local news features, and industry articles mentioning a business by name served as high-trust signals. The hotel experiment in New Orleans showed this most clearly: every hotel recommended by two or more AI search engines was featured in Conde Nast Traveler, Travel + Leisure, or Eater.
The "Invisible Business" Problem
The majority of businesses in every industry we tested were completely invisible to AI search engines. They did not appear in any response from any engine. This is not a ranking problem. It is an existence problem.
The invisible businesses shared a profile that was remarkably consistent across industries. They had invested heavily in a single discovery channel, usually Google, and had little to no presence anywhere else. A power washing company in Atlanta with 400+ Google reviews and top local pack placement did not exist in any AI response. A wedding photographer in Savannah with a page-one Google ranking for their primary keyword was absent from all three engines.
The mechanism is straightforward. AI search engines discover candidate businesses by searching Google and Bing, so traditional SEO matters as the entry ticket. But after finding candidates, the AI independently researches each one across reviews, directories, editorial content, and community discussions. If it finds nothing, it moves on. Businesses that are Google-only have nothing for the AI to find during that second stage.
What to do: Audit your presence outside of Google. Can you find your business mentioned by name on at least three independent platforms? If not, you have a visibility gap that no amount of Google optimization will close. Start with the directories and review sites specific to your industry, then pursue editorial mentions and community presence. The guide to showing up in AI search results covers this step by step.
How Local Services Perform in AI Search
Local service businesses, the plumbers, mechanics, dog groomers, power washers, moving companies, and wedding photographers, face the steepest challenge in AI search. As of June 2026, most local businesses have concentrated their digital presence on Google Business Profile, Google Ads, and Google reviews. That strategy is nearly useless for AI recommendations.
Across eight local service experiments, three patterns held consistently.
National chains and franchises were invisible. In the mechanic experiment, Firestone, Midas, and Christian Brothers had zero AI presence. In the dog groomer experiment, PetSmart and Petco grooming departments were absent. AI search engines consistently favored specialized independents over franchise operations because independent businesses tend to have more specific positioning, more detailed content about what makes them different, and more organic community discussion.
Directory coverage needed to be broad, not deep. A business with 500 Google reviews and zero Yelp reviews was invisible. A business with 100 Google reviews, 50 Yelp reviews, 30 Angi reviews, and a mention in a local blog post got recommended. AI search engines look for consensus across sources, not depth on a single source. The plumber experiment demonstrated this clearly: recommended businesses appeared on at least four different third-party sites.
Specific service pages outperformed generic websites. The businesses recommended had content answering the specific questions embedded in the query. "Emergency pipe burst repair in South Austin" gives an AI engine something to extract. "Plumbing services" does not. The same pattern appeared for moving companies, power washing companies, and wedding photographers.
The general local business experiment in Tampa confirmed these patterns across multiple service categories simultaneously: businesses visible to AI had broad directory coverage and community discussion, regardless of their Google ranking.
What to do: Claim every relevant directory listing, not just Google. Generate reviews across multiple platforms. Create specific service pages for each thing you do, each area you serve, and each problem you solve. Then earn editorial mentions in local blogs and community publications.
How Professional Services Perform in AI Search
Professional service providers, dentists, doctors, lawyers, therapists, accountants, financial advisors, insurance agents, and veterinarians, occupy a unique position in AI search. Trust signals matter more than in any other category, and the sources of trust are specific to each profession.
Across eight professional service experiments, the biggest finding was that industry-specific directories (the ones professionals pay thousands for) had almost no influence on AI recommendations.
Paid directory placements were ignored. Lawyers spending $50-200+ per click on Google Ads and hundreds per month on Avvo premium profiles got zero AI visibility. Therapists with Psychology Today premium badges were absent. Financial advisors paying $1,000-5,000 monthly for SmartAsset leads did not appear. AI search engines treat paid directory placements as advertising, not as editorial endorsement.
Professional credential directories did carry weight, but only specific ones. NAPFA and XY Planning Network listings helped financial advisors get recommended. AAHA accreditation appeared in veterinary recommendations. Martindale-Hubbell ratings surfaced for lawyers. The common thread: these are curated professional directories that filter members by qualifications, not by advertising spend. AI search engines distinguish between directories that verify quality and directories that sell placement.
