--- title: "AI Recommendation Index: what six models recommend" description: "A transparent index of 144 AI answers across four software categories, with share of voice, first-pick results, methodology, and study limitations." canonical: "https://nikoalho.fi/writing/ai-recommendation-index/" language: "en" --- > Canonical source: [https://nikoalho.fi/writing/ai-recommendation-index/](https://nikoalho.fi/writing/ai-recommendation-index/) [← writing](https://nikoalho.fi/writing/) GEO Published 2026 · 07 · 17 # AI Recommendation Index: what six models recommend A transparent index of 144 AI answers across four software categories, with share of voice, first-pick results, methodology, and study limitations. ![Niko Alho](https://nikoalho.fi/assets/niko-alho-headcut.webp) **Niko Alho**Operator in Turku · firsthand systems ![Editorial illustration for AI Recommendation Index: what six models recommend](https://nikoalho.fi/visuals/ai-recommendation-index-960.webp) ON THIS PAGE [01 The Current Index](#the-current-index) [02 The Method](#the-method) [03 Share of Voice Is Not Recommendation Position](#share-of-voice-is-not-recommendation-position) [04 What the Four Studies Already Show](#what-the-four-studies-already-show) [05 Limitations](#limitations) [06 The Quarterly Protocol](#the-quarterly-protocol) [07 How to Use the Index](#how-to-use-the-index) PROGRESS 0% ON THIS PAGE 7 sections [01 The Current Index](#the-current-index) [02 The Method](#the-method) [03 Share of Voice Is Not Recommendation Position](#share-of-voice-is-not-recommendation-position) [04 What the Four Studies Already Show](#what-the-four-studies-already-show) [05 Limitations](#limitations) [06 The Quarterly Protocol](#the-quarterly-protocol) [07 How to Use the Index](#how-to-use-the-index) TL ; DR tl;dr. 9-min read 4 category studies 1. 01 The index currently covers 144 answers : 36 per category across ecommerce, accounting, CRM, and project management software. 2. 02 Share of voice and first-pick rate measure different positions . A brand can appear in nearly every answer and still rarely lead the recommendation. 3. 03 The index is a dated snapshot, not a universal leaderboard. Prompts, language, market, model version, and live search all change the result. 4. 04 Cross-category scores are not collapsed into one magic number. Each category keeps its own prompt set and competitive context. ANSWERS 144 4 categories × 36 answers. MODEL PANEL 6 Same panel per category. BUYING PROMPTS 24 6 prompts per category. DIRECT ANSWER Q. What is the AI Recommendation Index? A. The AI Recommendation Index is a repeatable study of which brands AI assistants mention and recommend first across a fixed set of software-buying prompts. The June 2026 edition contains 144 answers from six models across four categories. It reports category-level share of voice, first-pick position, model variation, and cited sources without pretending the categories are directly interchangeable. EVIDENCE Each category used six buying prompts across the same six-model panel, producing 36 answers per category and 144 answers in total. The category articles publish their prompt framing, run date, model list, counts, and caveats. The **AI Recommendation Index** tracks which software brands AI assistants place on a buyer’s shortlist and which one they recommend first. The current edition combines four category studies run in June 2026. Each study used six buying prompts and the same six-model panel, producing 36 answers per category and 144 answers in total. This is not a global brand ranking. It is a transparent collection of category snapshots. A CRM answer and a Finnish ecommerce answer do not share the same buyer, language, or competitive set, so collapsing them into one proprietary score would create confidence the data does not support. ## The Current Index | Category | Market | Answers | Share-of-voice leader | Leader SoV | First-pick signal | | --- | --- | --- | --- | --- | --- | | [Ecommerce platforms](https://nikoalho.fi/writing/ai-ecommerce-platform-recommendations/) | Finland, Finnish prompts | 36 | Shopify | 97% | Category page