We asked the AI assistants your buyers actually use which software they recommend, across 10 go-to-market categories. Here is what they say, who is invisible, and why the answer depends on which AI you ask.
Every tool's position-weighted recommendation frequency across all engines and categories, normalized 0 to 100. A #1 pick counts more than a #5; breadth across categories is rewarded.
| Tool | AI-Visibility | Score | Mentions | #1 pick | Cats | |
|---|---|---|---|---|---|---|
| 1 | Otter.ai | 100 | 11 | 10 | 2 | |
| 2 | Jasper | 89.9 | 10 | 9 | 2 | |
| 3 | GitHub Copilot | 78.7 | 9 | 7 | 1 | |
| 4 | Beautiful.ai | 65.6 | 9 | 5 | 1 | |
| 5 | Descript | 63.1 | 16 | 1 | 4 | |
| 6 | Gamma | 56.6 | 9 | 4 | 1 | |
| 7 | Opus Clip | 51.6 | 6 | 5 | 1 | |
| 8 | ElevenLabs | 49.2 | 5 | 5 | 1 | |
| 9 | ChatGPT | 48.8 | 7 | 4 | 3 | |
| 10 | HireVue | 45.9 | 7 | 3 | 1 | |
| 11 | Pictory | 42.6 | 6 | 3 | 2 | |
| 12 | Cursor | 40.6 | 7 | 2 | 1 | |
| 13 | Tome | 36.9 | 8 | 1 | 1 | |
| 14 | Midjourney | 35.2 | 5 | 3 | 1 | |
| 15 | Synthesia | 29.7 | 6 | 2 | 2 | |
| 16 | Copy.ai | 29.5 | 7 | 0 | 1 | |
| 17 | Claude | 26.2 | 5 | 1 | 2 | |
| 18 | Copilot | 25.4 | 6 | 1 | 2 | |
| 19 | Runway | 24.6 | 3 | 2 | 1 | |
| 20 | Gemini | 23 | 5 | 0 | 2 | |
| 21 | Stable Diffusion | 21.6 | 6 | 0 | 1 | |
| 22 | Tabnine | 20 | 7 | 0 | 1 | |
| 23 | Rev | 19.7 | 3 | 1 | 2 | |
| 24 | Greenhouse | 19.7 | 2 | 2 | 1 | |
| 25 | Resemble AI | 18 | 3 | 1 | 1 |
Top 25 of 256 measured tools. Linked tools have a full AI-Visibility report card. Complete ranking in the open dataset.
Mostly, no. Below is each engine's top pick in every category. The five engines named the same single best tool in only 20% of categories, disagreeing in 8 of 10. Which AI a buyer happens to ask changes which tool they're told to buy.
| Category | GPT-OSSmemory | Qwen 3.6memory | Cohere Command-Amemory | Gemini 2.5 Flashgrounded | Agree? |
|---|---|---|---|---|---|
| AI image generators | Midjourney | Midjourney | MidJourney v7 | GPT Image 2 | ≠ |
| AI presentation tools | Beautiful.ai | Gamma | Beautiful.ai | Gamma | ≠ |
| AI video generators | Synthesia | Runway | Synthesia | Veo | ≠ |
| AI short-form video clipping | Opus Clip | Opus Clip | Descript | Opus Clip | ≠ |
| AI voice and text to speech | ElevenLabs Prime | ElevenLabs | Resemble.ai | ElevenLabs | ≠ |
| AI writing tools | Jasper | Jasper | Jasper | Jasper | = |
| AI coding assistants | GitHub Copilot | GitHub Copilot | GitHub Copilot | Claude Code | ≠ |
| AI transcription and meeting notes | Otter.ai | Otter.ai | Otter.ai | Otter.ai | = |
| AI chatbots and assistants | ChatGPT | ChatGPT | OpenAI ChatGPT-5 | Rasa | ≠ |
| AI recruiting software | HireVue | Eightfold AI | HireVue | hireEZ | ≠ |
The split above is not random. It tracks how each engine knows what it knows.
GPT-OSS and Qwen 3.6 and Cohere Command-A answer from training data. They reliably name the established, widely-written-about brands (the HubSpots and Mailchimps) and tend to miss anything newer than their training cut-off.
Gemini 2.5 Flash read live search results, so they surface the current and trending winners (newer category leaders in email, sales, and AI-visibility tooling) that the memory engines never mention.
AI assistants increasingly answer "what's the best tool for X" before a buyer ever scans a page of links, which makes being named by AI a surface distinct from your Google ranking. The engines we queried are public and reproducible (GPT-OSS, Qwen 3.6, Cohere Command-A, Gemini 2.5 Flash), each asked the identical question set, with the full raw output in the open dataset below.
When the grounded engines backed their answers with live web search, these are the domains they cited most. This is the map of where to be cited if you want AI to recommend you.
The consensus short list per category, blended across all 4 engines.
Run the AI-Visibility check to see where your product lands when buyers ask AI for a recommendation.
SOURCED: every recommendation is a recorded output of a live AI engine. Engines audited: GPT-OSS 120B (OpenAI open weights, model memory) [model-memory, 30 queries]; Qwen 3.6 (Alibaba, model memory) [model-memory, 30 queries]; Cohere Command-A (model memory) [model-memory, 30 queries]; Gemini 2.5 Flash (grounded) [web-grounded, 10 queries]. Across 10 AI-tool categories, the two model-memory engines answered all three phrasings per category; the three web-grounded engines (Gemini via API, ChatGPT and Perplexity via browser) answered one query per category, capturing live web sources where available. DERIVED: the AI-Visibility Score (position-weighted recommendation frequency, normalized 0-100), per-category leaderboards, cross-engine agreement, and source-citation tallies. HONESTY FLOOR: this measures what AI engines OUTPUT, not product quality or our opinion. Search-grounded engines reflect the live web; model-memory engines reflect training data and can name tools that do not exist or omit real leaders. Because the grounded engine is sampled less densely than the memory engines, the blended leaderboard leans slightly toward model-memory; the per-engine comparison below separates them. Outputs vary by phrasing, date, and engine; this is a dated snapshot, not a ranking endorsement. Browser passes (ChatGPT, Perplexity) are layered in as documented.
Open data: ai-recommendation-audit-2026.json, free to reuse with attribution to Nesyona. This measures what AI engines OUTPUT, not product quality; a dated snapshot, re-run to reproduce.
Couey, V. W. (2026). The AI Tools Recommendation Audit 2026 [Data set]. Nesyona.