Niche specialization was essential. "Full-service law firm" produced zero AI recommendations. "Flat-fee small business dispute attorney" appeared across engines. Dentists positioned around pediatric sedation got recommended. Therapists specializing in anxiety treatment appeared. Accountants focused on startup bookkeeping got visibility. Insurance agents with a defined client niche (young families, small business) outperformed generalist agents with broader marketing budgets. Generalist positioning makes it impossible for an AI engine to match you to a specific query.
Educational content demonstrated expertise. The recommended professionals published content answering the questions potential clients ask before hiring. "What to expect at your child's first dental visit." "How much does a business litigation attorney cost in Chicago." "When do young families need a financial advisor vs. DIY." Real estate agents who published neighborhood guides and market reports earned AI recommendations over agents with higher transaction volumes but no published expertise. Recruiters with industry-specific salary guides and placement data outperformed national staffing firms. This content serves double duty: it ranks on Google (stage 1 discoverability) and gives AI something to cite when building its recommendation narrative (stage 2).
What to do: Stop relying on paid directory placements as your primary discovery channel. Maintain credentials on quality-gated directories (NAPFA, AAHA, Super Lawyers). Publish educational content that answers pre-hire questions with specifics. Position your practice around a defined specialization rather than a broad service list.
How B2B and SaaS Products Perform in AI Search
B2B SaaS is the category where AI recommendations diverge most dramatically from traditional discovery channels. In the CRM experiment and the project management tool experiment, AI search engines recommended products based on community advocacy and use-case positioning rather than market share, G2 rankings, or advertising spend.
G2 and Capterra rankings were not determinative. As of June 2026, G2 sorts by review volume plus satisfaction score. Capterra sorts by sponsored placement. Neither method matches how AI search engines evaluate software. Products with fewer reviews but stronger community presence and clearer use-case positioning (Attio, Linear, Folk) outperformed category leaders. The CRM experiment showed Salesforce, the G2 category leader, appearing only as a "you'll outgrow the others eventually" caveat.
Community word-of-mouth was the dominant signal. Linear appeared in all three AI search engine responses for project management despite being a fraction of Asana's or Monday.com's user base. The reason: Linear is discussed extensively and positively in r/startups, r/programming, and developer communities. AI search engines weight community discussions heavily because they represent authentic user opinions rather than marketing.
Use-case-specific content won over generic feature pages. "CRM for 20-person startups without dedicated ops" is a query with specific constraints. Products that published content addressing those exact constraints (HubSpot's Startups program, Attio's "modern CRM for startups" positioning, Close's "built for sales teams" messaging) got recommended. Products with "all-in-one solution for teams of all sizes" messaging had weaker match signals.
Pricing transparency mattered. Products with published, transparent pricing gave AI search engines information to include in recommendations. Products with "contact sales" pricing could not be recommended for budget-conscious queries because the engine had no price signal to reference.
What to do: Publish dedicated landing pages for each buyer segment you serve. Build genuine presence in the communities where your target buyers evaluate tools (subreddits, Indie Hackers, Stack Overflow, Hacker News). Earn inclusion in editorial comparison content. Publish pricing clearly. Write your own comparison content that honestly positions your product against alternatives.
How Ecommerce Performs in AI Search
The running shoes experiment revealed a fundamental difference between how Google and AI search engines handle product purchase queries. Google answers "find this product at the best price." AI search engines answer "help me decide what to buy and where."
Expertise beat inventory. Amazon, the platform where most running shoes are purchased, appeared only as a caveat. Fleet Feet, Running Warehouse, and Road Runner Sports dominated because they published expert buying guidance. When the query included uncertainty ("not sure what I need," "good guidance on selection"), AI search engines recommended retailers that help buyers make decisions, not the ones with the largest catalog.
Advisory content was the primary signal. Fit quizzes, buying guides, recommendation tools by use case, and "first-time marathon trainer" content matched the guidance-seeking intent of the query. Retailers without expert content, even massive ones like Zappos and DSW, were absent because AI search engines had no basis to recommend them for advisory queries.
Enthusiast community presence mattered. r/running, r/RunningShoeGeeks, and marathon training forums extensively discuss where to buy. Retailers recommended by actual runners carried peer-validation signals that AI search engines weighted heavily.