reports model and local-platform variation | | [Accounting software](https://nikoalho.fi/writing/ai-accounting-software-recommendations-2026/) | US-framed, English prompts | 36 | QuickBooks, Wave, Xero | 86% each | QuickBooks led 24 of 36 answers | | [CRM software](https://nikoalho.fi/writing/ai-crm-software-recommendations-2026/) | English buying prompts | 36 | HubSpot, Zoho CRM | 97% each | HubSpot led 24 of 36 answers | | [Project management software](https://nikoalho.fi/writing/ai-project-management-software-recommendations-2026/) | English buying prompts | 36 | Jira | 92% | Jira led 8 of 36 answers | The table exposes the central pattern: mention rate alone is not enough. Accounting and CRM both produced ties at the top of share of voice, while the first-pick distribution separated a clear leader. ### Download the Q2 2026 dataset The response-level archive is published under **CC BY 4.0**. It contains the 144 message outputs, exact prompts, model identifiers, run timestamps, normalized mentions, first-pick proxies, and citations. Provider reasoning, API task IDs, token use, and costs are deliberately excluded. | File | Use | | --- | --- | | [Responses · JSON](https://nikoalho.fi/data/ai-recommendation-index/2026-q2/responses.json) | Full nested dataset for analysis and applications | | [Responses · CSV](https://nikoalho.fi/data/ai-recommendation-index/2026-q2/responses.csv) | Spreadsheet, warehouse, and notebook import | | [Category summary · JSON](https://nikoalho.fi/data/ai-recommendation-index/2026-q2/summary.json) | Aggregated mention and first-pick counts | | [Q2 manifest · JSON](https://nikoalho.fi/data/ai-recommendation-index/2026-q2/index.json) | Version, coverage, license, and distribution metadata | | [All waves · JSON](https://nikoalho.fi/data/ai-recommendation-index/index.json) | Stable machine-readable index for every quarterly release | | [GitHub repository](https://github.com/alhoniko/ai-recommendation-index) | Version history, citation metadata, and source files | | [Zenodo · DOI 10.5281/zenodo.21420271](https://doi.org/10.5281/zenodo.21420271) | Immutable Q2 2026 archive and scholarly citation | Required attribution: **AI Recommendation Index by Niko Alho**, linked to this page. The files are versioned by wave instead of being silently overwritten. The leading mention rate inside each category's own 36-answer prompt set. Categories are not a shared benchmark. Category leader share of voice — June 2026 % OF CATEGORY ANSWERS 97 86 97 92 Shopify · ecommerce FI QuickBooks · accounting HubSpot · CRM Jira · project management ## The Method Each category follows the same high-level design: 1. Define six buying questions covering generic, budget, use-case, and comparison intent. 2. Send the same six prompts to six models with web search enabled. 3. Record every named product, the opening recommendation, and the cited sources. 4. Normalize obvious product-name variants into one brand. 5. Calculate share of voice as answers naming the brand divided by 36. 6. Report the model panel, prompt framing, run month, results, and caveats on the category page. The panel reported in the category studies was GPT-5.5, GPT-5-nano, Claude Opus 4-8, Claude Haiku 4-5, Gemini 3.1 Pro, and Perplexity Sonar Pro. The point of listing versions is not permanence. It is the opposite: model versions are part of the observation and must be recorded because the next wave may differ. The downloadable response archive publishes all 24 prompts verbatim and keeps each answer attached to its category, model, market, language, and run timestamp. ## Share of Voice Is Not Recommendation Position AI share of voice answers: **was the brand present?** First-pick rate answers: **did the answer lead with it?