Direct-to-consumer brands can compete. Brooks and Hoka appeared in AI recommendations through their own websites because they published marathon-specific training content alongside product pages. Brands selling direct can bypass both Amazon and multi-brand retailers by publishing expertise that AI search engines match to advisory queries.
What to do: Publish use-case-specific buying guides that help uncertain buyers decide. Build recommendation tools (fit quizzes, selection filters, comparison calculators). Earn presence in enthusiast communities. Get featured in category editorial publications (Runner's World, Wirecutter, and equivalent for your vertical). Ecommerce brands optimizing for AI search see the strongest results from advisory content, not product listings.
How Hospitality and Travel Perform in AI Search
Hotels and travel agencies sit in a category where AI recommendations completely bypass the existing distribution infrastructure. In the hotel experiment, OTAs (Booking.com, Expedia, Hotels.com) had zero influence on AI recommendations. In the travel agent experiment, AI search engines recommended specialists with published destination expertise over agencies with broad inventory.
Travel editorial was the primary signal. Conde Nast Traveler, Travel + Leisure, Eater, and regional travel media drove hotel recommendations. Every hotel appearing in two or more AI responses was featured in a major travel publication. For hotels paying 15-25% OTA commissions per booking, AI search represents a direct discovery channel where editorial coverage matters more than distribution scale.
Narrative identity outperformed amenity lists. "Converted church complex with four bars" is a story AI can match to a "character and great bar" query. "350-room full-service hotel with meeting space" is not. Restaurants in San Diego showed the same pattern: "intimate 30-seat wine bar with Indian-inspired small plates" outperformed "fine dining in San Diego."
Independent properties beat chains. Marriott, Hilton, and every major chain's properties were absent from AI hotel recommendations despite massive advertising budgets and loyalty programs. Boutique and independent properties with editorial coverage and community discussion dominated. AI search engines matched subjective query terms ("character," "atmosphere," "great bar") to properties described with those exact attributes in editorial content.
Food and beverage coverage extended hotel visibility. Hotels whose bars or restaurants earned independent editorial coverage (featured in Eater, reviewed by food bloggers) gained additional citation sources. This is a parallel editorial channel that most hotels are not pursuing deliberately.
What to do: Pursue travel editorial coverage aggressively. Ensure your property has a narrative identity that publications can write about. If your bar or restaurant has its own story, pitch it to food media separately. Build presence in travel subreddits and community forums. Publish destination-specific content on your own site that demonstrates local expertise.
How Education and Childcare Perform in AI Search
The daycare experiment and tutor experiment revealed a category where trust signals are paramount and parenting communities carry outsized influence.
Parenting blogs and community discussions drove recommendations. Perplexity's daycare recommendations came almost entirely from a Nashville parenting blog and a Reddit thread. AI search engines treated parent-to-parent recommendations as the highest-trust signal for decisions involving children. Franchise brands and centers with large advertising budgets were absent.
State licensing and accreditation served as trust filters. Similar to professional credential directories, AI search engines distinguished between programs with verifiable quality standards and programs with advertising presence. State licensing information and accreditation status appeared in recommended providers' descriptions.
Age-specific and program-specific content earned matches. Daycare centers with content addressing specific developmental stages ("toddler Montessori program," "infant care ratios," "pre-K school readiness curriculum") gave AI search engines match criteria for specific queries. Centers with generic "childcare for all ages" messaging were invisible.
What to do: Build presence on parenting-specific platforms and community forums. Publish age-specific program descriptions and detailed curriculum information. Ensure state licensing and accreditation credentials are prominently displayed and verifiable. Pursue mentions in local parenting blogs and community guides.
Which Industries AI Handles Well vs. Poorly
AI search engines are not equally capable across all industries. As of June 2026, our research identified a clear spectrum based on how much structured, third-party content exists for a given category.
Industries Where AI Recommendations Are Strong
B2B SaaS and software. Extensive editorial comparison content, active community discussions, transparent pricing, and structured review data give AI search engines abundant material to work with. The CRM and project management experiments produced specific, well-reasoned recommendations with clear explanations.
Hospitality and dining. Travel editorial, food media, and active community discussion create a rich recommendation environment. AI hotel and restaurant recommendations were specific, opinionated, and matched query intent accurately.
Healthcare and professional services (with caveats). AI produced reasonable recommendations for dentists, doctors, and therapists, but with significant engine disagreement. The recommendations were better when queries included specific requirements (pediatric, anxiety specialization, fee-only fiduciary).
Industries Where AI Recommendations Are Weak
Home services and trades. Plumbers, power washing companies, and moving companies had the weakest AI recommendations. Few editorial sources cover these industries, community discussion is sparse compared to dining or travel, and most businesses in these categories have thin online presences beyond Google. AI recommendations existed but were less confident and more variable between engines.
Niche personal services. Med spas, dog groomers, and wedding photographers occupy a middle ground. Community discussions exist in niche forums, but editorial coverage is limited to a handful of local publications. These industries have high engine disagreement, meaning the three AI search engines rarely recommended the same business.
What to do: If you're in an industry where AI handles recommendations poorly, this is actually your opportunity. The competition for AI visibility is lowest in categories with sparse third-party content. Being the first business in your industry and city to build a multi-platform presence, publish specific service content, and earn editorial mentions creates a durable advantage before competitors realize the channel exists.
The Sources AI Pulls from by Industry Type
Different industry categories trigger different source types in AI recommendations. Understanding which sources matter for your industry determines where to invest your effort.
Local Services
Primary sources: Yelp, Angi, Thumbtack, HomeAdvisor, BBB, local blogs, and community subreddits (r/[city] threads). AI search engines treated local directory breadth as the primary signal. Businesses present across 4+ directories with recent reviews got recommended. Businesses on only one or two platforms were invisible.
Professional Services
Primary sources: Professional credential directories (NAPFA, AAHA, Super Lawyers, AVMA), local business publications, niche community forums, and professional association directories. Paid lead-generation platforms (SmartAsset, Psychology Today premium, Avvo) carried no weight. Quality-gated credential directories served as trust filters.
B2B and SaaS
Primary sources: Reddit (r/startups, r/SaaS, r/programming), Indie Hackers, editorial comparison articles, G2 review content (not G2 rankings), and brand-owned comparison pages. Community discussions were the dominant signal. Grok cites Reddit 13x more than other AI search engines, making Reddit presence especially important for B2B visibility on that engine.
Ecommerce
Primary sources: Enthusiast community forums, category editorial publications (Runner's World, Wirecutter), brand-published buying guides, and expert review content. Marketplace presence (Amazon, Shopify storefronts) carried almost no weight for advisory queries. Expert content and community peer-validation were the primary drivers.
Hospitality and Travel
Primary sources: Travel editorial (Conde Nast Traveler, Travel + Leisure), food media (Eater, local food blogs), travel subreddits (r/travel, r/[city] threads), and TripAdvisor content. OTA rankings (Booking.com, Expedia) had zero influence. Food and beverage coverage provided a secondary editorial channel.
Education and Childcare
Primary sources: Parenting blogs, community forums (r/[city] parenting threads, Facebook groups), state licensing databases, and local family publications. Childcare marketplace platforms (Care.com) had minimal influence. Parent-to-parent discussions carried the highest trust weight.
Local vs. B2B vs. Ecommerce: Where the Gaps Are
The gap between Google visibility and AI visibility is not uniform across categories. Our research revealed a clear hierarchy in how much AI search disrupts existing discovery patterns.
B2B SaaS: moderate disruption. Google organic results and AI recommendations share more overlap in B2B than in any other category. Products ranking well on Google for category queries also tend to have the editorial coverage and community presence AI search engines look for. The disruption is in how AI ranks within the recommendation set (community word-of-mouth over market share), not in which products appear at all.
Ecommerce: high disruption for advisory queries. When shoppers know what they want, AI adds little value over Google Shopping. When shoppers don't know what they want (the "help me decide" query), AI completely reframes discovery. Specialty retailers with expert content capture queries that would have gone to Amazon.
Local services: maximum disruption. The overlap between Google's local pack and AI recommendations was near zero in our experiments. Businesses optimized exclusively for Google are almost entirely invisible to AI search engines. This represents the largest opportunity gap because the competition for AI visibility in local services is currently negligible. Most local businesses don't show up in AI search, which means early movers face minimal competition.
Professional services: high disruption with high stakes. The disconnect between paid directory placements and AI recommendations means professionals spending thousands per month on lead-generation platforms are buying visibility on a channel AI search engines ignore. Redirecting even a fraction of that budget toward content and editorial presence produces AI visibility that paid placements cannot.