** Those are different commercial positions. A product named in 30 of 36 answers but placed fourth each time has strong shortlist coverage and weak recommendation priority. A product named in 18 answers and placed first in 12 may own a narrower use case more convincingly. This distinction is why the index does not publish a single blended score yet. Any weighting between coverage and position would be a business judgment, not a fact. The raw components are more useful. CRITERIA was the brand named? Share of voice did the answer lead with it? First-pick rate WIN Measures Shortlist coverage Recommendation priority Strong result Appears across prompt types Opens the answer repeatedly Failure mode Recognized but generic Visible only as an alternative Use Find coverage gaps Diagnose positioning gaps ## What the Four Studies Already Show ### 1\. High visibility can hide weak positioning Xero reached 86% share of voice in accounting but opened zero of the 36 answers. Zoho CRM matched HubSpot at 97% share of voice while HubSpot dominated first-pick position. “Mentioned everywhere” and “recommended first” are not synonyms. ### 2\. Prompt design changes the category Wave’s accounting visibility benefits from the free-software prompt. Linear appears more strongly in project-management prompts aimed at software teams. Notion enters the PM category even though it is not a traditional project-management product. The prompt set defines the market the model is being asked to construct. ### 3\. Language and geography matter The Finnish ecommerce study surfaced domestic platforms and a Finnish source graph that would not be captured by an English global prompt set. It should not be compared directly with the US-framed accounting study. ### 4\. Cited intermediaries matter The category studies repeatedly find comparison publishers and specialist editorial sites in the citations. Vendor pages are part of the source graph, but they do not control it. This is where [entity authority](https://nikoalho.fi/writing/entity-authority-for-llms/) and digital PR meet [LLM SEO](https://nikoalho.fi/writing/llm-seo/). ## Limitations The index is designed to be inspectable, not infallible. - **Small prompt panels.** Six prompts can expose a positioning pattern, but they do not represent every buyer question. - **Non-deterministic answers.** Live web search and generated output can change between identical runs. - **Model drift.** A dated model panel becomes historical as providers update routing and versions. - **Unequal markets.** The ecommerce study is Finnish; the others use different framing. Cross-category ranking would be misleading. - **Mention normalization.** Product aliases and parent brands require judgment. The public generator contains the normalization patterns, while the export keeps the unedited message text for re-analysis. - **First mention is only a proxy.** The first normalized brand mention can be context rather than a positive recommendation. Treat it as a reproducible positional signal, not sentiment analysis. The dataset makes the classification inspectable, but it does not make a non-deterministic model deterministic. A rerun can verify the method and still produce a different answer. ## The Quarterly Protocol A useful index must show change, not just one snapshot. For each new wave: 1. Freeze the core prompt set before the run. 2. Keep a small exploratory prompt set separate from the trend panel. 3. Record the exact model identifiers and run dates. 4. Run all brands through the same normalization rules. 5. Publish deltas in share of voice, first-pick rate, and cited-source concentration. 6. Keep old waves available so model drift is visible rather than overwritten. Do not edit a June result into an August result. Publish a new wave and preserve the baseline. The stable [all-waves manifest](https://nikoalho.fi/data/ai-recommendation-index/index.json) points to Q2 2026 now. Q3 and later releases will receive their own directory, response files, summary, and manifest under the same URL structure. ## How to Use the Index If a brand has low share of voice, audit category coverage and the sources retrieved for the missing prompts. If it has high share of voice but low first-pick rate, the problem is positioning: the web recognizes the brand but consistently frames it as an alternative. If performance varies heavily by model, inspect which sources each system retrieves and which category language appears around the brand. Then rerun the same panel after a meaningful release. The measurement loop is detailed in [how to measure AI share of voice](https://nikoalho.fi/writing/how-to-measure-ai-share-of-voice/) and [tracking AI citations](https://nikoalho.fi/writing/tracking-ai-citations/). The job is not to win a proprietary score. It is to understand where AI-mediated buying research includes the brand, how it positions it, and which source graph creates that answer. NEED A CATEGORY BASELINE? I run the same fixed-panel study for B2B software markets. [Book a 20-min intro →](https://nikoalho.fi/book/) Questions people actually ask FAQ · 5 Q01 How is AI share of voice calculated? + For each category, count the answers that name a brand and divide by the 36 answers in that category. If a brand appears in 27 answers, its category share of voice is 75%. The metric captures presence, not recommendation order or sentiment. Q02 What is first-pick rate? + First-pick rate counts how often a brand is the opening recommendation in an answer. It separates brands that are consistently shortlisted from brands the model actually leads with. Q03 Can I compare a CRM score directly with an ecommerce score? + No. Each category has a different market, prompt set, and in the Finnish ecommerce study, a different language and geography. Cross-category leader bars show the shape of each study, not which software brand is globally strongest. Q04 Is the index reproducible? + The exact prompts, model identifiers, timestamps, response text, normalized mentions, first picks, and citations are downloadable as JSON and CSV. Live-search answers remain non-deterministic, so a rerun is a new dated wave rather than a guaranteed reproduction of the same output. Q05 What should a brand do with the result? + First identify whether the gap is presence, first-pick position, or source coverage. Then inspect the cited intermediaries and the claims they repeat. Improve the underlying evidence and third-party corroboration, then rerun the same prompt panel. Sources & further reading 1. \[01\] [AI Optimization API](https://dataforseo.com/apis/ai-optimization-api) DataForSEO · 2026 documentation 2. \[02\] [LLM responses endpoint](https://docs.dataforseo.com/v3/ai_optimization/llm_responses/overview/) DataForSEO · 2026 documentation ![Niko Alho](https://nikoalho.fi/assets/niko-alho-profile.webp) Niko Alho I run agentic SEO and build custom AI for B2B companies. Based in Turku. [About →](https://nikoalho.fi/about/) KEEP READING ## More on geo. - [![Editorial illustration for LLM SEO: how to optimize for AI search and citations](https://nikoalho.fi/visuals/llm-seo.webp) GEO 2026 · 07 · 17 LLM SEO: how to optimize for AI search and citations A practical LLM SEO system for crawl access, source selection, citable passages, entity clarity, and… read →](https://nikoalho.fi/writing/llm-seo/) - [![Editorial illustration for Which accounting software does AI recommend?](https://nikoalho.fi/visuals/ai-accounting-software-recommendations-2026.webp) GEO 2026 · 06 · 23 Which accounting software does AI recommend? Which accounting software do AI assistants recommend? Results from 36 answers across six models, wit… read →](https://nikoalho.fi/writing/ai-accounting-software-recommendations-2026/) - [![Editorial illustration for Which CRM does AI recommend? 36 answers, 6 models](https://nikoalho.fi/visuals/ai-crm-software-recommendations-2026.webp) GEO 2026 · 06 · 23 Which CRM does AI recommend? 36 answers, 6 models Which CRM do AI assistants recommend? A 36-answer test across six models, with brand frequency, firs… read →](https://nikoalho.fi/writing/ai-crm-software-recommendations-2026/) [More writing →](https://nikoalho.fi/writing/) Booking June · 2 spots left · 20-min intro, no pitch [Book a slot →](https://nikoalho.fi/book/) ## Structured data ```json { "@context": "https://schema.org", "@type": "WebSite", "@id": "https://nikoalho.fi/#website", "url": "https://nikoalho.fi/", "name": "Niko Alho", "description": "Agentic SEO and custom AI builds for B2B