AI Search Engine Disagreement Across Industries
AI search engines disagree on the top recommendation in roughly half of all queries. Loudmink's research found 50% disagreement on the number-one pick across B2B categories. Our 26-industry experiment showed this disagreement varies by industry type.
Highest agreement (same business recommended by 2+ engines): Hotels, restaurants, and B2B SaaS. These categories have the most editorial content for AI to reference, creating more convergence on which businesses have strong reputations.
Lowest agreement (each engine recommended entirely different businesses): Lawyers, mechanics, power washing, and other local services with limited editorial coverage. In the lawyer experiment, eleven different firms were recommended across three engines with exactly one overlap.
What this means: Optimizing for a single AI search engine is insufficient. Each engine uses different source material, different weighting, and different retrieval methods. A business appearing in ChatGPT's response may be invisible to Perplexity, and vice versa. Multi-engine monitoring is the only way to understand your actual AI visibility.
The Loudmink AEO platform tracks your visibility across up to five AI search engines and identifies which queries and sources drive recommendations. Plans from $99/mo.
What to Do About It: A Cross-Industry Action Plan
The specifics vary by industry, but the underlying actions are consistent. Every business tested in our 26-industry experiment benefited from the same foundational work.
Build presence across 4+ third-party platforms relevant to your industry. For local services: Yelp, Angi, Thumbtack, BBB, local directories. For professional services: professional credential directories, industry associations, local business publications. For B2B: G2, Capterra, Reddit communities, editorial comparison sites. For ecommerce: category editorial publications, enthusiast communities, review platforms.
Generate reviews on multiple platforms, not just Google. AI search engines look for consensus signals. Split your review requests across the platforms your industry's AI recommendations draw from.
Publish specific, extractable content. Create pages addressing the exact questions and constraints people include in AI queries. Service-specific, location-specific, and use-case-specific content gives AI search engines something to work with. Structuring content for AI citations covers the formatting details.
Earn editorial mentions. Pitch local bloggers, contribute expert quotes, sponsor events that generate coverage, and pursue inclusion in "best of" roundups. Editorial content is the single most consistent recommendation driver across all 26 industries.
Participate in relevant online communities. Find where your customers discuss providers in your industry. Contribute genuinely useful information. When satisfied customers mention you by name in community discussions, it creates the peer-validation signals AI search engines weight most heavily.
Update everything monthly. AI search engines favor content published within the last 30 days. Directory profiles, website content, and blog posts all benefit from regular updates. A business that was visible three months ago may have dropped out if nothing has been refreshed.
Frequently Asked Questions
Does Google ranking affect AI search recommendations?
Indirectly, yes. AI search engines discover candidate businesses by searching Google and Bing, so ranking on traditional search is the entry ticket. But ranking well on Google is necessary, not sufficient. After finding candidates, AI independently researches each one across third-party sources and community discussions. A business ranking first on Google with no other online presence will still be invisible to AI recommendations.
Which AI search engine should I optimize for first?
Start with ChatGPT. As of June 2026, it has the largest user base at 900 million weekly active users. But do not stop there. Our experiments showed that each engine recommends different businesses for the same query. Perplexity, Gemini, Grok, and Claude each use different sources and weighting. A business visible on ChatGPT may be invisible on Perplexity.
How long does it take to start showing up in AI search?
For most industries, 2-4 months of consistent effort. The first month is infrastructure: claiming directory listings, optimizing profiles, beginning review generation across multiple platforms. Months 2-3 typically produce visibility for long-tail queries. Months 4-6 bring broader visibility for primary service terms. B2B SaaS products with strong community presence can see results faster.
Do I need to hire an AEO agency or can I do this myself?
You can do the foundational work yourself: claim directory listings, generate reviews, create specific content, and build community presence. Where DIY breaks down is multi-engine monitoring (each AI search engine behaves differently), content at the volume needed for visibility (20-40 articles per month for competitive categories), and post-publication verification. If you have a content team and time, start with DIY. If you need results in weeks instead of quarters, an AEO platform like Loudmink (from $99/mo) replaces $3,000-5,000/mo in agency costs.
Are the experiment results the same every time I ask?
No. AI search results change every time you ask. Loudmink's research found that AI search results vary between identical runs, with brand citation counts swinging up to 48%. This is why single-snapshot checks are unreliable and ongoing monitoring matters.