companies.", "inLanguage": "en", "publisher": { "@id": "https://nikoalho.fi/#person" }, "potentialAction": { "@type": "SearchAction", "target": { "@type": "EntryPoint", "urlTemplate": "https://nikoalho.fi/search/?q={search_term_string}" }, "query-input": "required name=search_term_string" } } ``` ```json { "@context": "https://schema.org", "@type": "Person", "@id": "https://nikoalho.fi/#person", "name": "Niko Alho", "givenName": "Niko", "familyName": "Alho", "url": "https://nikoalho.fi/about/", "image": "https://nikoalho.fi/og/default.png", "jobTitle": "Agentic SEO & Custom AI Consultant", "email": "mailto:contact@nikoalho.fi", "telephone": "+358401539426", "address": { "@type": "PostalAddress", "addressLocality": "Turku", "addressCountry": "FI" }, "knowsAbout": [ "Search Engine Optimization", "Agentic SEO", "Topical Authority", "Retrieval-Augmented Generation", "Large Language Models", "Custom AI Builds", "B2B SaaS Content Strategy", "Schema.org Structured Data", "Generative Engine Optimization" ], "knowsLanguage": [ "en", "fi" ], "worksFor": { "@id": "https://nikoalho.fi/#organization" }, "sameAs": [ "https://www.linkedin.com/in/nikoalho/", "https://github.com/alhoniko" ] } ``` ```json { "@context": "https://schema.org", "@type": "ProfessionalService", "@id": "https://nikoalho.fi/#organization", "name": "Niko Alho — SEO & AI Automation", "alternateName": "Niko Alho", "description": "Agentic SEO and custom AI builds for B2B companies.", "url": "https://nikoalho.fi/", "image": "https://nikoalho.fi/og/default.png", "logo": "https://nikoalho.fi/assets/logo-mark.svg", "email": "mailto:contact@nikoalho.fi", "telephone": "+358401539426", "priceRange": "$$$", "founder": { "@id": "https://nikoalho.fi/#person" }, "employee": { "@id": "https://nikoalho.fi/#person" }, "knowsLanguage": [ "en", "fi" ], "address": { "@type": "PostalAddress", "addressLocality": "Turku", "addressCountry": "FI" }, "areaServed": [ { "@type": "City", "name": "Turku" }, { "@type": "City", "name": "Helsinki" }, { "@type": "Country", "name": "Finland" }, { "@type": "Place", "name": "European Union" }, { "@type": "Place", "name": "Worldwide (remote)" } ] } ``` ```json { "@context": "https://schema.org", "@type": "TechArticle", "@id": "https://nikoalho.fi/writing/ai-recommendation-index/#article", "headline": "AI Recommendation Index: what six models recommend", "name": "AI Recommendation Index: what six models recommend", "description": "A transparent index of 144 AI answers across four software categories, with share of voice, first-pick results, methodology, and study limitations.", "image": "https://nikoalho.fi/og/ai-recommendation-index.png", "url": "https://nikoalho.fi/writing/ai-recommendation-index/", "datePublished": "2026-07-17T00:00:00.000Z", "dateModified": "2026-07-17T00:00:00.000Z", "inLanguage": "en", "isAccessibleForFree": true, "wordCount": 1379, "articleSection": "GEO", "keywords": "ai recommendation index, ai recommendations, llm share of voice, ai brand visibility, ai citation research", "author": { "@id": "https://nikoalho.fi/#person" }, "publisher": { "@id": "https://nikoalho.fi/#person" }, "mainEntityOfPage": { "@type": "WebPage", "@id": "https://nikoalho.fi/writing/ai-recommendation-index/" }, "about": { "@type": "Thing", "name": "GEO" }, "speakable": { "@type": "SpeakableSpecification", "cssSelector": [ "h1", ".tldr", ".article-body > .prose > p:first-of-type" ] }, "citation": [ { "@type": "CreativeWork", "url": "https://github.com/alhoniko/ai-recommendation-index" }, { "@type": "CreativeWork", "url": "https://doi.org/10.5281/zenodo.21420271" } ] } ``` ```json { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://nikoalho.fi/" }, { "@type": "ListItem", "position": 2, "name": "Writing", "item": "https://nikoalho.fi/writing/" }, { "@type": "ListItem", "position": 3, "name": "AI Recommendation Index: what six models recommend", "item": "https://nikoalho.fi/writing/ai-recommendation-index/" } ] } ``` ```json { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "How is AI share of voice calculated?", "acceptedAnswer": { "@type": "Answer", "text": "For each category, count the answers that name a brand and divide by the 36 answers in that category. 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